Estimation of New Weighted Controlled Switching Overvoltage by RBFN Model

Hasanpour, M.; Ghanbari, M.; Parvin-Darabad, V.

doi:10.22098/joape.2020.6987.1508

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	Estimation of New Weighted Controlled Switching Overvoltage by RBFN Model
Journal of Operation and Automation in Power Engineering
مقاله 5، دوره 8، شماره 3، اسفند 2020، صفحه 234-244 اصل مقاله (1.08 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2020.6987.1508
نویسندگان
M. Hasanpour¹؛ M. Ghanbari^* ¹؛ V. Parvin-Darabad¹^،^{، 2}
¹Department of Electrical Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran
²Department of Electrical Engineering, Golestan University, Gorgan, Iran
چکیده
Mitigating switching overvoltages (SOVs) and conducting well-suited insulation coordination for handling stresses are very important in UHV transmission Lines. The best strategy in the absence of arresters is controlled switching (CS). Although elaborate works on electromagnetic transients are considered in the process of designing transmission systems, such works are not prevalent in day-to-day operations. The power utility and/or operator have to carefully monitor the peak values of SOVs so this values not to exceed the safe limits. In this paper, we present a novel CS approach in dealing with EMTP/ATP environment, where trapped charge (TC) is intended to train a radial basis function network (RBFN) meta-model that is implemented to calculate SOVs. A new weighted maximum overvoltage factor proposed to find locations of critical failure risk due to SOVs occurred along transmission lines. Power utilities or design engineers can benefit from the presented meta-model in designing a well-suited insulation level without spending time for taking into account the feasible risk value. Besides, the operators can energize the lines sequentially upon their choice; i.e., a safe and proper energization.
کلیدواژه‌ها
Switching overvoltage؛ Transmission Line؛ Controlled Switching؛ RBFN

مراجع
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Estimation of New Weighted Controlled Switching Overvoltage by RBFN Model