Probabilistic Evaluation of Optimal Location of Surge Arresters on EHV and UHV Networks Due to Switching and Lightning Surges
Abstract
Switching surges are of primary importance in insulationcoordination of extremely high voltage and ultra-high voltagenetworks. However, in regions of high lightning activity or highground resistance insulation design, preferably, should be based onthe risk of failure caused by lightning and switching surges and theprobability of line outage, a combination of lightning and switchingflashover rates (SSFOR). This paper describes an effective installationof transmission line arresters (TLAs) to obtain a better protectionscheme (i.e., minimizing global risk to the network). As aconsequence, protection costs are reduced in accordance with thecosts of elements actually protected and the number of TLAs utilized.In order to accomplish this, a probabilistic method for calculatingthe lightning related failure and an artificial neural networkfor estimating the SSFOR are presented. A multicriteria optimizationmethod based on a genetic algorithm is also developed to determinethe optimum location of TLAs.Index Terms—Artificial neural network (ANN), failure-riskanalysis, genetic algorithm (GA), overvoltages, transmission-linearresters.
I. INTRODUCTION
THE insulation level of power networks at the planningstage is decided on the basis of the peak value of transientovervoltages. In general, with the increase of operatingvoltage, switching overvoltages rather than lightning surges determinethe insulation level of the network. Then, the insulationlevel of extremely high voltage (EHV) and ultra-high voltage(UHV) systems is largely determined by the switching overvoltages(i.e., the probability of switching surge flashover rate(SSFOR)) [1], [2].However, the SSFOR is normally not considered a standalonecriterion. In areas of low lightning activity, the SSFORmay be selected higher than for the areas of high activity, sincethe probability of lightning faults, which cause the breaker toreclose is high. So there should be a compromise between theSSFOR and the lightning flashover rate (LFOR) of the network.This concept is called the storm outage rate (SOR) and is theSSFOR multiplied by the LFOR [3]. Nevertheless, in regions of high ground resistance, lightning overvoltages of high magnitudecan occur that may lead to insulation failure. Thus, forbetter protection of EHV networks, lightning-related failure riskcan also be considered.For long EHV lines, preinsertion resistors (PIRs) are traditionallyused to limit the switching overvoltages. But recently,a trend has been to try to find alternatives to PIR with more activeuse of transmission-line surge arresters (TLAs) or by controlledswitching [4], [5]. The use of TLAs offers a robust andefficient alternative to PIR since the arresters can be located onselected points of the network to obtain the required control ofovervoltages.This paper presents a multicriteria simulation optimizationmethod to determine the optimal location of TLAs on the network.In order to do that, a statistical method to calculate thelightning-related risk and a meta model, constructed by usingthe artificial neural network (ANN) to estimate the SSFOR ofthe network, are introduced; then an optimization procedure,based on genetic algorithm (GA), is developed so as to minimizefailure risk of the network caused by the lightning and switchingsurges (i.e., determining the best position of TLAs). To achievebetter protection, the proposed method also considers the atmosphericconditions and arrester failures.Results of the studies were applied to a sample EHV networkto illustrate the method. The developed method has been codedin Matlab v. 7.1 and linked to an EMTP/ATP draw program toperform the network simulations directly.II. FAILURE-RISK ANALYSISA. Lightning Related FailuresThe LFOR and lightning-related failure risks can be calculatedbased on a statistical approach in which the Monte Carlosimulation is a very common method for this purpose. A MonteCarlo procedure consists of the following steps [6]: generationof random numbers to obtain those parameters of the lightningstrokes and the overhead line of random nature, of which the statisticalparameters are known; application of an incidence modelto deduce the point of impact of every lightning stroke; calculationof the overvoltage generated by each stroke; and calculationof the LFOR and risk of failure.In order to statistically evaluate lightning overvoltages foreach node of interest, for every network simulation, the maximumvoltage is stored.

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