Abstract: Online condition assessment of surge arresters is necessary for early detection of problems and subsequent replacement of the device to avoid unplanned downtime and/or damage to the equipment being protected. The most common technique of monitoring and assessing the condition of the arrester is extraction of the resistive component of the leakage current. There are many drawbacks to this method arising from inaccuracies in the model-based approach and deficiencies in the understanding of the leakage current signal. This paper presents leakage current signature analysis (LCSA) as a method of assessing the condition of the surge arrester where the harmonics components of the current signal, extracted using Bayesian spectrum estimation, are used as indicators of degradation. An experimental methodology comprising measurement of the leakage current of arresters undergoing constant-stress accelerated degradation testing is employed. Results indicate that a consistent increase in the DC component of the leakage current signature in the case of degrading samples is the most likely indicator of condition

Bokoro, P.

Doorsamy, W.

English

University of Johannesburg Institutional Repository

Condition Monitoring of Metal-oxide SurgeArresters using Leakage Current Signature AnalysisW. Doorsamy, and P. BokoroDepartment of Electrical and Electronic Engineering TechnologyUniversity of JohannesburgJohannesburg, South AfricaAbstract—Online condition assessment of surge arresters isnecessary for early detection of problems and subsequent replace-ment of the device to avoid unplanned downtime and/or damageto the equipment being protected. The most common techniqueof monitoring and assessing the condition of the arrester is ex-traction of the resistive component of the leakage current. Thereare many drawbacks to this method arising from inaccuracies inthe model-based approach and deficiencies in the understandingof the leakage current signal. This paper presents leakage currentsignature analysis (LCSA) as a method of assessing the conditionof the surge arrester where the harmonics components of thecurrent signal, extracted using Bayesian spectrum estimation, areused as indicators of degradation. An experimental methodologycomprising measurement of the leakage current of arrestersundergoing constant-stress accelerated degradation testing isemployed. Results indicate that a consistent increase in theDC component of the leakage current signature in the case ofdegrading samples is the most likely indicator of condition.Index Terms—Metal-oxide surge arrestors, online conditionmonitoring, leakage current signature, Bayesian spectrum esti-mation.I. INTRODUCTIONMetal-oxide varistors (MOV) are the most commonly usedtype of surge arresters for protecting equipment and systemsagainst direct or indirect effects of lightning surges and switch-ing transients [1], [2]. The need to ensure these equipment andsystems are effectively protected implores condition monitor-ing of the deployed surge arresters. Moreover, online conditionmonitoring of metal-oxide surge arresters (MOVs) is desirablebecause it is more conducive of a preventive maintenancestrategy for protection applications. Recent advancements inassessing MOV degradation affords the possibility of detectingproblems early [3], [4]. Leakage current monitoring is the mostpopular method as it can be used online, is non-invasive andis perhaps best suited for recent progress in understanding ofMOV degradation mechanisms [5]. However, in spite of thenumerous advantages of leakage current monitoring, there arestill deficiencies and drawbacks with its application which arediscussed here.This paper proposes overcoming some of these deficienciesand drawbacks through a new approach of analysing themeasured leakage current. The approach is based on extractionand analysis of the harmonic components of the leakagecurrent using Bayesian spectrum estimation.II. SURGE ARRESTER DEGRADATION AND CONDITIONASSESSMENTThe basic parallel resistance-capacitance (RC) circuit usedto model insulators is also the most widely accepted characteri-sation of MOVs [6]. This is because MOVs have been found tobehave similarly to insulating materials under nominal voltageand standard operating temperature [7]. The microstructuralcomposition of a typical MOV unit comprises zinc-oxidegrains and intergranular regions that constitute the resistiveand capacitive elements of the basic model. This modelforms the basis of leakage current monitoring on MOVsas most techniques involve extraction and analysis of themodel’s resistive component (iR). Essentially, a progressiveincrease in this resistive component is an indication of MOVdegradation. A number of variations of the resistive-currentextraction method and basic model have been presented interalia phase-shift estimation [8], variable compensation [9],and computational methods [10]. The reliance of bus voltagemeasurement is one of the major deficiencies common to allof the aforementioned techniques. Practical implementationfor purposes of online monitoring is therefore challenging ascontinuous measurement of operating voltage at each MOVinstallation point is required. Besides the inaccuracies of thevarious equivalent models presented over the years, selectionthereof still depends on the availability of system data, systemcomplexity and the designers judgment [11]. Model-basedleakage current monitoring inherits these complexities andinaccuracies. A more reliable, inexpensive and uncomplicatedmethod of assessing the leakage current of the MOV istherefore needed. The proposed LCSA method overcomesthese challenges as it only uses direct measurement of theleakage current to determine MOV condition without anyequivalent model or assumptions about the protected system.III. METHODOLOGYA. Leakage Current Signature Analysis (LCSA)A key driver in recent progress of signal processing is thegeneral application areas of process and condition monitor-ing. Different forms of time- and frequency-domain analysesare employed in various condition monitoring applications.Popular techniques include Wavelet Transforms, Short-TimeFourier Transforms and spectral analysis [12], [13].Figure 1: Experimental layout for conducting accelerateddegradation of MOV and testing leakage current based sig-nature analysisThis work proposes spectral analysis of the MOV’s leakagecurrent as means of assessing its condition. Techniques forspectrum estimation are categorised as parametric or nonpara-metric [14]. Bayesian spectrum estimation is a nonparametricmethod that does not make any assumptions about the sampledsignal. The technique determines the expected value of thesignal spectrum over the joint posterior probability distribu-tion of both signal and noise parameters [15]. It has beenshown to yield more accurate spectrum estimates for conditionmonitoring application relative to other well-known techniquessuch as the standard periodogram, Welsh and multiple signalclassification (MUSIC), particularly for low signal-to-noiseratios [16]. Additionally, the Bayesian technique offers betterresolution making it suitable for condition monitoring applica-tions where the change in a particular harmonic order indicatesthe presence of a problem.B. Experiment DesignIn order to investigate the viability of the proposed method,the leakage current of the MOV must be analysed at differentlevels of degradation. The experimental setup shown in Fig.2 was conceived for this purpose. The setup is used to con-duct standard IEEE accelerated degradation testing of MOVs[17]. The thermal chamber enables controlled application ofelevated temperatures. During the degradation testing, theleakage currents of the MOVs are recorded periodically usinga shunt resistance and data acquisition system. A fuse is usedfor protection of the measurement equipment in the eventof a short-circuit created when the MOV fails. Samples oflow-voltage ZnO varistors sized 20 mm are used for theexperiment.1) Accelerated thermal degradation: Standard laboratorydegradation tests for insulators are typically used for lifemodelling and failure process investigation [17]. These testsFigure 2: Experimental setup for conducting accelerated degra-dation of MOV and testing leakage current based signatureanalysisconsists of accelerated thermal degradation of the insulator tosimulate life degradation. Additional progressive or constantstresses may also be applied to simulate conditional failureprocess. Thermal degradation is modelled using the Arrhe-nius life model - given by (1) - through extrapolating lifedegradation times using the constant elevated temperature andthe period over which the tested device is subjected to thattemperature [18], [19].tt = ttest × 2.5(Ttest−Tstd)10 (1)where:tt : extrapolated time,ttest : accelerated degradation test time,Ttest : accelerated degradation test temperature,andTstd : standard temperature (40oC).For the presented investigation, each MOV sample is sub-jected to a constant elevated temperature of 135oC and exter-nal voltage supply equal to 85 % of its reference voltage i.e.0.85 V1mA. The test is conducted for a maximum period of 45min or until the MOV fails. For example, the full duration ofthe test (45 min) simulates continuous operation of the MOVfor 4523.67 hours (approximately 27 weeks) under 85 % ofits reference voltage.2) V -I testing: The V -I characteristic of an MOV is themain measure of its performance, and a direct indication of itscondition. This characteristic can be determined by progressiveapplication of dc voltages to the MOV and measurement ofthe resulting current.Degradation of an MOV causes a decrease in its abilityto clamp a surge voltage. This is reflected by a decreases inthe MOV’s reference voltage V1mA. Typically, a decrease ofan MOVs’ reference voltage in the range of 5 % to 10 %constitutes complete failure. Here, a 5 % drop in the V1mA ofthe arrester is considered as an indication of its failure. The V -I characteristics of 5 MOV samples were determined beforeand after the degradation tests. Figure 3 compares the healthyand degraded (to failure) V -I characteristics of an MOV wherethe V1mA decreased by 5.09 %.10-3 10-2 10-1 100Current (Idc) [mA]050100150200250Voltage (Vdc) [V]MOV (Degraded)MOV (Healthy)Figure 3: Experimental V -I characteristics of MOV obtainedbefore and after degradationIV. RESULTS AND DISCUSSIONThe Bayesian spectrum estimates of the 5 MOV units’leakage currents, recorded at selected times, were computed.The current was sampled at a frequency of approximately650 Hz. The posterior probability of four different harmoniccomponents of the leakage current were determined - i.e. dccomponent, fundamental (50 Hz), second order (100 Hz),and third order (150 Hz). Estimates for MOVs 1 and 2 areshown in figures 4 and 5, respectively. These specific MOVsremained operational for the full duration of the accelerateddegradation test. The V -I test conducted on these units afterthe degradation test indicated a negligible decrease in theV1mA value confirming non-failure. The dc component ofthese recorded signatures are significantly higher than theother components. The only observable trend is that the dccomponents first rise before fluctuating for the remainder ofthe test. Figures 6, 7 and 8 show the spectrum estimates forMOVs 3, 4 and 5, respectively. These MOVs failed during thetest at different times. Failure of these units was confirmedthrough the V -I test. Unlike with MOVs 1 and 2, the dccomponents of the leakage current signatures of the failed unitsexhibit a progressive increase with degradation. The initialrise in the leakage current dc component of the units thatdid not fail is an indication of degradation. The subsequentdecrease in the leakage current dc component follows thephysical degradation property exhibited by insulators called’self-healing’ [20], [21]. This essentially means that in somecases the surge arresters may undergo microstructural changesduring normal life degradation so as to become more resilientinstead of failing.V. CONCLUSIONMOVs perform a key function in protecting power systemequipment at various voltage levels. Monitoring the healthof the MOV units is vital in ensuring that these equipmentare well protected. Online monitoring methods are mostdesirable because it affords the opportunity for preventative0 1 2 3Harmonic Order00.20.40.60.8Posterior Probability1 min5 min10 min15 min20 min30 min45 minFigure 4: Spectrum estimates of MOV 1 leakage current afterselected intervals during accelerated degradation testing0 1 2 3Harmonic Order00.20.40.60.81Posterior Probability1 min5 min10 min15 min20 min30 min45 minFigure 5: Spectrum estimates of MOV 2 leakage current afterselected intervals during accelerated degradation testing0 1 2 3Harmonic Order00.020.040.060.080.1Posterior Probability1 min5 min10 min15 minFigure 6: Spectrum estimates of MOV 3 leakage current afterselected intervals during accelerated degradation testing0 1 2 3Harmonic Order00.10.20.30.40.5Posterior Probability1 min5 min10 min15 min20 minFigure 7: Spectrum estimates of MOV 4 leakage current afterselected intervals during accelerated degradation testing0 1 2 3Harmonic Order00.10.20.30.40.50.60.70.8Posterior Probability1 min5 min10 min15 min20 min30 minFigure 8: Spectrum estimates of MOV 5 leakage current afterselected intervals during accelerated degradation testingmaintenance. Leakage current monitoring does provide suchan opportunity, however all available variations of the methodare based on uncertain equivalent models and have practicaldeficiencies. In this paper, an LCSA method was proposed thatuses Bayesian spectrum analysis to determine the condition ofthe arrester. The method is not based on any equivalent modelor assumptions about the protected system. An experimentalsetup comprising accelerated degradation testing was usedto investigate the viability of the proposed method. Resultsindicate that the dc component of the leakage current spec-trum estimates progressively increase as the arrester degradestowards failure. The leakage current signature of the healthyarresters did no exhibit the same progressive increase. LCSAis a potential alternative to currently available methods asthis definitive trend could perhaps be used as an assessmenttechnique in practice.REFERENCES[1] M. Khodsuz, M. Mirzaie, and S. Seyyedbarzegar, “Metal oxide 245surge arrester condition monitoring based on analysis of leakage currentcomponents,” International Journal of Electrical Power and EnergySystems, vol. 66, pp. 188-93, 2015.[2] P. Osmokrovic,B. Loncar,S. Stankovic,and A. Vasic, “Aging of the over-voltage protection elements caused by over-voltages”, MicroelectronicsReliability, vol. 42, no. 12, pp. 1959-1966, 2002.[3] M. Khodsuz, and M. Mirzaie, “Monitoring and identification of metalox-ide surge arrester conditions using multi-layer support vector machine,”IET Generation, Transmission and Distribution, vol. 9, no. 16, pp.2501-2508, 2015.[4] N. Tsukamoto, and M. Ishii, “Repetitive Impulse Withstand Performanceof MetalOxide Varistors,” IEEE Transactions on Power Delivery, vol. 32,no. 4, pp.1674-1681, 2017.[5] G. R. Lira, and E. G. Costa, “MOSA monitoring technique based onanalysis of total leakage current,” IEEE Transactions on Power Delivery,vol. 28, no. 2, pp.1057-1062, 2013.[6] W. Bassi, and H. 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Condition monitoring of metal-oxide surge arresters using leakage current signature analysis

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Condition monitoring of metal-oxide surge arresters using leakage current signature analysis

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