ANN and Drives IEEE Papers

7/28/2019 ANN and Drives IEEE Papers

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Toma, S.; Capocchi, L.; Capolino, G.-A., "Wound-Rotor Induction Generator Inter-Turn Short-Circuits

Diagnosis Using a New Digital Neural Network," Industrial Electronics, IEEE Transactions on , vol.60,

no.9, pp.4043,4052, Sept. 2013

doi: 10.1109/TIE.2012.2229675

Abstract: This paper deals with a new transformation and fusion of digital input patterns used to train

and test feedforward neural network for a wound-rotor three-phase induction machine windings

short-circuit diagnosis. The single type of short-circuits tested by the proposed approach is based on

turn-to-turn fault which is known as the first stage of insulation degradation. Used input/output data

have been binary coded in order to reduce the computation complexity. A new procedure, namely

addition and mean of the set of same rank, has been implemented to eliminate the redundancy due to

the periodic character of input signals. However, this approach has a great impact on the statistical

properties on the processed data in terms of richness and of statistical distribution. The proposed

neural network has been trained and tested with experimental signals coming from six current sensors

implemented around a setup with a prime mover and a 5.5 kW wound-rotor three-phase induction

generator. Both stator and rotor windings have been modified in order to sort out first and last turns

in each phase. The experimental results highlight the superiority of using this new procedure in bothtraining and testing modes.

keywords: {asynchronous generators;electric machine analysis computing;fault diagnosis;machine

insulation;neural nets;power generation faults;binary code;current sensor;digital input pattern;digital

neural network;insulation degradation;interturn short circuit diagnosis;power 5.5 kW;prime

mover;turn-to-turn fault;wound rotor induction generator;wound rotor three phase induction

generator;Artificial neural networks;Neurons;Rotors;Sensors;Stator

windings;Training;Backpropagation;data preprocessing;digital measurements;fault

diagnosis;feedforward neural network;induction generators;rotor current;stator current;winding

short-circuits},

URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583

Opathella, C.; Singh, B; Cheng, D.; Venkatesh, B., "Intelligent Wind Generator Models for Power Flow

Studies in PSSE and PSSSINCAL," Power Systems, IEEE Transactions on , vol.28, no.2, pp.1149,1159,

May 2013

doi: 10.1109/TPWRS.2012.2211043

Abstract: Wind generator (WG) output is a function of wind speed and three-phase terminal voltage.

Distribution systems are predominantly unbalanced. A WG model that is purely a function of windspeed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate.

These errors add up and become pronounced when a single three-phase feeder connects several WGs.

Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow

applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-

fed induction generator and type-4 permanent magnet synchronous generator. The proposed

approach can be readily applied to any other type of WGs. The main advantages of these ANN models

are their mathematical simplicity, high accuracy with unbalanced systems and computational speed.

These models were tested with the IEEE 37-bus test system. The results show that the ANN WG

models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of

the proposed ANN WG models allow easy integration into commercial software packages such as

PSSE and PSSSINCAL and implementations are also shown in this paper.

keywords: {Artificial neural networks;Biological system modeling;Computational
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583


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modeling;Generators;Mathematical model;Neurons;Wind speed;Artificial neural networks;power

distribution systems;power flow;wind power generators},


Gastli, A.; Ahmed, M.M., "ANN-Based Soft Starting of Voltage-Controlled-Fed IM Drive System," Energy

Conversion, IEEE Transactions on , vol.20, no.3, pp.497,503, Sept. 2005

doi: 10.1109/TEC.2004.841522

Abstract: Soft starters are used as induction motor controllers in compressors, blowers, fans, pumps,

mixers, crushers and grinders, and many other applications. Soft starters use ac voltage controllers to

start the induction motor and to adjust its speed. This paper presents a novel artifical neural network

(ANN)-based ac voltage controller which generates the appropriate thyristors' firing angle for any

given operating torque and speed of the motor and the load. An ANN model was designed for that

purpose. The results obtained are very satisfactory and promising. The advantage of such a controller

are its simplicity, stability, and high accuracy compared to conventional mathematical calculation of

the firing angle which is a very complex and time consuming task especially in online control

applications.keywords: {induction motor drives;machine control;neurocontrollers;starting;thyristors;torque;voltage

control;ANN-based soft starting;artificial neural

networks;blowers;compressors;crushers;fans;grinders;induction motor;induction motor

controllers;mixers;online control applications;pumps;thyristor firing angle;torque;voltage controlled-

fed IM drive system;AC generators;Compressors;Fans;Grinding machines;Induction

generators;Induction motors;Neural networks;Thyristors;Torque control;Voltage control;AC voltage

controller;artificial neural network (ANN);firing angle;induction motor;soft starter;thyristor},


Malerba, D.; Esposito, F.; Ceci, M.; Appice, A., "Top-down induction of model trees with regression and

splitting nodes," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26, no.5,

pp.612,625, May 2004

doi: 10.1109/TPAMI.2004.1273937

Abstract: Model trees are an extension of regression trees that associate leaves with multiple

regression models. In this paper, a method for the data-driven construction of model trees is

presented, namely, the stepwise model tree induction (SMOTI) method. Its main characteristic is the

induction of trees with two types of nodes: regression nodes, which perform only straight-lineregression, and splitting nodes, which partition the feature space. The multiple linear model associated

with each leaf is then built stepwise by combining straight-line regressions reported along the path

from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple

models and have a "global" effect, while straight-line regressions at leaves have only "local" effects.

Experimental results on artificially generated data sets show that SMOTI outperforms two model tree

induction systems, M5' and RETIS, in accuracy. Results on benchmark data sets used for studies on

both regression and model trees show that SMOTI performs better than RETIS in accuracy, while it is

not possible to draw statistically significant conclusions on the comparison with M5'. Model trees

induced by SMOTI are generally simple and easily interpretable and their analysis often reveals

interesting patterns.

keywords: {learning by example;regression analysis;trees (mathematics);benchmark data sets;data

driven construction;global effect;internal regression nodes;learning by example;multiple linear
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806


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model;multiple regression models;regression trees;splitting nodes;stepwise model tree induction

method;straight line regression;top down induction;Induction generators;Linear regression;Machine

learning;Neural networks;Pattern analysis;Piecewise linear approximation;Piecewise linear

techniques;Regression tree analysis;Statistics;Tree data structures;Algorithms;Artificial

Intelligence;Cluster Analysis;Computer Simulation;Decision Support Techniques;Information Storage

and Retrieval;Numerical Analysis, Computer-Assisted;Pattern Recognition, Automated;Reproducibility

of Results;Sensitivity and Specificity},


Tsang, E. C C; Yeung, D.S.; Lee, J.W.T.; Huang, D. M.; Wang, X. Z., "Refinement of generated fuzzy

production rules by using a fuzzy neural network," Systems, Man, and Cybernetics, Part B: Cybernetics,

IEEE Transactions on , vol.34, no.1, pp.409,418, Feb. 2004

doi: 10.1109/TSMCB.2003.817033

Abstract: Fuzzy production rules (FPRs) have been used for years to capture and represent fuzzy,

vague, imprecise and uncertain domain knowledge in many fuzzy systems. There have been a lot of

researches on how to generate or obtain FPRs. There exist two methods to obtain FPRs. One is bypainstakingly, repeatedly and time-consuming interviewing domain experts to extract the domain

knowledge. The other is by using some machine learning techniques to generate and extract FPRs from

some training samples. These extracted rules, however, are found to be nonoptimal and sometimes

redundant. Furthermore, these generated rules suffer from the problem of low accuracy of classifying

or recognizing unseen examples. The reasons for having these problems are: 1) the FPRs generated are

not powerful enough to represent the domain knowledge, 2) the techniques used to generate FPRs are

pre-matured, ad-hoc or may not be suitable for the problem, and 3) further refinement of the

extracted rules has not been done. In this paper we look into the solutions of the above problems by

1) enhancing the representation power of FPRs by including local and global weights, 2) developing a

fuzzy neural network (FNN) with enhanced learning algorithm, and 3) using this FNN to refine the local

and global weights of FPRs. By experimenting our method with some existing benchmark examples,

the proposed method is found to have high accuracy in classifying unseen samples without increasing

the number of the FPRs extracted and the time required to consult with domain experts is greatly

reduced.

keywords: {fuzzy neural nets;fuzzy systems;knowledge acquisition;knowledge based

systems;knowledge representation;learning (artificial intelligence);fuzzy neural network;fuzzy

production rules;fuzzy systems;machine learning techniques;uncertain domain knowledge;Automaticcontrol;Fuzzy control;Fuzzy logic;Fuzzy neural networks;Fuzzy systems;Induction generators;Machine

learning;Power generation;Production;Refining},


Karanayil, B.; Rahman, M.F.; Grantham, C., "An implementation of a programmable cascaded low-pass

filter for a rotor flux synthesizer for an induction motor drive,"Power Electronics, IEEE Transactions on ,

vol.19, no.2, pp.257,263, March 2004

doi: 10.1109/TPEL.2003.823181

Abstract: This paper investigates a programmable cascaded low pass filter for the estimation of rotor

flux of an induction motor, with a view to estimate the rotor time constant of an indirect field

orientation controlled induction motor drive. Programmable cascaded low pass filters have been

traditionally used in stator flux oriented vector control of the induction motor. This paper extends the


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use of this filter to estimate the rotor flux for the indirect field orientation control by generating rotor

flux estimates from stator flux estimates. This is achieved by using a three-stage programmable

cascaded low pass filter. The three-stage programmable cascaded low-pass filter investigated in this

paper has resulted in excellent estimation of rotor flux in the steady-state and transient operation of

an indirect field oriented drive. The estimated rotor flux data have also been used for the on-line rotor

resistance identification with artificial neural network. Modeling and experiment results presented in

this paper demonstrate this method of estimating rotor flux clearly.

keywords: {cascade networks;induction motor drives;low-pass filters;machine vector control;magnetic

flux;neural nets;power engineering computing;programmable filters;rotors;artificial neural

networks;indirect field orientation control;induction motor drive;online rotor resistance

identification;programmable cascaded low-pass filter;rotor flux synthesizer;stator flux oriented vector

control;Induction generators;Induction motor drives;Induction motors;Low pass filters;Machine vector

control;Position control;Rotors;Stators;Steady-state;Synthesizers},


Mohamadian, M.; Nowicki, E.; Ashrafzadeh, F.; Chu, A.; Sachdeva, R.; Evanik, E., "A novel neuralnetwork controller and its efficient DSP implementation for vector-controlled induction motor

drives," Industry Applications, IEEE Transactions on , vol.39, no.6, pp.1622,1629, Nov.-Dec. 2003

doi: 10.1109/TIA.2003.819441

Abstract: An artificial neural network controller is experimentally implemented on the Texas

Instruments TMS320C30 digital signal processor (DSP). The controller emulates indirect field-oriented

control for an induction motor, generating direct and quadrature current command signals in the

stationary frame. In this way, the neural network performs the critical functions of slip estimation and

matrix rotation internally. There are five input signals to the neural network controller, namely, a shaft

speed signal, the synchronous frame present and delayed values of the quadrature axis stator current,

as well as two neural network output signals fed back after a delay of one sample period. The

proposed three-layer neural network controller contains only 17 neurons in an attempt to minimize

computational requirements of the digital signal processor. This allows DSP resources to be used for

other control purposes and system functions. For experimental investigation, a sampling period of 1

ms is employed. Operating at 33.3 MHz (16.7 MIPS), the digital signal processor is able to perform all

neural network calculations in a total time of only 280 s or only 4700 machine instructions. Torque

pulsations are initially observed, but are reduced by iterative re-training of the neural network using

experimental data. The resulting motor speed step response (for several forward and reverse stepcommands) quickly tracks the expected response, with negligible error under steady-state conditions.

keywords: {digital control;digital signal processing chips;induction motor drives;learning (artificial

intelligence);machine vector control;multilayer perceptrons;neurocontrollers;stators;1 ms;280

mus;33.3 MHz;DSP implementation;DSP resources;Texas Instruments TMS320C30 digital signal

processor;computational requirements minimisation;direct current command signals;forward step

commands;indirect field-oriented control;induction motor;iterative re-training;matrix rotation;motor

speed step response;neural network controller;neural network output signals;quadrature axis stator

current;quadrature current command signals;reverse step commands;shaft speed signal;slip

estimation;stationary frame;steady-state conditions;synchronous frame;three-layer neural network

controller;torque pulsations;vector-controlled induction motor drives;Artificial neural networks;DC

generators;Digital signal processing;Digital signal processors;Induction generators;Induction motor

drives;Induction motors;Instruments;Neural networks;Signal generators},


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Karayaka, H.; Keyhani, A.; Heydt, G.; Agrawal, B.; Selin, D., "Neural Network-Based Modeling of a Large

Steam Turbine-Generator Rotor Body Parameters from Online Disturbance Data," Power Engineering

Review, IEEE, vol.21, no.9, pp.62,62, Sept. 2001

doi: 10.1109/MPER.2001.4311621

Abstract: A novel technique to estimate and model rotor-body parameters of a large steam turbine

generator from real time disturbance data is presented. For each set of disturbance data collected at

different operating conditions, the rotor body parameters of the generator are estimated using an

output error method (OEM). Artificial neural network (ANN)-based estimators are later used to model

the nonlinearities in the estimated parameters based on the generator operating conditions. The

developed ANN models are then validated with measurements not used in the training procedure. The

performance of estimated parameters is also validated with extensive simulations and compared

against the manufacturer values.

keywords: {Artificial neural networks;Circuit simulation;Equivalent circuits;Fault detection;Induction

generators;Induction motors;Neural networks;Parameter estimation;Rotors;Voltage;Parameteridentification;artificial neural networks;large utility generators;rotor body parameters},


Tsang, E. C C; Wang, X.Z.; Yeung, D.S., "Improving learning accuracy of fuzzy decision trees by hybrid

neural networks," Fuzzy Systems, IEEE Transactions on , vol.8, no.5, pp.601,614, Oct 2000

doi: 10.1109/91.873583

Abstract: Although the induction of fuzzy decision tree (FDT) has been a very popular learning

methodology due to its advantage of comprehensibility, it is often criticized to result in poor learning

accuracy. Thus, one fundamental problem is how to improve the learning accuracy while the

comprehensibility is kept. This paper focuses on this problem and proposes using a hybrid neural

network (HNN) to refine the FDT. This HNN, designed according to the generated FDT and trained by

an algorithm derived in this paper, results in a FDT with parameters, called weighted FDT. The

weighted FDT is equivalent to a set of fuzzy production rules with local weights (LW) and global

weights (GW) introduced in our previous work (1998). Moreover, the weighted FDT, in which the

reasoning mechanism incorporates the trained LW and GW, significantly improves the FDTs' learning

accuracy while keeping the FDT comprehensibility. The improvements are verified on several selected

databases. Furthermore, a brief comparison of our method with two benchmark learning algorithms,namely, fuzzy ID3 and traditional backpropagation, is made. The synergy between FDT induction and

HNN training offers new insight into the construction of hybrid intelligent systems with higher learning

accuracy

keywords: {decision trees;fuzzy set theory;learning (artificial intelligence);neural nets;FDT;FDT

induction;GW;HNN;HNN training;LW;backpropagation;comprehensibility;fuzzy ID3;fuzzy decision tree

induction;fuzzy production rules;global weights;hybrid intelligent systems;hybrid neural

networks;learning accuracy;local weights;Algorithm design and analysis;Databases;Decision

trees;Entropy;Fuzzy neural networks;Fuzzy sets;Induction generators;Knowledge acquisition;Neural

networks;Production},


Lavrac, N.; Ganberger, D.; Turney, P., "A relevancy filter for constructive induction," Intelligent Systems
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952


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and their Applications, IEEE, vol.13, no.2, pp.50,56, Mar/Apr 1998

doi: 10.1109/5254.671092

Abstract: Some machine-learning algorithms enable the learner to extend its vocabulary with new

terms if, for a given a set of training examples, the learner's vocabulary is too restricted to solve the

learning task. We propose a filter, called the Reduce algorithm, that selects potentially relevant terms

from the set of constructed terms and eliminates terms that are irrelevant for the learning task.

Restricting constructive induction (or predicate invention) to relevant terms allows a much larger

explored space of constructed terms. The elimination of irrelevant terms is especially well-suited for

learners of large time or space complexity, such as genetic algorithms and artificial neural networks. To

illustrate our approach to feature construction and irrelevant feature elimination, we applied our

proposed relevancy filter to the 20- and 24-train East-West Challenge problems. The experiments

show that the performance of a hybrid genetic algorithm, RL-ICET (Relational Learning with ICET),

improved significantly when we applied the relevancy filter while pre-processing the data set

keywords: {computational complexity;feature extraction;filtering theory;genetic algorithms;learning

by example;relevance feedback;vocabulary;East-West Challenge problems;RL-ICET;Reduce

algorithm;constructed term space;constructive induction;data set pre-processing;extendablevocabulary;feature construction;hybrid genetic algorithm;irrelevant feature elimination;machine-

learning algorithm;performance;predicate invention;relational learning;relevancy filter;relevant

terms;space complexity;time complexity;training examples;Artificial neural networks;Computer aided

software engineering;Councils;Filters;Genetic algorithms;Induction generators;Machine learning;Space

exploration;Switches;Vocabulary},


Simoes, M.G.; Bose, B.K.; Spiegel, Ronald J., "Design and performance evaluation of a fuzzy-logic-based

variable-speed wind generation system," Industry Applications, IEEE Transactions on , vol.33, no.4,

pp.956,965, Jul/Aug 1997

doi: 10.1109/28.605737

Abstract: Artificial intelligence techniques, such as fuzzy logic, neural networks and genetic algorithms,

have recently shown promise in the application of power electronic systems. The paper describes the

control strategy development, design and experimental performance evaluation of a fuzzy logic-based

variable-speed wind generation system that uses a cage-type induction generator and double-sided

PWM power converters. The system can feed a utility grid maintaining unity power factor at all

conditions or can supply an autonomous load. The fuzzy logic-based control of the system helps tooptimize efficiency and enhance performance. A complete 3.5 kW generation system has been

developed, designed and thoroughly evaluated by laboratory tests in order to validate the predicted

performance improvements. The system gives excellent performance and can easily be translated to a

larger size in the field

keywords: {AC-AC power convertors;PWM power convertors;asynchronous generators;control system

synthesis;fuzzy control;machine control;machine testing;power station control;wind power

plants;wind turbines;3.5 kW;PWM AC-AC power conversion;cage-type induction generator;control

performance;control strategy development;double-sided PWM power converter;fuzzy logic

control;performance evaluation;unity power factor;utility grid;variable-speed wind generation

system;Artificial intelligence;Artificial neural networks;Control systems;Electric variables control;Fuzzy

control;Fuzzy logic;Fuzzy systems;Genetic algorithms;Induction generators;Power electronics},



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Angeline, P.J.; Saunders, G.M.; Pollack, J.B., "An evolutionary algorithm that constructs recurrent

neural networks," Neural Networks, IEEE Transactions on , vol.5, no.1, pp.54,65, Jan 1994

doi: 10.1109/72.265960

Abstract: Standard methods for simultaneously inducing the structure and weights of recurrent neural

networks limit every task to an assumed class of architectures. Such a simplification is necessary since

the interactions between network structure and function are not well understood. Evolutionary

computations, which include genetic algorithms and evolutionary programming, are population-based

search methods that have shown promise in many similarly complex tasks. This paper argues that

genetic algorithms are inappropriate for network acquisition and describes an evolutionary program,

called GNARL, that simultaneously acquires both the structure and weights for recurrent networks.

GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies

that are potentially excluded by the artificial architectural constraints imposed in standard network

induction methods

keywords: {optimisation;recurrent neural nets;GNARL;evolutionary algorithm;evolutionary

programming;genetic algorithms;population-based search methods;recurrent neuralnetworks;Artificial intelligence;Ash;Computer architecture;Evolutionary computation;Genetic

algorithms;Genetic programming;Induction generators;Network topology;Recurrent neural

networks;Search methods},


Umbaugh, S.E.; Moss, R.H.; Stoecker, W.V., "Applying artificial intelligence to the identification of

variegated coloring in skin tumors," Engineering in Medicine and Biology Magazine, IEEE, vol.10, no.4,

pp.57,62, Dec. 1991

doi: 10.1109/51.107171

Abstract: The importance of color information for the automatic diagnosis of skin tumors by computer

vision is demonstrated. The utility of the relative color concept is proved by the results in identifying

variegated coloring. A feature file paradigm is shown to provide an effective methodology for the

independent development of software modules for expert system/computer vision research. An

automatic induction tool is used effectively to generate rules for identifying variegated coloring.

Variegated coloring can be identified at rates as high as 92% when using the automatic induction

technique in conjunction with the color segmentation method.

keywords: {artificial intelligence;computer vision;expert systems;medical diagnosticcomputing;skin;artificial intelligence;automatic diagnosis;automatic induction tool;color

information;color segmentation method;computer vision;expert system;feature file paradigm;skin

tumors;software modules;variegated coloring identification;Artificial intelligence;Cancer;Classification

algorithms;Decision trees;Expert systems;Humans;Induction generators;Neural networks;Skin

neoplasms;Virtual colonoscopy},



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Mesemanolis, Athanasios; Mademlis, Christos, "A Neural Network based MPPT controller for variable

speed Wind Energy Conversion Systems," Power Generation, Transmission, Distribution and Energy

Conversion (MEDPOWER 2012), 8th Mediterranean Conference on , vol., no., pp.1,6, 1-3 Oct. 2012

doi: 10.1049/cp.2012.2034

Abstract: In this paper, an Artificial Neural Network (ANN) based Maximum Power Point Tracking

(MPPT) controller for Wind Energy Conversion Systems (WECS) is proposed, that achieves fast and

reliable tracking of the optimum rotational speed of the turbine and accomplishes maximum power

harvesting from the incident wind. The proposed control system can be implemented on any WECS

and requires minimum training for the ANN as well as a small number of artificial neurons. During the

training of the ANN, the WECS needs to operate simultaneously with a wind measurement system,

until a sufficient amount of data is collected on all operating regions of the wind turbine and the wind

turbine characteristics are determined. Next, the ANN is trained, having the rotational speed of the

shaft and the power output of the generator as input signals. As a result, the wind turbine can be

driven to the optimum rotor speed very fast and with high precision so as the MPPT controller can

follow the fast dynamics of the wind speed. Several simulation results are presented for the validation

of the effectiveness of the suggested MPPT control scheme and demonstrate the operationalimprovements.

keywords: {Artificial neural network;Induction generator;Maximum power point tracking;Variable

speed drive;Wind energy conversion system},


Brahmi, J.; Krichen, L.; Ouali, A., "Sensorless control of PMSG in WECS using artificial neural

network," Systems, Signals and Devices, 2009. SSD '09. 6th International Multi-Conference on , vol.,

no., pp.1,8, 23-26 March 2009

doi: 10.1109/SSD.2009.4956689

Abstract: This paper presents an artificial neural network (ANN) observer for a speed sensorless

permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). In order

to perform maximum power point tracking control of the wind generation system, it is necessary to

drive wind turbine at an optimal rotor speed. From the aspect of reliability and increase in cost, wind

velocity sensor is not preferred too. Wind and rotor speeds sensorless operating methods for wind

generation system using observer are proposed only by measuring phase voltages and currents.

Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed

mode. The robustness of the ANN against stator resistance variation is studied.keywords: {artificial intelligence;control engineering computing;direct energy conversion;electric

current measurement;machine vector control;neural nets;observers;optimal control;permanent

magnet generators;power control;synchronous generators;voltage measurement;wind power

plants;wind turbines;artificial neural network;maximum power point tracking control;observer;phase

current measurement;phase voltage measurement;sensorless control;speed sensorless permanent

magnet synchronous generator;variable-speed mode;wind energy conversion system;wind generation

system;wind turbine generator;wind velocity sensor;Artificial neural networks;Optimal

control;Permanent magnets;Rotors;Sensorless control;Synchronous generators;Wind energy;Wind

energy generation;Wind speed;Wind turbines;MPPT;Sensorless control;WECS;artificial neural network

observer},



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Yanjun Yan; Kamath, G.; Osadciw, L.A.; Benson, G.; Legac, P.; Johnson, P.; White, E., "Fusion for

modeling wake effects on wind turbines," Information Fusion, 2009. FUSION '09. 12th International

Conference on , vol., no., pp.1489,1496, 6-9 July 2009

Abstract: Wind turbine wakes cause power reduction and structural loading. A data-driven approach is

proposed for wake modeling to provide the azimuth, angular spread, and intensity of wakes. We

introduce three wind speed difference definitions for wake analysis. The wake identification is

automated using morphological imaging operators. The wake pattern is complicated by multiple

neighboring turbines. Four fusion schemes are proposed to draw a complete picture. A similarity based

clustering enhances the final fusion result by treating clusters, each with specific features, equally.

keywords: {aerodynamics;image processing;mechanical engineering computing;wakes;wind

turbines;data-driven approach;morphological imaging operators;multiple neighboring turbines;power

reduction;similarity based clustering;structural loading;wake analysis;wake effects modeling;wake

identification;wake pattern;wind turbines;Automation;Azimuth;Image processing;Potential

energy;Renewable energy resources;Wind energy;Wind farms;Wind power generation;Wind

speed;Wind turbines;Automation;Fusion;Image Processing;Wake;Wind Turbine},


Fernandez, E.; Mabel, M.C., "Analysis of the Influence of Control Parameters on Wind Farm Output: a

Sensitivity Analysis using ANN Modelling," Power Electronics, Drives and Energy Systems, 2006. PEDES

'06. International Conference on , vol., no., pp.1,4, 12-15 Dec. 2006

doi: 10.1109/PEDES.2006.344289

Abstract: Wind energy planners are interested in studies that highlight the impact of control input

parameters on the output of wind farms. Yet, there are few studies highlighting such investigations. It

has been observed that wind energy programs are being actively pursued in most developing

countries. In India, one of the states that is actively involved in wind energy power generation

programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind

farms concentration is being encouraged.

keywords: {neurocontrollers;power generation control;sensitivity analysis;wind power;wind power

plants;ANN modelling;India;Muppandal area;Tamil Nadu;power generation;sensitivity analysis;wind

energy planner;wind farm control;Artificial neural networks;Government;Helium;Sensitivity

analysis;Wind energy;Wind energy generation;Wind farms;Wind power generation;Wind speed;Wind

turbines;ANN models;Impact Assessment;Sensitivity Analysis;Wind Power Generation;Wind farms},


Phan Quoc Dzung; Anh Nguyen Bao; Hong-Hee Lee, "New artificial neural network based direct virtual

torque control and direct power control for DFIG in wind energy systems," Power Electronics and Drive

Systems (PEDS), 2011 IEEE Ninth International Conference on , vol., no., pp.219,227, 5-8 Dec. 2011

doi: 10.1109/PEDS.2011.6147250

Abstract: This paper presents direct power control (DPC) strategy for controlling power flow, direct

virtual torque control (DVTC) strategy for synchronizing double-fed induction generator (DFIG) with

grid and voltage oriented control (VOC) for controlling voltage of link capacitor. All strategies are

implemented on artificial neural network (ANN) controller to decrease the time of calculation in

comparison with the conventional DSP control system. The essence of three strategies is selection

appropriate voltage vectors on the rotor side converter. The network is divided in two types: fixed

weight and supervised models. The simulation results on a 4-kW machine are explained using


10/26

MATLAB/SIMULINK together with the Neural Network Toolbox.

keywords: {asynchronous generators;load flow control;machine

control;microcontrollers;neurocontrollers;power control;power convertors;power generation

control;torque control;voltage control;wind power plants;ANN controller;DFIG;DPC strategy;DSP

control system;DVTC strategy;Matlab-Simulink;VOC;artificial neural network controller;direct power

control;direct virtual torque control;double-fed induction generator;link capacitor voltage;neural

network toolbox;power 4 kW;power flow control;rotor side converter;voltage oriented control;wind

energy systems;Artificial neural networks;Hysteresis;Neurons;Rotors;Stators;Torque;Training;Artificial

Neural Network (ANN);Direct Power Control (DPC);Direct Virtual Torque Control (DVTC);Doubly-Fed

Induction Generator (DFIG);Grid-side converter (GSC);Rotor-side converter (RSC)},


Mohseni, M.; Niassati, N.; Tajik, S.; Afjei, E., "A novel method of maximum power point tracking for a

SRG based wind power generation system using AI," Power Electronics and Drive Systems Technology

(PEDSTC), 2012 3rd, vol., no., pp.330,335, 15-16 Feb. 2012

doi: 10.1109/PEDSTC.2012.6183350Abstract: A novel maximum power point tracking technique is introduced in this study, for a wind

power generation system, based on switched reluctance generator. This method is based on the rotor

speed control of the SRG, by adjusting the excitation current with respect to the wind speed, using an

artificial neural network (ANN). In order to achieve best performance, considering the non-linear

nature of the SRG wind power generation system, processes of optimization are performed, using the

genetic algorithm (GA). Results obtained by the optimizations were used to train the ANN. The

presented MPPT method is then modeled and simulated, in MATLAB/SIMULINK environment, in

order to investigate and verify its performance.

keywords: {genetic algorithms;maximum power point trackers;neural nets;power engineering

computing;reluctance generators;rotors;velocity control;wind power

plants;AI;ANN;GA;MATLAB/SIMULINK;MPPT method;SRG wind power generation system;artificial

neural network;genetic algorithm;maximum power point tracking method;nonlinear

nature;optimization processes;rotor speed control;switched reluctance generator;Energy

loss;Optimized production technology;Switches;Wind power generation;Artificial Neural

Network;Genetic Algorithm;Maximum Power Point Tracking;Switched Reluctance Generator;Wind

Power Generation},


Ren, Y. F.; Bao, G. Q., "Control Strategy of Maximum Wind Energy Capture of Direct-Drive Wind

Turbine Generator Based on Neural-Network," Power and Energy Engineering Conference (APPEEC),

2010 Asia-Pacific , vol., no., pp.1,4, 28-31 March 2010

doi: 10.1109/APPEEC.2010.5448343

Abstract: The wind power varies mainly depending on the wind speed. Many methods have been

proposed to track the maximum power point (MPPT) of the wind, such as the fuzzy logic (FL), artificial

neural network (ANN) and Neuro-Fuzzy. In this paper, a variable speed wind generator MPPT based on

artificial neural network (ANN) is presented. It is designed as a combination of the generator speed

forecasting model and neural network. The ANN is used to predict the optimal speed rotation using

the variation of the wind speed and the generator speed as the inputs. The wind energy control

system employs a permanent magnet synchronous generator connected to a DC bus using a power


11/26

converter is presented. The performance of the control system with the proposed ANN controller is

tested for wind speed variation. System simulation results have confirmed the functionality and

performance of this method.

keywords: {fuzzy neural nets;maximum power point trackers;neurocontrollers;permanent magnet

generators;power convertors;power generation control;synchronous generators;wind turbines;DC

bus;artificial neural network;direct-drive wind turbine generator;fuzzy logic;generator speed

forecasting model;maximum power point trackers;neuro-fuzzy;permanent magnet synchronous

generator;power converter;wind energy capture;wind energy control;wind speed;Artificial neural

networks;Control systems;Energy capture;Fuzzy logic;Predictive models;Wind energy;Wind energy

generation;Wind forecasting;Wind speed;Wind turbines},


Vale, Z.A.; Morais, H.; Faria, P.; Soares, J.; Sousa, T., "LMP based bid formation for virtual power

players operating in smart grids," Power and Energy Society General Meeting, 2011 IEEE, vol., no.,

pp.1,8, 24-29 July 2011

doi: 10.1109/PES.2011.6039853Abstract: Power system organization has gone through huge changes in the recent years. Significant

increase in distributed generation (DG) and operation in the scope of liberalized markets are two

relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased

importance, and is being seen as a paradigm able to support power system requirements for the

future. This paper proposes a computational architecture to support day-ahead Virtual Power Player

(VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a

resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation

module. Due to the involved problems characteristics, the implementation of this architecture requires

the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource

and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource

scheduling. The paper presents a case study that considers a 33 bus distribution network that includes

67 distributed generators, 32 loads and 9 storage units.

keywords: {energy resources;load forecasting;particle swarm optimisation;power engineering

computing;power markets;smart power grids;LMP based bid formation;artificial intelligence

techniques;artificial neural networks;bid formation module;distributed generation;energy resource

scheduling;evolutionary particle swarm optimization;forecasting module;liberalized markets;load

forecasting;locational marginal price;resource optimization;smart grids;virtual power players;Artificialneural networks;Contracts;Electricity supply industry;Energy resources;Smart grids;Wind

forecasting;Artificial Intelligence;Artificial Neural Networks;Energy Resources Management;Intelligent

Power Systems;Locational Marginal Prices (LMP);Particle Swarm Optimization},


Abdel-Khalik, A.S.; Elserougi, A.; Massoud, A.; Ahmed, S., "Control of doubly-fed induction machine

storage system for constant charging/discharging grid power using artificial neural network," Power

Electronics, Machines and Drives (PEMD 2012), 6th IET International Conference on , vol., no., pp.1,6,

27-29 March 2012

doi: 10.1049/cp.2012.0177

Abstract: A large-capacity low-speed flywheel energy storage system based on a doubly-fed induction

machine (DFIM) basically consists of a wound-rotor induction machine, and a back-to-back converter


12/26

for rotor excitation. It has been promoted as a challenging storage system for power system

applications such as grid frequency support/control, power conditioning, and voltage sag mitigation.

This paper presents a power control strategy to charge/discharge a flywheel doubly-fed induction

machine storage system (FW-DFIM) to obtain a constant power delivered to the grid. The proposed

controller is based on conventional vector control, where an artificial neural network (ANN) is used to

develop the required rotor current component based on the required grid power level and the

flywheel instantaneous speed. This technique is proposed for power levelling and frequency support

to improve the quality of the electric power delivered by wind generators, where a constant power

level can be delivered to the grid for a predetermined time depending on the required power level and

the storage system inertia. The controller is designed to avoid overloading stator as well as rotor

circuits while the flywheel charges/discharges. The validity of the developed concept in this paper,

along with the effectiveness and viability of the control strategy, is confirmed by computer simulation

using Matlab/Simulink for a medium voltage 10MJ/1000hp FW-DFIM example.

keywords: {Doubly-fed induction machine;ac machines;flywheel storage system;neural network;vector

control},


Dong Lei; Wang Lijie; Hu Shi; Gao Shuang; Liao Xiaozhong, "Prediction of Wind Power Generation

based on Chaotic Phase Space Reconstruction Models," Power Electronics and Drive Systems, 2007.

PEDS '07. 7th International Conference on , vol., no., pp.744,748, 27-30 Nov. 2007

doi: 10.1109/PEDS.2007.4487786

Abstract: The development of wind generation has rapidly progressed over the last decade, but it must

be integrated into power grids and electric utility systems. However, it cannot be dispatched like

conventional generators because the power generated by the wind changes rapidly because of the

continuous fluctuation of wind speed and direction. So it is very important to predict the wind power

generation. This paper discusses why the wind power generation can be predicted in short-term, and

how to setup the construction of an ANN (artificial neural network) prediction model of wind power

based on chaotic time series. The analysis of modeling with low dimensions nonlinear dynamics

indicates that time series of wind power generation have chaotic characteristics, and wind power can

be predicted in short-term. Phase space reconstruction method can be used for ANN model design.

The data from the wind farm located in the Saihanba China are used for this study.

keywords: {chaos;load forecasting;neural nets;nonlinear dynamical systems;phase space

methods;power system simulation;time series;wind power plants;ANN prediction model;artificialneural network;chaotic phase space reconstruction models;chaotic time series;electric utility

systems;nonlinear dynamics;power grids;wind farm;wind power generation;Artificial neural

networks;Chaos;Mesh generation;Power generation;Power grids;Power system modeling;Predictive

models;Wind energy;Wind energy generation;Wind power generation;chaotic dynamic

system;forecast;neural network;wind power prediction},



13/26

Muyeen, S. M.; Hasanien, H.M.; Tamura, J., "Reduction of frequency fluctuation for wind farm

connected power systems by an adaptive artificial neural network controlled energy capacitor

system,"Renewable Power Generation, IET, vol.6, no.4, pp.226,235, July 2012

doi: 10.1049/iet-rpg.2010.0126

Abstract: Frequency fluctuations are a major concern for transmission system operators and power

grid companies from the beginning of power system operation due to their adverse effects on modern

computer-controlled industrial systems. Because of the huge integration of wind power into the power

grid, frequency fluctuations are becoming a serious problem, where randomly varying wind power

causes the grid frequency fluctuations of the power system. Therefore, in this paper, the minimisation

of the frequency fluctuation of a power system, including a wind farm, is proposed using an energy

capacitor system (ECS). A scaled-down, multi-machine power system model from Hokkaido prefecture,

Japan, is considered for the analysis. A novel adaptive artificial neural network (ANN) controller is

considered for controlling the DC-bus connected ECS. The control objective is to standardise the line

power of the wind farm, taking into consideration the frequency deviation. The effects of wind power

penetration levels, as well as load variations, are also analysed. The proposed control method is

verified by simulation analysis, which is performed with PSCAD/EMTDC using real wind speed data.The adaptive ANN-controlled ECS was found to be an effective means of diminishing the frequency

fluctuation of multi-machine power systems with connected wind farms.

keywords: {adaptive control;neurocontrollers;power capacitors;power generation control;power

grids;wind power plants;ANN controller;DC-bus connected ECS;PSCAD-EMTDC;adaptive ANN-

controlled ECS;adaptive artificial neural network controlled energy capacitor system;adaptive artificial

neural network controller;computer-controlled industrial systems;grid frequency fluctuation

reduction;load variations;power grid companies;power system operation;scaled-down multimachine

power system model;transmission system operators;wind farm connected power systems;wind power

penetration levels;wind speed data},


Alexiadis, M.C.; Dokopoulos, P.S.; Sahsamanoglou, H. S., "Wind speed and power forecasting based on

spatial correlation models," Energy Conversion, IEEE Transactions on , vol.14, no.3, pp.836,842, Sep

1999

doi: 10.1109/60.790962

Abstract: Wind energy conversion systems (WECS) cannot be dispatched like conventional generators.

This can pose problems for power system schedulers and dispatchers, especially if the schedule ofwind power availability is not known in advance. However, if the wind speed can be reliably forecasted

up to several hours ahead, the generating schedule can efficiently accommodate the wind generation.

This paper illustrates a technique for forecasting wind speed and power output up to several hours

ahead, based on cross correlation at neighboring sites. The authors develop an artificial neural

network (ANN) that significantly improves forecasting accuracy comparing to the persistence

forecasting model. The method is tested at different sites over a year

keywords: {correlation methods;forecasting theory;neural nets;power generation planning;power

generation scheduling;power system analysis computing;wind;wind power;wind power plants;artificial

neural network;cross correlation;forecasting accuracy;power system dispatchers;power system

schedulers;spatial correlation models;wind power availability schedule;wind power forecasting;wind

power generation;wind speed forecasting;Artificial neural networks;Job shop scheduling;Power system

modeling;Power system stability;Predictive models;Weather forecasting;Wind energy;Wind energy


14/26

generation;Wind forecasting;Wind speed},


Walker, R. C.; Early, H. C., "HalfMegampere MagneticEnergyStorage Pulse Generator,"Review of

Scientific Instruments , vol.29, no.11, pp.1020,1022, Nov 1958

doi: 10.1063/1.1716044

Abstract: Energy is stored in the magnetic field of a large aircore transformer having a very low

impedance, tightly coupled secondary winding. The energy can be effectively delivered in less than 5

msec to a noninductive load, having a resistance of less than 10-4

ohm.


Methaprayoon, K.; Yingvivatanapong, C.; Wei-jen Lee; Liao, J.R., "An Integration of ANN Wind Power

Estimation Into Unit Commitment Considering the Forecasting Uncertainty," Industry Applications,

IEEE Transactions on , vol.43, no.6, pp.1441,1448, Nov.-dec. 2007

doi: 10.1109/TIA.2007.908203

Abstract: The development of wind power generation has rapidly progressed over the last decade.With the advancement in wind turbine technology, wind energy has become competitive with other

fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of

wind power. The current practice ignores wind generation capacity in the unit commitment (UC),

which discounts its usable capacity and may cause operational issues when the installation of wind

generation equipment increases. To ensure system reliability, the forecasting uncertainty must be

considered in the incorporation of wind power capacity into generation planning. This paper discusses

the development of an artificial-neural-network-based wind power forecaster and the integration of

wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic

concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this

paper.

keywords: {neural nets;power engineering computing;power generation planning;power generation

scheduling;wind power plants;ANN model;Lawton City;artificial-neural-network;forecasting

uncertainty;power generation planning;unit commitment;unit commitment scheduling;wind

energy;wind power estimation;wind power forecaster;wind power generation;wind turbine

technology;Capacity planning;Fluctuations;Power generation;Reliability;Uncertainty;Wind

energy;Wind energy generation;Wind forecasting;Wind power generation;Wind turbines;Artificial

neural network (ANN);confidence interval;short-term wind power forecast;wind forecast uncertainty},URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978

Gershman, Daniel J.; Zurbuchen, T.H., "Modeling extreme ultraviolet suppression of electrostatic

analyzers," Review of Scientific Instruments , vol.81, no.4, pp.045111,045111-8, Apr 2010

doi: 10.1063/1.3378685

Abstract: In addition to analyzing energy-per-charge ratios of incident ions, electrostatic analyzers

(ESAs) for spaceborne time-of-flight mass spectrometers must also protect detectors from extreme

ultraviolet (EUV) photons from the Sun. The required suppression rate often exceeds 1:107

and is

generally established in tests upon instrument design and integration. This paper describes a novel

technique to model the EUV suppression of ESAs using photon ray tracing integrated into SIMION, the

most commonly used ion optics design software for such instruments. The paper compares simulation

results with measurements taken from the ESA of the Mass instrument flying onboard the Wind


15/26

spacecraft. This novel technique enables an active inclusion of EUV suppression requirements in the

ESA design process. Furthermore, the simulation results also motivate design rules for such

instruments.

keywords: {mass spectrometers;space vehicle electronics;ultraviolet

spectrometers;0760Rd;0775+h;0787+v},


Fidalgo, J.N.; Peas Lopes, J.A.; Miranda, V., "Neural networks applied to preventive control measures

for the dynamic security of isolated power systems with renewables," Power Systems, IEEE

Transactions on , vol.11, no.4, pp.1811,1816, Nov 1996

doi: 10.1109/59.544647

Abstract: This paper presents an artificial neural network (ANN) based approach for the definition of

preventive control strategies of autonomous power systems with a large renewable power

penetration. For a given operating point, a fast dynamic security evaluation for a specified wind

perturbation is performed using an ANN. If insecurity is detected, new alternative stable operating

points are suggested, using a hybrid ANN-optimization approach that checks several feasiblepossibilities, resulting from changes in power produced by diesel and wind generators, and other

combinations of diesel units in operation. Results obtained from computer simulations of the real

power system of Lemnos (Greece) support the validity of the developed approach

keywords: {control system analysis computing;control system synthesis;diesel-electric power

stations;neurocontrollers;optimal control;power system analysis computing;power system

control;power system security;power system stability;wind power plants;artificial neural

network;autonomous power systems;computer simulation;control design;control simulation;dynamic

security evaluation;isolated power systems;optimization approach;preventive neurocontrol

strategy;renewable energy resources;wind-diesel hybrid power systems;Artificial neural

networks;Control systems;Hybrid power systems;Neural networks;Performance evaluation;Power

system control;Power system dynamics;Power system measurements;Power system security;Power

systems},


Karayaka, H.B.; Keyhani, A.; Agrawal, B.L.; Selin, Douglas A.; Heydt, G.T., "Identification of armature,

field, and saturated parameters of a large steam turbine-generator from operating data," Energy

Conversion, IEEE Transactions on , vol.15, no.2, pp.181,187, Jun 2000doi: 10.1109/60.866997

Abstract: This paper presents a step by step identification procedure of armature, field and saturated

parameters of a large steam turbine-generator from real time operating data. First, data from a small

excitation disturbance is utilized to estimate armature circuit parameters of the machine.

Subsequently, for each set of steady state operating data, saturable mutual inductances Lads and

Laqs are estimated. The recursive maximum likelihood estimation technique is employed for

identification in these first two stages. An artificial neural network (ANN) based estimator is used to

model these saturated inductances based on the generator operating conditions. Finally, using the

estimates of the armature circuit parameters, the field winding and some damper winding parameters

are estimated using an output error method (OEM) of estimation. The developed models are validated

with measurements not used in the training of ANN and with large disturbance responses

keywords: {damping;inductance;machine windings;maximum likelihood estimation;neural nets;power


16/26

system analysis computing;recursive estimation;steam turbines;turbogenerators;armature

parameters;artificial neural network;damper winding parameters;excitation disturbance;field

parameters;generator operating conditions;output error method;parameters identification;recursive

maximum likelihood estimation;saturable mutual inductances estimation;saturated parameters;steady

state operating data;steam turbine-generator;training;Artificial neural

networks;Circuits;Damping;Maximum likelihood estimation;Parameter estimation;Shape;Shock

absorbers;State estimation;Steady-state;Voltage},


Fraile-Ardanuy, J.; Wilhelmi, J.R.; Fraile-Mora, J.J.; Perez, J.I., "Variable-speed hydro generation:

operational aspects and control," Energy Conversion, IEEE Transactions on , vol.21, no.2, pp.569,574,

June 2006

doi: 10.1109/TEC.2005.858084

Abstract: The potential advantages of variable-speed hydroelectric generation are discussed in this

article. Some general aspects concerning the efficiency gains in turbines and the improvements in

plant operation are analyzed. The main results of measurements on a test loop with an axial-flowturbine are reported. Also, we describe the control scheme implemented, which is based on artificial

neural networks. To confirm the practical interest of this technology, the operation of a run-of-the-

river small hydro plant has been simulated for several years. Substantial increases in production with

respect a fixed-speed plant have been found.

keywords: {hydraulic turbines;hydroelectric power stations;neurocontrollers;power generation

control;artificial neural networks;axial-flow turbines;hydroplants;plant operation improvements;test

loop;variable-speed hydrogeneration;Artificial neural networks;Costs;Hydraulic turbines;Hydroelectric

power generation;Power generation;Production;Propellers;Synchronous generators;Testing;Wind

energy generation;Artificial neural network (ANN);operation limits of hydroturbines;regenerative

frequency converters;variable-speed hydro generation},


Lepri, S.T.; Nikzad, S.; Jones, T.; Blacksberg, J.; Zurbuchen, T.H., "Response of a delta-doped charge-

coupled device to low energy protons and nitrogen ions," Review of Scientific Instruments , vol.77,

no.5, pp.053301,053301-9, May 2006

doi: 10.1063/1.2198829

Abstract: We present the results of a study of the response of a delta-doped charge-coupled device(CCD) exposed to ions with energies less than 10 keV. The study of ions in the solar wind, the majority

having energies in the 15 keV range, has proven to be vital in understanding the solar atmosphere

and the near Earth space environment. Delta-doped CCD technology has essentially removed the dead

layer of the silicon detector. Using the delta-doped detector, we are able to detect H+

and N+

ions with

energies ranging from 1 to 10 keV in the laboratory. This is a remarkable improvement in the low

energy detection threshold over conventional solid-state detectors, such as those used in space

sensors, one example being the solar wind ion composition spectrometer (SWICS) on the Advanced

Composition Explorer spacecraft, which can only detect ions with energies greater

than 30 keV because of the solid-state detectors minimum energy threshold. Because this threshold is

much higher than the average energy of the solar wind ions, the SWICS instrument employs a bulky

high voltage postacceleration stage that accelerates ions above the 30 keV detection threshold. This

stage is massive, exposes the instrument to hazardous high voltages, and is therefore problematic


17/26

both in terms of price and its impact on spacecraft resources. Adaptation of delta-doping technology in

future space missions may be successful in reducing the need for heavy postacceleration stages

allowing for miniaturization of space-borne ion detectors.

keywords: {charge-coupled devices;cosmic ray apparatus;position sensitive particle detectors;solar

atmosphere;solar cosmic ray particles;solar radiation;solar wind;2940Gx;9660Vg},


Pena, F.L.; Duro, R.J., "A virtual instrument for automatic anemometer calibration with ANN based

supervision," Instrumentation and Measurement, IEEE Transactions on , vol.52, no.3, pp.654,661, June

2003

doi: 10.1109/TIM.2003.814703

Abstract: A fully automatic anemometer calibrator intended for performing fast and accurate

calibrations has been developed to fulfill the increasing demand and strict requirements of the wind

energy industry. Different sensors are connected to a computer where a virtual environment acquires

and processes the incoming signals and controls a wind tunnel, allowing the calibration of the

anemometer at the pre-selected air speed values. An important part of the resulting complex virtualenvironment is a supervising system, based on artificial neural networks and able to check and handle

the possible malfunctions and deviations within the calibration process.

keywords: {anemometers;calibration;geophysics computing;neural nets;virtual instrumentation;wind

tunnels;artificial neural network supervision;automatic anemometer calibration;computerised

sensor;virtual instrument;wind energy;wind tunnel;AC motors;Artificial neural networks;Associate

members;Calibration;Electrical equipment industry;Fluid flow

measurement;Hardware;Instruments;Virtual environment;Wind energy},


Maene, N.; Cornelis, J.; Biermans, F.; Van den Bosch, A., "Quench experiments on superconductive

coils," Magnetics, IEEE Transactions on , vol.21, no.2, pp.702,705, Mar 1985

doi: 10.1109/TMAG.1985.1063746

Abstract: A set of four similar superconductive coils of 166 mm inner winding diameter were wet-

wound with NbTi wire for subsequent application in various configurations: one single coil, a pair of

coils or all four coils stacked on top of each other and connected in series. The height of the winding

was 41 mm and the thickness 9 mm. The detailed observation of the time-dependence of the current

and voltage with a data processing system yielded information on the time scale of the quenchpropagation. In the single coil and the pair of coils the time dependence of the quench resistance with

time was derived from an analysis of the current and the coil voltages during the transient. With the

four coils the time required for the current to decrease from 90 % to 10 % gets shorter with increasing

quench current. Variations of 1.6 s to 0.3 s were observed in this configuration. At the maximum

current a magnetic induction of at least 2.5 T was reached in a volume of over 2.5 litres. The self-

inductance of this system was 1.28 Henry and the stored energy attained 22 kJ.

keywords: {Superconducting coils;Boring;Magnetic flux;Niobium compounds;Superconducting

coils;Superconducting magnets;Superconductivity;Testing;Titanium compounds;Voltage;Wire},


Fouad, R. H.; Ashhab, M. S.; Mukattash, A.; Idwan, S., "Simulation and energy management of an

experimental solar system through adaptive neural networks," Science, Measurement & Technology,


18/26

IET, vol.6, no.6, pp.427,431, November 2012

doi: 10.1049/iet-smt.2011.0201

Abstract: In this study, the authors consider a solar system which consists of a solar trainer that

contains a photovoltaic panel, a DC centrifugal pump, flat plate collectors, storage tank, a flowmeter

for measuring the water mass flow rate, pipes, pyranometer for measuring the solar intensity,

thermocouples for measuring various system temperatures and wind speed meter. The various

efficiencies of the solar system have been predicted by an artificial neural network (ANN) which was

trained with historical data. The ANN fails to predict the efficiencies accurately over the long-time

horizon because of system parts degradation, environmental variations, date changes within the year

from the modelling date and presence of modelling errors. Therefore the ANN is adapted using the

error between the ANN-predicted efficiency and the efficiency measurement from the appropriately

selected sensors and efficiency laws to update the network's parameters recursively. The adaptation

scheme can be performed online or occasionally and is based on the Kaczmarz's algorithm. The

adaptive ANN capability is demonstrated through computer simulation.


Opathella, C.; Singh, B; Cheng, D.; Venkatesh, B., "Intelligent Wind Generator Models for Power Flow

Studies in PSSE and PSSSINCAL," Power Systems, IEEE Transactions on , vol.28, no.2, pp.1149,1159,

May 2013

doi: 10.1109/TPWRS.2012.2211043

Abstract: Wind generator (WG) output is a function of wind speed and three-phase terminal voltage.

Distribution systems are predominantly unbalanced. A WG model that is purely a function of wind

speed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate.

These errors add up and become pronounced when a single three-phase feeder connects several WGs.

Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow

applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-

fed induction generator and type-4 permanent magnet synchronous generator. The proposed

approach can be readily applied to any other type of WGs. The main advantages of these ANN models

are their mathematical simplicity, high accuracy with unbalanced systems and computational speed.

These models were tested with the IEEE 37-bus test system. The results show that the ANN WG

models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of

the proposed ANN WG models allow easy integration into commercial software packages such as

PSSE and PSSSINCAL and implementations are also shown in this paper.keywords: {Artificial neural networks;Biological system modeling;Computational

modeling;Generators;Mathematical model;Neurons;Wind speed;Artificial neural networks;power

distribution systems;power flow;wind power generators},



19/26

Pinto, J. O P; Bose, B.K.; da Silva, L.E.B., "A stator-flux-oriented vector-controlled induction motor drive

with space-vector PWM and flux-vector synthesis by neural networks," Industry Applications, IEEE

Transactions on , vol.37, no.5, pp.1308,1318, Sep/Oct 2001

doi: 10.1109/28.952506

Abstract: A stator-flux-oriented vector-controlled induction motor drive is described where the space-

vector pulsewidth modulation (SVM) and stator-flux-vector estimation are implemented by artificial

neural networks (ANNs). ANNs, when implemented by dedicated hardware application-specific

integrated circuit chips, provide extreme simplification and fast execution for control and feedback

signal processing functions in high-performance AC drives. In the proposed project, a feedforward

ANN-based SVM, operating at 20 kHz sampling frequency, generates symmetrical pulsewidth

modulation (PWM) pulses in both undermodulation and overmodulation regions covering the range

from DC (zero frequency) up to square-wave mode at 60 Hz. In addition, a programmable cascaded

low-pass filter (PCLPF), that permits DC offset-free stator-flux-vector synthesis at very low frequency

using the voltage model, has been implemented by a hybrid neural network which consists of a

recurrent neural network (RNN) and a feedforward neural network (FFANN). The RNN-FFANN-based

flux estimation is simple, permits faster implementation, and gives superior transient performancewhen compared with a standard digital-signal-processor-based PCLPF. A 5 HP open-loop volts/Hz-

controlled drive incorporating the proposed ANN-based SVM and RNN-FFANN-based flux estimator

was initially evaluated in the frequency range of 1.0-58 Hz to validate the performance of SVM and the

flux estimator. Next, the complete 5 HP drive with stator-flux-oriented vector control was evaluated

extensively using the PWM modulator and flux estimator

keywords: {PWM invertors;feedforward neural nets;frequency control;induction motor drives;low-

pass filters;machine vector control;magnetic flux;neurocontrollers;recurrent neural

nets;stators;voltage control;1 to 58 Hz;20 kHz;5 hp;60 Hz;ANN;DC offset-free stator-flux-vector

synthesis;application-specific integrated circuit chips;artificial neural networks;digital-signal-

processor;feedback signal processing functions;feedforward ANN-based SVM;feedforward neural

network;flux estimator;flux-vector synthesis;frequency control;hybrid neural network;neural

networks;overmodulation region;programmable cascaded low-pass filter;recurrent neural

network;sampling frequency;space-vector PWM;space-vector pulsewidth modulation;square-wave

mode;stator-flux-oriented vector control;stator-flux-oriented vector-controlled induction motor

drive;stator-flux-vector estimation;symmetrical pulsewidth modulation pulses generation;transient

performance;undermodulation region;very low frequency;voltage control;voltage model;Feedforward

neural networks;Frequency estimation;Frequency synthesizers;Induction motor drives;Neuralnetworks;Pulse width modulation;Recurrent neural networks;Space vector pulse width

modulation;Stators;Support vector machines},


Rahman, M.A.; Ashraful Hoque, M., "Online self-tuning ANN-based speed control of a PM DC

motor," Mechatronics, IEEE/ASME Transactions on , vol.2, no.3, pp.169,178, Sep 1997

doi: 10.1109/3516.622969

Abstract: This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control

scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training

algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of

the ANN. The complete system is implemented in real time using a digital signal processor controller

board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the


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proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC

motor drive system under different operating conditions. The comparative results show that the ANN-

based speed control scheme is robust, accurate, and insensitive to parameter variations and load

disturbances

keywords: {DC motors;adaptive control;angular velocity control;feedforward neural

nets;neurocontrollers;permanent magnet motors;real-time systems;self-adjusting systems;tuning;DC

motors;adaptive learning;biases;digital signal processor controller;feedback;feedforward neural-

network;online self-tuning;permanent magnet motors;real time system;speed control;Adaptive

control;Artificial neural networks;Control systems;DC motors;Digital control;Digital signal

processors;Programmable control;Real time systems;Signal processing algorithms;Velocity control},


Nguyen, C.T.-C.; Howe, R.T., "An integrated CMOS micromechanical resonator high-Q oscillator," Solid-

State Circuits, IEEE Journal of, vol.34, no.4, pp.440,455, Apr 1999

doi: 10.1109/4.753677

Abstract: A completely monolithic high-Q oscillator, fabricated via a combined CMOS plus

Documents

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