Artificial neural network-based software tool for calculating the lightning performance of high-voltage transmission lines

Abstract

An artificial neural network (ANN) is addressed for evaluating the lightning performance of high-voltage transmission lines. Several structures, learning algorithms and transfer functions were tested to produce a model with the best generalising ability. Actual input and output data, collected from operational Hellenic high-voltage transmission lines, were used in the training, validation and testing process. The method is coded in a comprehensive software program to be used by electric power utilities as a useful tool for the design of electric power systems, as an alternative to the conventional analytical methods. The aims of the paper are to describe in detail the proposed ANN method and the developed software tool and to present the results obtained by its application to operational Hellenic transmission lines of 150 kV and 400 kV. The ANN tool's results are compared with results produced from a conventional method and real records of outage rate showing a quite satisfactory agreement. overvoltages in distributions lines (13) and for lightning protection of high-voltage transmission lines (14). The calculation of the lightning performance of overhead transmission lines presents many uncertainties owing to the random nature of the lightning phenomenon and the lack of reliable data. This is why all the existing methods for the calculation of lightning performance are based on empirical and approximating equations. ANNs can be an effective alternative solution to this problem, as they can present great accuracy and, in many cases, better results using only actual line data in the lightning performance calculations. Moreover, the efficient and economic implementation of ANNs with today's computer technology makes them very attractive tools. In this paper, an ANN method is proposed to identify the lightning performance of high-voltage transmission lines. By testing several combinations of different structures, learning algorithms and transfer functions, we have developed an ANN that presented the best generalising ability among all the other combinations. Actual input and output data, collected from operational Hellenic high- voltage transmission lines w ere used in the training, validation and testing process. The developed method was coded in a comprehensive software program and applied to several operational Hellenic transmission lines of 150 kV and 400 kV so that its accuracy could be validated. The results obtained are compared with those produced using conventional methods and with real records of outage rate.