Fault Diagnoses for Three Phase Inverter of the Power ...xajzkjdx.cn/gallery/38-dec2019.pdf · Fault classification is executed with the help of the Modified Adaptives Neuros-Fuzzy

Fault Diagnoses for Three Phase Inverter of the

Power Electronic Converter using DWT and

MANFIS

Mrs.Sujatha S, Assistant Professor, BMCE, Sasthamcotta

Ms. Hyma H S, Assistant Professor, BMCE, Sasthamcotta

Abstract: In industry, inverters that are centered on power electronics (such as, ‘3’-Phase Pulse

Width Modulation (PWM)) acts as the chief components. As there are numerous faults that are

present in the inverter, its performance debases. For the failure detection of disparate switches

and also tracing manifold kinds of faults that happen in an inverter, a fault diagnostics frame is

utilized which has a pattern recognition system and also machine learning technology as its

integral part. An integrated technique aimed at the process of fault diagnosis (FD) and

classification during open as well as short circuit faults that occur in ‘3’-phase inverter circuits

are proposed here. Initially, the discontinuity in currents as a consequence of the fault is detected

by utilizing Discrete Wavelet Transform (DWT), followed by which the features for FD are

extracted. Information concerning the transistor faults together with the equivalent fault ID can

well be on the fault dictionary. Fault classification is executed with the help of the Modified

Adaptives Neuros-Fuzzy Inference System (MANFIS). As of the experimental outcomes, it is

evident that the combination of DWT with MANFIS yields superior outcomes than the existent

techniques.

Key words: Fault Diagnosis, DWT, Power electronics converters, Adaptive Neuro-Fuzzy

Inference System (ANFIS), Modified ANFIS (MANFIS),

1. INTRODUCTION

Nowadays, Power Electronics (PE) is utilized by the majority of the techniques that go after

power conversion. The technological advancement of PE is a vital milestone that occurred during

the past century [1]. The PE devices are utilized increasingly in adjustable speed drives [2], arc

furnaces, escalators, bulk rectifiers, elevators, power supplies, fluorescent lamps, escalators,

Journal of Xi'an University of Architecture & Technology

Volume XI, Issue XII, 2019

Issn No : 1006-7930

Page No: 323

larger air conditioning systems, et cetera and also a rapid augmentation on renewable energy

generation [3]. PE systems at high power level handle static as well as rotating equipment for the

generation, transmission, in addition to distribution that is managing large measures of power

[4]. Quality and reliability are provided for today’s PE products and immense importance is

placed on high power density, high efficiency, along with low cost [5]. The PE equipment

incorporated silicon carbide (SiC) targeting high voltage, high power applications, and also

gallium nitride (GaN) targeting low voltage, low power applications under commercialization

[6].

PE systems play a major role in techniques that employ microprocessors encompassing

microcontrollers, Digital Signal Processors (DSP), along with Field Programmable Gate Arrays

[7] and the advance of Very Large Scales Integration (VLSI) and Ultra-Large-Scales Systems

(ULSS). An vital issue that the PE-Converters (PECs) face is the design of these devices.

Traditionally, PECs design is developed in the form of sequence, but it encompasses some cons,

such as the converter exhibit bad dynamic performance or unanticipated behavior in opposition

to disturbance along with uncertainties [8]. Also, the converters and inverters based system have

some faults. The faults are recognized by using some methods.

Generally, the PECs have five categories such as AC-DC converter [9] (Controller Rectifier),

DC-DC converter (DC Chopper), AC-AC converter (AC voltage regulator), DC-AC converter

(Inverter) and static switches. For the optimum harvesting of energy and also the integration of

affable energy systems, PE technology is a must. New and emerging power devices are

incorporated in the PE development, whose switching speed and efficiency’s performances are

higher. PE will mostly be utilized extensively in numerous applications in the future [10].

The remaining section is given as: Section 2 delineates the associated works. Section 3

detailed the proposed technique. Section 4 offers the investigational results. Section 5 deduces

the paper.

2. RELATED WORK

Seyed Saeid Moosavi et al. [11] generated an artificial neural network (ANN)-centered method

for FD subsequent to pattern extraction. For ANN training, the pattern below AC–DC converter

malfunction was employed. It encompassed ‘4’ disparate phases of switches fault centered on



Issn No : 1006-7930

Page No: 324

simulation as well as experimental outcomes. Made certain the accuracy together with the

oversimplification of the commenced pattern, capacitor size changes, load, along with speed

variations were regarded. The outcomes authenticated the simulation outcomes thoroughly.

Bilal Djamal Eddine Cherif and Azeddine Bendiabdellah [12] examined an analysis and

discovery of an open-circuit fault on a 3-phase 2-level voltage source inverter fed induction

motor. It was centered upon the DWT with the neural network (NN) aimed at the Insulated-gates

bipolar transistor (IGBT) open-circuit FD. Several experiments that were performed in the LDEE

laboratory authenticated the disparate attained outcomes for assessing effectiveness as well as the

pros of the joined DWT-NN.

Marjan Alavi et al. [13] introduced the 'FD-and-isolation’ technique aimed at faulty metal-oxide

semi-conductor field-effect transistors on a 3-phase PWM voltage sources inverter (VSI).

Voltage space stands as the time-free domain wherein the switching signals were analyzed. The

FD time was ‘1’ PWM carrier period and it was rapid compared to current centered traditional

techniques. The load, PWM switching frequency, along with the feedback loop was not included

while taking the outcome. The technique addressed the dependability issue of multi-level

inverters on renewable electrical generation systems as well as could dramatically lessen the

needed sensors.

V. Gomathy and S. Selvaperumal [14] exhibited a technique aimed at FD and also a

classification for open- along with short-circuits faults in 3-phase inverter circuits. The

discontinuity on currents that is brought about by means of a fault was detected by means of

DWT together with principal-component-analysis. The FD was extracted. A fault dictionary was

employed to attain details concerning transistor faults along with their equivalent fault

identification. Fault classification was carried out by means of a fuzzy logic along with a

relevance vectors machine (RVM). Investigational outcomes confirmed that the CSO-RVM gave

better outcomes than the CSO-fuzzy logic system.

Mehdi Salehifar et al. [15] developed a technique that could execute the fault-tolerance control

of a 5-phase BLDC motor called the finite controls set-models predictive control, which was

plain, flexible and fast. An FD that was centered upon the available data as of the control block

was employed. It was vigorous, simple, in addition, could localize manifold open circuit faults.



Issn No : 1006-7930

Page No: 325

The FD in addition to Fault-Tolerance control was integrated in a 5-phase BLDC motor drive. So

as to authenticate the presented theory, simulation along with investigational outcomes was

performed on a 5-phase 2-level VSI providing a 5-phase BLDC motor.

3. PROPOSED METHODOLOGY

For guaranteeing the safety along with reliability, the requirement of FD is more in the

diagnostic system. The prediction of faulty component/region is dealt with the fault analysis as

of the phase voltage, and also current features here. The prediction of the faulty component helps

for identifying the output signal variation that averts the damage to the load that is fixed at the

inverter end, which in turn offers safety and precaution. The faulty circuit is the incessant

unexpected circuit’s behavior. IGBT failures are classified as i) intermittent gate misfiring, ii)

open circuit and iii) short circuit faults. Centered upon wavelet transform (WT), every phase of

the 3-phase inverter circuit is examined. The transform coefficients’ standard deviation is

inputted to the classifier for the fault type’s identification. Thus, the FD of ‘3’-phase voltage

source inverter is done by the following steps:

Initially, the disparate fault features along with non-faulty conditions as of the inverter’s

phase voltage output are extracted utilizing DWT.

After that, by means of extracting the transform coefficients’ standard deviation, the fault

dictionary is built.

The identification of the fault-type is performed centered upon the CUT parameters in the

MANFIS classifiers. The proposed method’s architecture is exhibited in figure1.



Issn No : 1006-7930

Page No: 326

Figure 1: Architecture of the proposed method

3.1 Three Phase Inverter

The conversion of direct to alternating current is performed by the inverter. PWM

technique is utilized for controlling the switches. Inverter encompasses in total '3'-phases and

each of which encompasses '2' IGBT switches. Intersective method is employed to create the

PWM waveform. In this method, a triangle waveform is taken as a reference signal. The

modulation waveform is contrasted with a reference signal using a comparator. Inverter power

faults are bifurcated into i) short and ii) open circuit. IGBT stays in off-state for the open circuit

fault condition. In most cases, a short circuit causes an excessive flow current, which is detected

by the standard protection system, and shut down is carried out. Open-circuit fault happens when

the bonding wires are hefted on account of the thermic cycling, but it does not cause a shutdown,

instead, it demeans the system performance. Figure 2 exhibits the 3-phase voltage source

inverter’s basic structure.

Figure 2: Basic structure of three-phase voltage source inverter

3.1 Feature Extraction using DWT

This is performed to ameliorate the difference in the current change that occurs between the

transistor base drive short - open circuit fault as well as other faults, for instance, intermittent



Issn No : 1006-7930

Page No: 327

misfiring across inverter switching devices, load disturbance along with single line to ground at

machine terminal. Here, with the assist of DWT, the feature extraction is performed. In the signal

processing, the DWT decomposes an input signal in to a collection of elementary waveforms,

termed wavelets and gives the means to analyze the signal via examining the coefficients of

these wavelets. For detecting the change in the output parameter, DWT is employed. In general,

a localized region of a bigger signal is examined by wavelet. Wavelet analysis, unlike other

signal processing methods, shows higher competence analyzing patterns, break-down point’s

judgments along with discontinuity examination. Further, the analysis and synthesis of the

original signal can well be performed with reduced consumption time. The signals are analyzed

by using filters at different frequencies and scales. The Low Pass Filter together with High Pass

Filter is used in the analysis of low frequency and high-frequency signal respectively. The WT is

a method used for analyzing the signals. The DWT is a distinct case of the WT, which offers a

dense depiction of a signal that can efficiently be computed. The mathematical expression for

DWT is provided by

p

p

kp 2

2kqkd

2

1q,pDWT (1)

Where kd implies the discrete signal that is signified as a function of its coefficients,

signifies the mother wavelet (window function), p and q indicate the time scale parameters, k

implies the discrete-time along with total co-efficients in the DWT, p2 signifies the variable for

scaling, p2k indicates the variable for shifting, and p2

1 implies the energy normalization

component to make certain the same scale as the mother wavelet. The original signal sequence

kd could be signified by a sum of the entire elements, simply put, the sum of the entire details

and the approximation components at the last level of decomposition, For example, for two

levels of decomposition, the representation is:

kWfkDfkDfkWfkDfkd 2222111111 (2)

where 1D and 2D implies the detail elements of the 1st and 2nd decomposition levels

respectively, 1W and 2W are the approximation components of the 1st and 2nd decomposition



Issn No : 1006-7930

Page No: 328

levels, jf implies the coefficient of the detail or approximation components at the j th

decomposition level, as well as kWfkDfkWf 222211 .

In the Multi-Resolution Analysis implementation for DWT, the scaling along with

wavelet functions are provided by

qt22t p2p

q,p

(3)

qt22t p2p

q,p

(4)

Where, tq,p denotes the scale function, and tq,p is the wavelet function of time t .

3.2 Fault Dictionary Database

A database consisting of faults information that the simulators use to ascertain the fault coverage

is stated as a fault dictionary. The diagnostic system uses the fault dictionary to identify the fault

type while diagnosing the issue. Here, a fault dictionary is created by means of extracting the

transform coefficients’ standard deviation, which is taken as of [14].

3.3 MANFIS

A MANFIS system is utilized to classify the kinds of faults subsequent to getting the

standardized peak values of the wavelet coefficients of fault signals. ANN in addition to fuzzy

logic is utilized in the ANFIS structure, thus it encompasses pros of both. Its inference system

embodies a collection of fuzzy IF-THEN rules, which encompasses a learning capability for

approximating the non-linear functions.

The ANFIS structural design comprises ‘5’ layers. The initial layer ascertains the membership

functions (MF) of the input values, which is commonly called the fuzzification layer. The 2nd-

layer generates the firing strengths for the rules, thus, it is termed as a rule layer. By means of

dividing every value for the total firing strength, the 3rd-layer normalizes the calculated firing

strengths. The normalized values together with their consequence parameter set are inputted to

the 4th-layer and its output is called the defuzzificated ones, which are inputted to the final-layer

to acquire the last output.



Issn No : 1006-7930

Page No: 329

The normal ANFIS model utilizes the bell-MF whereas the proposed work uses Gaussian kernel

MF for ameliorating the rule generation process's performance. So the ANFIS is called modified

ANFIS (MANFIS). The two basic rules of ANFIS are specified in the below equations.

Rule 1: If 1cW is iS and 2cW is iT then,

i1ciicii zWvuWRules (5)

Rule 2: If 1F is 1iS and 2F is 1iT then,

1i1ci1ici1i1i zWvWuRules (6)

Where iS , iT , 1iS and 1iT specifies the fuzzy sets. ciW

and 1ciW values represent the

different extracted wavelet coefficients of fault signals. iu , iv , iz , 1iu , 1iv & 1iz values are the

parameter set. The layers in ANFIS are elucidated as follows

Layer-1: every node stands as an adaptive node encompassing a node function

ciSi,1 WOi

(7)

Here, ciW implies the input to node i . Each node adapts to a function parameter. The

output from each node is a degree of member-ship value that is provided by the input of the

member-ship function (MF). The MF that is utilized in the proposed work is Gaussian kernel

MF. This is specified in the succeeding equation.

2

i

2

ii

Sz2

vuexp

i

(8)

Where, iu , iv and also iz are the MFs parameters that could change the MF shape. The

parameters are alluded to as the premise parameters.

Layer 2: Every node here stands as a fixed node, which is labeled ANFIS, outputs the product of

the entire incoming signals.

)W()W(QO 1ciSciSii,2 ii (9)



Issn No : 1006-7930

Page No: 330

This layer i,2B output signifies the rule’s firing strength.

Layer 3: similar to layer-2, each node here stands as a fixed node that is labeled as N. The i th

node gauges the proportion of the firing strength of the ‘ i th’ rule and the sum of the entire rules'

firing strengths. And its output is called as normalized firing strengths.

6....2,1i,Q

QQO

i

i

ii,3

(10)

Layer 4: every single node similar to layer-1 is basically an adaptive node encompassing a node

function.

iii,4 Rules.QO (11)

Here, iQ implies the normalized FS as of the previous layer and iRules signifies the

system’s rule. The parameters that are employed are termed as succeeding parameters.

Layer 5: The single node that is present here is also a fixed node that is labeled ANFIS, which

calculates the general outcome as the summation of the entire incoming signals. And, the circle

node is symbolized as

i i

i ii

i

i

ii,5Q

RulesQRulesQO (12)

4. RESULT AND DEISCUSION

In this part, the proposed FD system’s performance is analyzed. The proposed work is

functioning in the platform of MATLAB/Simulink. The proposed FD system centered on

MANFIS’s performance is weighed against the existing Cuckoo Search Optimization-Relevance

Vector Machine (CSO-RVM) system centered on standard deviation, accuracy, along with

simulation time. The voltage on phase A, phase B, along with phase C of the inverter circuit is

regarded as input for the evaluation procedure. The output signal for non-faulty conditions of

phase A, phase B together with phase C is exhibited in Figure 3,



Issn No : 1006-7930

Page No: 331

Figure 3: Output signal for no-faulty conditions of phase A, B and C

Figure 4: Demonstrate the performance of the proposed MANFIS with the existing CSO-RVM

in terms of accuracy

Figure 4 shows the proposed MANFIS’s performance with the CSO-RVM centered upon

accuracy metric. The proposed MANFIS has 93.45% accuracy but the existing CSO-RVM has



Issn No : 1006-7930

Page No: 332

88.78% accuracy which is below the proposed work. It deduced that the proposed work has

enhanced performance when weighted with the existing methodology.

Figure 5: comparison graph for the proposed MANFIS with the existing CSO-RVM based on

the standard deviation

Figure 5 compared the proposed MANFIS’s performance with the existing CSO-RVM in

respects to standard deviation. The proposed MANFIS has a 347.89 standard deviation but the

existing CSO-RVM has a 296.32 standard deviation that is lower than the proposed method.

Thus, it deduced that the MANFIS has enhanced performance when weighted against the

existing method.



Issn No : 1006-7930

Page No: 333

Figure 6: Illustrate the performance of the proposed MANFIS with the existing CSO-RVM

based on simulation time in seconds

Figure 6 shows the proposed MANFIS’s performance with the existing CSO-RVM in terms of

simulation time in seconds. The proposed MANFIS took 0.35s for simulating the work but the

existing CSO-RVM takes 0.57s time for completing the task. Hence, it proved that the proposed

work has superior performance to that of the existing methodology.

5. CONCLUSION

The PE’s reliability is of supreme significance in aerospace, industrial, commercial, as well as

military applications. Faults that occur in power converters are categorized as i) short circuit, ii)

open circuit, and iii) degradation faults. Short circuits (S-C) in most cases bring about a

condition in which an excessive measure of current overflows that is readily detected and acted

upon by standard protection systems. An integrated technique aimed at the process of fault

diagnosis (FD) and classification during open as well as short circuit faults that occur in ‘3’-

phase inverter circuits are proposed here. Initially, a discontinuity of currents is detected that

occurred because of the fault utilizing DWT and the features are extracted. Then, the MANFIS

performs the Fault classification. The proposed system’s performance is compared with the

existent systems regarding standard deviation, accuracy as well as simulation time. The proposed

system gave a better performance when weighed in opposition to the existent method.



Issn No : 1006-7930

Page No: 334

REFERENCES

1. Molina, Marcelo G, “Energy storage and power electronics technologies: A strong

combination to empower the transformation to the smart grid”, Proceedings of the IEEE,

vol. 105, no. 11, pp. 2191-2219, 2017.

2. Bhim Singh, Bhuvaneswari G., and Sabha Raj Arya, “Review on power quality solution

technology”, Asian Power Electronics Journal, vol. 6, no. 2, pp. 19-27, 2012.

3. Chi-Seng Lam, Yongheng Yang, Qing Zhong, Yandong Chen, and Keng-Weng Lao,

“Advanced power electronic converters and power quality conditioning”, Journal of

Electrical and Computer Engineering, 2018.

4. Xinghuo Yu, Carlo Cecati, Tharam Dillon, and M. Godoy Simoes, “The new frontier of

smart grids”, IEEE Industrial Electronics Magazine, vol. 5, no. 3, pp. 49-63, 2011.

5. Fred Lee, C., Qiang Li, Zhengyang Liu, Yuchen Yang, Chao Fei, and Mingkai Mu,

“Application of GaN devices for 1 kW server power supply with integrated magnetics”,

CPSS Transactions on power electronics and applications 1, no. 1 (2016): 3-12.

6. Fei Fred Wang, and Zheyu Zhang, “Overview of silicon carbide technology: Device,

converter, system, and application”, CPSS Transactions on Power Electronics and

Applications, vol. 1, no. 1, pp. 13-32.

7. Núñez-Pérez, José Cruz, José Ricardo Cárdenas-Valdez, Katherine Montoya-Villegas, J.

Apolinar Reynoso-Hernández, José Raúl Loo-Yau, Christian Gontrand, and Esteban

Tlelo-Cuautle, “FPGA-based test bed for measurement of AM/AM and AM/PM

distortion and modeling memory effects in RF PAs”, Integration, the VLSI Journal,

vol. 52, pp. 291-300, 2016.

8. Sébastien Mariéthoz, Stefan Almér, and Manfred Morari, “Integrated control and circuit

design; an optimization approach applied to the buck converter”, In Proceedings of the

American Control Conference, pp. 3305-3310, 2010.



Issn No : 1006-7930

Page No: 335

9. Oliveira, J. G., Schettino H., V. Gama, R. Carvalho, and Hans Bernhoff, “Implementation

and control of an AC/DC/AC converter for double wound flywheel application”,

Advances in Power Electronics, 2012.

10. Yang Yongheng Huai Wang, Ariya Sangwongwanich, and Frede Blaabjerg, “Design for

reliability of power electronic systems”, In Power Electronics Handbook, pp. 1423-1440,

2018.

11. Seyed Saeid Moosavi, Abdesslem Djerdir, Youcef Ait-Amirat, Davood Arab Khaburi,

and Abdoul N'Diaye, “Artificial neural network-based fault diagnosis in the AC–DC

converter of the power supply of series hybrid electric vehicle”, IET Electrical Systems in

Transportation, vol. 6, no. 2, pp. 96-106, 2016.

12. Bilal Djamal Eddine Cherif, and Azeddine Bendiabdellah, “Detection of two-level

inverter open-circuit fault using a combined DWT-NN approach”, Journal of Control

Science and Engineering, 2018.

13. Marjan Alavi, Danwei Wang, and Ming Luo, “Short-circuit fault diagnosis for three-

phase inverters based on voltage-space patterns”, IEEE transactions on industrial

electronics, vol. 61, no. 10, pp. 5558-5569, 2014.

14. Gomathy V, and Selvaperumal S., “Fault detection and classification with optimization

techniques for a three-phase single-inverter circuit”, Journal of Power Electronics,

vol. 16, no. 3, pp. 1097-1109, 2016.

15. Mehdi Salehifar, and Manuel Moreno-Equilaz, “Fault diagnosis and fault-tolerant finite

control set-model predictive control of a multiphase voltage-source inverter supplying

BLDC motor”, ISA transactions, vol. 60, pp. 143-155, 2016.



Issn No : 1006-7930

Page No: 336

Documents

Fault Diagnoses for Three Phase Inverter of the Power ...xajzkjdx.cn/gallery/38-dec2019.pdf · Fault classification is executed with the help of the Modified Adaptives Neuros-Fuzzy