TY - JOUR ID - 578 TI - Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Abravesh, M.M AU - Sheikholeslami, A AU - Abravesh, H. AU - Yazdani asrami, M. AD - Department of Electrical Engineering, Hadaf Institute of Higher Education, Sari, Iran AD - Department of Electrical Engineering, Noshirvani University of Technology, Babol, Iran Y1 - 2016 PY - 2016 VL - 4 IS - 2 SP - 235 EP - 241 KW - Surge arresters KW - Residual voltage KW - Big Bang – Big Crunch algorithm KW - Hybrid Big Bang – Big Crunch algorithm DO - 10.5829/idosi.JAIDM.2016.04.02.12 N2 - Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this paper, Big Bang – Big Crunch and Hybrid Big Bang – Big Crunch optimization algorithms are used to selects optimum surge arrester model equivalent circuit parameters values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer.The proposed algorithms are applied to a 63 kV and 230 kV metal oxide surge arrester. The obtained results show that using this method the maximum percentage error is below 1.5 percent. UR - https://jad.shahroodut.ac.ir/article_578.html L1 - https://jad.shahroodut.ac.ir/article_578_564d25a1c25858e62346d530acbaaa91.pdf ER -