1. CONTENTS INTRODUCTION POWER QUALITY DEFINITION INCREASED
INTEREST IN POWER QUALITY CAUSES OF POWER QUALITY PROBLEMS POWER
QUALITY DISTURBANCES AUTOMATIC POWER QUALITY DISTURBANCE
CLASSIFIERS POWER QUALITY MONITORING REAL TIME MONITORING SYSTEM
ANALYSIS OF POWER QUALITY MEASUREMENTS BENEFITS OF POWER QUALITY
MONITORING CONCLUSION REFERENCES 2
2. INTRODUCTION The aim of power system is to supply electrical
energy or power to customers. Non linear loads,utility switching
and fault clearing produce disturbances that affect the quality of
this delivered power. In the present scenario,electric power is
viewed as an integral product with certain characteristics,which
can be measured, predicted,guaranteed and improved. The term `power
quality emerged as a result of this new emphasis. 3
3. Contd.. Power quality means the quality of the normal
voltage supplied to our homes, factories, etc. It is based on the
extent of variation of the voltage and current waveforms from the
ideal pure sinusoidal waveforms of fundamental frequency. To
improve the power quality,it is necessary to know what kind of
disturbances occurred. A power quality monitoring system that is
able to automatically detect,characterise and classify disturbances
on electrical lines is thus required. 4
4. AN OVERVIEW : TOPICS COVERED POWER QUALITY DISTURBANCES
TYPES OF DISTURBANCES AUTOMATIC POWER QUALITY DISTURBANCE
CLASSIFIER MONITORING POWER QUALITY MONITORS REAL TIME MONITORING
SYSTEM DATA ANALYSIS BENEFITS OF POWER QUALITY MONITORING 5
5. POWER QUALITY DEFINITION As per IEEE 100 Authoritative
Dictionary of IEEE Standard Terms,Power Quality is defined as `The
concept of powering and grounding electronic equipment in a manner
that is suitable to the operation of that equipment and compatible
with the premise wiring system and other connected equipments.
Power Quality is the set of parameters defining the properties of
the power supply as delivered to the user in normal operating
conditions,in terms of the continuity of voltage and voltage
characteristics. 6
6. INCREASED INTEREST IN POWER QUALITY Power Quality problems
cost US business a loss of more than 15 billion dollars a year,as
per IBM studies. Equipments have become more sensitive to voltage
disturbances. Equipments like rectifiers cause voltage
disturbances. Power Quality is measurable with the advanced modern
electronic equipments. Growing awareness of users. Increased
emphasis on efficiency and reliability at a limited cost. 7
7. CAUSES OF POWER QUALITY PROBLEMS Difficult to point an exact
cause for a specific problem. Broadly divided into 2 categories:
1.Internal causes i)About 80% of Power Quality problems originate
within a business facility. ii)Due to large equipments start or
shut down,improper wiring and grounding,overloaded circuits or
harmonics. 2.External causes i)About 20% of Power Quality problems
originate within the utility transmission and distribution system.
ii)Due to lightning strikes,equipments failure,weather conditions
etc. 8
8. POWER QUALITY DISTURBANCES Power Quality disturbances can be
divided into 2 basic categories: 1.Steady-state variations:-Small
deviations from the desired voltage or current values. i)voltage
fluctuations ii)voltage and current unbalance iii)harmonic
distortion iv)high frequency voltage noise 2.Events:-Significant
sudden deviations of voltage or current from the nominal or ideal
wave shape. i)interruptions ii)voltage sag iii)voltage swell
iv)transients 9
9. 1.i) VOLTAGE FLUCTUATION Fast changes or swings in the
steady state voltage magnitude Due to variations of total load of a
distribution system, action of transformer tap changers, switching
of capacitor banks,etc. If the variations are large enough or in a
certain critical frequency range,it can affect the performance of
the equipment. 10
10. 1.ii) VOLTAGE AND CURRENT UNBALANCE Voltage unbalance is
marked by a difference in the phase voltages,or when the phase
separation is not 120 degrees. Current unbalance is similar,except
the values are for current,instead of voltage. Causes of voltage
and current unbalance:- i)large or unequal distribution of single
phase load. ii)equipments which simply require single phase but at
line to line voltage(a 415 V welder). iii)unbalanced 3 phase loads.
11
11. 1.iii) HARMONIC DISTORTION Deviation of voltage and current
waveforms from the ideal pure sinusoidal waveforms of fundamental
frequency. Non-fundamental frequency components are called
harmonics. Due to non linear loads and devices in the power system.
12
12. 1.iv) HIGH FREQUENCY VOLTAGE NOISE Non periodic high
frequency components in supply voltage. Caused mainly due to arc
welding or operation of electrical motor. Analysis needed only if
it leads to some problem with power system or end user equipments.
13
13. 2.i) INTERRUPTIONS Supply interruption occurs when voltage
at supply terminals is close to zero. Normally initiated by faults
which subsequently trigger protection measures. Based on the
duration, interruptions are subdivided into: 1)Sustained
interruptions, which are terminated through manual restoration or
replacement. 2)Temporary interruptions ,which last less than 2
minutes and terminated through automatic restoration. 3)Momentary
interruptions, which are terminated through self restoration.
14
14. 2.ii) VOLTAGE SAG Decrease in the RMS value of the voltage,
ranging from a half cycle to few seconds(less than 1 minute).
Referred to as under voltage, if continues for longer duration.
Causes: 1)Faults on the transmission or distribution networks.
2)Connection of heavy loads. Consequences: 1)Malfunction of
microprocessor based control systems. 2)Loss of efficiency in
electrical rotating machines. 15
15. 2.iii) VOLTAGE SWELL Momentary increase of the voltage, at
the power frequency, outside the normal tolerances with duration of
more than 1 cycle, and typically less than 1 minute. Referred to as
over voltage', if continues for longer duration. Causes: 1)Start
and stop of heavy loads. 2)poorly regulated transformers
Consequences: 1)Flickering of lighting and screens. 2)Damage of
sensitive equipments. 16
16. 2.iv)TRANSIENTS Sub cycle disturbances of very short
duration that vary greatly in magnitude. Mainly subdivided into:
1)Impulsive transient, where there is a large deviation of the
waveform for a very short duration in one direction, followed
possibly by a couple of smaller transients in both directions.
2)Oscillatory transient, where there is a ringing signal or
oscillation following the initial transient. 17
17. AUTOMATIC POWER QUALITY DISTURBANCE CLASSIFIERS Used to
classify various power quality disturbances. Consist of 3 main
units, namely , 1)Pre-processing unit Disturbance signal is passed
to this unit It has 2 function blocks: segmentation feature
extraction 2)Processing unit(power quality classifier) Extracted
features are used to classify various power quality disturbances.
3)Post-Processing unit(decision making) Classifiers information is
used to make the final decision in this unit. 18
18. BLOCK DIAGRAM OF AUTOMATIC POWER QUALITY DISTURBANCE
CLASSIFIERS 19 . Segmentation Feature Extraction Classification
Decision Making Additional processing Input Output Pre-processing
Event segments Processing Post-processing Input : Disturbance
waveform, voltage v(t) and current i(t). Output : Class or type of
disturbance.
19. 1) SEGMENTATION It is a pre processing technique. Divides
data sequence into 1. Transition segments with a large and sudden
change in signal. 2. Event segments with a stationary signal from
which features can be extracted. 20
20. 2) FEATURES EXTRACTION It is the transformation of the raw
signal from its original form to a new form, from which suitable
information can be extracted. Extracted features by signal
processing are used as input to the power quality classification
system. Methods to extract features are : Parametric methods(model
based) Non-parametric methods(transform based) 21
21. Contd.. 1) Parametric methods(model based) Obtain residual
signal by fitting the captured waveform into the chosen model. Use
the residual signal to detect transition points and thus to analyse
and characterise the disturbance. E.g.:-Kalman filter model. 2)
Non-parametric methods(transform based) Find singular points from
multi-state decomposition of power quality signal. E.g.:-Wavelet
transform, short term Fourier Transform. 22
22. 3) POWER QUALITY CLASSIFIER The automatic classifiers used
to classify various power quality disturbances are: Deterministic
classifiers Designed with limited amount of data and sufficient
power system expert knowledge. E.g.:-Rule based expert system,
Fuzzy expert system. Statistical classifiers Suitable when large
amount of data from training of the classifiers is available.
E.g.:-Artificial Neural Network. 23
23. CLASSIFICATION APPROACHES i) ARTIFICIAL NEURAL NETWORK
BASED CLASSIFIERS It recognises a given pattern by experience which
is acquired during the learning or training phase when a set of
finite examples is presented to the network. This set of finite
examples is called training set. Neurons in the network adjust
their weight vectors according to certain learning rules, in the
training phase. After training, knowledge required to recognise
patterns is stored in the neurons weight vectors. Network is then
tested with a set of finite examples, called the testing data
set(testing or generalisation). The main drawbacks of ANN based
classifiers are: Need of training phase. Requirement of retraining
the entire ANN for every new power quality event . 24
24. ii) EXPERT SYSTEM BASED CLASSIFIER It is implementation of
knowledge from power quality experts ,in automatic classification
systems, by developing a set of classification rules in a expert
system. The expert system consists of a set of rules ,where the
real intelligence by human experts is translated into artificial
intelligence for computers. The 3 basic elements of expert system
are: Inference engine or control procedure mechanism Draws
inference based on previously available knowledge. Controls the
flow of analysis. Knowledge reservoir Collection of static
knowledge. Represented by production or if-then rules User
interface Facilitates the communication between users and the
expert system. 25
25. BASIC STRUCTURE OF AN EXPERT SYSTEM 26
26. iii)FUZZY EXPERT SYSTEM BASED CLASSIFIERS Fuzzy logic
system has strong inference capabilities of expert system as well
as power of natural knowledge representation. Rules of this
Artificial Intelligence technique are based on human experience and
expertise. It has mainly 4 elements, namely Fuzzifier Inference
engine Knowledge base Defuzzifier 27
27. Contd.. Fuzzifier Maps crisp numbers into fuzzy sets.
Needed in order to activate rules which are in terms of linguistic
variables having fuzzy sets associated with them. This step is
called fuzzy matching,which calculates the degree that the input
data match the conditions of the fuzzy rules. Inference engine Maps
fuzzy sets into fuzzy sets. Handles the way in which the rules are
combined. There are 2 common approaches for the inferences, namely,
1) Clipping method,which cuts off the top of the membership
function,where value is higher than the matching degree. 2) Scaling
method,which scales down the membership function in proportion to
the matching degree. 28
28. Contd.. Knowledge base Is a set of fuzzy rules expressed as
a collection of if-then statements,provided by the experts.
Defuzzifier Maps output fuzzy sets into crisp numbers. Widely used
defuzzification methods are : center of area(COA or centroid)
method ,which derives the crisp number by calculating the weighed
average of the output fuzzy sets. maximum of membership (MOM)
method,which chooses the value with maximum membership degree as
the crisp number. 29
29. FUZZY LOGIC SYSTEM 30
30. DISADVANTAGES OF FUZZY CLASSIFIER The system time response
slows down with the increase in the number of rules. The accuracy
of the system is highly dependent on the knowledge and experience
of human experts. Rules should be updated with time. Rules are not
adaptable according to the variation in data. The weighing factors
in the fuzzy sets should be refined with time. 31
31. 4) DECISION MAKING This stage is usually merged with the
classification stage in most of the power quality classifiers.
Proper decision tool is required to increase the accuracy of
classification. Examples for decision making tools are expert
system and fuzzy logic system. 32
32. CASE STUDY Step 1:-A disturbance waveform is chosen and
given as input to the segmentation block,which segregates it into
transition segments and event segments. Step 2 :-The event segments
are given to the block for feature extraction, where fourier
analysis and wavelet analysis are used to get 8 unique features of
a given waveform ,which are fuzzy inputs: i. Fundamental voltage
component,Vn ii. Phase angle shift ,PASn iii. Total harmonic
distortion,THDn iv. Number of peaks of the wavelet coefficients,Nn
v. Energy of the wavelet coefficients,EWn vi. Oscillation number of
the missing voltage,Osn vii. Lower harmonic distortion,TSn viii.
Oscillation number of the RMS variations,RNn 33
33. Contd.. Step 3:-The 8 inputs are given to the block for
classification.Here,the classifier is assumed to be fuzzy expert
system based. Let Ai,Bi,Ci,Di,Fi,Gi,Hi and Ki be the triangular
membership functions for the 8 inputs respectively,where i can
range from 0 to 10. The outputs are the 8 power quality
disturbances ,namely,Voltage fluctuation,Voltage
unbalance,Noise,Harmonic distortion,Voltage sag,Voltage
swell,Interruptions and Transients. Step 4:-Consider the case of a
waveform with its features extracted using wavelet
transforms,fuzzified as Vn=A2, PASn=B2, THDn=C3, Nn=D1, EWn=F1,
Osn=G1, TSn=H1, RNn=K1. Step 5:-Utilising the fuzzy if-then rules
prepared based on experience and expertise,the disturbance is
detected to give the output as Transient=1.Rule used here is,If
Vn=A2,THDn=C3,and PASn=B2,then Transient=1 . Step 6:-Using the
maximum of membership method of defuzzification,hence the transient
is detected to be the power quality disturbance (output). 34
34. POWER QUALITY MONITORING It is a multi-pronged approach to
identifying,analyzing and correcting power quality problems. Helps
to identify the cause of power system disturbances. Helps to
identify problem conditions before they cause interruptions or
disturbances,in some cases. Objectives for power quality monitoring
are generally classified into: Proactive approach Intended to
characterise the system performance. Helps to understand and thus
match the system performance with customer neeeds. Reactive
approach Intended to characterise a specific problem. Performs
short term monitoring at specific customers or at different loads.
35
35. POWER QUALITY MONITORS Commercially available monitors are
classified into: 1)PORTABLE MONITORS Used for troubleshooting after
an event has taken place. Subdivided into: I. Voltage recorders
Recorders digitize voltage and current signals by taking samples of
voltage and current over time. Used for continuous monitoring of
steady state voltage variations. Most important factor to consider
when selecting and using a voltage recorder is the method of
calculation of the RMS value of the measured signal. II.
Disturbance analyser Designed to capture events affecting sensitive
devices. Thresholds are set and recording starts the moment when a
threshold value is exceeded. 36
36. PORTABLE MONITOR 37
37. 2)PERMANENT MONITORS These monitors are permanently
installed full system monitors , strategically placed throughout
the facility ,letting the users know any power quality disturbance
as soon as it happened. Characterise the full range of power
quality variations. Record both the triggered and sampled data.
Triggering depends on RMS thresholds for RMS variations and on wave
shape for transient variation. Real time monitoring system is an
example. 38
38. PERMANENTLY INSTALLED FULL SYSTEM MONITOR 39
39. REAL TIME MONITORING SYSTEM This permanent monitoring
system has the following components :- 1) Measurement instruments
Involves both the voltage recorder and disturbance analyser. Has a
trigger circuit to detect events. Includes a data acquisition board
to acquire all the triggered and sampled data. 2) Monitoring
workstation Used to gather all information from the measuring
instruments. Periodically send information to a control
workstation. 40
40. . 3) Control workstation This station configures the
parameters of measuring instruments. Gathers and stores the data
coming from the remote monitoring workstations. Does the data
analysis and export. 4) Control software This software drives the
control workstation. Does the analysis and processing of data.
Algorithms used for processing varies according to the system used.
Algorithms used may be based on wavelet transforms or expert
systems or some other advanced technique. 41
41. . 5) Database server Database management system should
provide fast and concurrent access to many users without critical
performance degradation. Also,it should avoid any form of
unauthorized access. 6) Communication channels Selection of
communication channel strongly depends on monitoring
instruments,connectivity functions and on their physical locations.
Some of the possible channels are fixed telephone channels by using
a modem and mobile communication system by using a GSM modem.
42
42. CONFIGURATION OF REAL TIME MONITORING SYSTEM 43
43. DATA ANALYSIS OF POWER QUALITY MEASUREMENTS Analysis is
done by the control software and the method of analysis depends on
the type of disturbance. Main objective of an analyser is to
identify the type of event. Analyser looks for parameters in the
measured data to characterise the waveform. Since individual
inspection of all wave shapes is not easy due to the large size of
database, a few characteristics are extracted from the measured
data, mainly magnitude and duration. Since database has a lot of
information and recorded data, analyser extracts only the relevant
disturbances. 44
44. Contd.. Analyser groups the captured events in a number of
classes. These classes are made by comparing the captured waveforms
with the ideal waveforms. This classification is called disturbance
classification. By comparing the captured events with libraries of
power quality variation characteristics and correlating with system
events, causes of variations can be determined. Every electrical
disturbance has an associated waveform which describes its
characteristics, which provides important clues to locate the
source of electrical problem. 45
45. METHODOLOGY OF DATA ANALYSIS 46
46. BENEFITS OF POWER QUALITY MONITORING Ensures power system
reliability. Identify the source and frequency of events. Helps in
the preventive and predictive maintenance. Evaluation of incoming
electrical supply and distribution to determine if power quality
disturbances are impacting. Determine the need for mitigation
equipments. Reduction of energy expenses and risk avoidances.
Process improvements-monitoring systems allows to identify the most
sensitive equipments and install power conditioning systems
wherever necessary. 47
47. CONCLUSION Electric power quality,which is a current
interest to several power utilities all over the world,is often
severely affected by various power quality disturbances like
harmonics and transient disturbances.Deterioration of power quality
has always been a leading cause of economic losses and damage of
sensitive equipments. Various types of power quality disturbances
are analysed.Automatic Power Quality Disturbance Classifiers are
discussed in detail,along with different classification
approaches,with a case study. Power Quality Monitoring systems and
techniques are presented,emphasizing the real time monitoring
systems.Data analysis and benefits of Power Quality Monitoring are
also presented. 48
48. REFERENCES ALEXANDER KUSKO and MARC.C.THOMPSON.(2007).Power
Quality in Electrical Systems.New York:Mc Graw-Hill.
D.SAXENA,K.S.VERMA and S.N.SINGH.(2010).Power Quality Event
Classification:an Overview and Key Issues.International Journal of
Engineering,Science and Technology.2(3),pp.186-199. NEHA
KAUSHIK.(2013).Power Quality,its Problem and Power Quality
Monitoring.International Journal of Electrical Engineering and
Technology.4(1),pp.46-57. ROGER.C.DUGAN and
MARK.F.McGRANGHAN.(2012).Electrical Power Systems Quality.2nd
ed.McGraw-Hill. YUAN LIAO and JONG-BEOM LEE.(2004).A Fuzzy Expert
System for Clasifying Power Quality Disturbances.Electrical Power
and Energy Systems.26,pp.199-205. http://www.slideshare.net.
49