1. INTERNATIONAL CONFERENCE ON “CONTROL, AUTOMATION, COMMUNICATION AND ENERGY CONSERVATION -2009, 4th-6th
June 2009
1
Power System Switching Transients Analysis
Using Multiresolution Signal Decomposition
S.A.Deokar, L.M.Waghmare, M.D.Takale
Abstract—Deregulation and embedded generation are two important reasons for the recent interest in power quality. Other important
reasons are the increased emission of disturbances by equipment and the increased susceptibility of equipment, production processes,
and manufacturing industry to voltage disturbances. Thus it is essential to establish a real time power quality monitoring system to detect
power quality disturbance. Wavelet Transform (WT) is a mathematical tool, which provides an automatic detection of Power Quality
Disturbance (PQD) waveforms, especially using Daubechies family. Several types of Wavelets Network algorithms have been considered
for detection of power quality problems. But both time and frequency information are available by Multi Resolution Analysis (MRA) alone.
This paper presents the application of wavelet transform to detect, localize, and extract power switching disturbance. A power system
switching transients have been simulated using MATLAB-7.01. The key idea underlying the approach is to decompose a given disturbance
signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed
using multiresolution signal decomposition techniques. The proposed technique appears to be robust for detection and localization of
power quality disturbances created due to capacitor switching and load switching.
Index Terms— Power Quality, Power Switching Transients, Multiresolution Signal Decomposition (MSD), Wavelet Transform (WT), Voltage
Swell, Squared Wavelet Transform Coefficient
—————————— ——————————
1 INTRODUCTION
Nowadays electrical devices are becoming smaller due to the
proliferation of electronic equipment which makes the
devices more sensitive to power quality disturbances.
Worldwide, the electric power industry is undergoing vast
regulatory changes. These deregulated structures of power
system have forced electric supply companies to supply high
quality of electric power to their customers. At present, the
cost of electric power is determined by the amount of power
delivered, i.e., Rs or $/kWh. In future, it is likely that cost of
electric power will also be determined by the quality of the
delivered power to the customer. As such, the quality of pow-
er has been an important concern in recent years and has cap-
tured a great deal of attention from electric utilities and their
customers.
Poor quality disturbances are generated both on the utility
and customer sides. In order to determine the causes and
sources of disturbances, one must have the capability to
detect and localize those disturbances and further classify the
types of disturbances. Some manual procedures have been
developed but it requires large amount of data and associated
effort and such procedures are costly and inefficient. A more
efficient approach is thus required in this power quality as-
sessment. In this paper an attempt has been made to imple-
ment wavelet transformation based multiresolution signal
decomposition (MSD) technique to detect and localize of
power quality disturbances. The basic idea is to decompose a
given disturbance signal into other signals that represent a
smoothed version and a detailed version of the original sig-
nal. The detailed version is indeed a wavelet transforms of the
original signal that indicates the occurrence of the disturbance
event, the frequency content of the event, and the gradient
(first derivative) of the events. The proposed technique ap-
pears to be robust for detection and localization of power
quality disturbances created due to capacitor switching and
load switching. The uniqueness of the squared wavelet trans-
form coefficients is also studied which will lead to an auto-
matic scheme for classifying various types of power quality
disturbances which will be a valuable alternative to the tradi-
tional transform approaches.
2 POWER QUALITY DISTURBANCES
The International Electro technical Commission (IEC)-61000-4-
30[158, page 15], defines the power quality as follows: Cha-
racteristics of the electricity at a given point on an electrical
system, evaluated against a set not to the performance of
equipment but to the possibility of measuring and quantifying
the performance of the power system [1]. Any deviation of
current or voltage from the ideal is a power quality
disturbance. Disturbance can be a voltage disturbance or a cur-
rent disturbance, but it is often not possible to distinguish be-
tween the two. As per IEEE Std-1159, 1250 definition and caus-
es of few disturbance signals are discussed as given below.
2.1 Voltage dip or Sag
A voltage drop is only sag if sag voltage is between 10% and
90% of the nominal voltage. A voltage dip is caused by switch-
ing operations associated with a temporary disconnection of
supply, the flow of heavy current associated with starting of
heavy motors or the flow of fault currents and the lightning
strokes.
2.2 Voltage Interruption
Voltage interruption or supply interruption is a condition in
which the voltage at supply terminals is close to zero. Close to
the zero is by the IEC defined as “lower than 1% of the declared
voltage” and by the IEEE as “lower than 10%”. The causes can
be a blown fuse, or breaker opening which leads down to the
shut down of the power system set up causing huge financial
loss.
————————————————
• S.A. Deokar is with the AISSMS College of Engineering, Pune- 411001
Email: s_deokar2@rediffmail.com
• L.M.Waghmare is with the S.G.G.S.College of Engineering and Technol-
ogy , Nanded-431606. E-mail: lmwaghmare@yahoo.com
• M.D.Takale is with the J.S.P.M, C.O.E, Pune - 411028.
E-mail mdtakale@gmail.com
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2. 2
2.3 Voltage increase or Swell
A Voltage swell is an increase in r.m.s. voltage or current be-
tween 1.1 to 1.8 p.u. at the power frequency for durations from
0.5 cycle to 1 minute. Swells can be caused by switching off a
large load, Single line to ground fault, energizing a large capaci-
tor bank. They appear on the unfaulted phase of a three phase
circuit with a single phase short circuit. They can also occur
after rejection of some load.
2.4 Transients
Voltage disturbances shorter than sags or swells are classified
as transients and are caused by the sudden changes in the
power system. On the basis of the duration, transient over vol-
tages can be divided into switching surge (duration in the
range of milliseconds) and impulse spike (duration in the range
of microseconds). Surges are high energy pulses arising from
power system switching disturbances either directly or as a
result of resonating circuits associated with switching devices.
In particular capacitor switching can cause resonant oscillations
leading to overvoltage three to four times the nominal rating.
Impulses on the other hand result from direct or indirect
lightning strokes, arcing, insulation breakdown, etc.
3. WAVELET TRANSFORM:ATOOL FOR SIGANL
ANALYSIS
Wavelet Transform provides the timescale analysis of the non-
stationary signal [1]. It decomposes the signal to time scale re-
presentation rather than time- frequency representation.
Wavelet transform expands a signal into several scales belong-
ing to different frequency regions by using translation (shift in
time) and dilation (compression in time) of a fixed wavelet
function known as Mother Wavelet. Wavelet based signal
processing technique is one of the new tools for power system
transient analysis and power quality disturbance classifica-
tion and also transmission line protection [2].
In other words, the wavelet transform reacts the most to the
gradient of a given signal. The more irregular the disturbance,
the higher the gradient will be. In most power signals, the wa-
veshape of a disturbance event is irregular compared to that of
its background signal. As a consequence, the wavelet coeffi-
cients associated with the disturbance event will have very
large magnitudes compared to those of a disturbance-free
waveform. The Multiresolution Signal Decomposition (MSD)
technique decomposes a given signal into its detailed and
smoothed versions [4]. In power quality disturbance signals,
many disturbances contain sharp edges, transitions, and jumps.
By using the MSD technique, the power quality (PQ) distur-
bance signal is decomposed into two other signals; one is the
blurred version of the PQ disturbance signal, and the other is
the detailed version of the PQ disturbance signal that contains
the sharp edges, transitions, and jumps. Therefore, the MSD
technique discriminates disturbances from the original signal
which analyses them separately.
3.1 Selection of Wavelet
The choice of analyzing wavelets plays a significant role in de-
tecting and localizing various types of power quality distur-
bances. For short and fast transient disturbances Daub4 and
Daub6 wavelets are better while for slow transient disturbances
Daub8 and Daub10 are suitable. The selection of appropriate
mother wavelet without knowing the types of transient distur-
bances is a challenging task. To avoid complexity we apply one
type of mother wavelet in the whole course of detection for all
types of disturbances. At the lowest scale i.e. scale 1, the mother
wavelet is most localized in time and oscillates rapidly within
very short period of time. As the wavelet goes to higher scale
the analyzing wavelets become more localized and oscillate less
due to the dilation nature of the wavelet transform analysis.
Hence due to higher scale signal decomposition fast and short
transient disturbances will be detected at lower scales whereas
slow and long transient disturbances will be detected at higher
scales. Thus we can detect both fast and slow transients
with a single type of analyzing wavelets. The wavelettransform
is performed by dilating a mother wavelet in the course of
analysis rather than by contracting a mother wavelet.
We choose Daub4 because it is most localized i.e. compactly
supported in time. As per IEEE standards, Daubechies wavelet
transform is very accurate for analyzing Power Quality Distur-
bances among all the wavelet families, for transient faults. The
Daubechies family wavelets are shown in Fig 1.
Fig. 1. Daubechies wavelets family.
4. MULTIRESOLUTION SIGNAL
DECOMPOSITION
Let co (n) is a discrete-time signal recorded from a physical
measuring device. This signal is to be decomposed into de-
tailed and blurred representations. From the MSD tech-
nique, the decomposed signals at scale 1 are c1(n) and d1(n),
where c1(n) is the smoothed version of the original signal,
and d1
(n) is the detailed representation of the original signal
co(n) in the form of wavelet transform coefficients [4]. They
are defined as
( ) )(2)( 01 ncnkhnc
k
⋅−= ∑ (1)
( ) )(2)( 01 ncnkgnd
k
⋅−= ∑ (2)
Where h(n) and g(n) are the associated filter coefficients that
decompose co(n) into c1(n) and d1(n) respectively. If the h(n)
and g(n) are of four coefficient then it is called as Daube-
chies wavelet with four coefficients or Daub4 and for that of
six coefficient is known as Daubechies wavelet with six coef-
ficients or Daub6 and other choices of filter coefficient are
possible such as ones with 8, 10, etc., coefficients, and with
such choices the analyzing wavelets are called Daub8,
Daub10, etc. The next higher scale decomposition is now
based on the c1(n) signal. The decomposed signal at scale 2
is given by
( ) )(2)( 12 ncnkhnc
k
⋅−= ∑ (3)
( ) )(2)( 12 ncnkgnd
k
⋅−= ∑ (4)
In general, the decomposed signal at ‘m’ scale based on
‘(m-1)’ signal is given by
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3. 3
( ) )(2)( 1 ncnkhnc m
k
m −⋅−= ∑ (5)
( ) )(2)( 1 ncnkgnd m
k
m −⋅−= ∑ (6)
A Figure 2. shows the decomposition of cm-1(n) into cm(n) and
dm(n) using filters h(n) and g(n), respectively. These filters
determine the wavelet used to analyze the signal cm-1(n).
After the signal cm-1(n) is filtered by h(n) and g(n), it is then
decimated by a factor of two according to Eq.(5) and Eq.(6),
respectively. The resulting signal from h(n) is cm
(n) a
smoothed version of the original signal cm-1(n) because h(n)
filter has a low pass frequency response. A signal dm(n), is
the difference between the original signal cm-1(n) and the
smoothed signal cm(n). In other words, signal dm(n), contains
the details that have been removed from signal cm-1(n). Signal
dm
(n) is called wavelet transform coefficient at scale ‘m’.The
time resolutions of cm(n) and dm(n) are now half that of cm-
1(n) due to the decimation by a factor of two. As a result,
cm(n) has N sample points for the entire observation time,
then signals cm(n) and dm(n) will have N/2 sampling points
for the same observation period. The highest possible fre-
quency at this scale is half of that of the original, since the
sampling interval has been doubled [4].
5. SIMULATION RESULTS FORTHE
DETECTION OF POWER SYSTEM
TRANSIENTS
A transient phenomenon due to capacitor switching and
load switching is analyzed and simulated using Matlab-7.01
which is discussed below.
5.1 Capacitor Switching
Let us now perform the wavelet transform upon an actual
disturbance waveform c0(n) shown in Fig. 3. This waveform
is due to capacitor energizing disturbance event. The capaci-
tor of 0.1µF was energized by 100V, 50Hz supply at time
30msec. The number of sample points of signal c0
(n) is 10000
and the recording time is T = 20msec. Thus, the time resolu-
tion of this signal is 20/10000=2msec with a sampling fre-
quency of 2000X50 = 10 kHz.
Let us perform a step-by-step wavelet transformation upon
this capacitor energizing disturbance waveform. Daube-
chies' wavelet with four coefficients or Daub4 for short is
chosen as a mother wavelet.The original disturbance signal
co(n) is decomposed into smoothed signal c1(n) and detailed
signal d1(n) respectively. A computer code to perform a one-
scale wavelet transform, daubwt, is written using Matlab -
7.01. The number of sample points of both signals c1
(n) and
d1
(n) is 5000 points which is half of the length of signal co
(n)
as shown in Fig. 4. (a) and (b) respectively. So now the time
resolution of c1(n) signal is 20/5000=4msec. Signal c1(n)
looks very much similar to signal c0
(n), but the magnitude of
c1(n) is different than the magnitude of c0(n) and has less
details or higher frequency components as compared to the
original signal c0
(n). The signal d1
(n) is detailed or higher
frequency component of original signal c0(n) i.e. the higher
frequency component, which has been removed from the
signal c1(n) is recorded as detailed signal d1(n). These detail
components are wavelet transform coefficients at scale 1.
The detail signal d1(n) clearly indicates the occurrence of the
disturbance event. Figure 3. Shows that at the capacitor
energizing event, the voltage step change is the largest in the
waveform. The wavelet transform is very sensitive to signal
irregularities or changes, hence the wavelet coefficients at
this location are the largest.The wavelet transform of signal
c1(n) at scale 2 can be determined from the previous imme-
diate scale, i.e. scale 1. According to Eqs. (3) and (4), signals
c2(n) and d2(n) can be obtained by decomposing signal c1(n)
with Daub4 as a mother wavelet. The number of samples in
c2
(n) and d2
(n) is half that of signal c1
(n) i.e. 2500 as shown in
Fig. 4. (c) and (d). Signal c2(n) is smoothed version and d2(n)
is detailed version of signal c1(n). Similarly the c0(n) can be
decomposed into smoothed signal c3(n) and detailed signal
d3(n) at scale 3 by using immediate smoothed signal c2(n) as
shown in Fig. 4 (a) and (b) and so on. The above discussion
shows that the signal cm-1(n) can be decomposed into
smoothed version cm(n) and detailed version dm(n). The de-
tailed version dm(n) indicates the occurrence of power sys-
tem disturbance.
In order to enhance the detection of disturbance the detailed
Fig. 2. A Two-Scale Signal Decomposition
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
-200
0
200
(a) Sample Points
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
-50
0
50
(b) Sample Points
0 500 1000 1500 2000 2500
-200
0
200
(c) Sample Points
0 500 1000 1500 2000 2500
-20
0
20
(d) Sample Points
Fig. 4. Wavelet decomposition (a) Smoothed signal c1
(n) and (b) De-
tailed signal d1
(n) at scale 1. (c) Smoothed signal c2
(n) and (d) De-
tailed signal d2
(n) at scale 2
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
-100
-50
0
50
100
Sampling Points
Voltage(volts)
Fig. 3. Capacitor Energizing Disturbance Waveform
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4. 4
or higher frequency signals are squared. Fig. 6. shows the
squared detailed signal at scale 1, scale 2, scale 3, scale 4 and
scale 5 respectively. Squared wavelet transform coefficients
are very useful quantities in indicating disturbance activi-
ties. If a disturbance event is characterized by abrupt voltage
changes, the squared detailed signals are the excellent indi-
cator.
The squared detailed signal not only defines but also identi-
fies the transient duration. Therefore, squaring wavelet
transform coefficients is advisable if the wavelet transform
coefficients by themselves are inadequate to indicate the
disturbance features, i.e., in this case the beginning and du-
ration of the event.
5.2 Load Switching
Let us now perform the wavelet transform upon another
actual disturbance waveform c0(n) shown in Fig. 7. (a). This
waveform is due to load switching event. The load was in-
itially on and switched off at 2.5msec followed by switched
on 5msec as shown in Fig. 6. (a). The squared detailed wave-
form of this disturbance at scale 1, scale 2, scale 3, scale 4
and scale 5 is shown in Fig. 7. (b) to Fig. 7. (f) respectively.
Now, let us compare Daub4's, Daub6's, Daub8's, and
Daub10's squared detailed signal on the same signal (load
switchng) at scale 1. Fig. 8.(a) to (d) shows squared detailed
signal from Daub4 through Daub10 respectively, which in-
dicate the initialization of the line energizing event. Howev-
er, only Daub4's squared detailed signal is able to detect
harmonic disturbances that followed the line energizing
event. These harmonic features are denoted by small ampli-
tudes that appear periodically in time.
From above, it is clear that the wavelet transform with
Daub4 as a mother wavelet detects and extracts disturbance
features better than Daub6, Daub8, and Daub10 because it
detects the initialization of disturbance and the presence of
harmonic distortion.
6. CONCLUSION
The wavelet transform is utilized to detect various types of
electric power quality disturbances because it is sensitive to
disturbed signal but insensitive to the constant signal beha-
vior. Power quality disturbances are a natural application of
the wavelet transform because these disturbances are gen-
0 200 400 600 800 1000 1200
-500
0
500
(a) Sample Points
0 200 400 600 800 1000 1200
-50
0
50
(b) Sample Points
0 100 200 300 400 500 600
-500
0
500
(c) Sample Points
0 100 200 300 400 500 600
-20
0
20
(d) Sample Points
Fig. 5. Wavelet decomposition (a) Smoothed signal c3(n) and (b) Detailed
signal d3(n) at scale 3. (c) Smoothed signal c4(n) and (d) Detailed signal d4(n)
at scale 4.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
-200
0
200
(a) Sample Points
volatge(volts)
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
0
1000
2000
(b) Sample Points
0 500 1000 1500 2000 2500
-100
0
100
200
(c) Sample Points
0 200 400 600 800 1000 1200
0
2000
4000
(c) Sample Points
0 100 200 300 400 500 600
0
200
400
(e) Sample Points
0 50 100 150 200 250 300
0
5000
10000
(f) Sample Points
Fig. 6. (a) Capacitor energizing disturbance waveform Squared detailed
signal (b)d1(n) at scale 1 (c)d2(n) at scale 2 (d)d3(n) at scale 4 (e)d4(n) at scale
4 (e)d5(n) at scale 5.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
-200
0
200
(a) Sample Points
volatge(volts)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
-0.1
0
0.1
0.2
(b) Sample Points
0 500 1000 1500 2000
-1
0
1
(c) Sample Points
0 200 400 600 800 1000 1200
0
2
4
(d) Sample Points
0 100 200 300 400 500 600
0
20
40
(e) Sample Points
0 50 100 150 200 250 300
0
100
200
(f) Sample Points
Fig. 7. (a) Squared detailed signal (b)d1
(n) at scale 1 (c)d2
(n) at
scale 2 (d)d3
(n) at scale 4 (e)d4
(n) at scale 4 (e)d5
(n) at scale 5
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0
0.1
0.2
(a) Sample points
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0
0.05
0.1
(b) Sample points
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0
0.02
0.04
(c) Sample points
0 500 1000 1500 2000 2500 3000 3500 4000 4500
0
0.05
0.1
(d) Sample points
Fig. 8. (a) Squared detailed signal (a)d1
(n) at scale 1 at Daub4,
(b)d1
(n) at scale 1 at Daub6, (c)d1
(n) at scale 1 at Daub8, (d)d1
(n) at
scale 1 at Daub10
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5. 5
erally irregular compared to their background waveform
The power quality disturbance signal is decomposed into
two versions, i.e., the smoothed and detail versions. The
detailed versions are actually the high frequency compo-
nents of the disturbance signal. The squared detailed signals
at various scales are the fingerprints of power system dis-
turbances. The squared detailed signal of wavelet transform
characterized the given signal as the local frequency com-
ponents, the duration of the disturbance, and the step-
change in the disturbance event. These features are ade-
quate to characterize the transient type events.
7. REFERENCES
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[3]. S. Santoso, E.J Powers, W.M Grady, "Power quality disturbance
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S.A.Deokar has completed his graduation in Electrical Engineering
from Shivaji University, Kolhapur in 1992 and post graduate from Pune
University in 2006. He is persuing his PhD from S.G.G.S. College of
Engineering and Technology, Nanded since July, 2007. He is presently
working as a Assistant Professor in Electrical Engineering at AISSMS ,
College of Engineering in Pune University.He is also BEE Certified
Energy auditor.He has 10 international paper on his credit.His area of
interest is power Quality,Renewable energy,Energy Audit and Energy
Conservation.
L.M.Waghmare has completed his PhD in Instrumentation and Control
from IIT Roorkee and presently working as a Professor and Head of
Instrumentation Engineering department at S.G.G.S.Institute of Engi-
neering and Technology, Nanded.
M.D.Takale has completed his postgraduation from Bharati Vidyapeeth
in 2005 and presently working as a Assistant Professor at JSPM Col-
lege of Engineering in Pune University.He has 21 years of teaching and
administrative experience.
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