A Novel Voltage Sag Source Detection Method Using ...

A Novel Voltage Sag Source Detection Method Using TFR Technique Mohamed F.Faisal1, Azah Mohamed2, Hussain Shareef3

Abstract – Voltage sag is considered as the major power quality disturbance that can disrupt the operation of sensitive equipment. Many power utilities around the world install power quality recorders at strategic sites to monitor the frequency and severity of voltage sags that may affect their customers. The immediate identification of sources of voltage sags is very critical in providing information to customers to resume back their operations. The sources of voltage sags can originate either from upstream or downstream with respect to the monitoring point. This paper presents a novel method to identify the sources of voltage sags using a signal processing technique called the S-transform (ST). An index named S-Transform Disturbance Power (STDP) was developed to identify the origin of the voltage sags. The results of this study showed that the novel method is superior to the other methods in detecting the origin of both symmetrical and asymmetrical voltage sag in actual power systems. Copyright © 2009 Praise Worthy Prize S.r.l. All rights reserved.

Keywords: Power quality, Signal processing, Sources of voltage sags, S-Transform

I.

Introduction

Voltage sag is considered as the major power quality disturbance in the power supply industry as it can disrupt the operation of sensitive equipment and cause hours of downtime. The information about the actual sources and causes of voltage sags is very important to the plant engineers who had experienced sudden production stoppage due to these disturbances. Such information is important for making decision to resume back operation after being interrupted by voltage sag. If the source of the sag is internal, then the plant engineer needs to troubleshoot his internal electrical systems in order to identify the source of the problem. And if the source is external, the plant can resume back operation. Therefore, in order to enhance services to customers, it is imperative that power utilities are able to detect voltage sags in their networks and then share the relevant information with their customers. One of the most important steps in managing voltage sags is to install a permanent on-line power quality monitoring system (PQMS) at selected sites either at the main substations or at the point of common coupling (PCC) between customers and the power utility. The PQMS, shown in Fig. 1, comprises of a power quality recorder (PQR) and is connected via a communication network to transmit the power quality measurement data to the power utility engineers. The summaries of the power quality events would be send to the engineers’ mobile phones using the short message system (SMS). Example of information received through the SMS is shown in Fig. 2. The information shows voltage sag had

occurred at a substation named TKBU. The voltage magnitudes were 105% (red), 82% (yellow) and 116% (blue). The duration of the sag was 380 ms. Details of the data are also accessible through a desktop computer in the engineer’s office. This information would enable them to respond promptly to undesirable system performances. A1 Send SMS

Current Input

PQMS Server

Voltage Input

Access using Desktop computer 33 kV

132 kV

Source

TF1

33 kV

PQR

X

X

TF2

J

TF3

K

11 kV

X A2 DF1

X

DF2

X

DF3

X

A3 B X

C D

DF4

X 33 kV

E 415 Volt

Fig. 1. Configuration for power quality monitoring system (PQMS)

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

source detection. And in section 4, the performances of the existing sag source detection methods are compared with the novel method in identifying the origins of symmetrical and asymmetrical voltage sags recorded in Malaysia.

Fig. 2. Example of information sent to mobile hand phone

However, the sources and causes of voltage sags are only known after site inspections have been conducted. Frequent site inspection is required and this is a time consuming activity. Therefore, customers may have to wait longer for the correct information before production can recommence. As shown in Fig. 1, the sources of the sags can originate either from distribution feeders (DF1 to DF 4) based on faults at points A1 to E, or from the transmission feeders (TF2 and TF3) due to faults at point J, K or other points in the transmission networks. To expedite the diagnosis of voltage sags, an automated process is required. Automation will convey fast and accurate information to customers to assist them in making decision to resume back operation after the occurrences of voltage sags. This information is also very important to resolve dispute if the sag originated from a customer’s installation and had affected other customers’ sensitive equipment. In this paper, the concept of automatic sag source detection is studied comprehensively. The concept of sag source detection is shown in Fig. 3. A PQR is installed at point M in a power supply network. If voltage sag occurs in the network, the PQR will detect and record the disturbance depending upon its voltage threshold settings which are basically based on the definition of voltage sags [1]. The source of sag can originate either from upstream or downstream with respect to point M. Upstream side can be defined as the side that supplies the fundamental power into the monitoring point at steady state conditions whereas the downstream side is defined as the side that leaves the fundamental power from the monitoring point. Active Power M Upstream System

M - Monitored Point

Downstream System

Power Quality Recorder

Fig. 3. Concept of upstream and downstream voltage sags

The following section presents an overview of existing sag source detection methods. In section 3, the novel method named the S-Transform Disturbance Power (STDP) method is introduced for performing sag

II.

Overview on existing methods

II.1.

Voltage-Current (V-I) amplitude method

This is the earliest known method for sag source detection. It is based on the assumption that currents measured at the monitoring point will increase during downstream events and decrease during upstream event [2]. The phasors for the fundamental components of line voltages Vk = |Vk| e jϕu,k and currents Ik = |Ik| e jϕi,k are calculated based on the measured waveforms. |Vk | and |Ik | are phasor lengths, while ϕu,k and ϕi,k are phasor angles for voltage and current. The subscript, k represents red, yellow or blue phase. Generally, a phase angle is defined as ϕk = ϕ u,k - ϕ i,k. During voltage sag, the points of (|Ik|, |Vkcosϕk|) are calculated using the linear function for each phase individually in order to investigate its slope. If the slope is > 0, then the source is said to be at upstream, and if the slope is < 0 then the source is in downstream. The graphical representation of the V-I method is shown in Fig. 4. This method is termed as method M1. V

V

Pre Sag

Pre Sag

Slope < 0 Slope > 0 Post Sag

(a)

Post Sag I

I

Upstream sag

(b) Downstream sag

Fig. 4. Characteristics of voltage and current for upstream and downstream sag

II.2.

Disturbance Power flow (DP) method

A method to identify the sources of voltage sags based on the flow of power and energy during the disturbances was proposed in [3]-[4]. This method relies on the fact that energy tends to flow towards a disturbance source during a disturbance. This method is termed as method M2. If the value of the disturbance power during voltage sag is negative, then the source of the sag is upstream. And if the value of the disturbance power is positive, it indicates that the source of the sag originates from downstream. The disturbance power (DP) can be represented as: DP = ip3φ (t ) − ip SS (t )

(1)

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

where ip 3φ (t ) is the total three-phase instantaneous

Ze =

power and ip SS (t ) is the predisturbance steady state instantaneous power. The main problem related to the disturbance power flow method is related to its’ derivation. The sinusoidal voltage and current waveforms were first converted to rms values and multiplied with the phase angles to calculate the disturbance powers. The conversion to rootmean-square (rms) values will average out all the original signal data. The application of this method to detect upstream sources of sags in a system as in Fig. 1 is very much questionable as it may not detect any minor changes in the fundamental power flow due to faults at locations B, C, D, E, J and K. The changes in the rms currents due to faults at these points are very minor and may not be detected in the rms plots. Another problem related to the power flow method is linked to the behavior of the constant kVA loads. For long duration, the current values drawn by the constant kVA loads will increase for both upstream and downstream faults in order to maintain constant kVA load requirement. If this condition occurs, the DP method will give wrong results in the detection of upstream sag source. II.3.

Incremental impedance (I-I) method

Another method to pinpoint the origin of voltage sag was developed based on the basis of incremental impedance of the non-disturbance side [5]. The sign of the fundamental frequency positive sequence resistance estimated at a metering point was proposed as the indicator to determine the origin of voltage sag. The equivalent circuit for calculating the positive sequence resistance is shown in Fig. 5.

I

M

X Z1 = R1 + jX1

S

E1

Supply system

Z2 = R2 + jX2 V

E2

S

Customer system

Fig. 5. Equivalent circuit for sag source analysis for incremental impedance

In the above figure, complex parameters Z1 and E1 are equivalent impedance and internal voltage source of the supply system respectively while Z2 and E2 are the parameters for the customer system. Point M represents the monitoring point. The fundamental-frequency positive-sequence resistance or the real part of the impedance Ze can be calculated using the following equation:

ΔV Vduring − V pre = ΔI I during − I pre

(2)

where Vpre and Ipre are pre-sag voltage and current and Vduring and Iduring are during sag voltage and current, respectively. If the Real (Ze) > 0, the source of the sag is upstream with respect to the monitored point and if Real (Ze) < 0, the source of the sag is downstream from the monitored point. This method is termed as method M3 in this paper.

II.4.

Incremental impedance (I-I) method

The approach using a distance relay algorithm [6] is slightly different from the previously mentioned methods. The basic idea is to analyze the impedance seen during the event. The impedance is estimated using the voltage and current phasors at the monitored location. For a downstream fault the impedance value can be calculated as: V (3) Z seen = = Z 2 + ΔZ I Where V and I are the measured voltage and current phasors, ΔZ is a function of the fault resistance and load and characteristics. If Z SAG < Z PRE −SAG angle( Z SAG ) > 0 , the sag source is said to be upstream.

If Z SAG > Z PRE − SAG and angle( Z SAG ) < 0 , then the sag source is in downstream. This method is termed as method M4. II.5.

The performances of existing methods

The performances of the existing methods (M1, M2, M3 and M4) for the detection of sag sources were studied in [8]-[9]. In [8], methods M1 and M2 were found to give low accuracies in the detection of upstream sag sources due to asymmetrical faults in the networks. The accuracies were only 50 % to 70%. A more detailed performance test for voltage sag source detection was studied in [9]. The performances of methods M1, M2, M3 and M4 were evaluated for the detection of sag sources due to symmetrical and unsymmetrical faults in the transmission networks. The overall accuracies for the detection of sag sources due to symmetrical faults were 87%. And the results for detection of sag source due to asymmetrical faults were only 65%. Based on these results, there is a need for a better method to identify the origins of voltage sags.

III. The novel sag source detection using TFR technique Signal processing is an attempt to find a better form for a set of information from a recorded signal, either by

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

reshaping it or filtering out selected parts that are sometimes label as noise. The interest in exploiting signal processing techniques for analyzing power quality measurement data is due to the fact that signal processing techniques can extract meaningful and valuable information from the recorded signals. In this paper a novel method based on a signal processing technique is propose for identifying the origins of voltage sags due to both symmetrical and asymmetrical faults. This novel method was developed from an advanced signal processing technique known as the S-transform (ST). The ST produces the time frequency representations (TFR) for the recorded voltage and current waveforms. The time averaged amplitudes and spectral contents for signals are then extracted from the TFR. These values are used to develop an index named as the S-transform disturbance power (STDP). The STDP will be used to determine the origins of the voltage sags. Details on the S-transform and STDP are presented in the sub-sections below.

time. Each element of the S-matrix is a complex number and will be used to determine the sources of the voltage sags. This information can also be plotted as timefrequency contours for visual observation. Examples of the S-transform contours for voltage sags are shown in Fig. 6. The first row shows all the three phase voltage disturbances in per unit (pu). The second row shows the S-transform contours for the respective phase voltages and voltage sags can seen in all the three phases. The S-transform can also be applied in the diagnosis of the disturbance currents. Examples of the S-transform contours for the phase currents are shown in Fig. 7. The first row shows the current waveforms in per unit (pu) and the second row illustrates the S-transform contours for the respective phase currents.

III.1. Derivation of the S-transform

The S-transform of a function h(t) can be defined as a Continuous Wavelet Transform (CWT) multiplied by a − i 2πfτ phase factor e [10]-[11]. The CWT is a series of correlations of the time series with a function called a wavelet. ∞

W (τ , d ) =

∫

Fig. 6. Three phase voltages and S-transform contours

h(t )ω (t − τ , d )dt

(4)

−∞

where ω (t − τ , d ) is the mother wavelet, t and τ are both time, and the dilation factor d is the inverse of the frequency f. The mother wavelet can be further expanded as: −

f

ω (t , f ) =

t2 f 2 2 e −i 2πft

(5) 2π According to equation (5), the mother wavelet does not satisfy the admissibility condition of having a zero mean, and therefore it is not strictly a CWT [12]. Writing out equation (4) explicitly gives the new S-Transform (ST) equation as shown below, S (τ , f ) =

f 2π

e

∞

∫

−

h(t )e

(t −τ ) 2 2 e − i 2πft dt

(6)

−∞

The phase factor in equation (6) is a phase correction of the definition of the CWT. It eliminates the concept of wavelet analysis by separating the mother wavelet into two parts, the slowly varying envelope (the Gaussian function) which localizes in time, and the oscillatory − i 2πft exponential kernel e which selects the frequency being localized [13]. Basically the ST is the time localizing Gaussian that is translated while the oscillatory exponential kernel remains stationary. The output of the ST is an M x N matrix called the Smatrix whose rows pertain to frequency and columns to

Fig. 7. Three phase currents and S-transform contours

III.2. Development of a novel sag source detection method using the S-transform

In this paper the S-transform is used to develop an index named as the S-transform disturbance power (STDP). The derivation of the STDP is explained in the sub-section below. III.2.1 ST Disturbance Power

Based from equation 6, the time frequency representation for any voltage signal, let say red phase voltage, vR , can be represented as:

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

STVR =

f 2π

∞

∫

−

vR (t )e

(t −τ )2 2 e −i 2πft dt

−∞

(7)

where STVR is the S-matrix of size m x n for voltage signal vR . Now obtaining the maximum values from each column of STVR matrix, it is possible to construct a column vector, VSTR, of size n which represents the time component of vR . This VSTR is called ST voltage for red phase. Using similar procedure, ST voltage for yellow phase, VSTY and blue phase VSTB can be obtained independently. Next, the time frequency response for any current signal, let say red phase current iR , can be represented as: STI R =

f 2π

∞

∫

−

iR (t )e

IV.1. Analysis on upstream sources of voltage sags

(t −τ ) 2 2 e − i 2πft dt

−∞

(8)

where STIR is the S-matrix of size m x n for current signal iR . By obtaining the maximum values from each column of STIR matrix, it is possible to construct a column vector, ISTR of size n which represents the time component of iR . This ISTR is called ST current for red phase. Similarly one can obtain ISTY and ISTB for yellow and blue phase currents independently. Finally, the following equation is used to calculate the S-transform disturbance power (STDP) or STP,

STP = [(VSTR ⋅ ∗I STR ) + (VSTY ⋅ ∗I STY ) + (VSTB ⋅ ∗I STB )] (9) where the operator .* represents element by element multiplication. The same source detection methodology as used by the disturbance power flow, method M2, in determining the origin of voltage sags is adopted by STDP. If the value of the STPD is > 0, then the sag source is said to be a downstream event. On the other hand if the STDP value is < 0, then the sag source is registered as an upstream cause. This novel method is termed as method M5.

IV.

the network comprise of composite loads and hence will exhibit recovery characteristics during voltage sags. The recording of voltage sag data in the network was done using a permanent on-line PQMS installed at feeder DF1. A period of 36 months was selected for this study. During this period, two hundred and thirty seven (237) numbers of voltage sags were recorded. The origins of all the sags were later verified based on correlation done with the local power utility event database. One hundred eighty three (183) numbers of voltage sags originated from upstream and fifty four (54) numbers of voltage sags originated downstream from DF1. These data were used to evaluate the accuracy of all the sag source detection methods. Details on the analyses are presented in sections IV.1 and IV.2.

Results and discussion

In this section, the existing sag source detection methods (M1, M2, M3 and M4) and the novel method (M5) are evaluated for their accuracies in identifying the origins of actual voltage sags recorded at a substation in Malaysia. The substation is a step down substation that converts voltages from 132 kV to 33 kV. The single line diagram of the network is as shown in Fig. 1. The network comprises of a mesh transmission and radial distribution networks. The customers’ loads connected in

The first analysis was to evaluate the accuracy of all the methods in the detection of upstream sag sources due to symmetrical and asymmetrical faults originating from both the transmission and distribution networks. Sample results on the sag source analysis are explained in the next subsections. And the overall results for the upstream sag source detections are shown in Table 8. IV.1.1 Example on analysis of symmetrical sags due to upstream faults from the distribution networks

Faults at points B, C, D, E, J and K in Fig. 1 are considered upstream sources to the PQMS at DF1. Sample data for symmetrical voltage sags due to a 33 kV circuit breaker flashover at point D (distribution feeder DF4) are shown in Table 1 and the voltage/current plots in Fig. 8. The results obtained by methods M1, M2, M3 M4 and M5 are shown in Fig. 9 and summarized in Table 2. As shown in Fig. 8, during the sags all the three phase currents experienced sudden reductions due to disconnection of customers’ loads and motor loads slowing down. These conditions were then followed by sudden increased in order to compensate for the drop in voltage as to maintain the constant three phase power requirement. At the end of the sag, the currents showed another sudden increase due to restarting of motors. The motor loads restarted back once the fault was isolated. As mentioned in [14]-[15], transient currents occurring at the instants of voltage sag and voltage recovery are directly proportional to the voltage drop and not to the remaining voltage magnitude. TABLE I DESCRIPTION OF VOLTAGE SAGS DUE TO UPSTREAM SYMMETRICAL FAULTS IN DISTRIBUTION NETWORK

Phases % Voltage (rms) Duration (ms)

Red 42 269

Yellow 43 282

Blue 37 298

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

Upstream source from distribution networks

Incorrect

Incorrect

Correct

Correct

Correct

IV.1.2 Example on analysis of symmetrical sags due to upstream faults from the transmission networks

Fig. 8. Voltage and current waveforms due to upstream symmetrical faults in distribution network

Fig. 9.Results of sag source detection due to upstream symmetrical faults in distribution system

The graphical results of the sag source detection methods are shown in Fig. 9. The first row shows the results of method M1. The results of methods M2, M3, M4 and M5 are shown in sequence in the second row. The results of method M3 and M4 are printed on the second graph (voltage/current plot) in the second row. Overall method M1 failed to detect the sag source as it only identified the sag source for the red phase and not for the other two phases. The other results showed that methods M3, M4 and M5 (STDP) correctly identified the sag sources to be upstream from feeder DF1. Method M2 failed to identify the origin of the voltage sags. The cause of the errors was due to the erratic behavior of the load current during the voltage sag disturbance. As can be seen in Fig. 8, the load currents experienced a sudden drop between 200 to 250 ms. The reduction in voltage was followed by a sudden current increase in all the phases between 250 to 400 ms. Based on RMS conversion, the RMS current experienced sudden increase and gave false indication for the sag source to be downstream. The S-transform however, detected no increase in the current values between 250 to 400 ms and thus method M5 detected the sag source to be upstream. The summary of all the results is shown in Table 2. TABLE 2 SUMMARY OF SAG SOURCE DETECTION DUE TO UPSTREAM SYMMETRICAL FAULTS IN DISTRIBUTION NETWORK

Sag source

M1

M2

M3

M4

M5

An example for symmetrical voltage sags originating from the transmission network due to a fault at point J is shown in Table 3 and in Fig. 10. The results of the sag source analyses are shown in Table 4. The results showed that all the methods correctly identified the origins of the voltage sags. The fundamental disturbance power flow i.e. method M2, showed a reduction in power during the sag event and this signifies an upstream sag source. Method M5 also showed a reduction in the ST disturbance power flow thus indicating upstream sag source. All the three phases showed lower values for voltage and current during the post sag compared to presag thus indicating upstream sag source for method M1. The impedance during the sag (Zsag) has a lower value than during pre sag (Zpre-sag). Moreover, the phase angle for the Zsag is larger than zero. These values indicated that the sag source is upstream according to method M4. The value of the real part of the impedance Ze is larger than zero and this indicated the sag source as upstream from the monitored point for method M3. TABLE III DESCRIPTION OF VOLTAGE SAGS DUE TO UPSTREAM SYMMETRICAL FAULTS IN TRANSMISSION NETWORK

Phases % Voltage (rms) Duration (ms)

Red 75 139

Yellow 71 144

Blue 75 131

Fig. 10.Voltage and current waveforms due to upstream symmetrical faults in transmission network TABLE IV SUMMARY OF SAG SOURCE DETECTION DUE TO UPSTREAM SYMMETRICAL FAULTS IN TRANSMISSION NETWORK

Sag source

M1

M2

M3

M4

M5

Upstream sag source from transmission networks

Correct

Correct

Correct

Correct

Correct

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

IV.1.3 Example on analysis of asymmetrical sags due to upstream faults from distribution networks

IV.1.4 Example on analysis of asymmetrical sags due to upstream fault from transmission networks

Next, all the five methods were evaluated in the detection of sag sources due to upstream asymmetrical faults from the distribution networks. Sample data for asymmetrical voltage sag is shown in Table 5 and in Fig. 11. The cause of the voltage sag was due a cable fault in the distribution networks at point C (Fig. 1). Since the neutral of the distribution network is impedance grounded, any single line to ground fault will cause a sag in 1 phase and swells in the other 2 healthy phases [16]. And as the event was a single-phase sag, no inrush currents were observed at end of the voltage sags in a three-phase network unlike for a three-phase symmetrical sag [17]. The summary of the analysis are shown in Table 6.

The next analysis was to evaluate the performance of all the methods in detecting the source of asymmetrical voltage sag originating from the transmission network. A sample data for asymmetrical voltage sag is shown in Table 7 and in Fig.12. The cause of the voltage sag was due to a lightning strike at point J (Fig. 1). Since the event is a three-phase sags, load recovery currents can be observed during the voltage sags. The results of the sag source detection showed that only methods M2 and M5 correctly identified the source of the voltage sags. Methods M1, M3 and M4 gave false indication for the sag sources. The causes of the errors were due to the erratic behavior of the load currents for all the three phases. The erratic current values contributed to the error in calculating the impedance value for method M3. The current phase angles, which are used in deriving method M4, experienced reversed in direction thus giving wrong value for the impedance as the magnitude and angle will be incorrect.

TABLE V Description of voltage sags due to upstream asymmetrical faults in distribution network Phases Red Yellow Blue % Voltage 146 171 54 Duration (ms) 268 373 331

TABLE VII DESCRIPTION OF VOLTAGE SAGS DUE TO UPSTREAM ASYMMETRICAL FAULT IN THE TRANSMISSION NETWORK

Phases % Voltage Duration (ms)

Red 82 96

Yellow 82 61

Blue 54 116

Fig. 11.Voltage and current waveforms due to upstream asymmetrical faults in distribution network TABLE VI SUMMARY OF SAG SOURCE DETECTION DUE TO UPSTREAM ASYMMETRICAL FAULTS IN DISTRIBUTION NETWORK

Upstream sag source Upstream sag source from transmission networks

M1

M2

M3

M4

M5

Fig.12. Voltage and current waveforms due to upstream asymmetrical faults in transmission network

Incorrect

Correct

Correct

Correct

Correct

IV.1.5 Overall results on analysis of upstream sags The overall results of the upstream sag source detections are shown in Table 8.

The results of this study showed that all methods except method M1 (V-I) correctly identified the source of the upstream sag. The cause of the errors for method M1 were due to the existence of voltage swells in the two healthy phases. All the current profiles generally remain within steady state values except for the red phase which increased slightly during the sag event. These values caused the slope for the V-I plot (red phase) to be < 0 thus indicating a downstream sag source.

TABLE VIII OVERALL RESULTS FOR UPSTREAM SAG SOURCE DETECTION Numbers of sag sources detected Sag Typ No. of M1 M2 M3 M4 M5 source e events A C 42 0 0 42 42 42 B C 46 46 46 46 46 46 A D 48 0 0 48 48 48 B D 47 0 47 0 0 47 % 25.1 50.8 74.3 74.3 100 Mnemonic A

Description Distribution networks

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

B C D

Transmission networks Symmetrical sags Asymmetrical sags

The novel method M5 registered 100% accuracy in the detection of both upstream symmetrical and asymmetrical sag sources. The other methods registered accuracies of 25.1% to 74.3% for detection of upstream sag sources. The results of this analysis proved that method M5 is superior in the detection of upstream sag sources than the other existing sag source detection methods. Fig.14. Results of sag source detection due to downstream asymmetrical fault

IV.2 Analysis on downstream source of voltage sags

The next study is to evaluate the performances of all the methods in the detection of downstream sag sources. The downstream voltage sags were generated due to faults downstream from the monitoring point at DF1 (Fig. 1). Example on the diagnosis of asymmetrical downstream voltage sags (Fig. 13) due to a fault at point A1 is shown in Fig. 14. The results of the diagnosis showed that all methods except for method M1 correctly identified the source of the voltage sag as a downstream event. The causes of the errors for method M1 were due to the existence of voltage swells in the two healthy phases. The current profile for the yellow phase increased slightly during the sag event and decreased to zero once the fault was isolated. These values caused the slopes for the V-I plots to be > 0 thus indicating an upstream sag source.

The next example is on the diagnosis of symmetrical downstream voltage sag due to a fault at point A3. The voltage and current waveforms due to the symmetrical faults are shown in Fig. 15. The results of the diagnosis showed that all methods correctly identified the source of the voltage as a downstream event.

Fig. 15. Voltage and current waveforms due to downstream symmetrical fault

IV.2.1 Overall analysis of downstream voltage sags

Fig.13. Voltage and current waveforms due to downstream asymmetrical fault

The overall results for sag source detection for 54 numbers of voltage sags are shown in Table 9. All the existing methods successfully detected the origins for symmetrical voltage sags. Perfect results were also seen for all methods except for method M1, in detecting the sag sources for asymmetrical voltage sags. The novel method M5 (STPD) registered perfect accuracy in identifying downstream sag sources due to both symmetrical and asymmetrical faults in the distribution networks. TABLE 9 OVERALL RESULTS FOR DOWNSTREAM SAG SOURCE DETECTION Numbers of sag sources detected No. of Sag M1 M2 M3 M4 M5 Types events source B C 27 27 27 27 27 27 B D 27 0 27 27 27 27

Mohamed F.Faisal, Azah Mohamed, Hussain Shareef

% Mnemonic A B C D

50

100

100

100

100

Description Distribution networks Transmission networks Symmetrical sags Asymmetrical sags

V.

Conclusion

In this paper, the performance of a novel method developed from the S-transform was compared with the existing methods in detecting the origins of voltage sags. From the analyses done on 237 numbers of voltage sag data, the novel method (M5) scored perfect results in identifying the sources of voltage sags - upstream and downstream from the monitoring point. The accuracies of other methods for upstream sag source detection were 25.14% (M1), 50.82% (M2) and 74.32% (M3 and M4). For downstream sag source, all the methods except for method M1 (V-I) correctly identified all the sag sources. Method M1 has an accuracy of 50 % for detecting downstream sag source. Overall, this study showed that the novel method (M5) is superior to the other methods in the detection of upstream and downstream voltage sag sources.

Acknowledgements This work was carried out with the financial support from the Universiti Kebangsaan Malaysia under the research grant UKM-GUP-BTT-07-25-151.

[9]

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[12]

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Mohamed Fuad Faisal received his BSc from CWRU in Cleveland, USA and M.Sc from UiTM Shah Alam Selangor in 1991 and 2002 respectively. He is a Power Quality & Energy Efficiency Engineer with TNB Malaysia. He is current a Phd student at Universiti Kebangsaan Malaysia (UKM). He is also a student member of IEEE.

Professor Azah Mohamed received her B.Sc from University of London in 1978 and M.Sc and Ph.D from Universiti Malaya in 1988 and 1995, respectively. She is a professor at the Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia. Her main research interests are in power system security, power quality, artificial intelligence and distributed generation. She is a senior member of IEEE

Hussain Shareef received his BSc with honours from IIT, Bangladesh, MS degree from METU, Turkey, and PhD degree from UTM, Malaysia, in 1999, 2002 and 2007, respectively. He is currently a lecturer in the Department of Electrical, Electronics, and Systems Engineering, Universiti Kebangsaan Malaysia. His current research interests are power system deregulation, power quality and power system distribution automation.