a comparative study of power distribution over voltages classification using feed forward artificial...

8/18/2019 A Comparative Study of Power Distribution Over voltages Classification Using feed forward Artificial Neural Networ…

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A Comparative Study of Power Distribution Over

voltages Classification Using feed forward ArtificialNeural Networks and General Regression Neural

Networks Pascal Dieu Seul Assala, Haoyong Chen

#

South China University of Technology , Guangzhou , 510641 PR-China School of Electric Power

Abstract- Power outages due to overvoltage and following damages affect a large part of

power distribution networks. A right identification system for the overvoltage can help in

fast intervention and shorten the outage duration. In the present work, authors present a

comparative study of distribution overvoltage identification. The study is conducted with

data simulated from ATP-EMTP with a network designed from data of real case studies

conducted on 10kV distribution network of the city of Qingyuan electrified by the China

Southern Power Grid. The comparison study is conducted with data from time domain

analysis and Wavelet Packet Decomposition (WPD) analysis. Two identification tools are

used: the General Regression Neural Network (GRNN) and the Feed forward Artificial

Neural Network (FANN). Training and identification performances are compared at the

end of the study bringing out some specificities of the two identification tools regarding

identifications of the direct strike lightning overvoltages, temporary overvoltages and

capacitor bank energization overvoltages.

Keywords: Power distribution, neural networks, over voltages, wavelet packet

decomposition

# Corresponding AuthorE-mail : [email protected]

I. INTRODUCTION

Overvoltage in power distribution system

is the first cause of damage in electronics

[1]. A power outage is also a phenomenon

induced by distribution overvoltage events.

In order to protect a sub-network, a

protection relay will trigger to stop the

energy generated from the surge event to

flow towards the protected area. This


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triggering will sometimes be repeated in a

sequence to attempt to re-energize the

protected area until the surge is detected to

delay longer and, therefore, the protection

apparatus will definitely shut down the

protected network area awaiting for the

distribution network response team to

manually come to inspect the possible

causes and ensure the security of the

affected network prior to the next

energization balanced study of overvoltage

identification between the most used

pattern recognition tool the FANN and the

GRNN a new generation of neural

networks (NN) introduced by Specht [2]

belonging to the probabilistic neural

networks (PNN) family.

Regardless of the source provoking the

event, over voltages are harmful to power

network system lines and can cause

insulator breakdown and can decrease

magnetic circuit quality. In a power

transformer, this will bring a loud noise

and higher Foucault currents circulating in

the magnetic circuit leading to transformer

heating. Voltage surge will often cause

equipment damage, maloperation of

protective devices and even loss of human

life. In the area of power quality, several

research works have been done providing

very good results for overvoltage

classification. The short-time Fourier

transform (STFT) [3] is a deterministic

versus statistical method analysis [4], a

covariance analysis [5]. Support vector

machine (SVM) theory used for

classification is combined with wavelet

transform (WT) method [6] and has also

been associated with S-transform as a

discrimination tool [7]. ANN pattern

recognition capabilities have been used in

Ref. [8]. In Ref. [9], ANN is combined

with the Fisher discriminant function

(FDF).

At present, overvoltage classification

works have gained a lot of attention from

the researchers and many research works

have brought about many advances in the

area. GRNN and FANN capabilities are

used with different datasets simulated on

ATP based on a real-world distribution

system of Qingyuan city, which is a part of

China’s Southern Power Grid. Processed

samples are generated from time domain

and time-frequency domain analysis of

simulated data. Results obtained highlight

the advantages and specific aspects of

using GRNN and FANN for applications

of overvoltage classification.


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1.Wavelet Packet Decomposition

Wavelet packet decomposition (WPD)theory emerges from wavelet signal

processing. It was introduced by

Coifmanand Wickerhauser [10]. WPD

gives a more complete signal time

frequency analysis compared to the

discrete wavelet transform (DWT). The

DWT recursively processes a signal using

the pyramid algorithm [11]. In DWT, the

discrete signal of length N to be processed

is sent to two mirror filters. The output of

the low-pass filter consists of N /2 wavelet

coefficients ar ={a

r

1,a

r

2,a

r

3,....a

r

( N /2)!1}

being the approximation of the signal at

scale r of wavelet signal analysis. The

high-pass filter outputs the detail

coefficients d r ={d

r

1,d

r

2,d

r

3,....d

r

( N /2)!1} at

the resolution level r . The difference

between WPD and DWT is that the DWT

will only use the wavelet coefficients for

processing from one resolution level to

another while the WPD will use both low-

pass and high-pass filters’ output. This

gives a full signal sub-band representation.

FigureFig! # presents the pyramidal

processing of WPD. Shannon wavelet

packets with low-pass filter " and high

pass filter # from Ref. [11] as in Eqs. (1) &

(2) respectively are used in the present

study.

Fig. 1: Wavelet Packet Decomposition

Tree.

2. Feedforward Artificial Neural

Networks

A feed forward artificial neural network

(FANN) is a type of classic artificial

neural networks (ANN) that are based on

the first model of human neuron presented

by McCulloch and Pitts. The artificial

neuron in the model used on FANN is a

mathematical unit built on a set of inputs, a

summation unit, an activation function and


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an output. Mathematically, a neuron is

presented as modeled in Eq. (3),where X is

the neuron with number of n inputs and

one output y(X), g is the activation

function; it computes the neuron output

based on the net function Netf that is

mathematically modeled in Eq. (4). The

sigmoid activation function is presented in

Eq. (5).

In FANN, a neuron located in layer L$

only sends information to neurons located

at layer L$+1 and only receives signals

from neurons of layer L$!1 [12]. Three-

layer FANNs are very powerful tools for

pattern recognition that can be used to

implement a wide range of real life

applications requiring a decision-making

[13]. The network in this study is a three-

layer FANN. Training algorithm and

network information are as presented in

Table 1. A three-layer FANN at its first

layer (also called input layer) will receive

an input pattern of the overvoltage data to

be identified; the hidden layer is made of

neurons, each of them computes its output

independently of others of the same layer.

The output layer in our case is a unique

neuron that outputs the identified type best

matching the input pattern.

Table 1: Basic Characteristics of the FANN.

The mathematical expression of Sigmoid

activation function is presented in Eq. (5).

3. General Regression Neural

Networks

Firstly developed by Specht [2], a GRNN

is a type of probabilistic neural network.

For a known joint probability density

function f ( x, y), the regression of y gives an

input X as presented in Eq. (6). X is a

vector of size p; p%1.

Element Value and details

Activation function

Sigmoid

1

Topology Feedforward

multilaye perceptron

Number of layers 3

Learning type and

algorithm

Supervised, LMS


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f ( x, y) in the ongoing overvoltage

identification study is unknown. Assuming

that the first partial derivative of f is small

at any x and the underlying density is

continuous, as stipulated in Ref. [2], for

our GRNN, neurons will use the third

activation function in table 1 of Ref. [14]

for computing the probability estimator of

f ( X ,Y ) presented in Eq. (7).

Combining Eqs. (6) & (7) and calculating

different integration yields to the generic

model of GRNN. (Eq. (5) in Ref. [2])

presents the mathematical model. In regard

to the work conducted in the present paper,

the model (Eq. (9) in Ref. [2]) presented in

Eq. (8) has been chosen.

&(X)=

!i=1

m

Aiexp

"#$

%&'

! D2

i

2'2

!i=1

m B

iexp

"

#$

%

&'

! D2

i

2'2

(8)

Where, D2i =( X ! X i)T ( X ! X i).

In Eq. (8), Ai and B

i are defined following

Eq. (9). Ai & B

i are updated during the

clustering process each time a training

sample that belongs to the group

represented by the cluster center is

processed during the training. When a

training sample difference from all the

cluster centers is greater than a predefined

limit r , it becomes a new cluster center.

4. Data Construction

4.1. Overvoltage Types

Three types of over voltages have been

selected to support the undergoing study:

the direct strike lightning surge, the

temporary overvoltage and the capacitor

bank energization overvoltage.

Direct Strike Lightning Overvoltage:

The most devastating overvoltage type in

power distribution lines is the lightning

overvoltage. It can affect the power

network by inducing overvoltage or a

direct strike on a power cable. For both

cases, the resulting overvoltage is usually


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of very high amplitude, a steep front, and

low energy. The power distribution

network modeled on ATP for running the

simulations is a three-phase overhead

network without protection cable. For that,

only the direct strike lightning surge has

been taken into account for analysis. When

lightning strikes on the overhead cable, the

voltage is raised from the normal 10!15kV

to several MV. The adjacent lines are also

affected and some effect isdepicted in Fig.

Fig. 2.

Fig. 2: Overvoltage Resulting from a

Lightning Direct Strike on Phase C of the

Overhead Line.

The voltage of the phase at which

lightning directly strikes embeds the

impulses with the highest amplitude at the

striking instant; the waveform is rapidly

damped in the following 5ms. The

adjacent phases are affected in a different

way. Figure 2 shows two differences

in the time domain analysis that will be

used in the next stage of our work for

online detection of lightning strike

overvoltage. In the first 3 ms, the voltage

of the struck phase will be directly damped

after the lightning strike while adjacent

phases will present some few peaks at the

highest amplitude before being damped.

Figure 3 shows a typical lightning impulse

recorded in one phase.

Fig. 3: Measured Lightning Impulse

Waveform.

Temporary Overvoltage: Temporary

overvoltage isthe result of phase-to-ground

fault clearance. A temporary overvoltage

can provoke rising voltage waveform up to

2.5 p.u. of the host distribution network

voltage amplitude [15]. A typical

temporary overvoltage resulting from

Phase B to ground fault clearance is shown

in Figure 4.


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Fig.4: Temporary Overvoltage Waveform

in a Three-Phase Overhead Distribution

Network.

The bold dot on the plot in Figure 4

materializes the instant at which the phase-

to-ground default appears and the high

increase of voltage appears from the

instant at which the default is cleared.

Capacitor Bank Energization

Overvoltage: The principal use of

capacitor banks in power distribution

networks is to improve the power factor.

This is done by providing the reactive

energy Q consumed by a load using a

source different from the power generator

or the distribution transformer. The

capacitor bank can be located at different

points in the network chosen taking into

account cable characteristics and the

amount of reactive power to be produced.

Among three types of overvoltages under

study, the capacitor bank energization

overvoltage is the type that lasts longer

and produces lower peaks in the

waveform. The amplitude of capacitor

bank energization overvoltage depends on

the host network voltage level at the time

of energization and the initial charge of the

capacitor. An overvoltage resulting from

energization of the capacitor bank initially

discharged with line voltage above 90% of

the maximum value is presented in Figure

5.

Fig. 5: Waveform of Capacitor Bank

Energization Overvoltage.

4.2. Time Domain Analysis

In the time domain analysis, data used for

identification are recorded from three

nodes in the network following the

description in Ref. [16]. A vector is built

from the same phases’ signal propagated

to each of the three recorders installed on


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the network. The three data sets calculated

are then concatenated to build an input

vector for the specific overvoltage. The

seven parameters calculated at a recorder

for a specific phase as proposed in Ref.

[16] are presented below from Eqs. (10) to

(16).

ST is the sampling period, t 0 the time at

which the overvoltage occurs and T the

considered period of the overvoltage

waveform for the time domain analysis.

1. Rising time from occurrence to 20% of

maximum value calculated in t 0

to t 1

interval:

R02=t 1!t 0 (10)

2. Rising time from 20 to 90% of the

maximum value calculated in t 1 to t

2

interval:

R09=t 2!t

1 (11)

3. Time from occurrence to falling down

to half of its maximum value:

TTH=t 5!t

0 (12)

4. Absolute rising slope from 20% to

maximum value:

ARS=max(A)*0.8

(t 3!t

0 )

(13)

5. The form factor of the signal:

FF= RMS(A)

Mean(A) (14)

Mean(A) stands for the average value of

the signal. RMS(A) and Mean(A)

calculated over the studied period T

bounded by t 0 & t 5.

6. The ripple factor of the signal:

RF=max( A)!min( A)

Mean( A) (15)

7. The signals’ peak time ratio over its

period:

PTR=t 4!t 2TTH

(16)

4.3. Frequency Band Analysis

The spectral analyses of the three types of

overvoltages under study are the guides for

the choice of frequency bands of the

signals to be retained as parameters for

identification. Figures 6-8 depict the

spectrum of a direct strike overvoltage, a

temporary overvoltage and a capacitor

bank energization overvoltage

respectively.


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Fig. 6: Direct Strike Lightning

Overvoltage Spectrum.

Fig. 7: Temporary Overvoltage Spectrum.

Fig. 8: Capacitor Bank Energization

Overvoltage Spectrum.

The second and third stages of the analysis

in the present work use data provided by

WPD analysis.

In the second stage, signals are grouped

into 1 kHz bandwidth frequency groups

and their energy will be calculated.

Harmonics number 1 and 2 are excluded in

the first group of 1 kHz frequency

bandwidth. Every recorder will be

processed and 10 parameters computed

and concatenated with those from other

two recorders to build a 30-element input

vector for every overvoltage sample.

The choice of data in the third stage is

performed as follows:

Looking back at Figures 6 – Figure 8, it

appears that a right choice of few

parameters can be performed to obtain a

better classification results for a pattern

recognition tool. Keeping the original

WPD with sub-band groups of 1 kHz

bandwidth, the spectral analysis shows that

signals of the first, the third the seventh

and the tenth groups have significant

information that can be used to classify the

three types of over-voltages under study.

This last step will give performances of the

FANN and GRNN in classification using

rightly chosen classification input data’s.


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Data analyzed in the present work are

sampled at 500 kHz, analysis and further

signal processing are performed under

Matlab. The comparative study is

conducted under three categories of inputs.

From time domain analysis, data formally

used for internal and external overvoltage

classification will be used for the first level

of evaluation. It is obvious that the

obtained results will be far lower than

those obtained with the internal and

external classification in Ref. [16]. The

main objective in this part will be to

collect some information about the

adaptability of two types of neural

networks in classification with a non-

accurate choice of input data’s.

The second stage of the analysis will be

dedicated to the frequency band analysis

of different inputs. Simulated data’s will

be processed with the WPD and specific

sub-bands will be considered for building

input data for classification. The goal inthis second part will be to evaluate the

neural network performances with a larger

input vectors.

The third stage will be about the

evaluation of classification performances

using input vector characteristics that are

also based on the precedent figures. Data

are obtained from WPD analysis, the

energy of selected frequency bands will be

calculated to serve as elements of every

input vector. In this case, input vector

elements are not normalized. An extra

evaluation is performed with data of the

third stage normalized to p.u. of

100(V max the line-to-ground peak

voltage.

A discussion from the previously

mentioned cases will follow in Sec. 7 to

bring out specific information about the

conducted comparative study.

5. Performance Evaluation

As stipulated in Sec.5.3, the analysis is

performed with three types of over-

voltages analyzed in frequency domain

and in wavelet sub-band decomposition

domain. The results are evaluated in time

spent for training the NN for different

sizes of input data on the same computer

and the ratio of right identified input data

over the total test data available. The

complete comparison results are presented

in Table 2.


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Table 2: Table of Performance Details.

1. GRNN built on smoothing parameter

'=0.0701 and 46 cluster centers.






'=0.0204 and 18 cluster centers; data

normalized to p.u. of 100(V m

ax the line-

to-ground peak voltage.

6. Discussion

With regard to the study carried out in the

present paper, there are some observations

that can be obtained from interpreting the

obtained results. At the first sight, we can

confirm with Ref. [14] that regardless of

the output performance, training GRNN is

hundreds of times faster than training

FANN for the same type and amount of

training data.

The GRNN performs best for normalized

input vectors; the FANN despite the time

taken for training can provide a better

output performance for raw data non-

normalized and regardless of the size of

the input. Normalizing input data is very

crucial for improving the performance of

GRNN

II. CONCLUSION

In the present paper, authors have focused

the study on enlightening some usage tips

of the GRNN and FANN. GRNN is

Input NN Inpu

t

Trai

ning

Training Test Ident

ificat

ion

data

type

type vecto

r size

samp

les

time (s) samp

les

Resu

lts

(%)

Time

domain

FANN 21 54 0.734571 142 54.22

GRNN 1 21 54 0.0281182 142 98.6

WPD FANN 30 61 4.862906 163 75.5

GRNN 2 30 61 0.023164 163 65

FANN 12 61 2.013288 163 81.12

GRNN 3 12 61 0.032108 163 69.5

FANN 4 12 61 2.1602197

0

163 83.11

GRNN 4 12 61 0.0100708 163 91.21


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relatively new to the public and is not yet

widely used as FANNs are well known for

their speed in processing despite the

memory size used to store a complete

trained GRNN that is evaluated to be far

higher than the required memory for

storing the FANN. FANN appears to

behave better in case of non-normalized

data or input data vectors that have a high

variance between equivalent elements.

Adjusting the parameter r in Section 4 for

evaluation of D2

i or modifying the

minimum required output error does not

significantly improve the performance of

GRNN.

FANN and GRNN are both good tools for

autonomous decision making applications ,

although the GRNN performance

outperforms the FANN’s, the FANN can

still be an indicated tool for data cases

where normalization of inputs cannot help

reduce the first partial derivative of inputs

to model suitable data for better

performance of GRNN.

III. ACKNOWLEDGMENT

This work was supported in part by National

Natural Science Foundation of China

(51177049) and by National Science Fund for

Excellent Young Scholars (51322702).

!"#"!"$%"&

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