Characterization of Voltage Dips due to Faults and...

International Journal of Engineering Technology, Management and Applied Sciences

www.ijetmas.com February 2016, Volume 4, Issue 2, ISSN 2349-4476

121 Miss.Priyanka N.Kohad , Mr..S.B.Shrote

Characterization of Voltage Dips due to Faults and Induction

Motor Starting

Miss. Priyanka N.Kohad1 , Mr..S.B.Shrote2

Department of Electrical Engineering & E &TC Pune, Maharashtra India

Abstract: This paper focuses on events that cause a temporary decrease in the fundamental frequency voltage

magnitude. It is very important to improve the power quality levels.The main cause of voltage dips due to faults and

induction motor starting based on characterization is discussed From these discussion. the modified IEEE

distribution system is designed & simulated in PSCAD which can be used to locate the faults. Finally at the time

experimentation the data obtained from PSCAD is given to MATLAB program, in MATLAB program feature

extraction is carried out using wavelet transform. From magnitude of coefficient various statistical parameter are

calculated and used as an input to ANN for characterization of voltage dips due to faults and induction motor

starting

Keywords: Power quality,Voltage dips, power system faults, induction motor, wavelet transform, ANNs

I. INTRODUCTION

An electrical power system is expected to deliver undistorted sinusoidal rated voltage continuously at rated

frequency to the end users. A PQ problem can be defined as “any problem manifested in voltage, current,

or frequency deviations that results in failure or mal-operation of utility or end user equipment.”. Over the

last ten years, voltage dips have become one of the main topics concerning power quality among utilities,

customers and equipment manufacturers. Voltage Dip is a power quality problem that is prevalent in any

power system. It is said to be one of the main problems of power quality. Voltage dips have attracted a lot

of attention due to the problems that cause to equipment like adjustable speed drives, computers, industrial

control systems etc. The main causes of voltage dip are due to faults and large rating induction motor

starting. Since most of the electrica energy conversion to mechanical energy is done by the induction

motor. Modern power electronic equipment is sensitive to voltage variation and it is also the source of

disturbances for other customers. This increased sensitivity of the equipments to voltage dips has

highlighted the importance of quality of power, the electric utilities and customers have become much

more concerned about the quality of electric power service.

Voltage sags are referred to as voltage dips in Europe. IEEE defines voltage sags as a reduction in voltage

for a short time. The duration of voltage sag is less than 1 minute but more than 10 milliseconds (0.5

cycles). The magnitude of the reduction is between 10 percent and 90 percent of the normal root mean

square (rms) voltage at 50 Hz [11].

The major causes of voltage sags in electrical networks are:

Voltage dips due to Faults

Voltage dips due to Motor Starting

Voltage Dips due to Transformer energization

Extreme loading on a working induction motor can also cause a voltage dip in the network . To achieve the

goal, the results obtained from practical experiments in some special cases of voltage dip were studied.

Therefore, the presented results may not be regarded as general.

In this paper the simulation approach has been chosen to assess the effects of voltage dip on the

performance of induction motor. Therefore, as there are no restrictions in simulating under different

conditions, obtaining more complete and comprehensive results is possible.

For this purpose the simulations has been done by means of PSCAD software and MATLAB to ensure the

accuracy and precision of simulation in presence of voltage dips, the simulation results are compared

with the experimental results which are taken fro the experimental work. Then，the effects of the degree




of voltage dips and their start time on the motor performance are investigated. II. The modified IEEE

distribution test feeder system

Figure1 shows modified IEEE distribution test feeder. The system data is given in circuit parameters. The

objective is to discriminate the voltage dips due to faults and induction motor starting in a power system. This information may be used to take proper countermeasures to maintain the bus voltage during system faults

within specified limits.

Figure1: The modified IEEE distribution test feeder

Figure 2: Single-line diagram of the system simulated in PSCAD Software for induction motor starting




The voltage dip due to induction motor starting can be obtained by using time breaker logic.The

signals obtained from PSCAD are further analyzed using wavelet transform. The wavelet transform

decomposed the signal up to six decomposition levels by using Daubachies Db4 wavelet. The decomposition

gives approximations and detailed coefficients. The detailed coefficients at level 4 obtained from DWT are

further subjected to various statistical parameters for increasing the detection accuracy.Then these extracted

features are provided as an input to ANN for classification of voltage dips due to faults and induction motor

starting.

III. Method of Evaluation

Algorithm to classify the voltage dips due to faults and induction motor starting (simulation)

The modified IEEE distribution test feeder system is simulated in PSCAD.

The voltage dips is observed in the system voltage due to the creation of different faults like LG,

LL, LLG, LLL and LLLG. The faults are created in the circuit by using timed fault logic for

specifying the instant of fault and the duration. The voltage dip due to induction motor starting can be

obtained by using time breaker logic.

The Voltage waveform on four buses are plotted. But the study has been conducted on bus no.1.

The signals obtained from PSCAD are further analyzed using wavelet transform.

Discrete wavelet transform is calculating using Db4 wavelet up to sixth level.

Then various statistical parameters such as maximum value, standard deviation, variance,

skewness, kurtosis and energy are calculated of detailed coefficient at level 4 for increasing detection

accuracy.

The six different statistical parameters are given as input to the neural network. By using

Generalized feed forward neural network (GFNN) for six parameters gives 100% results in

simulation analysis i.e. 100% classification of voltage dips due to faults and induction motor starting

is done.

Algorithm to classify the voltage dips due to faults and induction motor starting (Experimental)

The experimental setup is arranged for classifying the voltage dips due to faults and induction

motor starting.

In an experimental analysis, the voltage dips are observed in the system voltage due to the

different faults like LG, LL, LLG, LLL, LLLG and starting of induction motor.

In order to acquire data DSO is used to capture the voltage dip signal. Then voltage dip waveform

are observed and captured on monitor instantly and save the data for further analysis.

Discrete wavelet transform is calculating using Db4 wavelet up to sixth level.

Then various statistical parameters such as maximum value, standard deviation, variance,

skewness, kurtosis and energy are calculated of detailed coefficient at level 4.

The six different statistical parameters are given as input to the neural network. By using

Generalized feed forward neural network (GFNN) for six parameters gives 100% results in

experimental analysis i.e. 100% classification of voltage dips due to faults and induction motor

starting is done.

IV. RESULT AND DISCUSSION

Wavelet transform approach (Simulation)

The signals obtained from PSCAD are further analyzed using wavelet transform. The wavelet




transform decomposed the signal up to six decomposition levels using db4 wavelet. The decomposition

gives approximations and detailed coefficients.

The decomposed signal for voltage dips are due to different faults like LG, LL, LLG, LLL, LLLG and

induction motor starting are as shown below.

Figure 3 :a) LG Fault, b)LL Fault c) LLG Fault d)LLL Fault e)LLLG Fault of Simulation f) Wavelet

decomposition of signal of voltage dip due to induction motor starting

Figure 3(a) shows the original signal and wavelet decomposition of waveforms of voltage signal up to sixth

level of LG fault i.e. (phase c to ground fault). The original signal shows the voltage dip due to LG fault. The

effect of LG fault can be more clearly visualized in D4 level. Figure 3(b) shows the original signal and

wavelet decomposition of waveforms of voltage signal up to sixth level of LL fault).Here fault involves phase

B and phase C. The original signal shows the voltage dip due to LL fault. The effect of LL fault can be

more clearly visualized in D4 level. Figure 3(c) shows the original signal and wavelet decomposition of




waveforms of voltage signal up to sixth level of LLG fault. Here fault involves phase A and phase C along

with the ground.

The original signal shows the voltage dip due to LLG fault. The effect of LLG fault can be more clearly

visualized in D4 level. Figure 3(d) shows shows the original signal and wavelet decomposition of waveforms

of voltage signal up to sixth level of LLL fault. Here fault involves all the three phases A, B and C

respectively. The original signal shows the voltage dip due to LLL fault. The effect of LLL fault can be more

clearly visualized in D4 level. Figure 3(e) shows the original signal and wavelet decomposition of

waveforms of voltage signal up to sixth level of LLLG fault. Here fault involves all the three phases A, B and

C along with the ground. The original signal shows the voltage dip due to LLLG fault. The effect of LLLG

fault can be more clearly visualized in D4 level. The wavelet decomposition of waveforms of voltage signal

up to sixth level using Db4 wavelet of induction motor starting is shown in figure.3 (f).

Wavelet transform approach (Experimental) The decomposed signal for voltage dips are due to faults like LG, LL, LLG, LLL, LLLG and

induction motor starting are as shown below.

Figure 4 :a) LG Fault, b)LL Fault c) LLG, Fault d)LLL Fault e)LLLG Fault of Experimentation f) Wavelet decomposition of signal of

voltage dip due to induction motor starting




From wavelet transform approach, classification of voltage dip due to faults and induction motor starting are

not possible by visual inspection. Because of this drawback various statistical parameters such as maximum

value, standard deviation, variance, skewness, kurtosis and energy are calculated.

Similarly if the worked done on statistical parameters such as maximum value, standard deviation,

variance, skewness, kurtosis and energy. It is clear that with the help of visual inspection of various statistical

parameters of voltage dips due to different faults and induction motor starting is not an easy task to classify

properly.Hence for proper classification, ANN technique is used.

ANN based classification

One of the most critical difficulties in constructing the ANN is the choice of number of hidden layers

and the number of neurons for each layer. Multilayer perceptron (MLP) and Generalized feed forward neural

network (GFNN) are used in this study.

The six different statistical parameters such as maximum value, standard deviation, variance, skewness,

kurtosis and energy are calculated for level 4 detailed coefficient is given as input to neural network. ANN

with transfer function TanhAxon, the learning rule used is momentum-0.7000, step size is 1.00000 and

maximum epochs are 1000 no.s is used to train the network. The training percentage is 75% and testing

percentage is

25%.The network is then tested and trained for various no. of processing elements in hidden layer. After

performing no. of iteration, at a certain value of processing element then 100% accuracy i.e. voltage dips due

to faults and induction motor starting are completely classified.

Simulation

Figure 5 indicates that when number of processing are taken as 14, then 80 % accuracy is obtained.

Experimental

Figure 6: indicates that when number of processing element is taken as 13, then 100% accuracy is




obtained. The voltage dip classification is performed for faults like LG, LL, LLG, LLL, LLLG and

induction motor starting.

By using Generalized feed forward neural network (GFNN) for six parameters such as maximum value,

standard deviation, variance, skewness, kurtosis and energy gives 100% results in simulation as well as

experimental analysis. Hence, the classification of voltage dips due to faults and induction motor starting is

done by using ANN technique from which there is 100% accuracy.

V.CONCLUSION

The modified IEEE distribution test feeder System is simulated in PSCAD. The data obtained from

simulation is in time domain. With the help of magnitude of voltage and duration of events, the cause of

voltage dips cannot discriminate properly. Hence in order to obtain correct classification the Wavelet-ANN

approach is used.

By using Generalized feed forward neural network (GFNN) for six parameters such as maximum value,

standard deviation, variance, skewness, kurtosis and energy gives 100% results in simulation as well as

experimental analysis i.e. 100% classification of voltage dips due to various types of faults and induction

motor starting is done.

REFERENCES

[1] Ozgur Gencer, Semra Ozturk, Tarik Erfidan, “A new approach to voltage sag detection based on wavelet

transform”, Electrical power and Energy system.

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Wavelet Transforms" International Journal of Computer, Vol. 18.No.1, April 2010, pp 61-66.

[3] Memon. A.P, T.R Mohamad, Z. A. Memon, “Detection of power Quality Disturbance using wavelet

Transform Techniques” International Journal for the advancement of science and Arts, Vol.1, Jan 2010.

[4] Julio Barros∗, Ramón I. Diego, Matilde de Apráiz, Applications of wavelets in electric power quality: Voltage

events, Electric Power Systems Research.

[5] Dolores Borrás, M. Castilla, Member, IEEE, Narciso Moreno, and J. C. Montaño, Senior Member, IEEE,

“Wavelet and Neural Structure: A New Tool for Diagnostic of power system Disturbances” IEEE

Transaction on industry application Vol.37, No.1, January/February 2001

[6] S. Suja, Jovitha Jerome, " Power Signal Disturbance Classification Using Wavelet Based Neural

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