A critical review of detection and classication of power quality eventsOm Prakash Mahelan, Abdul Gafoor Shaik, Neeraj GuptaCentre for ICT, Indian Institute of Technology, Jodhpur 342011, Indiaa rti cle in foArticle history:Received 17 June 2014Received in revised form18 August 2014Accepted 26 August 2014Available online 16 September 2014Keywords:Power systemFeature extractionPower qualityPQ disturbancePQ event classierSegmentationabstractRequirement of greensupplywithhigher qualityhas beenconsumers demandaroundtheglobe.Theelectrical powersystemisexpectedtodeliverundistortedsinusoidal ratedvoltageandcurrentcontinuously at rated frequency to the consumers. This paper presents a comprehensive review of signalprocessing and intelligent techniques for automatic classication of the power quality (PQ) events andaneffect of noiseondetectionandclassicationof disturbances. It is intendedtoprovideawidespectrum on the status of detection and classication of PQ disturbances as well as an effect of noise ondetection and classication of PQ events to the researchers, designers and engineers working on powerquality. More than 150 research publications on detection and classication techniques of PQdisturbances have been critically examined, classied and listed for quick reference.& 2014 Elsevier Ltd. All rights reserved.Contents1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4962. Power quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4963. Automatic power quality events classier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4964. Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4975. Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4975.1. Fourier transform based methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4975.2. S-transform based methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4985.3. Hilbert Huang transform based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4985.4. Wavelet transform based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4985.5. Miscellaneous feature extraction techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4985.6. Comparative study of PQ events detection techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4996. Articial intelligence classication techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4996.1. Support vector machine based classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4996.2. Neural network based classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4996.3. Fuzzy expert system based classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5006.4. Neuro-fuzzy system based classication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5006.5. Genetic algorithm based classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5006.6. Miscellaneous classication systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5006.7. Comparative study of PQ events classication techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5017. Effect of noise on PQ event classiers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5018. Future scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5019. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502Contents lists available at ScienceDirectjournal homepage: www.elsevier.com/locate/rserRenewable and Sustainable Energy Reviewshttp://dx.doi.org/10.1016/j.rser.2014.08.0701364-0321/& 2014 Elsevier Ltd. All rights reserved.nCorresponding author.E-mail addresses: opmahela@gmail.com (O.P. Mahela), saadgafoor@iitj.ac.in (A.G. Shaik), ngtaj@iitj.ac.in (N. Gupta).Renewable and Sustainable Energy Reviews 41 (2015) 4955051. IntroductionConsiderablechangesinabusinessenvironmenthaveincreasedthe use of sensitive electronic components, computers, programmablelogic controllers, protectionandrelayingequipments whichhaveincreased the power consumptions [1]. Increasing consumer expecta-tions with the requirement of green supply around the globe, whereintegration of renewable energy sources to the distribution grid is thefocusareaof smart grid, electrical powersystemsareexpectedtodeliver power supply continuously at high quality to the consumers.Economyof anycountrysufferswithhugelosseswhentherearevoltage or currentabnormalities presentin the power delivery.Anydeviation/disturbance manifestedinthe voltage, current andfre-quencyfromthestandardratingistreatedasapowerquality(PQ)problemthat results in failure or malfunctioning of electrical/electronicequipments [2]. Power quality disturbances increase the risk of black-out;especiallybecauseofthefailureofinterdependenciesbetweensub-networks andassociateddynamical propagations. To preventthese issues customers are willing to invest in the on-site equipmentsto ensure higher level of quality supply such as uninterrupted powersupply (UPS) and stabilizers even though these are very costly [3]. Itshows the importance of power quality towards economic distributionof the energy.Renewable energy integration and smart transmission systems,well equipped withmodern control equipments, increase theapplicationsof nonlinearandelectronicallyswitcheddevicesindistributionsystemswhichexaggerateproblemssuchasharmo-nics,icker, voltage sag/swell, voltage regulation, load unbalancinganddeviationsinphaseaswellasfrequency. Several solid-stateelectronic/power-electronic devices have been developed, studiedandproposed to theinternational scienticcommunitywith thegoal of improving supplied power quality in last two decades [4].This causes increased operational and planning complexity ofelectricitysupplynetworks whichrequires increasedattentionfor quality of power supply [5]. The transient oscillations are alsoseeninpower networks whenpower electronicallycontrolledcapacitors are switched across a node/bus.The harmonics and power sinusoids modulated by low frequencysignals are also observed in electrical networks due to sudden changesin the operating conditions such as load disturbances, networkcontingencies, and output of renewable energy sources [610]. Theseevents may impose penalties to the consumers in terms of supportingancillary services cost such as cost of reactive power, re-dispatch costandloadcurtailmentcosttoremovedisturbingload. Therefore, PQevents need to be monitored and mitigated to maintain standard ofsupplyandeconomic operatingconditions. Incomplexandlargepowersystems, duringoperations, ahugeamountofmeasuredPQeventsdatamakeanalysisdifcultformonitoringprograms. There-fore, asmarttool andmethodologyarerequiredfordetectionandclassicationof thisdataforclearandsmartunderstandingtotheutilities, regulators and customers about the operating requirementsunder suddenchangeof operatingconditions [11]. Inthis regard,feature extraction and classication are the most important part of thegeneralized PQ event classication system where PQ event detectionrequires the feature extraction fromthe disturbances. To make analysismore effective; a set of better features should be available to report thedisturbance signal efciently [4]. Thereafter, this set of extractedfeatures can be used for the classication process.This paper aims at presenting a comprehensive review on thetopic of power quality events detection and classication techni-ques/methodologies. Over150publications[1171]arecriticallyreviewed and classied into six major categories. Therst [117] ison general power quality and their economics as well as standards.Thesecondcategory[1823]isontheautomaticpowerqualityevents classierandgeneral ideasof segmentationandfeatureextraction of PQ disturbances. The third category [2487] includesfeature extractiontechniqueswhich issub-classiedinto Fouriertransformbasedmethods [2436], S-transformbasedmethods[3744], Hilbert Huang transform based methods [4554], wavelettransform based methods [5569], and miscellaneous techniques[7087]. Thefourthcategory[88164] isonarticial intelligentclassicationtechniqueswhichisfurtherclassiedintosupportvector machine based classication [89102], neural networkbased classication [103115], fuzzy-expert system based classi-cation[116129], neuro-fuzzysystembasedclassication[130136], geneticalgorithmbasedclassication[137145] andmis-cellaneous classication systems [146164].The fth and nalcategory [165171] is on the effect of noise on PQ events classica-tion. Howeversome publications includemorethan onecategoryand have been classied based on their dominanteld.This paper is divided into nine sections. Starting with anintroductioninSection1, Section2describespowerqualityandSection 3 covers the automatic power quality event classier. Thesegmentation of PQ disturbances, techniques for feature extractionfrom PQdisturbancesandarticial intelligentclassicationtech-niques are covered under Sections 46. The effect of noise on PQevent classication and future scope are presented in Sections 7 and 8.Finally the concluding remark is included in Section 9.2. Power qualityThetermpower quality(PQ) isgenerallyappliedtoawidevariety ofelectromagneticphenomenaoccurringwithinapowersystemnetwork[2]. Theabilityofthepowersystemstodeliverundistorted voltage, current and frequency signals is termedas quality of power supply [12]. Unexpected variation of thevoltage or current from normal characteristics can damage or shutdown the critical electrical equipments designed for specicpurpose. Suchvariations happeninelectrical networks withagreat frequency due to a competitive environment and continuouschange of power supply. In a highly evolved electrical system PQsensitive demands can be classied as (i) digital economy (such asbanking, share market and railways), (ii) continuous processmanufacturingindustries, and(iii)fabricationandessential ser-vices. Costincurredtooperateall theabovetypesofloadsvaryfrom3to120perkVAperevent[12]. Thisishugeandgreatlyaffectseconomicoperationof powerindustries. TomitigatePQissues; customersarealsoequippedwithsomeback-upinstru-mentsapart fromgridsupply[13]. AccordingtoIEEEstandard1159-1995[14], thePQdisturbancesincludeawiderangeofPQphenomenanamelytransient (impulsiveandoscillatory), shortduration variations (interruption, sag and swell), frequency varia-tions, long duration variations (sustained under voltages andsustainedovervoltages)andsteadystatevariations(harmonics,notchand icker) witha time scale whichranges fromtensof nanoseconds to steady state. Inigo Monedero et al. [15] denedPQ disturbances based on UNE standard in Spain as given in Table 1.IEEEStd. 1459-2010[16] includesdenitionsformeasurementofelectric power quantities under sinusoidal, non-sinusoidal, balanced,andunbalancedconditions. In[17], authorspresentedanIEEEStd.1459 power magnitude measurement system working as a part of aPQ improvement structure.3. Automatic power quality events classierBefore 1990s some manual congurations were used for monitor-ing and managing the quality of power supply due to lack of signalprocessing and intelligent techniques advancement. The decision wasbasedontheoperator/expert intuitiontomaintainqualitywithina certain range. As technology evolved, smart signal processingO.P. Mahela et al. / Renewable and Sustainable Energy Reviews 41 (2015) 495505 496techniques(suchaspattern-recognition, datamining, internet-net-working, and articial intelligence) with intelligent instruments (suchas computers, digital signal processors, mass storage, information andcommunications) are adopted time to time. A basic block diagram ofautomatic PQ event classier is given in Fig. 1 [18]. The disturbancesignal is passed through the pre-processing unit having two functionblocks; segmentation and feature extraction. The extracted features ofdisturbances arepassedthroughprocessingunit for classication.Finally, the post-processing unit gives an actual decision regarding thetype of disturbance.4. SegmentationSegmentation divides the data sequence into stationary and non-stationary parts [19]. Events are the segments in between transitionsegments. The feature is extracted from the event segments becausethe signal is stationary and normally contains the information that isunique enough to distinguish among different types of disturbances.To capture the disturbance waveform period, a triggering method isrequiredtogetstartandendtimeinstantofPQevent[20,21]. Thecurrent methodsusedfordetectingPQdisturbancesarebasedonapoint-to-point comparisonof adjacent cycleor apoint-to-pointcomparison of the RMS values of the distorted signal with itscorresponding pure signal and/or frequency domain transformed data.The recent methods proposed for this purpose are classied asparametric (or model based) and non-parametric (or transformbased).The parametric methods include techniques such as Kalman lter (KF)andauto-regressive(AR) models whilenon-parametrictechniquesincludeshort-termFouriertransform(STFT)andwavelettransform(WT) [22].5. Feature extractionFeature extraction fromPQdisturbances is also known asdetectionofdisturbances. ExtractedfeaturesareusedtoclassifythePQevents. Thereafter, theclassier'sinformationisusedtomakethenaldecisionthroughapost-processingunit[18]. Theselection of suitable features of PQ events is extremely importantfor classication. Features may directly be extractedfromtheoriginal measurementeitherfromsometransformeddomainorfrom the parameters of signal models [23]. In this context, recentdevelopments regarding feature extractiontechniques are dis-cussed in the subsections as detailed below.5.1. Fourier transform based methodsThebestknowntechniqueforfrequencydomainanalysisisthe Fourier transform (FT), where it represents a signal as sum ofsinusoidal termsofdifferentfrequencies[24]. FTissuitableforstationarysignalsandextractingspectrumat specicfrequen-cies; however it is unable to resolve any temporary informationassociatedwithuctuations[25]. Onevariant of FT; theshorttime Fourier transform(STFT) divides the signal into smallsegments where each segment can be assumedto be stationary[26]. In this regard, STFT determines the sinusoidal frequency andphasecontentsof local sectionsof signal astheychangeoverFig. 1. Block diagram automatic power quality event classier.Table 1PQ disturbances classication [15].Type Disturbance type Time RangeMin. value Max. valueFrequency Slight deviation 10 s 49.5 Hz 50.5 HzSevere deviation 47.0 Hz 52.0 HzVoltage Average voltage 10 min 0.85 Un 1.1 UnFlicker 7%Sag Short 10 ms1 s 0.1 U 0.9 ULong 1 s1 minLong-time disturbance 41 minUnder voltage Short o3 min 0.99 ULong 43 minSwell Temporary short 10 ms1 s 1.1U 1.5 kVTemporary long 1 s1 minTemporary long-time 41 minOver voltage o10 ms 6 kVHarmonics and otherinformation signalsHarmonics THD48%Information signals Included in otherdisturbancesO.P. Mahela et al. / Renewable and Sustainable Energy Reviews 41 (2015) 495505 497time. Also, it extractsseveral framesof signalstobeanalyzedwithawindowthatmoveswithtime. Withamovingwindow,the relation between the variance of frequency and the time canbe identied [27,28]. It is difcult to analyze non-stationarysignals withSTFT[29]; however it has beenappliedtonon-stationarysignalswhenoperatinginaxedwindowsize[30].Discrete STFT is used for timefrequency analysis of non-stationarysignals. It decomposesthetime-varyingsignalsintotimefrequency domain components [31]. Discrete Fourier trans-form (DFT) represents the discrete signals that repeat themselvesinaperiodicfashionfromnegativetopositiveinnitywhereasfast Fourier transform (FFT) gives exactly the same result as theDFT in much less time [32].In[33], authorspresenteduniquefeaturesthat characterizePQevents and methodologies to extract them from recorded voltage and/orcurrentwaveformsusingFourierandWaveletTransforms(WT).In[34], authorsusedwindowedFFTforpowerqualityassessment.The windowed FFT is a time version ofthe discrete time FT. It wasexperimentally proved that WT is better than STFT [29]. The schemesbased on STFT, S-transform,and Kalmanlter have been developedefciently for detecting PQ events [35,36].5.2. S-transform based methodsThe S-transformis a timefrequency tool generated by thecombinationof WTandSTFT[37]. Itproducesatimefrequencyrepresentation of a time series. It uniquely combines a frequency-dependent resolution that simultaneously localizes the realandimaginaryspectra. ThebasicfunctionfortheS-transformisGaussian modulated co-sinusoids [38]. In the case of non-stationarydisturbanceswithnoisydata; theS-transformprovidespatternsthat closelyresemblethedisturbancetypeandthus requires asimple classication procedure [28].In[39], authorsproposedasimpleandeffectivemethodforclassication and quantication of ten typical kinds of powerqualitydisturbances using S-transform. In [40], authors presenteda real-time power quality disturbances classication by usingahybridmethodbasedonS-transformanddynamicswherethedynamics is used to reduce run time. In [41], a S-transform basedneural network structure is presented for automatic classication ofpower quality disturbances. The S-transform technique is integratedwithneural networkmodel withmulti-layerperceptiontocon-struct the classier. Power quality analysis using discrete orthogonalS-transformis presented in [42]. Multi-resolution S-transformbasedfuzzyrecognitionsystemforpowerqualityeventsispro-posedin[43]. Thisisbasedonavariablewidthanalysiswindowwhich changes with frequency according to user dened function.In [44], authors proposed more suitable fast variants of the discreteS-transform(FDST)algorithmtoaccuratelyextractthetimeloca-lized spectral characteristics of non-stationary signals.5.3. Hilbert Huang transform based methodsThe Hilbert Huang transform (HHT), a novel signal processingalgorithm wasproposedin1998byDr. Huangwhichconsistsoftwodistinctprocesses[45]. Thesignaltobeanalyzedisdecom-posedusingtheempirical modedecomposition(EMD) processinto intrinsic mode function (IMF) that have meaningful instanta-neous frequencies and amplitudes. The EMDdecomposes thesignal into IMFs in such a way that the IMFs are sorted from thehighest frequencytothe lowest frequency. Once the signal isdecomposed into IMFs, the Hilbert transform can then be appliedto each IMF giving the instantaneous amplitude and instantaneousfrequencyversustimecurve. Thiscombinationof EMDprocessandHilberttransformisknownastheHHT[46,47]. Inarowofdevelopment, multiple HHT variants are demonstrated in thecontext of PQ detection and classication.In [48], authors developed a method based on combination of EMDand Hilbert transform for assessment of power quality events. How-ever, an approach for power quality disturbances classication usingHHT and Relevance Vector Machine (RVM) is presented in [49]. In [50],authors presented the multi-disturbance complex power quality signalHHTdetectionmethodusingtheHHT algorithm. In[51], avoltageicker is analyzedusingtheorthogonal Hilbert HuangTransform(OHHT) instead of HHT which can be used in the analysis of nonlinearand non-stationary signals. In [52], authors proposed a novel methodbased on mathematical morphology and HHT which is used to detectand analyze power quality disturbances. In [53], authors presented theHilbert-Huang method with modications for timefrequency analysisof distorted power quality signals. In [54], authors presented a methodtoextract efcient features of the PQdisturbances using HilbertTransformandtoclassifythedisturbancesignal usingradial basisfunction (RBF) neural networks with more improved methodology.5.4. Wavelet transform based methodsThe wavelet transform (WT) is a mathematical tool, much like FT,that decomposes a signal into different scales with different levels ofresolution by dilating a signal prototype function. The WT is based ona square-integral function and group theory representation. The WTprovides a local representation (in both time and frequency) of a givensignal; thereforeit is suitablefor analyzingasignal wheretimefrequency resolution is needed such as disturbance transition eventsinpowerquality[55,56]. TheWTisclassiedintodiscretewavelettransform (DWT) and continuous wavelet transform (CWT) [57].In [58], authors introduced the use of WT and multi-resolutionsignal decompositionasapowerful analysistool forPQevents.A wavelet based method for detecting, localizing, quantifying andclassifyingshort durationPQdisturbancesispresentedin[59].In[60], authorsintroducedacompressiontechniqueforpowerdisturbancedataviaDWT andwaveletpackettransform(WPT).In[61], authorsproposedamodel of disturbancedetectionforharmonics and voltages using wavelet probabilistic network whichisatwo-layerarchitecturecontainingthewaveletlayerandtheprobabilistic network. Consequently, a novel approach for the PQdisturbances classicationbasedontheWT andself-organizinglearning array systemis proposed in [62]. In [63], authorsintroducedanewperspectivefortheIEEEstandard1459-2000denitions using the stationary wavelet transform(SWT) fordeningpowercomponents, powerfactors, andpollutionfactor.In[64], PQindicesthatwererecommendedin[65]and[66]areredenedinthetimefrequencydomainusingWPT. AwaveletnormentropybasedeffectivefeatureextractionmethodforPQdisturbanceclassicationis presentedin[67]. In[68], authorsproposeda WTandS-transformbasedapproachfor islandingdetection and disturbance due to load rejection in the distributedgeneration (DG) based hybrid system. In [69], authors introducedthe un-decimated wavelet transform to compute power quantitiesusing complex wavelet coefcients. The important issue related tothe useof wavelet methodis thechoice of suitable wavelet. Thecomputational cost increases withanincrease in lter length.Table 2 provides characteristics of four commonly used wavelets.5.5. Miscellaneous feature extraction techniquesApartfromtheabovetechniquesmentionedinSections5.15.4,some other techniques have played a signicant role in PQ detectionand classication. De-noising techniques with change point approachforwaveletbasedpowerqualitymonitoringareusedanddemon-strated in [70]. Parallel computing for timefrequency feature extrac-tion of PQ disturbances is presented in [71]. Hybrid wavelet and HTO.P. Mahela et al. / Renewable and Sustainable Energy Reviews 41 (2015) 495505 498with frequency shifting decomposition for PQ analysis are presented in[72]. Several other techniques such as GaborWigner transform (GT)[73], Kalmanlter[74,75], Fuzzy-ARTMAP-waveletnetwork[76], TTTransform[77], Curve tting [78], DWT Transformand waveletnetwork [79], Hybrid soft computing technique [80], parametricspectral estimation method [81], extended Kalman ltering [82], linearcombiners [83], higher order statistics and case based reasoning [84],Adaline [85], digitallters [86], short-time correlation transform [87]have played an important role in PQ event detection and classicationin recent years.5.6. Comparative study of PQ events detection techniquesComparative study of PQ events detection techniques is carried outbased on critical reviews of publications [2487]. The computationalefciency of major power quality disturbance detection techniques todetect PQ disturbances is given in Table 3. The comparison of main PQdisturbance analysis methods is detailed in Table 4.6. Articial intelligence classication techniquesAbroaddenitionof articial intelligence (AI) canbe theautomation of activities that are associated with human thinkingsuchasdecisionmaking, problemsolving, learning, perception,and reasoning [88]. In this regard recent developments in AI toolsof interest to the electric power community are detailed below.6.1. Support vector machine based classicationThe foundation of Support Vector Machine (SVM) has beendevelopedbyVapnik[89], wherestatistical learningtheorybeingthe basis provides a new pattern recognition approach. SVMs are a setof relatedsupervisedlearningmethodsusedforclassicationandregression. They belong to family of generalized linear combiners [90].In[91], authors presentedanidenticationmethodfor PQevents based on N-1 SVMs classier which is particularly effectiveintheautomaticclassicationofvoltagedisturbances[92]. Con-sequently, Whei-Lin et al. [93] presented one-versus-one approachbasedSVMwhichcanprocessthemultipleclassicationsof PQdisturbances. The SVMmethod is better than optimal timefrequencyrepresentation(OTFR) andisalowcomplexityeventclassier [94]. Anintegratedmodel for recognizingPQdistur-bances using wavelet multi-class SVM is presented in [95]. A SVMclassier combinedwithDWTtorecognizethetypeof powersystemPQis presentedin[96]. The direct acyclic graphSVMcorrectly classies the PQ events with high degree of accuracy andless training as well as testing times incomparisonto otherkernel-basedlearningtechniquesandANNbasedmethods[97].Theclassicationof PQeventsbasedonwavelettransformandSVMis presented in [98100]. The number of SVMand thecompactness of different clusters areenhancedusingmodiedimmune optimization algorithm in classication of non-stationarypower signals usingmodiedTT-transformandSVM[101]. In[102], authors presented classication of PQ events using SVM andhigher order statistical features.6.2. Neural network based classicationNeural networks (NN) represent the promising new generationof information processing systems. They are good at tasks such aspatternmatching, classication, functionapproximation, optimi-zationanddataclustering[103]. Theclassicationandfunctionapproximation capabilities of articial neural networks (ANN)Table 4Comparison of main methods of PQ disturbances analysis.Sr.no.Method Advantages Disadvantages1 STFT Successfully used for stationary signals where properties ofsignals do not evolve in time. Simple in implementationNot suitable for non-stationary signal as it does not track signal dynamics properly dueto limitation ofxed window width2 HHT Useful in feature extraction of distorted waveform, generatesquadrature signal by which instantaneous amplitude and phasecan be easily evaluatedLimited only for narrow band conditions3 ST Fully convertible from time domain to 2-D frequency translationdomain and then to Fourier frequency domainBased on block processing manner and does not satisfy real-time requirement,incorrect measurement of harmonics due to dependency of frequency window widthon central frequency4 WT Provide local representation in both time and frequency.Therefore, suitable where good timefrequency resolution isrequired.Strongly inuenced by noise present in the signal, suffering from spectral leakage andpicket fence effects5 GT It has high signal to noise ratio and good timefrequencyresolutionUse limited at high frequencies, computational complexity is directly associated withsampling frequencyTable 3Computational efciency of PQ detection techniques.Sr.no.PQdisturbancePercentage efciency of PQ detection techniquesHilbert Huangtransform [48]S-transform[48]Discrete wavelettransform [79]Fast Fouriertransform[156]1 Sag 100 100 98.67 952 Swell 100 100 99.33 983 Harmonic 95 100 99.33 1004 Flicker 100 100 98.67 895 Notch 100 83 97.33 6 Spike 95 77 7 Transient 98 100 98.67 1008 (13) 98 100 98.18 9 (23) 89 100 98.18 10 (17) 96.36 11 (27) 98.18 Table 2Wavelet characteristics.WaveletnameOrthogonalpropertyCompactsupportSupportwidthFilterslengthSymmetryHaar 1 2 Symlets 2N-1 2N Near tosymmetryDaubechies 2N-1 2N Near tosymmetryCoiets 6N-1 6N NoN-Sample points for entire observation time.O.P. Mahela et al. / Renewable and Sustainable Energy Reviews 41 (2015) 495505 499have been employed in power quality studies, fault, and harmonicssource classication.In [104], authors proposedandimplemented aNN approach tothe classication of power systemdisturbance waveforms. Twodifferent NN paradigms, the common feed-forward neural network(FFNN) andtime delay neural network (TDNN) are investigated.In[105], a method for automatically detecting, localizing and classifyingvarioustypesof disturbancewave-shapefaultispresented. ANNbasedapproachtonon-intrusiveharmonicsourceidenticationisproposed in [106]. In [107], authors proposed a novel method basedonwavelet and ANNfor transmission line fault detection andclassication using oscillo-graphic data. In [108], some patterns basedonDWTarestudiedfordetectionandidenticationof bothlowfrequencydisturbanceslikeicker, harmonics, andhighfrequencydisturbances like transient and sags. A wavelet based articial neuralnetwork classier for recognizing power quality disturbances isimplemented andtestedin[109]. In[110], a feature extractionmethod based on centre clustering is obtained which is used as aninput toANNforPQevent classication. PQevents classicationbasedonS-transformandprobabilisticNNis presentedin[111].Classication of PQ events using a balanced neural tree is presentedin[112]. AdualneuralnetworkbasedmethodologytodetectandclassifysingleandcombinedPQdisturbancesisproposedin[113].Theradialbasis function(RBF)neuralnetworkforrecognitionandclassication of PQ events is presented in [114,115].6.3. Fuzzy expert system based classicationFuzzy classication system is based on Mamdani type rules toevaluate the information provided by the linguistic variable inputs[116,117]. Fuzzy logic refers to a logic system that generalizes theclassical two-valued logic for reasoning under uncertainty. Itis motivated by observing that human reasoning can utilizeconceptsandknowledgethatdonothave welldenedorsharpboundaries [118]. A fuzzy expert-system is an expert system thatuses a collection of fuzzy sets and rules instead of Boolean sets forreasoning about data [119].In[120], authorsproposedthedesignof atool toquantifyPQparameters using wavelets and fuzzy sets theory. A hybrid techniquefor characterizing PQ events using a linear Kalmanlter and a fuzzy-expert system is presented in [121]. An approach for the detection andclassication of single and combined PQ disturbances using fuzzy logicand a particle swarm optimization algorithm is proposed in [122]. In[123], authors presented an approach for the classication of PQ datausing decision tree and chemo-tactic differential evolution based fuzzyclustering. Anapproachfor PQtimeseries dataminingusingS-transform based fuzzy expert system is presented in [124]. In [125],authorspresentedanapproachforthevisuallocalization, detectionandclassicationof various non-stationarypower signals usingavariety of windowing techniques. In [126],a hybrid scheme using aFourier linear combiner and a fuzzy expert systemfor the classicationof transient disturbance waveforms in a power system is presented. In[127], authorsproposedawavelet-basedextendedfuzzyreasoningapproach to PQdisturbance recognition and identication. An adaptivefuzzy self-learning technique for detection of abnormal operation ofelectrical systems is presented in [128]. A data compression techniquefor power waveform using adaptive fuzzy logic is presented in [129].6.4. Neuro-fuzzy system based classicationNeuro-fuzzybasedmethodshaveadvantagesofhandlinganykind of information, manage imprecise, partial, and vague orimperfect information. Thesemethods alsohaveadvantages toresolveconictsbycollaborationandaggregation, self-learning,self-organizingandself-tuningcapabilities. Further, thereis noneedof priorknowledgeof relationshipof data, mimichumandecision making process, and fast computation using fuzzy num-ber operations [130,131].In [132], authors described a cork stopper quality classicationsystemusingmorphological lteringandcontourextractionbyfollowing a feature extraction method and a fuzzy-neural networkas a classier. In [133], an approach based on a four step algorithmcombining 3-D space referential representation, principal compo-nent analysis andneuro-fuzzybasedautomaticclassicationispresented. Adaptive-neuro-fuzzy-inferencesystemapproachfortransmissionlinefaultclassicationandlocationispresentedin[134,135]. Amethodfor mitigationof voltagesags withphasejumps byUPQCwithparticle-swarmoptimization(PSO) basedadaptive neuro-fuzzy system is presented in [136].6.5. Genetic algorithm based classicationGenetic Algorithm(GA) is asearchalgorithmbasedonthemechanicsofnaturalselectionandnaturalgenetics. Itcombinessurvival of thettest among string structures with a structured yetrandomized information exchange to form a search algorithmwithsomeof theinnovative air of humansearch[137]. TheGAisconsidered to be an excellent intelligent paradigm for optimizationusing a multipoint, probabilistic, randomand guided searchmechanism [138].In [139], authors described an innovative and fuzzy-based adaptiveapproachtothemeteringof powerandRMSvoltageandcurrentemployingGA. Power systemmodel validationfor PQassessmentapplications using GA is presented in [140]. The GA is introduced asa powerful tool for monitoring and supervising power systemdisturbancesgeneratedduetodynamicperformanceofpowersys-temsin[141]. In[142], a jumpinggenesparadigmintheformat ofhierarchical GA is proposed for optimizing the power voltage controlsystems. AnewmethodusingenhancedGAfor placement of PQmonitors is proposed in [143]. An analytic method of power qualityusingextensionGAandwavelet transformis presentedin[144].In[145], authorsdescribedthedesignof aGAthatoptimizestheS-transformfor analysis andclassicationof theperturbations inelectrical signals.6.6. Miscellaneous classication systemsDetection and classication of PQevents in real-time areimportant considerations to electric utilities. The design anddevelopmentofarulebasedsystemforintelligentclassicationof PQdisturbancesusingS-transformfeaturesarepresentedin[146,147]. Rule-basedandwavelet-multi-resolutiondecomposi-tionforPQeventsclassicationispresentedin[148]. AnexpertTable 5Strength and weakness of AI techniques.Sr. No. Attributes AI techniquesNN ANN SVM FL GA ES1 Uncertainty tolerance 2 Imprecision tolerance 3 Knowledge representation 4 Adaptability 5 Maintainability 6 Learning ability 7 Explanation ability 8 Data mining 9 Generalized Performance NNNeural network, ANNArticial neural network, SVMSupport vectormachine, FL Fuzzy logic, GA Genetic algorithm, ES Expert system; AITechnique with higher number of is more preferred for specic purpose.O.P. Mahela et al. / Renewable and Sustainable Energy Reviews 41 (2015) 495505 500system for PQ classication is presented in [149,150]. Several otherPQclassication techniques such as Warping Classier [151],Digital lteringandmathematical morphology[152], Hardwareand software architecture [153155], Hidden Markov models andvector quantization [156], recurrence quantication analysis [157],Phasor data records andsequence of events [158], Multi-wayprincipal componentanalysis[159], nearestneighborrule[160],Transient-meter [161], Easy VI program [162], inductive inferenceapproach [163], and fault current limiting high-temperaturesuperconductorcable[164]haveplayedanimportantroleinPQevent classication in past years.6.7. Comparative study of PQ events classication techniquesComparativestudyof PQevents classicationtechniques iscarried out based on critical reviews of publications [88164]. Thestrength and weakness of different AI techniques for power qualitydisturbanceclassicationareanalyzedandpresentedinTable5.The comparison of different power quality disturbances classica-tion methods is provided in Table 6.7. Effect of noise on PQ event classiersThesignalscapturedbymonitoringdevicesareoftenaccom-paniedwithnoisetherebyaffectingtheextractionof importantfeatures fromthe signal. Noise has an adverse effect on theperformanceof waveletbasedeventdetection, timelocalizationand classication schemes due to the difculty of separating noiseanddisturbances[165]. Thedisturbedcomponentsofthewave-formcarry the most important informationfor detectionandclassication. When PQ data is decomposed using wavelet trans-form most of the disturbance components are reected at higherfrequency bands which are also occupied by noise. Therefore, evenif the magnitude of noise level present is not very high comparedtofundamental componentformanyPQdisturbancesitiscom-parable to disturbance energy at these bands. Hence, the presenceof noisedegradesthedetectioncapabilityof wavelet basedPQmonitoring system [166].In this regard, a de-noising technique with a change-pointapproachforwavelet-basedpower-qualitymonitoringispresentedin[167]. Consequently, performanceimprovementofpowerqualitydisturbances classicationbasedonanewde-noisingtechniqueispresented in [168]. Also recognizing noise inuenced PQ events withintegrated feature extraction and neuro-fuzzy network is presented in[169]. In[170], authorsproposedasimpleyet effectivede-noisingtechnique using inter- and intra-scale dependencies of waveletcoefcientstode-noisePQwaveformdataforenhanceddetectionandtimelocalizationof PQdisturbances. Thede-noisingtechniquewith a spatial noise-suppression method for wavelet-based PQ mon-itoring is presented in [171].Abrief comparative study of error introduced due to thepresenceofnoiseinclassicationofPQdisturbancesusingANNand SVM is presented in Table 7.8. Future scopeThe present thrust inPQresearchis of real-time analysis;therefore many researchers have given so many enhancements inreal-time detection and mitigation. However, the PQanalysistechnologystill has awaytogrowespeciallyfor real-timePQevents. Therefore, there is a need to work on real-time PQ eventsto develop new techniques for classication and mitigation. Thereis also a need to develop a generalized approach for detection andclassication of single and multiple PQ events. On this basis, it ispossible to improve the study of blackout conditions.9. ConclusionAcritical andcomprehensiveliteraturereviewonthedetectionand classication of PQ events in the electrical power systemis carriedout. This paper presents a literature survey on detection/feature extraction techniques such as wavelet transform, S-transform,Fourier transform, Hilbert-Huang transform and articial intelligencetechniques for PQ classication such as SVM, ANN, Fuzzy logic, and GA.The effect of noise on PQ event classication is also outlined. Finally, atthe end of paper future scope for research in the eld of power qualitydetection and classication techniques has been presented.According to developed review, it can be concluded that commonlyused techniques for detection of PQ disturbances are WT, ST, FT andHHT. The comparative study of these techniques will help in selectingthe particular technique for specic application. Regarding eventclassication, trendhasbeentheuseof algorithmbasedonANN,Table 6Comparison of PQ events classication methods.Sr.No.Method Advantages Disadvantages1 NN High classication accuracy for mixed PQ disturbances Less efcient under noisy conditions2 ANN High accuracy for real time applications and provides mathematicalexibilityConvergence speed and accuracy depends on the network architecture aswell as noise in the signal3 SVM Potential to handle large features, provide stable solution to quadraticoptimization, high learning processesPoor classication accuracy when training samples are minimum4 FL Accurate in modeling and analyzing complex systems Training set for every case isxed, hence not suitable for new disturbances5 GA Accurately classify PQ disturbances generated due to dynamic performanceof power system and damped sub-harmonic signalsHigh computational time6 ES Can be used with or without limited data Expensive system, slow in execution, it is difcult to draw conclusion ifassumptions and actual situations do not match exactlyTable 7Effect of noise on PQ classication.Sr. no. 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