View
215
Download
0
Category
Preview:
Citation preview
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
AUSTRALIAN JOURNAL OF BASIC AND
APPLIED SCIENCES
ISSN:1991-8178 EISSN: 2309-8414 Journal home page: www.ajbasweb.com
Open Access Journal Published BY AENSI Publication © 2016 AENSI Publisher All rights reserved This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/
To Cite This Article: R. Sakthivel and Dr. M. Arun., Thermal Power System Stabilizer Design Using H∞ Robust Technique Based On
Enhance ABC Optimal Power System. Aust. J. Basic & Appl. Sci., 10(15): 261-271, 2016
Thermal Power System Stabilizer Design Using H∞ Robust Technique
Based On Enhance ABC Optimal Power System
1R. Sakthivel and 2Dr. M. Arun
1R. Sakthivel, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamil Nadu, India. 2Dr. M. Arun, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002,
Chidambaram, Tamil Nadu, India.
Address For Correspondence: R. Sakthivel, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002,
Chidambaram, Tamil Nadu, India. E
A R T I C L E I N F O A B S T R A C T
Article history:
Received 26 August 2016 Accepted 10 October 2016
Published 18 October 2016
Keywords:
PSS, RPSS, CPSS, H∞, Enhanced ABC
As power systems are Complex, very large and geographically distributed, it is difficult
to solve the low frequency oscillation problems. Therefore, it is most vital to implement a robust power system stabilizer with efficient optimization method. This paper
proposes a method which deals with thermal power system stability under deregulated
environment. In this work, a robust thermal power system stabilizer is implemented to meet the objective of frequency stability. In order to stabilize the tie-line power and
frequency oscillations effectively, an optimization technique of Enhanced Artificial Bee
Colony Algorithm is analyzed. The robust thermal power system stabilizer (RPSS) is designed using enhanced ABC to implement the controllers for dynamical systems in
electrical engineering. The results of proposed PSS with H∞ optimization and the
Conventional power system stabilizer (CPSS) are compared. An experimental results show that in comparison with other techniques, enhanced ABC is more effective to
produce desired response.
INTRODUCTION
Power System Stabilizers (PSS) are the most familiar and efficient devices to damp the power system
oscillations produced by interruptions. The transient stability of a system can be enhanced by providing suitably
tuned power system stabilizers on selected generators to provide damping to critical oscillatory modes. Properly
tuned Power System Stabilizers (PSS) will initiate a component of electrical torque in phase with generator rotor
speed deviations consequential in damping of low frequency power oscillations in which the generators are
involving. The input to stabilizer signal may be one of the locally accessible signals such as changes in rotor
speed, accelerating power, rotor frequency, electrical power output of generator or any other suitable signal.
This stabilizing signal is remunerated for phase and gain to result in suitable component of electrical torque that
results in damping of rotor oscillations and thereby increase power transmission and generation capabilities.
Constantly increasing complexity of electric power systems has enhanced interests in evolving superior
methodologies for Power System Stabilizers (PSS). Dynamic stability transient and considerations are among
the main disputes in the reliable and effective power system operations. Low Frequency Oscillation (LFO)
modes have been observed when power systems are interconnected by weak tie lines. The LFO mode, with
weak damping, is also known as electromechanical oscillation mode, and it generally occurs in the frequency
range of 0.1 to 2 Hz. PSSs are the very effective devices for damp out these oscillations.
262 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
In Soliman (2014), a simple analytical method is suggested to compute the set of three terms of a stabilizing
PSSs. Therefore, PID controller based stabilization of the interval plant and PSS based phase lead compensator
is dealt using generalized Kharitonov’s theorem. Furthermore, sufficient and necessary constraints to
characterize the efficient stabilizing three term controllers are derived by implementing the Routh–Hurwitz
criterion to all of segment/vertex plants.
In Abdul-Ghaffar et al., (2013), a design of a SMIB power system for the stability enhancement with PID-
PSS has been implemented, in which, Hybrid Particle Swarm-Bacteria Forging Optimization (PSBFO)
technique is used to optimize its parameters. A PID based on real coded GA is developed in Duman and Ozturk
(2010) to improve power system dynamic, in which the parameters of the proposed stabilizer are corrected by
using real coded GA. In Ramya and Selvi (2015), a dynamic simulator is developed to simulate a synchronous
power plant for practical application. To verify the control devices using Real Time Interface in virtual
environments, the SMIB model is executed in DSP(Digital Signal Processor) of dSPACE hardware, which is a
platform for real time simulation. Yang proposed a capable metaheuristic BA. The author suggests that the BA
have superior performance over GA and PSO Yang (2010). Thermal energy refers to the energy present in a
system by feature of its temperature. The average translational kinetic energy possessed by free particles in
thermodynamic equilibrium may also be mentioned to as the thermal energy per particle.
Thermal energy is the portion of the thermodynamic or core energy of a system that is responsible for the
temperature of the system. The thermal energy of a system measures with its size and is therefore an extensive
property. It is not a state function of the system unless the system has been built so that all changes in internal
energy are due to thermal energy changes, as a result of heat transfer. Otherwise thermal energy is dependent on
the way by which the system achieved its temperature. Thermal energy can be converted into and out of other
types of energy, and is not generally a conserved quantity.
In the conventional PSS, the normal controller circuit is used. It balances the load coming from the thermal
power plant. Here the value of Kp, Ki and Kd value are constant and the output will be the normal output. While
using H∞ technique, value of Kp, Ki and Kd value are constant and the output as like as normal PSS output but
compared to normal PSS, H∞ technique output will be better.
In this, a thermal based power system stabilizer in deregulated environment is proposed. The proposed
enhanced ABC optimization technique is compared with conventional PSS and PSS with H∞ optimization
technique.
Literature Of Review:
Surveyed on automatic generation control in power systems are discussed. Several configurations of power
systems such as single area thermal system, single area hydro system and Multi area interconnected are
addressed. The various control techniques used in several configurations also studied (Kamali and
WahidaBanu, 2013)
Performance of three area reheat thermal power system is enhanced by using SMES energy storage unit and
response is related with and without considering energy storage unit in all areas. Damping oscillations, settling
time, peak overshoot are improved, when associated to the response of system without considering SMES unit.
General PI controller gain values are enhanced using Integral Time Absolute Error (ITAE) performance index
criterion (Jagatheesan et al., 2014).
Analyzed on dynamic performance of Load Frequency Control (LFC) of three area interconnected
hydrothermal reheat power system by using Artificial Intelligent and PI Controller. In the proposed scheme,
control methodology established using conventional PI controller, Fuzzy Logic controller (FLC) and Artificial
Neural Network (ANN) for three area interconnected hydro-thermal reheat power system (Surya Prakash and.
Sinha, 2010).
A new technique fuzzy logic PI controller is intended for automatic load frequency control of
interconnected power systems. The controller performances Fuzzy logic PI method is in work for a Load
Frequency Control for Generation of Interconnected Power System. The proposed controller can lever the non-
linearity and at the same time faster than other conventional controllers. The effectiveness of the projected
controller in increasing the damping of local and inter area modes of oscillation is established in a two area
interconnected power system (Ramanand Kashyap et al., 2013).
Proposed an Automatic Generation Control (AGC) of interconnected two area Hydro-Thermal System
using usual integral and fuzzy logic controllers. Effects of different number of inputs and triangular membership
functions for fuzzy logic controller on dynamic responses have been discovered. 1% step load perturbation has
been considered happening either in individual area or occurring simultaneously in all the areas (AshisTripathya
et al., 2012).
In (Akshay Kumar et al., 2015), a system based on Sliding mode control (SMC) is developed for the control
of TCSC and PSS to enhance the dynamic stability of the single machine infinite bus’s (SMIB). Generally, a
Sliding mode controller can give good performance in presence of both disturbance like unknown nonlinear
263 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
function and uncertainties. Kanthalakshmi Srinivasan et al., (2014) developed a Fuzzy based Sliding Mode
Control PSS to improve the stability and dynamic response of the power system under fault conditions.
A modern robust load frequency controller for two area interconnected power system is offered to quench
the deviations in frequency and tie line power due to various load disturbances. The dynamic model of the
interconnected power system is established without the integral control. The area control error is also not
involved. The frequency and derivatives are zero under regular operation and after the disturbance effects are
expired. Then the problem is reorganized as the problem of state transfer from the initial steady state to final
steady state without oscillations in less time (VenkataPrasanth and Jayaram Kumar, 2009).
Optimal proportional-plus-integral controller with Particle swarm optimization is intended for load
frequency control of a two area thermal power system. The model is determined an optimization problem and a
diminishing the error function is derived for improving the performance of convergence to the solution. To
enhance the parameters of the PI controller, fuzzy logic technique and the particle swarm optimization algorithm
are used (Amitesh Kumar et al., 2014).
Load Frequency Control (LFC) of isolated two-area and single area re-heat inter-connected thermal power
system has been carried out by the classical controllers and performed on the system at 1% step load
perturbation in nominal loading conditions. Responses of deviation in tie-line power , deviation in frequencies
and choosing the optimum controller gain values to have well dynamic responses of the system have been
plotted, keeping in view the characteristics such as rise time, settling time, oscillations & peak overshoot (2014).
A proportional-integral-derivative controller (PID controller) is a control loop feedback
mechanism (controller) generally used in industrial control systems. A PID controller computes an error value
as the difference between a measured process variable and a exact set point. The controller efforts to minimize
the error by adjusting the process through the utilization of a manipulated variable.
The PID controller algorithm comprises three separate constant parameters, and is accordingly sometimes
known as three-term control: the proportional, the integral and derivative values, denoted P, I, and .Generally
put, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation
of past errors, and D is a future error predictions, based on current rate of change. The weighted sum of these
three actions is used to regulate the process via a control element such as damper, the power supplied to a
heating element or a position of a control valve. For a discrete time case, the term PSD, for proportional-
summation-derivative, is frequently used.
Some applications may need using only one or two terms to provide the appropriate system control. This is
attained by setting the other parameters to zero. A PID controller will be called a PI, PD, P or I controller
without the respective control actions. PI controllers are fairly common, since derivative action is delicate to
measurement noise, whereas the non-appearance of an integral term may prevent the system from reaching its
target value due to the control action.
The PID control scheme is named after its three correcting conditions whose sum comprises the
manipulated variable (MV). The proportional, derivative and integral terms are summed to determine the output
of the PID controller. The final equation of the PID algorithm is:
𝑢(𝑡) = 𝑀𝑉(𝑡) = 𝐾𝑝𝑒(𝑡) + 𝐾𝑖 ∫ 𝑒(𝜏)𝑑𝜏 + 𝐾𝑑𝑑
𝑑𝑡𝑒(𝑡)
𝑡
0
Where
Kp: Proportional gain, a tuning parameter
Ki: Integral gain, a tuning parameter
Kd: Derivative gain, a tuning parameter
e: Error =SP-PV
t: Time or instantaneous time (the present)
𝜏: Variable of integration; takes on values from time 0 to the present t.
Equivalently, the transfer function in the Laplace Domain of the PID controller is
𝐿(𝑠) = 𝐾𝑝 +𝐾𝑖
𝑠⁄ + 𝐾𝑑𝑠
Where S: complex number frequency
H∞ Robust Design Technique Based On Enhance Abc Optimal Power System Stabilizer:
H∞
H∞ methods are used to synthesize controllers attaining stabilization with guaranteed performance. To
use H∞ methods, a control designer expresses the problem as a mathematical optimization problem and then
finds the controller that resolves this optimization. H∞ techniques have the benefit over classical control
techniques in that they are eagerly applicable to multivariate system problems with cross-coupling between
channels. But non-linear constraints such as saturation are generally not well-handled.
264 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
Artificial Bee Colony (ABC):
In ABC algorithm, the processes of the exploration and exploitation contrast with each other, so the two
abilities should be well balanced for attaining good optimization performance. Owing to the search form of
ABC algorithm, a candidate solution would be generated by moving the preceding one towards another solution
selected randomly from the population. The ABC algorithm has previously proved to be a very effectual
technique for solving global optimization. ABC is not only a high performance optimizer which is very simple
to understand and implement. Yet, ABC could be slow to converge and sometimes trap in a local optimal
solution.
Enhanced Artificial Bee Colony Algorithm:
Fig. 1: Flow chart for Enhanced Artificial Bee Colony algorithm
In order to further enhance the performances of ABC, three main changes are made by introducing the best-
so-far solution, inertia weight and acceleration coefficients to alter the search process. In addition, the search
form of ABC is good at exploration but poor at exploitation. Therefore, to improve the exploitation, the
modification forms of the employed bees and the onlooker ones are different in the second acceleration
coefficient.
In initialization, EABC similar to ABC starts by associating all employed bees with arbitrarily generated
food sources. After initialization, the food source populations are subject to repetitive cycles of the search
processes of the employed bees, the onlooker bees and the scout bees. The main differentiation between EABC
and ABC is how bees get the candidate solutions. In EABC, an employed bee initially works out three new
solutions by three different solution search equations, and then selects and identifies the best one as the
candidate solution. Here, due to calculating the candidate solution before the employed bee choose where they
should go to discover, the process of calculating new food position is called ‘predict’. Subsequent to the bees
‘predict’ new candidate solution by three different solution search equations, they pick the best one from the
three solutions as the candidate solution. If the fitness values of the candidate solution is superior than the best
fitness value achieved so far, then the employed bee’s moves to this new food source and synchronously leaves
the old one, or else it remains the prior food source in its mind. If all employed bees have completed this
process, they share the fitness information with the onlookers, each of which prefers a food source according to
probability. As in the case of the employed bee, an onlooker ‘predicts’ three alteration on the position in her
memory, and then chooses the best one as the candidate source and verifies the fitness value of the candidate
source. Offering that the fitness value of the candidate source is better than that of the preceding one, the bee
would memorize the new position and forget the old one. In EABC, the three solution search equations are
265 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
separately calculated, but influence each other by the selected best solution. Overall the EABC has inherited the
clever sides of the other three algorithms.
Modelling Of Thermal Power System:
The power system taken in this study is modeled as a single synchronous generator connected through a
parallel transmission line to a very large network approximated by an infinite bus (SMIB) based on thermal
units. The state variables measured here be speed deviation and power system acceleration. Let Pm and Pc
signifies the mechanical and electrical power respectively. The developed Simulink model of the thermal power
system is shown in the following figure
Fig. 2: Block diagram of the proposed SMIB power system
Fig. 3: Simulink model of with (RPSS) and (CPSS) control for the PSS
A speed limiter is a governor utilized to bound the top speed of a vehicle. For some classes of vehicle and in
some jurisdictions they are a statutory requirement, for some other vehicles the manufacturer offers a non-
statutory system which may be fixed or programmable by the driver. A steam boiler component in which heat is
applied to intermediate pressure steam, which has given up some of its energy in extension through the high
pressure turbine.
A turbine is a rotatory mechanical device that extracts energy from a fluid flow and translates it into
useful work. A turbine is a machine with at least one moving part termed as rotor assembly, which is a shaft or
drum with attachment of blades. Moving fluid performances on the blades so that they move and impart
rotational energy to the rotor.
266 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
Simulation Results:
An optimization method is examined here for PID gains setting. Response of active power, terminal
voltage, stator angle voltage, speed deviation was observed. Comparison of the robust optimization technique
enhanced ABC with the conventional PSS and PSS with H∞ technique show that optimization technique can
attain excellent robustness, while the design process used in much simpler.
In this thermal PSS design, the robustness of PSS should be evaluated in different loading conditions and
different operating conditions. The change of operating conditions corresponds to the changes of transmission
line parameters and the active and reactive powers.
The quantitative results of the comparison of the static and dynamic performances (Static error, damping
coefficients, peak time and settling time) with CPSS, thermal PSS (RPSS) and with Horch et al.,(2014) and
Sakthivel and Arun (2016) of the different parameters are shown in table I and II. The proposed Table I and
Table II clearly show the efficiency of the proposed method in comparison with CPSS. Comparing the results of
the system it can be directly identified that very large developments of static and dynamic performances of the
system with the RPSS in comparison with the application of the CPSS and also outperforms over Horch et
al.,(2014) and Sakthivel and Arun (2016). Simulation results demonstrate the good damping performance of the
robust designed thermal PSS with enhanced ABC. Results show that enhanced ABC, an optimization method is
more effective to damp out oscillations.
I.Under-exited mode x=0.5 , y=0.85 , z=0.1802
267 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
II. Nominale mode x=0.3, y=0.85, z=0.1102:
268 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
c) Stator Angle Voltage:
III. Over-excited mode x=0.2 ,y=0.85, z=0.6760:
269 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
Table I: Damping Coefficients ‘α’ and static error ‘ξ’ in the closed loop system with RPSS and CPSS in different operating conditions of
the power system
Reactive
Power
αPSS ξPSS αPSSH∞ ξPSSH∞ αWPSSEABC ξWPSSEABC αTHPSSEABC ξTHPSSEABC
-0.2033 0.6574 0.00119 0.6846 0 0.7121 0 0.7322 0
-0.2449 0.6564 0.0012 0.6853 0 0.7211 0 0.7441 0
-0.1238 0.6695 0.00112 0.6960 0 0.7321 0 0.7510 0
-0.3402 0.6671 0.00089 0.7038 0 0.7382 0 0.7624 0
-0.6840 0.6574 0.00071 0.6877 0 0.7401 0 0.7743 0
Table II: Settling time ‘Ts’ and peak time ‘Tp’ in the closed loop system with RPSS and CPSS in different operating conditions of the power
system
Reactive
Power
TS PSS TP PSS TS PSSH∞ TP PSSH∞ TS WPSSEABC TP WPSSEABC TS THPSSEABC TP THPSSEABC
-0.2033 0.93 0.51 0.6 0.464 0.38 0.34 0.298 0.28
-0.2449 0.92 0.51 0.594 0.461 0.372 0.334 0.291 0.261
-0.1238 0.65 0.5 0.59 0.46 0.367 0.3217 0.269 0.25
-0.3402 0.81 0.46 0.549 0.435 0.3211 0.312 0.258 0.233
-0.6840 0.84 0.47 0.56 0.44 0.303 0.304 0.251 0.219
270 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
Conclusion:
This paper proposed a systematic model for automated power system stabilizer using enhanced ABC
algorithm to improve damping and reducing tie line power oscillations in deregulated environment. In this work,
the thermal power is given as input and the output is compared with the CPSS and Wind PSS. The Simulink
model was developed and tested using MATLAB software. The response of speed deviation, stator terminal
voltage, Interior angle and active power were plotted. Results of simulation show that enhanced ABC optimized
controller provides good damping performance. Comparison with the existing methods shows that the proposed
controller can achieve good robustness over the other controllers. In future, fuzzy controller or ANN controller
can replace the PID controller in order achieve more robust power system stabilizer.
REFERENCES
Abdessamad Horch, Abdellatif Naceri, Ahmed Ayad, 2014. Power system stabilizer design using H∞
robust technique to enhance robustnesse of power system” IEEE.
Abdul-Ghaffar, H., E.A. Ebrahim, M. Azzam, 2013. Design of PID controller for power system
stabilization using hybrid particle swarm-bacteria foraging optimization. WSEAS Trans Power Syst, 8: 12-23.
Akshay Kumar, P.C. Panda, S.C. Swain, 2015. Coordinated Design of PSS and Sliding Mode Based TCSC
Controller for Enhancing Dynamic Stability of Power System, Australian Journal of Basic and Applied
Sciences, 9(16): 289-293.
Amitesh Kumar, Ajay Kumar Singh, Mukesh Kumar Singh, 2014. Atul Sharma, Load Frequency Control
with Thermal and Nuclear Interconnected Power System Using Optimized Controller”, 2: 2.
AshisTripathya, Ajit Kumar Mohantyb, Shubhendu Kumar Sarangic, 2012. Automatic Generation Control
of an Interconnected Hydro-Thermal System Using Fuzzy Logic and Conventional Controller, International
Journal of Scientific & Engineering Research, 3: 8.
Bhateshvar, Y.K., H.D. Mathur, 2014. Frequency Stabilization for Thermal-Hydro Power System with
Fuzzy Logic Controlled SMES Unit in Deregulated Environment, IEEE.
Chung-Liang Chang, Chuan-Sheng Liu, K.O. Chun-Kuang, 1995. Experience with power system stabilizers
in a longitudinal power system, IEEE Trans. on Power Systems, 10(1): 539-545.
Dilip Parmar, Amit ved, 2015. Performance Analysis of Transient Stability and Its Improvement Using
Fuzzy Logic Based Power System Stabilizer. International Journal of Engineering Development and Research,
3: 2.
Duman, S., A. Ozturk, 2010. Robust design of PID controller for power system stabilization by using real
coded genetic algorithm. Int Rev Electr Eng., 5: 925-31.
Horch, A., A. Naceri, A. Ayad, 2014. Power system stabilizer design using H∞ robust technique to
enhance robustnesse of power system, IEEE.
2014. International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264) 2: 2.
Jagatheesan, K., B. Anand, Abhilash Das, 2014. Improved Dynamic Performances Of Multi-Area Reheat
Thermal Agc Power Systems With Energy Storage Unit, IJRET: International Journal of Research in
Engineering and Technology, 03: 07.
Kallol Das, Priyanath Das, Sharmistha Sharma, 2012. Load Frequency Control Using Classical Controller
in An Isolated Single Area and Two Area Reheat Thermal Power System, International Journal of Emerging
Technology and Advanced Engineering, 2: 3.
Kamali, S.M., Dr. R.S.D. WahidaBanu., 2013. A Detailed Analysis of Automatic Generation Control In
Power Systems , International Journal of Emerging Trends in Engineering and Development, 3: 6.
Kanthalakshmi Srinivasan, Latha Ramasamy, Kanagaraj Jeganathan, 2014. Design of Power System
Stabilizer using Fuzzy based Sliding Mode Control Technique. Australian Journal of Basic and Applied
Sciences, 8(18): 90-99.
Kundur, P., D.C. Lee, H.M. Zein El-Din, 1981. Power System Stabilizers for Thermal Units: Analytical
Techniques and On-Site Validation, IEEE Trans, PAS-100, 1: 184-198.
Lod Tapin, Dr. Ram Krishna Mehta, 2014. Overview and Literature Survey of Power System Stabilizer In
Power Systems, International Journal of Engineering Research and Development, 10: 6.
Neharika Gupta, Shiv Narayan, 2015. Design of power system stabilizer using robust controltechniques”
IEEE.
Ramanand Kashyap, Prof. S.S. Sankeswari, Prof. B.A. Patil, 2013. Load Frequency Control Using Fuzzy PI
Controller Generation of Interconnected Hydro Power System, International Journal of Emerging Technology
and Advanced Engineering, 3: 9.
Rajeev Gupta, Bandyopadhyay, B., A.M. Kulkarni, 2003. Design of power system stabilizer for single
machine system using robust fast output sampling feedback technique”65: 3.
Ramya, R., K. Selvi, 2015. Real time simulation of single machine infinite bus system using dSPACE
controller board. Power electronics and renewable energy systems. India: Springer, pp: 783-92.
271 R. Sakthivel and Dr. M. Arun, 2016
Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271
Ravi. V and K. Dr. Duraiswamy, 2012. A Novel Power System Stabilization Technique using Advanced
Genetic Algorithm Optimization Approach, Bonfring International Journal of Power Systems and Integrated
Circuits, 2: 1.
Sakthivel, R., M. Arun, 2016. Wind Energy Power System Stabilizer design using H∞ Robust Technique
based on Enhance ABC Optimal Power System” International journal of computer applications, 149: 11.
Sanjana Roy, Dr. Bhushan Bansal, 2014. Power System Stabilizer in terms of Operation, Dynamics, and
Control Technique, International Journal of Science, Technology & Management, 03: 07.
Shin, J.H., J.G. Lee, 2009. A Tuning Method for the PSS of a Large Thermal Power Plant and its
Application to Real Power System: Part I-Selection of parameters by Off-line Simulation”, Journal of KIIEE.,
23: 12.
Soliman, M., 2014. Parameterization of robust three-term power system stabilizers. Electr Power Syst Res.,
117: 172-84.
Surya Prakash, S.K. Sinha, 2010 . Load frequency control of three area interconnected hydro-thermal reheat
power system using artificial intelligence and PI controllers, International Journal of Engineering, Science and
Technology, 4(1): 23-37.
VenkataPrasanth, B., Dr. S.V. Jayaram Kumar, 2009. Load Frequency Control For A Two Area
Interconnected Power System Using Robust Genetic Algorithm Controller, Journal of Theoretical and Applied
Information Technology.
Yaser Soliman Qudaih; Yasunori Mitani; Tarek Hassan Mohamed, 2012. Wide-
Area Power System Oscillation Damping Using Robust Control Technique, IEEE.
Yang, X.S., 2010. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for
Optimization (NISCO 2010). In: Gonzalez JR, et al., editors. Studies in computational intelligence, 284: 65-74.
Recommended