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# Power quality-disturbances and monitoring Seminar

Presented as a b-tech seminar in NITC

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### Power quality-disturbances and monitoring Seminar

1. 1. SURABHI VASUDEV B110556EE S8 EEE,B2 BATCH
2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. BASIC STRUCTURE OF AN EXPERT SYSTEM 26
27. 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. 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. 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. 30. FUZZY LOGIC SYSTEM 30
31. 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. 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. 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. 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. 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. 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. 37. PORTABLE MONITOR 37
38. 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. 39. PERMANENTLY INSTALLED FULL SYSTEM MONITOR 39
40. 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. 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. 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. 43. CONFIGURATION OF REAL TIME MONITORING SYSTEM 43
44. 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. 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. 46. METHODOLOGY OF DATA ANALYSIS 46
47. 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. 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. 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
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