Abstract--This paper presents the detection and classifications

of power quality disturbances using time-frequency signal analysis. The method used is based on the pattern recognition approach. It consists of parameter estimation followed classification. Based on the spectrogram time-frequency analysis, a set of signal parameters are estimated as input to a classifier network. The power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The parameter estimation is characterized by voltage signal in rms per unit, waveform distortion, harmonic distortion and interharmonic distortion. A rule based system is developed to detect and classify the various types of power quality disturbances. The system has been tested with 100 data for each power quality event at SNR from 0dB to 50dB to verify its performance. The results show that the system gives 100 percent accuracy of power quality signals at 30dB of SNR.

Index Terms-- Spectrogram, pattern classification, power quality, time-frequency analysis, Signal to Noise Ratio (SNR).

I. INTRODUCTION ith the rapid advance in industrial applications that rely on sophisticated electronic devices, a demand for power

quality and reliability has become a great concern because the smallest interruption of power quality event can cause equipment failure, data loss and loss revenue. Voltage disturbances are the most frequent cause of a broad range of disruption in industrial and commercial power supply systems. These disturbances often referred to as power quality problems, which significantly affect many industries [1]. Major causes of power quality related revenue losses are interrupted manufacturing processes and computer network downtime.

The examples abound in semiconductor industry, chemical industry, automobile industry, paper manufacturing, and e-commerce. A report by Consortium for Electric Infrastructure to Support a Digital Society (CEIDS) [2] shows that the U.S. economy is losing between $104 billion and $164 billion a year due to outages and another $15 billion to $24 billion due to power quality phenomena. The conventional methods

Abdul Rahim Abdullah is a student at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia - (email: abdulr@utem.edu.my)

Ahmad Zuri Shaameri is with the Faculty of Electrical Engineering, Universiti Teknologi Malaysia - (email: ahmadzs@yahoo.com)

currently used by utilities for power quality monitoring are primarily based on visual inspection of voltage and current waveforms [2]. Therefore, a highly automated monitoring software and hardware is needed in order to provide adequate coverage of the entire system, understand the causes of these disturbances, resolve existing problems, and predict future problems.

This paper looks at the use of time-frequency representation in the interpretation of power quality disturbances. Spectrogram distribution is performed to detect and classify the power quality events. By using its time-frequency characteristics, each of the disturbance signals features is distinguished for the classification of the respective types of power quality problems.

II. POWER QUALITY PHENOMENA The term power quality refers to a wide variety of

electromagnetic phenomena that characterize the voltage and current at a given time and at a given location on the power system. According to the International Electrotechnical Commission (IEC), electromagnetic phenomena are classified into several groups as shown in Table 1 [3], [4]. This paper focused on seven types of power quality problems: voltage swell, voltage sag, interruption, harmonic, interharmonic, transient and notching.

A. Voltage Swell A swell is defined as an increase in rms voltage at the

power frequency for durations from 0.5 cycles to 1 minute. Typical magnitudes are between 1.1 and 1.8pu.

B. Voltage Sag Voltage sag is a decrease to between 0.1 to 0.9pu in rms

voltage at the power frequency for duration of 0.5 cycles to 1 minute.

C. Interruption An interruption occurs when the supply voltage or load

current decreases to less than 0.1pu for a period of time not exceeding 1 minute.

D. Harmonic Harmonics are sinusoidal voltages or currents having

frequencies that are integer multiples of the frequency at which the supply system is designed to operate (50Hz for

Detection and Classification of Power Quality Disturbances Using Time-Frequency Analysis

Technique Abdul Rahim Abdullah, Ahmad Zuri Shaameri, Abd Rahim Mat Sidek and

Mohammad Razman Shaari

W

The 5th Student Conference on Research and Development SCOReD 2007 11-12 December 2007, Malaysia

1-4244-1470-9/07/$25.00 2007 IEEE.

Malaysia). Harmonics combine with the fundamental voltage or current and produce waveform distortion [10].

E. Interharmonics Interharmonics are signal components at frequencies that are not integer multiples of the power system frequency. For example, a 190 Hz component in a 50 Hz system [1]. To correctly measure an interharmonic component requires a measurement window which is also an integer multiple of the interharmonic cycle length.

TABLE I

CATEGORIES AND TYPICAL CHARACTERISTICS OF POWER SYSTEM ELECTROMAGNETIC PHENOMENA

Categories Typical spectral content

Typical duration

Typical voltage magnitude

1.0 Transients 1.1 Impulsive 1.2 Nanosecond 1.3 Millsecond 1.2 Oscillatory 1.2.1 Low frequency 1.2.2 Medium frequency 1.2.3 High frequency

5 ns rise 1 ms rise

0.1 ms rise

< 5 kHz 5-500 kHz 0.5-5 MHz

< 50 ns

50 ns-1 ms > 1 ms

0.3-50 ms 20 ms 5 ms

0-4 pu 0-8 pu 0-4 pu

2.0 Short duration variations 2.1 Instantaneous 2.1.1 Sag 2.1.2 Swell 2.2 Momentary 2.2.1 Interruption 2.2.2 Sag 2.2.3 Swell 2.3 Temporary 2.3.1 Interruption 2.3.2 Sag 2.3.3 Swell

0.5-30 cycles 0.5-30 cycles

0.5 cycles -3 s

30 cycles-3 s 30 cycles-3 s

3 s-1 min 3 s-1 min 3 s-1 min

0.1-0.9 pu 1.1-1.8 pu

< 0.1 pu 0.1-0.9 pu 1.1-1.4 pu

< 0.1 pu 0.1-0.9 pu 1.1-1.2 pu

3.0 Long duration variations 3.1 Interruption, sustained 3.2 Undervoltages 3.3 Overvoltages

> 1 min > 1 min > 1 min

0.0 pu

0.8-0.9 pu 1.1-1.2 pu

4.0 Voltage imbalance 5.0 Waveform distortion 5.1 DC offset 5.2 Harmonics 5.3 Interharmonics 5.4 Notching 5.5 Noise 6.0 Voltage fluctuations 7.0 Power frequency variations

0-100th H 0-6 kHz

broad-band

< 25 Hz

steady state

steady state steady state steady state steady state steady state

Intermittent

< 10 s

0.5-2%

0-0.1% 0-20% 0-2%

0-1%

0.1-7%

F. Transient The term transient has been used in the analysis of power

system variations for a long time. Transient can be classified into two categories: impulse and oscillatory. Impulsive transients are normally characterized by their rise and decay times. These phenomena can also be described by their spectral content. For example, a 1.2/50s 2000V impulsive transient rises to its peak value of 2000 V in 1.2 us and decays to half its peak value in 50s. Oscillatory transients with a primary frequency component greater than 500 kHz and a typical duration measured in microseconds are considered high-frequency oscillatory transient. A transient with a primary frequency component between 5 and 500 kHz with duration measured in tens microseconds is termed a medium-frequency transient and considered as a low-frequency

transient if frequency component less than 5 kHz and duration from 0.3 to 50 ms.

G. Notching Notching is a switching or other disturbance of the normal

power voltage waveform, lasting less than 0.5 cycles which is initially of opposite polarity than the waveform and is thus subtracted from the normal waveform in terms of the peak value of the disturbance voltage. This includes complete loss of voltage for up to 0.5 cycles [4]. Notching is caused by the normal operation of power electronics devices when current is commutated from one phase to another.

III. TIME-FREQUENCY ANALYSIS Time-frequency analysis is motivated by the analysis of

non-stationary signals whose spectral characteristics change in time [4]. Spectrogram is one of the time-frequency analysis techniques and it represents a three-dimensional plot of the signal energy with respect to time and frequency [9]. The spectrogram is the result of calculating the frequency spectrum of windowed frames of a compound signal. Usually, spectrograms are calculated from the time signal using the short-time Fourier transform (STFT). In practice, the STFT is computed at a finite set of discrete values of . Moreover, due to the finite length of the windowed sequence, the STFT is accurately represented by its frequency sample as long as the number of frequency samples is greater than the window length [6], [7]. For an arbitrary discrete-time waveform of length N, the spectrogram time-frequency representation is calculated as follows:

( )10 and 10

)( )( 1,2

1

0

2

= =

MkNn

enmwmxM

knPM

m

Mkmj

x

(1)

x(n) is the input signal, w(n) is the window function, N is the number of samples and M is the windows length. Analysis results based on the spectrogram were made by simulation using MATLAB. Fig. 1 and 2 show the simulation results for swell and transient signal. For each time-frequency distribution, the corresponding time series data and spectrum are provided. Fig. 1a and 2a are the time series data that generates corresponding time-frequency distribution function in Fig. 1b and 2b. The amplitude of the distribution is expressed as the color intensity of the plot. The highest power is represent as red color while the lowest power by blue color.

0 50 100 150 200 250 300 350-1.5

-1

-0.5

0

0.5

1

1.5Power Quality Signal

Amplitu

de (V

pu)

Time (msec) Fig. 1a. Swell voltage in time representation

Fig. 1b. Swell voltage in time-frequency representation

0 50 100 150 200 250 300 350-1

-0.5

0

0.5

1

1.5Power Quality Signal

Am

plitu

de (V

pu)

Time (msec) Fig. 2a. Transient voltage in time representation

Fig. 2b. Transient voltage in time-frequency representation

IV. SYSTEM DESIGN The time-frequency distribution function is used as a tool to

extract the most salient features that represent the power quality phenomenon [5]. Before the application of this transform, a pre-processing of the signals is required to normalize them, because they are collected from various voltage levels in the distribution system. The time-frequency distribution is employed to generate a set of parameters which are used in classification process. The parameters are voltage (rms), harmonic distortion, interharmonic distortion, wave distortion and voltage pulse. The flow chart for analysis and classification of the power quality events is depicted in Fig. 3.

Fig. 3. Flow chart for analysis and classification of power quality events.

The rms (root mean square) is one of the parameters of the

signal. From the spectrogram time-frequency distribution (1), the rms can be defined as below;

( )

10 10

,)(1

0

= =

MkNn

knPnXM

kxrms

(2)

N is the number of samples and M is the windows length.

Transient

Normal voltage

Normal voltage Normal voltage Swell

Information of the harmonic distortion is needed especially to detect harmonic in power quality signal. When the waveform is nonsinusoidal but periodic with a period of one cycle (of the power system frequency, about 50 or 60 Hz), current and voltage waveforms can be decomposed into a sum of harmonic components [3]. The equation below is used to detect maximum power of harmonic distortion (HD) frequency component.

( )[ ]

harmonic 1002

10

,max)(

th

xh

HD

h

Nn

hnPnP

=

(3)

Besides that, voltage often contains interharmonic components that are not a multiple integer of the power system frequency [10]. For example, a 50 Hz signal distorted with a 155 Hz interharmonic. So, the parameter of interharmonic is required to classify interharmonic signal. The maximum power of interharmonic distortion (IhD) frequency component is defined as below.

( )[ ]

nicinterharmo

10

,max)(

th

xh

IhD

h

Nn

hnPnP

=

(4)

Waveform distortion includes all deviations of the voltage

waveform from the ideal sine wave [8]. The distortion can consists all the following possibilities: harmonic and, interharmonic distortion. The following equation is used to present the maximum power of waveform distortion (WD) frequency component in a power quality signal.

10 )()( )( )()( )()(

>==

>==

NnnPnPifnPnPnPifnPnP

HDIhDIhD

IhDHDHDWD

(5)

V. RULE-BASE FOR CLASSIFICATION OF POWER QUALITY EVENTS

To formulate rules, strict threshold values are set by a comprehensive analysis and comparison of derived features from each category of power quality event. The rules based to classify the power quality events are listed below:

Rule 1: if Vpu < 0.1pu and Duration > 10ms THEN Interruption Rule 2: if Vpu 0.1pu and Vpu 0.9pu and Duration > 10ms THEN Sag Rule 3: if Vpu 1.1pu and Vpu 1.4pu and Duration > 10ms THEN Swell Rule 4: if HD=Yes and IhD=No and Vpu > VThreshold THEN Harmonic Rule 5: if HD=No and IhD=Yes and Vpu > VThreshold THEN Interharmonic Rule 6: if WD=Yes and Vpu < VThreshold THEN Notching Rule 7: if WD=Yes and Vpulse=1 THEN Transient

Rule 8: if WD=No and Vpu < 1.1pu and Vrms > 0.9pu THEN Normal

VI. RESULTS Analysis results were made based on the several parameter

estimations obtained from the time-frequency distribution of power quality signals. The parameter estimations are voltage in rms per unit (Vrmspu), maximum value of waveform distortion (WD) frequency component, maximum value of harmonic distortion (HD) frequency component and maximum value of interharmonic distortion (IhD) frequency component.

Fig. 4 and 5 show the transient signal and its respective analysis results. The transient voltage can be characterized by a voltage pulse in the Vrmspu as shown in Fig. 5a, while Fig. 5b indicates that the transient frequency components exist in the WD signal. The frequency components consist of harmonic HD and IhD as shown in Fig. 5c and Fig. 5d.

Fig. 6 and 7 show the harmonic signal and its parameter estimation results. From the Vrmspu, the magnitude level of voltage signal increases more than its threshold value as shown in Fig. 7a. Its frequency distortion is observed in the WD in Fig. 7b. The harmonic signal is characterized by the presents of HD in Fig. 7c and IhD is zero as shown in Fig. 7d. From the characterization, a simple rule base system can be developed for the classification process of power quality events.

0 50 100 150 200 250 300 350-1

-0.5

0

0.5

1

1.5Power Quality Signal

Amplitu

de (V

pu)

Time (msec) Fig. 4. Transient signal

0 50 100 150 200 250 3000.9998

1

1.0002

1.0004a) Voltage Signal (Vrms pu)

Time (msec)

Vrm

s pu

0 500 1000 1500 2000 2500 3000 35000

1

2

3

4x 10-4 b) Maximum Value of WD Frequency Component

Time (msec)

Pow

er

0 500 1000 1500 2000 2500 3000 35000

1

x 10-4 c) Maximum Value of HD Frequency Component

Time (msec)

Pow

er

0 500 1000 1500 2000 2500 3000 35000

1

2

3

4x 10-4 d) Maximum Value of IhD Frequency Component

Time (msec)

Pow

er

Fig. 5. Analysis results for transient.

0 50 100 150 200 250 300 350-1.5

-1

-0.5

0

0.5

1

1.5Power Quality Signal

Amplitu

de (V

pu)

Time (msec) Fig. 6. Harmonic signal

0 50 100 150 200 250 3001.0019

1.0019

1.0019

1.0019

1.0019a) Voltage Signal (Vrms pu)

Time (msec)

Vrm

s pu

0 500 1000 1500 2000 2500 3000 35006

8

10

12x 10-4 b) Maximum Value of WD Frequency Component

Time (msec)

Pow

er

0 500 1000 1500 2000 2500 3000 35006

8

10

12x 10-4 c) Maximum Value of HD Frequency Component

Time (msec)

Pow

er

0 500 1000 1500 2000 2500 3000 3500-1

-0.5

0

0.5

1d) Maximum Value of IhD Frequency Component

Time (msec)

Pow

er

Fig. 7. Analysis results for harmonic

In order to measure the distortion amount independent from

the scale of the original signal, we used the signal to noise ratio, SNR which is defined as

noise

xdB P

PSNR log10= (6)

where Px and Pnoise corresponds to the original signal and noise power [8]. The SNR measurement is important because normally the power quality signals are having noise. By simulation, the system has been tested by using 100 data of each power quality event. The SNR measurement is tested from 0 to 50dB. Fig. 8 shows the performance evaluation of this system in terms of the accuracy the data versus SNR for each power quality events. The result shows the system gives the correct classification for all power quality events starting point at 30dB. Voltage swell gives the best performance amongst the other events. It gives 100% accuracy at SNR of 2.5dB. For a normal event, it gives the 100% accuracy at the noise level SNR of 27.5dB.

0

20

40

60

80

100

120

0 2.5 5 7.5 10 12.5 15 17

.5 20 22.5 25 27

.5 30 32.5 35 37

.5 40 42.5 45 47

.5 50

SNR dB

Num

ber o

f Dat

a

NormalSwellSagInterruptionHarmonicTransientNotchingInterharmonic

Fig. 8. SNR for each power quality event

VII. CONCLUSION In short, the detection and classification system is developed by using the spectrogram time-frequency analysis technique. It

Transient Harmonic

Interharmonic

Notching

Swell

Sag

Interruption

Normal

performs the extraction of the required time information, rms measurement, harmonic, interharmonic and waveform distortion of power quality signals. The performance of the rule-based classifier is demonstrated and verified by a set of simulated disturbance waveforms. In addition, each power quality signals have been tested by using 100 data with SNR from 0dB to 50dB. The result is the power quality signals give 100 percent accuracy at 30dB of SNR.

VIII. REFERENCES [1] H.J.Bollen, Y.H. Gu, Signal Processing of Power Quality Disturbances.

Wiley-Interscience, 2006. [2] A. Kusko, T. Thompson, Power Quality in Electrical Systems. McGraw

Hill, 2007. [3] IEEE Recommended Practice for Monitoring Electrical Power Quality.

IEEE Std 1159-1995 Approved Jun. 14, 1995. [4] Hasniaty, A. Mohamed, A. Hussain, Automating Power Quality

Disturbance Analysis Using the IPQDA Software Tool, in Proc. 2006 IEEE 4th Student Conference On Research and Development Conf., pp. 211-214.

[5] A.Galili, T.K.Kamel, M. Ypussef, Power Quality Disturbance Classification Using The Inductive Approach, IEEE Trans. Power Delivery, vol. 19, pp. 1812-1818, Oct. 2004.

[6] A.R. Abdullah, A.Z. Shaameri, Real time Power Quality Monitoring System Based On TMS320CV5416 DSP Processor, in Proc. 2005 IEEE Power Electronics and Drives Systems Conf., pp. 1668-1672.

[7] A.R. Abdullah, N.M. Saad, A.Z. Shaameri, Power Quality Monitoring System Utilizing Periodogram and Spectrogram Analysis Techniques, in Proc. 2007 IEEE International Conference on Control, Instrumentation and Mechatronics Engineering., pp. 770-774.

[8] O.N. Gerek, D.G. Ece, 2-D Analysis And Compression of Power-Quality Event Data, IEEE Trans. Power Delivery, vol. 19, pp. 791-798, Apr. 2004.

[9] B. Boashah, Time-Frquency Signal Analysis and Processing- A Comprehensive Reference, Elsevier, London, 2003.

[10] Y. Sallehhudin, H. A. Abu, et al., A guide Book on Power Quality, Tenaga Nasional Berhad, Malaysia, 1995.