The document provides an overview of power quality monitoring and automatic power quality disturbance classification. It defines power quality and discusses increased interest in power quality. It describes various power quality disturbances like voltage fluctuations, harmonics, sags, and swells. It then discusses automatic power quality disturbance classifiers which use techniques like segmentation, feature extraction, and classification to identify different disturbance types. Neural networks and expert systems are presented as methods for automatic classification. The document emphasizes the importance of power quality monitoring and classification systems.

1. SURABHI VASUDEV
B110556EE
S8 EEE,B2 BATCH

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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)
 Classifier’s information is used to make the final decision in
this unit.
18

19. 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.

20. 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

21. 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

22. 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

23. 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

24. 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 neuron’s 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

25. 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

26. BASIC STRUCTURE OF AN EXPERT SYSTEM
26

27. 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

28. 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

29. 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

30. FUZZY LOGIC SYSTEM
30

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. PORTABLE MONITOR
37

38. 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

39. PERMANENTLY INSTALLED FULL SYSTEM
MONITOR
39

40. 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

41. .
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

42. .
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

43. CONFIGURATION OF REAL TIME MONITORING
SYSTEM
43

44. 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

45. 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

46. METHODOLOGY OF DATA ANALYSIS
46

47. 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

48. 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

49. 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

50. 50

51. 51