Intelligent Identification of Flicker Source in Distribution Systems

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Published in IET Generation, Transmission & Distribution

Received on 16th August 2009

Revised on 11th February 2010

doi: 10.1049/iet-gtd.2009.0447

ISSN 1751-8687

Intelligent identification of flicker

source in distribution systemsN. Eghtedarpour E. Farjah A. KhayatianSchool of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran

E-mail: [email protected]

Abstract:The authors propose a new intelligent method to identify the source of flicker in distribution systems.

By measuring and sampling the bus voltages, a flicker index is extracted using multi-resolution S-transform. Time

frequency resolution characteristic of S-transform is shown to be a suitable candidate for assessment and

identification of flicker source. The new flicker index is selected from complex S-transform matrix, which takes

into account both amplitude and frequency of flicker. An multi level percepteron (MLP) neural network as a

powerful classifier is trained with the bus flicker indices to detect existence of flicker source in contaminating

bus. The source of flicker is modelled with an amplitude modulation scheme, and both flickers without

harmonics and with harmonics are considered. A 14-bus distribution system was simulated in ATP/EMTP to

test the method. Results show that by measuring voltages in proper buses, contaminating bus or zone can be

detected using a few measuring points.

1 Introduction

The quality of power has become an increasing concern forelectric utilities and their customers over the last decade.

This is the consequence of increasing use of solid-stateswitching devices, non-linear loads and similar devices.One of the most harsh power quality events in powersystems is voltage flicker. According to IEC, voltageflicker is a periodic or stochastic fluctuation of voltage, themagnitude of which is within +10% of rated voltage and

with frequencies between 0.5 and 25 Hz.

A load causing flicker is often connected to the powernetwork at medium-voltage level resulting in flickerpropagation throughout a wide area of the network andaffecting a large number of customers. Typical examples ofsuch loads are arc furnaces, welding machines and also

wind turbines.

Besides causing malfunction of phase-locked loops(PLLs), electronic controllers and protection devices,

voltage flicker can result in flickering of electronic,

incandescent, fluorescent lamps and cathode ray tubes,which can be sensed by customers and cause irritatingeffects. Therefore removing or decreasing these harsh

effects is of great importance. The first step in thisapproach is to locate the source which causes voltage flickerin the system so that the source of flicker can be detected,and by installing proper devices or upgrading the system,these disturbances can be removed or reduced.

In[1], Hughes described a technique that by measuringcurrent as well as voltage, and deriving the utility sourceimpedance, the voltage sag and flicker measured at acustomers point of supply can be separated into two-

component events caused by the customer being monitoredand events already on the utility system. Identification offlicker sources by separation of flicker effect from theindividual consumers connected to the same bus wasproposed in [2]. Knowing the short-circuit impedance ofthe network and simultaneously measuring branch currents,the disturbing branch can be investigated by this method.In [35], a method was proposed for determining thedirection to a flicker source by purely exploiting the sign ofthe flicker power: A positive sign indicates an upstreamflicker source, whereas a negative flicker power indicates adownstream flicker source with respect to a monitoring

point. In a similar work, a method for detecting thedominant flicker source from a multi-flicker source networkwas proposed in [6]. Despite simplicity of such methods,

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& The Institution of Engineering and Technology 2010 doi: 10.1049/iet-gtd.2009.0447

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source identification in wide networks needs a large numberof measuring points and also these methods do not includeany mechanism to trace a particular flicker source. On theother hand, since flicker is usually a medium durationevent, it also needs a long duration of time to identify themain location of flicker source.

In this paper, a new approach is presented foridentification of the bus or a zone, which contains thesource of flicker in the distribution system. This process isimplemented by sampling the voltage waveform in a fewnumber of buses and using neural networks. In order todetect the contaminating bus, an index is extracted byS-transform, which is essentially a variable window short-time Fourier transform (STFT). S-transform window

width varies inversely with frequency and produces a timefrequency representation of a time-varying signal byuniquely combining the frequency-dependent resolution

with simultaneously localising real and imaginary spectra. Acomparison between Fourier-based methods and wavelettransform to characterise flicker in the frequency domain

was proposed in [7]. The wavelet transform has shownsuperior performance comparing to other methods.S-transform is similar to wavelet transform but with aphase correction and both the amplitude and phasespectrum of the signal are obtained. S-transform of a powerdisturbance signal provides contours, which closelyresembles the disturbance pattern. Although, features suchas standard deviation of frequency-time contours andamplitude factor in [810] have been used for detectionand classification of different power quality events, butflicker assessment using S-transform has not beenconsidered yet. Regarding the importance of flickerespecially in distribution systems, in this paper a new indexis extracted from S-transform magnitude time contourand is used to identify the location of contaminating loadin the network.

The paper is organised as follows. A brief introduction toS-transform is discussed in Section 2. Voltage flicker modelis introduced in Section 3. In Section 4, the proposed flickermodel is analysed by S-transform and the correspondingfeature is introduced. Neural network and training process

is described in Section 5. Selection of metering locations isrepresented in Section 6. Simulation and test results for asample 14-bus distribution system are given in Section 7and finally in Section 8 conclusions are drawn.

2 Multi-resolution S-transform

The Fourier transform of a time-varying signal h(t) isgiven by

H(f) =1

1h(t)ei2pft dt (1)

The spectrum H(f) is referred to as the time-averagedspectrum. If the signal h(t) is multiplied point by point

with a window functiong(t), then the resulting spectrum iscalculated by

H(f) =1

1h(t)g(t)ei2pft dt (2)

The S-transform is obtained from (2) by defining a particularwindow function in the form of a normalised Gaussian as

g(t) = 1s

2p

e(t2/2s2) (3)

and then allowing the Gaussian window to be a function oftranslation t and dilation (window width) s. The window

width s is made proportional to the inverse of frequencyand is chosen as

s(f) = 1a

+b

|f

|

(4)

Ifa 0,s(f) denotes the S-transform and forb 0,s(f)denotes an STFT. Typical values ofbvary between 0.333 and5, which provide different frequency resolutions. For lowfrequencies a higher value of band for high frequencies alower value of b is chosen to provide suitable frequencyresolutions.

S-transform produces a multi-resolution analysis like abank of filters with a constant relative bandwidth (constantQanalysis).

Substituting (3) and (4) in (2), we obtain the S-transformofh(t) as

S(t,f) =1

1h(t)g(t t,f)ei2pft dt (5)

SinceS(t,f) is a complex number, it can also be written as

S(t,f) = A(t,f)eif(t,f) (6)

whereA(t,f) is the amplitude S-spectrum and w(t,f) is thephase of S-spectrum. It can be noted that the S-transformimproves the STFT in the sense that it has a better

resolution in phase space (i.e. a more narrow time windowfor higher frequencies) giving a fundamentally more soundtime frequency representation.

It is noticeable to consider the filtering effect of theS-transform when it is applied to the signal. The transformis obtained by multiplying the signal point by point with aGaussian window (g(t) in (3)). The Fourier transform ofthe Gaussian window is obtained as

G(v,f) = e2ps2v2 = e2p(a2/f2 )v2 (7)

More details can be followed in [11]. The exponential termin (7) is the frequency-dependent localising window and iscalled the Voice Gaussian. It plays the role of a low-pass

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filter for each particular voice. By examining different valuesofa (different window width) and choosinga 0.25 andplotting the Fourier transform of the window for different

values of f in Fig. 1, the low-pass filtering effect ofS-transform can be concluded.

The discrete version of the S-transform is calculated bytaking advantage from the efficiency of the fast Fouriertransform (FFT) and the convolution theorem. Thediscrete Fourier transform of the sampled signal h(kT),k 0, 1, . . .,N2 1 is defined as

H n

NT

= 1

N

N1k=0

h(kT)ei(2pnk/N) (8)

and discrete version of the S-transform ofh(kT) is obtained as(by lettingf n/NTand t jT)

S jT, n

NT

=N1m=0

H m+ n

NT

G(m,n)ei(2pmj/N) (9)

where

G(m,n) = e(2p2m2a2/n2) (10)

a 1/b,j,m,n 0, 1, . . .,N2 1 andNis the total numberof samples.

The computation of the multi-resolution S-transform is

very efficient using convolution theorem and FFT. Thecomputational steps are outlined as follows[8]:

1. Denote n/NT, m/NT, kT and jT as n, m, k and j,respectively, during all computations.

2. Obtain discrete Fourier transform H[m] of the originaltime-varying signal h(k), with N points and samplinginterval T, using FFT routine from (8).

3. Compute the localising Gaussian windowG[n, m] for therequired frequencyn using (10).

4. Shift the spectrumH[m] to H[m+ n] for the frequencyby using convolution theorem.

5. DetermineB(n, m) H(m+ n).G(m+ n).

6. Compute inverse Fourier transform ofB(n,m) frommtojto give the row ofS[n, j] corresponding to the frequencyn.

7. Repeat Steps 3, 4 and 5 until all of the rows ofS[n, j]corresponding to all discrete frequencies have been obtained.

The total number of operations for computing S-transformisN(N+NlogN).

The multi-resolution S-transform output is a complex

matrix, the rows of which are the frequencies and thecolumns are the time values. Each column thus representsthe local spectrum for that point in time. Frequencytimecontours having the same amplitude spectrum are used todetect and localise power disturbance events [710]. Amesh three-dimensional (3D) of the S-transform output

yields frequency time, magnitude time and frequencymagnitude plots. The original software code developed byStockwell [11]in Matlab has been modified by the authorsfor flicker studies.

3 Voltage flicker model

The accurate modelling of voltage flicker is a crucial task fortesting any proposed algorithm. The voltage flicker waveformcan be modelled as an amplitude modulated (AM)

waveform in addition to some harmonic components. Themodulated signal is equivalent to the sum of sinusoidalcomponents with random frequencies and amplitudes. Forthe sake of simplicity, in the first stage of analysis, theharmonics are neglected. Harmonic components will be

Figure 1 Fourier transform of the Gaussian window for different values of f



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considered in a later stage to investigate their effects on themethod for localising flicker source. Mathematically, if theharmonic components are neglected, the voltage flicker canbe expressed by the following model[12]

h(t) = A0 +M

i=1(Ai(t) cos(vit+ fi)) cos(v0t+ f0)

(11)

where A0is the nominal amplitude of the voltage, v0is thesupply frequency, f0is the supply phase angle and Aiis theamplitude of the voltage flicker with frequency vi andphase angle fi. An equivalent discrete model is given by

h(n) = A0 +Mi=1

(Ai(n) cos(vin+ fi))

cos(v0n + f0)

(12)

Equation (11) will be used for simulating flicker source in thetest systems.

4 Voltage flicker in S-transform

In this section, the behaviour of S-transform for a voltagewaveform with flicker effect is investigated. Regarding themodel described in Section 3, the waveform for a simulated

voltage flicker signal with 10% fluctuation and frequency of5 Hz is shown in Fig. 2. The corresponding 3D S-

transform of flicker signal along with its magnitudetimeplots is shown inFigs. 3and 4.

For a suitable frequency resolution considering low-frequency contents of voltage flicker, the value of a ischosen to be 0.25. As it is obvious from Figs. 2 and 3,there is a close relation between fluctuations of voltageflicker and magnitudetime contour. This characteristic isused in extracting the desired feature for flicker evaluation.

The new flicker index that can be used as a measure ofoccurrence of flicker is derived from magnitude time

contour of S-transform and is defined by

FI= maxi

(maxj|si,j|) min

i(max

j|si,j|) (13)

whereFIis the flicker index and si,jis the element in theithrow and jth column of complex matrix S.

As (5) shows, S-transform depends on both frequency andamplitude of the signal. The magnitudetime contours of

two signals with the same amplitude and differentfrequency are not the same and since flicker is usually

voltage fluctuations with variable and stochastic frequency,the magnitudetime contour of two different signals withsame amplitude but different frequency may yield differentflicker index. This issue is illustrated in Figs. 5 7, wherethe magnitude time contours of flicker waveforms withthe same flicker amplitude of 10% but with different flickerfrequencies of 3, 10 and 25 Hz are tested. In order tomitigate the impact of frequency changes on flicker index,this index is modified by normalising (13) based on thefollowing criteria:

The flicker index of all voltage measurements based on(13) is computed.

Figure 2 Pure sine wave flicker with amplitude 10% and

frequency of 5 Hz

Figure 3 3D S-transform plot for flicker waveform ofFig. 3

Figure 4 Magnitudetime contour of S-transform for

flicker waveform ofFig. 2



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The computed indices are normalised to the maximumflicker index in the measurement group.

With the above modifications, the computed flickerindices will be independent of frequency, since the absolute

values have been replaced by relative values. This is a key

benefit of the new flicker index, which will reduce thecomputation cost of training in neural network detectionscheme. In the next section, this index will be used as aninput to an MLP neural network for flicker detection.

5 Neural network

Artificial neural networks (ANN), which are parallel-distributed information processing units with differentconnection structures and processing mechanism, areparticularly suitable to link the different variables of aphysical system, where the relationship between the

independent and the dependent variables are not easilyquantifiable [13]. ANNs have been used in a broad rangeof applications including: pattern classification, patternrecognition, mapping, optimisation, prediction andautomatic control. The application of ANNs in differentpower system operation and control strategies has lead toacceptable results[14 16]. In this paper, ANN is used as aclassifier for flicker detection and its source identification ina distribution power system.

For intelligent identification of flicker source in adistribution power system, an MLP neural network as

shown inFig. 8is considered. Training a network by back-propagation involves three stages: the feed-forward of theinput training pattern, the back-propagation of theassociated error and the adjustment of the weights. Duringthe learning process, the ANN weights are adapted inorder to create the desired output vectors. The inputs tothe ANN are the flicker indices calculated based onS-transform. The outputs of the ANN are the status ofexistence of a flicker source in a certain bus. The numberof inputs to the neural network is the same as the numberof measurements in the system and the number of outputsfrom ANN is equal to the number of buses. LevenbergMarquardt Back-propagation algorithm is used in the

training of neural network. Weights and biases are updatedby Gradient Descent with Momentum Weights andBiases. MATLAB Neural Network toolbox is used for

Figure 5 Magnitude time contours of S-matrix for

waveform of 10% flicker amplitude and flicker frequencies

of 3 Hz

Figure 6 Magnitude time contours of S-matrix for

waveform of 10% flicker amplitude and flicker frequencies

of 10 Hz

Figure 8 Schematic of the MLP neural network for flicker

detection

Figure 7 Magnitude time contours of S-matrix forwaveform of 10% flicker amplitude and flicker frequencies

of 25 Hz



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ANN training. Transfer functions between input and hiddenlayer are logarithmic sigmoid; hyperbolic tangent sigmoidfunctions are selected for other layers. In the training stage,neural network outputs are considered as binary numbers, thatis, the contaminating bus is chosen 1 and non-contaminatingare 0. To identify the flicker source location, the output of

ANN is analysed as follows, if all values excluding themaximum are less than 0.5, the bus with the maximum valueis considered as the contaminating bus. When there are morethan one value greater than 0.5, these buses are doubtful ofbeing flicker source and a zone containing these buses isconsidered as contaminating buses. Therefore in a largenetwork seeking to find the contaminated bus is restricted to afew number of buses (usually 2 or 3) which can be studied

with the methods proposed in[1]or[2]to identify the mainlocation of flicker source.

6 Determination of number and

location of measurement pointsDue to the high cost of monitoring devices, optimal selectionof measurement points and their numbers is a crucial task; sothat the whole network can be observed using minimumnumber of monitoring devices.

In this part, an algorithm is proposed for selecting thenumber and locations of measurement points. Theproposed algorithm is as follows:

1. In the simulation process, voltages of all buses are sampled.

2. The modified flicker index is then calculated for differentcases of flicker amplitude to find sensitivity of each bus inflicker identification.

3. The standard deviation of indices for each measurementin different simulation cases is calculated.

4. The calculated values in Step 3 are sorted decreasingly.

5. In each stage, the input vector of neural network (flickerindices for each measurement) is added according to thesorted value and the output (location of flicker source) is

analysed and this process is continued till the outputreaches a satisfactory result.

The flowchart of the proposed algorithm is summarised inFig. 9.

7 Simulation results

To verify the accuracy of the proposed method inidentification of flicker source location, a sample 14-busdistribution system as shown in Fig. 10 is simulated in

ATP/EMTP. The distribution system data are presented in

the Appendix. For modelling the flicker source a three-phase resistor bank with variable resistance that simulates(11) is used to model the flicker source. This is a simple

model of arc furnace; which is the main source of flicker inpower systems.

According to the method presented in Section 6, themeasuring points are selected in Bus 1, Bus 3 and Bus 4.

Figure 9 Flowchart for the selection of number and

location of measurements

Figure 10 Studied 14-bus distribution system



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The selected neural network is a two-layer perceptron with3 neurons in the input and 16 neurons in the hidden layer.For training ANN, data from flickers of amplitudes 1, 4and 10% and frequency of 8 Hz in different buses ofsystem are used. The flicker indices of measuring buses 1, 3and 4 are used to train the ANN. It is worth noting that as

discussed in Section 4, the effects of frequency changes inthe magnitudetime contours are compensated bynormalisation process and there is no need to train ANNfor different frequency of flicker.

Calculated indices in three different cases of voltagefluctuations for the 14-bus system are given in Table 1.

These indices are used as inputs for training the ANN.Test results for flickers with amplitude of 2, 6 and 8% and

frequency of 8 Hz are presented in Table 2. As resultsshow when the contaminating load is connected in buses 1,2, 3, 4, 13 and 14, the proposed algorithm can exactlyidentify the flicker source. However, for the remaining

buses when the contaminating load is in these buses thealgorithm identifies more than one bus as the flickersources. Simulation results show that when the buses in thenetwork are closely located (like Bus 8 and Bus 9) or whenthere is a kind of symmetry in the network (like Bus 11and Bus 12 to Bus 10), a group of buses may be identifiedas flicker sources. To verify the validity of the method fordifferent flicker frequency event, the network is tested forfrequencies of 4 and 15 Hz and with different amplitudes.

The results are shown inTables 3and 4.

7.1 Real signal consideration

In order to test the robustness of the method when realsignals are analysed, the effects of different disturbances areaddressed in this section.

Flicker is a fluctuating voltage with frequency between0.5 and 25 Hz. Thus, considering the amplitudemodulation effect of flicker, maximum and minimumfrequency in the signal is in the range 2575 Hz.Regarding the cut-off frequency of the Gaussian window, itcan be deduced from Fig. 1 that components over theflicker range will be filtered.

In analysing the actual power system disturbance data, thereal waveform captured and recorded by a digital recorder isusually contaminated with different distortions. There are

Table 1 Simulated indices for ANN training

Contaminatingbus no.

Measurements 1% 4% 10%

1

V1 0.8936 0.8937 0.9001

V3 0.9021 0.9022 0.908

V4 1 1 1

2

V1 1 1 1

V3 0.9964 0.9964 0.9967

V4 0.9853 0.9854 0.9865

3

V1 0.6885 0.6879 0.6954

V3 0.7688 0.7684 0.7735

V4 1 1 1

4

V1 0.8154 0.8158 0.8268

V3 1 1 1

V4 0.8572 0.8575 0.8662

5

V1 0.6933 0.6923 0.6981

V3 0.7723 0.7717 0.7753

V4 1 1 1

6

V1 0.6022 0.6027 0.6173

V3 0.6399 0.6405 0.6537

V4 1 1 1

7

V1 0.6945 0.6925 0.6945

V3 0.7732 0.7718 0.7726

V4 1 1 1

8

V1 0.6929 0.6914 0.6953

V3 0.772 0.771 0.7733

V4 1 1 1

9

V1 0.7997 0.7997 0.8082

V3 0.9374 0.9373 0.9386

V4 1 1 1

Continued

Table 1 Continued

Contaminatingbus no.

Measurements 1% 4% 10%

10

V1 0.6881 0.6882 0.6993

V3 0.7685 0.7686 0.7764

V4 1 1 1

11

V1 0.6961 0.6924 0.6961

V3 0.7715 0.7717 0.7738

V4 1 1 1

12

V1 0.6939 0.6921 0.694

V3 0.7727 0.7715 0.7723

V4 1 1 1

13

V1 0.6933 0.691 0.6922

V3 0.7723 0.7707 0.7709

V4 1 1 1

14

V1 0.6943 0.6924 0.6943

V3 0.773 0.7717 0.7725

V4 1 1 1



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Table 3 Output of trained ANN for flicker of 4 Hz with different amplitude

Amplitude of fluctuations

Flicker source10% 8% 6% 4% 2% 1%

Selected bus in the ANN Output

1 1 1 1 1 1 1

2 2 2 2 2 2 2

3 3 3 3 3 3 3

4 4 4 4 4 4 4

5 5, 6, 7 5, 6 5 5 5 5

6 5, 6 6 6 5, 6 6 6

7 7 6, 7 7 7 7 7

8 8, 9 8, 9 8 8 8 8

9 8, 9 9 9 8, 9 9 9

10 10, 11, 12 10, 11, 12 10 10, 12 10 10

11 10, 12 10, 11, 12 11 10, 11, 12 11 11

12 10, 11, 12 10, 11, 12 12 10, 11, 12 12 12

13 13 13 13 13 13 13

14 14 14 14 14 14 14

Table 4 Output of trained ANN for flicker of 15 Hz with different amplitude



Selected bus in the ANN output

1 1 1 1 1 1 1

2 2 2 2 2 2 2

3 3 3 3 3 3 3

4 4 4 4 4 4 4

5 5, 6, 7 5, 6 5 5 5 5

6 5, 6 6 6 5, 6 6 6

7 7 6, 7 7 7 7 7

8 8, 9 8 8 8 8 8

9 8, 9 9 9 8, 9 9 9

10 10, 11, 12 11, 12 10 10, 12 10 10

11 10, 12 10, 11, 12 11 10, 11 11 11

12 10, 11, 12 10, 11, 12 12 10, 11, 12 12 12

13 13 13 13 13 13 1314 14 14 14 14 14 14



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mean. In[18], the authors showed that the total noise powerdoes not depend on the sample rate, where nis the number ofbits in the A/D converter andfsis the sample rate.

The mapping between an analogue sample, xa(m), and itsquantised value, x(m), can be expressed as x(m) Q[xa(m)]

where Q[.] is the quantising function. The quantisationnoise is defined as e(m) x(m) 2 xa(m). The noise has auniformly distributed probability density function in (2D/

2, D

/2), where D

2V/2n

is the quantisation step size foran n-bit quantiser with an amplitude range of+V volts.The signal-to-quantisation noise ratio can be approximated

as: SQNR(n) 6ndB [19]. Thus for an 8-bit DSP, theSQNR would be approximately 48 dB, which is a very lownoise to affect the FI substantially.

The proposed algorithm has also been implemented in a37-bus IEEE test system. The results are not reported herebut are completely promising.

8 Conclusion

This paper discussed a new method to identify the location offlicker source in a distribution system. By sampling voltage in

Figure 11 Robustness evaluation of flicker index in presence of harmonics and inter-harmonics

a Signal of 10% pure flickerb Magnitudetime plot of signal ac Signal of 10% flicker mixed with 3rd and 5th harmonic amplitude of 20 and 15% and interharmincs of frequency 125, 180 with amplitudeof 5%dMagnitudetime plot of signal c

Table 5 Output of trained ANN for flicker of 8 Hz mixed with harmonics for different amplitude



Selected bus in the ANN output

1 1 1 1 1 1 1

3 3 3 3 3 3 3

5 5, 6, 7 5, 6, 7 5 5 5 5

7 7 6, 7 7 7 7 7



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Conductor#2

Z(R+jX) in ohms per mile0.7526+j1.1814 0.1580+j0.4236 0.1560+j0.5017

0.7475

+j1.1983 0.1535

+j0.3849

0.7436+j1.2112B in micro Siemens per mile

5.6990 21.0817 21.6905

5.1795 20.6588

5.4246

Network equivalent impedance

Rth(ohm) Lth(mH)

5 1.43 1.43 21.6 7.21 7.21

5 1.43 21.6 7.21

5 21.6

Transformer data

Rated voltage: 66/20 Kv

R1: 0.7 V, R2: 0.06 V

X1: 11.6 mH, X2: 1.07 mH

Vector group: Dyn1

Table 6 Load data

Bus no. Load Ph-1 Ph-2 Ph-3

kW kVar kW kVar kW kVar

2 Y-PQ 160 110 120 90 120 90

7 Y-PQ 0 0 170 125 0 0

9 D-Z 0 0 230 132 0 0

11 Y-Z 128 86 0 0 0 0

12 D-PQ 385 220 385 220 385 220

13 Y-PQ 485 190 68 60 290 212

Table 7 Line data

Conductor type Line length (mile) Line no.

conductor #1 3 1 2

conductor #1 1 1 3

conductor #1 3 2 4

conductor #1 3 3 5

conductor #1 3 4 6

conductor #1 3 5 6

conductor #2 2 6 7

conductor #2 5 7 8

conductor #2 0.2 8 9

conductor #1 4 8 10

conductor #2 5 10 11






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Intelligent Identification of Flicker Source in Distribution Systems

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