International
Journal of Electrical Engineering
and Technology
(IJEET), ISSN 0976 –
INTERNATIONAL
JOURNAL
OF ELECTRICAL
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 1, January-June (2012), © IAEME
ENGINEERING & TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 3, Issue 1, January- June (2012), pp. 112-122
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IJEET
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APPLICATION OF WAVELET TRANSFORM FOR MONITORING
SHORT DURATION DISTURBANCES IN DISTRIBUTION
SYSTEMS
A.V.PADMAJA (M.Tech)
S.V.U.COLLEGE OF ENGINEERING
EEE DEPARTMENT
S.V.UNIVERSITY
TIRUPATHI,(A.P)
INDIA
Email:paddu.avp@gmail.com
Ms.V. USHA REDDY, M.Tech (Ph.D)
ASSISTANT PROFESSOR
S.V.U.COLLEGEOFENGINEERING
EEE DEPARTMENT
S.V.UNIVERSITY
TIRUPATHI. (A.P)
INDIA
ABSTRACT
This paper presents features that characterize power quality disturbances from recorded
voltage waveforms using wavelet transform. The concept of Discrete Wavelet Transform
for feature extraction of power quality disturbance signals has been incorporated as a
powerful tool for detecting and classifying power quality disturbances like Sag, Swell
and Interruption and decomposed up to 4 levels using Db4 wavelet. The Multiresolution
analysis is used to classify and quantify the power quality disturbances which obtained by
plotting the standard deviations of the decomposed signal at different resolution level.
The clear information for detection of power quality disturbances are obtained by plotting
the entropy of the decomposed signal at different levels.
Keywords
power quality, detection of disturbances, Daubenchies , discrete wavelet transform,
Multiresolution analysis, entropy.
I. INTRODUCTION
Electric Power Quality is a very important issue as far the power supply utilization is
concerned. The increased requirements on supervision, control and performance in
modern power systems make power quality monitoring a common practice for utilities.
Studies of power quality phenomena have emerged as an important subject in recent
years due to renewed interest in improving the quality of the electric supply. Poor power
quality may cause many problems for affected loads such as malfunctions, instabilities
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 1, January-June (2012), © IAEME
and short lifetime. In order to improve electric power quality, the sources and causes of
disturbances must be known before appropriate mitigating action can be taken and
continuous recording of disturbance waveforms is necessary. To determine the causes
and disturbances one must detect and localize those disturbances and classify the types of
the disturbances. New tools are required to extract all relevant information from the
recordings in an automatic way.
Wavelet Transform is a mathematical tool like Fourier transform, which provides an
automatic detection of power quality disturbance waveforms, especially using
Daubechies family. Wavelet Transform provides the time-scale analysis of the nonstationary signal. It decomposes the signal to time scale representation rather than time
frequency representation. A voltage event is an abnormal and temporary variation of the
magnitude of voltage supply. Voltage dips, short interruption and voltage swells are the
most important events in voltage signals for detecting and supply that produce huge
losses in industrial and commercial due to sensitivity of equipment to the voltage
variations.
Santoso et al [1] proposed wavelet transform technique for the detection and
localization of the actual power quality disturbances. Gaoda et al. [2] introduced the use
of wavelet transform and multiresolution signal decomposition as a powerful analysis
tool that can be used to monitor and measure the system response to distorted signal.
Heydt and Galli [3] proposed wavelet techniques for the identification of the power
system transient signals. Fuzzy logic control technique has been discussed by Hiyama et
al [4] to enhance power system stability using static VAR compensator. A hybrid scheme
using a Fourier linear combiner and a fuzzy expert system for the classification of
transient disturbance waveforms in a power system has been presented by Dash et al [5].
Styvaktakis et al [6] developed an expert system to classify different types of power
system events and offer useful information in terms of power quality. Huang et al [7]
presented a neural-fuzzy technology based classifier for the recognition of power quality
disturbances. Olivier et al [8] investigated the use of a continuous wavelet transform to
detect and analyze voltage sags and transients. They developed an efficient and simple
algorithm for detecting and measuring power quality analysis. He and Starzyk [9]
proposed a novel approach for power quality disturbances classification based on wavelet
transform and self organizing learning array system. Wavelet transform has been utilized
here to extract feature vectors for various power quality disturbances based on multi
resolution analysis. Lin and Wang [10] proposed another model for power quality
detection for power system disturbances using adaptive wavelet networks. Elkalashy et
al [11] used discrete wavelet transforms to detect high impedance faults due to leaning
trees. The need to analyze power quality signals to extract their distinctive features made
Gargoom et al [12] to use Hilbert transform and Clarke transform for the classification of
power quality signals and they compared the performance of these techniques with
wavelet transform. A new method has been proposed by Peres and Barros [13] for the
online real-time detection and classification of voltage events in power systems. They
used wavelet analysis for the detection and estimation of time related parameters of an
event and used extended Kalman filtering for the confirmation of the event.
This paper deals with the use of discrete wavelet transform and multiresolution
analysis to detect and analyze voltage sag, swell, and interruption. Daubechies wavelet
transform is very accurate for analyzing Power Quality Disturbances among all the
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 1, January-June (2012), © IAEME
wavelet families, for transient faults. The names of the Daubechies family wavelets are
written as DbN, where N is the order, and db the “surname” of the wavelet. The
localization and duration of the disturbance has been detected using the first
decomposition level i.e. the detail DI of the first decomposition level clearly indicates
the time interval of the event and hence can classify the phenomena as momentary or
temporary. The shape of the standard deviation curve provides information about the
harmonic content in the signal and also the entropy curve of the signal at different
decomposition levels can classify the disturbances present in the signal.
Fig.1 Daubechies family wavelets
II. DETECTION AND LOCALIZATION OF DISTURBANCES
This paper presents multiresolution signal decomposition technique as a powerful tool for
detecting and classifying disturbances in the electrical distribution system. The proposed
technique will deal with the problem not only in
time domain or frequency domain, but in a wavelet domain which covers both the time
and frequency domains. Multiresolution signal decomposition technique can detect and
diagnose defects and provide early warning of impending power quality problems. Using
the properties of the wavelet and the features in the decomposed waveform one will have
the ability to extract important information from the distorted signal at different
resolution levels and classify the types of disturbance.
Power quality disturbances that are analyzed using discrete wavelet transform and
multiresolution analysis are
• Normal/Pure sinusoidal signal
• Sinusoidal signal with intermediate sag
• Sinusoidal signal with intermediate swell
• Sinusoidal signal with intermediate interruption
All these signals were analyzed using Wavelet Transform. Here we have chosen
Daubechies 4 at level 4 for the analysis. Fig. 2(a) shows the coefficients of detail level DI
of a pure sinusoidal signal and reveals the coefficients of detail level 1 of a sinusoidal
signal having intermediate sag. It is observed that sufficiently large detail coefficient
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 1, January-June (2012), © IAEME
occurs around at 400th sample and at 800th sample. Similarly Fig. 2(b) shows the
coefficients of detail level 1 of the signals with intermediate swell and interruption.
Figure 2 a
Figure 2 b
Fig.2 Magnitude of detail coefficients of DWT (a) Pure sinusoidal signal and Sinusoidal
signal with intermediate sag (b) Sinusoidal signal with intermediate swell and Sinusoidal
signal with intermediate interruption.
The wavelet transform coefficients with high values indicate the power quality
disturbance events and the exact location of the disturbance. The other part of the
decomposed signal of detail d1 is smooth indicating that the signal follows some regular
patterns in those periods without having any electrical noise. Detail d1 shows the exact
location of the disturbance.
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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The standard deviations are plotted against the decomposition level to obtain the
standard deviation curve. The standard deviation curves for the low frequency (50 Hz)
signal and high frequency (154.7 kHz) signal are shown in Fig.3 and Fig.4. The basic
difference in the two curves is that for low frequency signal there is one peak and for
high frequency signal there are two peaks.
Fig.3 Standard deviation curve for low frequency signal
Fig.4 Standard deviation curve for high frequency signal
III. DETECTION ALGORITHM
In this proposed algorithm the various level multi-resolution analysis features to find the
detail coefficients threshold value. Initially the fundamental frequency and the phase
angle shift for each window are also compute.
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The detection algorithm is shown in Figure 5
IV. RESULTS AND DISCUSSIONS
Fig. 6 shows the standard deviation curves for 50 Hz. signals with power quality
disturbances i.e. sag, swell and interruption respectively. It is seen that the peak value of
the curve detects the type of disturbance.
Fig 5 Detection algorithm
For example the peak value of standard deviation curve in Fig. 6(a) is less that that at Fig.
3 indicating that there is sag in the disturbed signal. For interruption the peak is still less
than that in sag. Similarly for swell the peak of the curve is higher than that of the ideal
signal. Hence the standard deviation curves clearly detect the type of the disturbance.
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(a)
(b)
(c)
Fig.6 Standard deviation curves for low frequency signal with pquality disturbances (a)
sag (b) swell (c) interruption
Fig 7 shows the curve of entropy of different short duration disturbed signals at different
decomposition levels. Initially the magnitude of the curve for swell is high and that for
interruption is low. By observing the nature of the entropy curves of different disturbed
signals with respect to the normal signal the type of disturbance can be easily detected.
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Fig.7 Entropy curve for different power quality disturbances at low frequency
The standard deviation curves for high frequency signal are shown in Fig. 8. It is
found that the second peak of the curve remains same irrespective of the presence of
disturbance in the signal. By observing and monitoring the changes occurring at the first
peak with respect to the peak for normal sinusoid we can categorically define the type of
disturbance. The first peak of standard deviation curve is less in case of sag and still less
in the case of interruption as compared with the normal signal.
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 1, January-June (2012), © IAEME
(a)
(b)
(c)
Fig.8 Standard deviation curves for high frequency signal with power quality
disturbances (a) sag (b) swell (c) interruption
Fig.9 shows the entropy curve for different power quality disturbances at high frequency.
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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Fig.9 Entropy curve for different power quality disturbances at high frequency
V. CONCLUSIONS
This paper presents a new method for the detection and classification of voltage
events in the method proposed uses the results obtained in the simultaneous application
of the discrete wavelet transform and an extended Kalman filtering to the samples of
voltage supply for more accurate detection and estimation of the magnitude and duration
of a voltage event. The standard deviation curves both for low and high frequency signals
have been analyzed. Using different Properties of discrete wavelet transform one can
detect, localize and classify different short duration disturbances in a
signal.
ACKNOWLEDGEMENT
The authors would like to thank the managements of s.v.u.college of engineering,
tirupathi for providing the required facilities to do this work.
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