Title :
Probabilistic Evaluation of Optimal Location of Surge Arresters on EHV and UHV Networks Due to Switching and Lightning Surges
Author :
Shariatinasab, Reza ; Vahidi, Behrooz ; Hosseinian, S.H. ; Ametani, Akihiro
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
Switching surges are of primary importance in insulation coordination of extremely high voltage and ultra-high voltage networks. However, in regions of high lightning activity or high ground resistance insulation design, preferably, should be based on the risk of failure caused by lightning and switching surges and the probability of line outage, a combination of lightning and switching flashover rates (SSFOR). This paper describes an effective installation of transmission line arresters (TLAs) to obtain a better protection scheme (i.e., minimizing global risk to the network). As a consequence, protection costs are reduced in accordance with the costs of elements actually protected and the number of TLAs utilized. In order to accomplish this, a probabilistic method for calculating the lightning related failure and an artificial neural network for estimating the SSFOR are presented. A multicriteria optimization method based on a genetic algorithm is also developed to determine the optimum location of TLAs.
Keywords :
arresters; earthing; genetic algorithms; lightning protection; neural nets; power engineering computing; power transmission lines; power transmission protection; probability; surge protection; EHV networks; UHV networks; artificial neural network; failure risk; genetic algorithm; ground resistance insulation design; insulation coordination; lightning activity; lightning surges; multicriteria optimization method; probabilistic evaluation; probabilistic method; protection scheme; surge arresters optimal location; switching flashover rates; switching surges; transmission line arresters installation; Arresters; Artificial neural networks; Costs; Flashover; Insulation; Lightning; Optimization methods; Surge protection; Transmission lines; Voltage; Artificial neural network (ANN); failure-risk analysis; genetic algorithm (GA); overvoltages; transmission-line arresters;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2009.2027477