Every reliability analysis effort, in some way, involves searching the state space of the system for those states that represent the events of interest, typically failure of the system or a given node to meet the demand. This essentially translates into a search procedure to efficiently identify states to be examined and then using a mechanism to evaluate these states. Traditionally, reliability analysis methods are based either on an implicit or explicit enumeration process or Monte Carlo sampling. More recently, methods based on artificial intelligence have been investigated both as an alternative to Monte Carlo for the search process as well as state evaluation techniques in conjunction with the Monte Carlo methods. This paper will examine the conceptual basis of overall reliability evaluation process and explore the role of artificial intelligence methods in this context. It will also provide some examples of application to the reliability analysis of hybrid systems involving conventional and alternative energy sources.
SINGH, CHANAN and WANG, LINGFENG (2008) "Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 16: No. 3, Article 2. Available at: https://journals.tubitak.gov.tr/elektrik/vol16/iss3/2