{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:48:45Z","timestamp":1753886925198,"version":"3.41.2"},"reference-count":8,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2011,12,19]],"date-time":"2011-12-19T00:00:00Z","timestamp":1324252800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2012,1]]},"abstract":"<jats:p>In order to assess the reliability of distribution systems, more and more researchers are directing their attention to the artificial intelligent method, and several reliability indices have been proposed, such as basic load point indices and system performance indices. Artificial neural network is recently established as a useful and much promising too, applied to variety of power systems engineering. This paper presents ANN version for evaluating the reliability of distribution power systems (DPSs), in the proposed algorithm, the ANN used to predicted (RPS) using historical data method constructed according to the backpropagation learning rule. At the same time, System indices such as SAIFI and SAIDI of real distribution system are computed and compared with results generated by network method. The result obtained by proposed method gives acceptable reliability indices and can also found that the deviation of computed values by the proposed method is less than 1% and needs running time on ASUN network environment of less than 2\u2009s. The ANN approach demonstrates advantage over the network method.<\/jats:p>","DOI":"10.1155\/2012\/560541","type":"journal-article","created":{"date-parts":[[2011,12,19]],"date-time":"2011-12-19T21:00:46Z","timestamp":1324328446000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reliability Evaluation of Distribution Power Systems Based on Artificial Neural Network Techniques"],"prefix":"10.1155","volume":"2012","author":[{"given":"Mohammoud M.","family":"Hadow","sequence":"first","affiliation":[]},{"given":"Ahmed N.","family":"Abd Allah","sequence":"additional","affiliation":[]},{"given":"Sazali P.","family":"Abdul karim","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2011,12,19]]},"reference":[{"key":"e_1_2_6_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-1346-3"},{"volume-title":"Reliability Evaluation of Power Systems","year":"1995","author":"Billinton R.","key":"e_1_2_6_2_2"},{"key":"e_1_2_6_3_2","doi-asserted-by":"crossref","unstructured":"MidenceD. 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