2014
DOI: 10.1049/iet-smt.2013.0094
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Development of an adaptive neural‐fuzzy inference system based meta‐model for estimating lightning related failures in polluted environments

Abstract: Owing to the random nature of the lightning phenomenon, the statistical based methods such as Monte Carlo simulation are the best tool to perform the lightning-related studies. However, Monte Carlo simulation is complex and time consuming, especially, for larger networks where a lot of regions must be investigated, separately. Although some researches have been carried out to evaluate the lightning flashover outage rates based on artificial intelligence (AI) techniques, but, so far, any work has not been repor… Show more

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Cited by 11 publications
(10 citation statements)
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“…Lightning trip-out is a severe problem that threatens power supply. Almost 40-70% outages on power networks are caused by lightning [1][2][3]. The power frequency arc established after lightning flashover is the main reason that gives rise to short circuit fault and lightning trip-out.…”
Section: Introductionmentioning
confidence: 99%
“…Lightning trip-out is a severe problem that threatens power supply. Almost 40-70% outages on power networks are caused by lightning [1][2][3]. The power frequency arc established after lightning flashover is the main reason that gives rise to short circuit fault and lightning trip-out.…”
Section: Introductionmentioning
confidence: 99%
“…The ANFIS network is a model that can map the input characteristics to a single‐valued output or a decision associated with the output. A fuzzy inference system can only be applied to the modelling of systems whose rule structure is essentially predetermined by the user's interpretation of the characteristics of the variables in the model [33, 34]. Furthermore, rule structure is always constructed using an IF_THEN representation.…”
Section: Anfis Meta‐modelmentioning
confidence: 99%
“…Boonchuay and Ongsakul (2012) applied particle swarm optimization to assess bidding strategies for a power generation firm. Shariatinasab et al (2014) used neural networks to replace simulation in evaluating lightning flashover outage avoidance strategies. Nuclear power risk assessment was supported by artificial intelligence models reported by Pourali (2014).…”
Section: Artificial Intelligence In Engineering Risk Managementmentioning
confidence: 99%