A new wavelet network based method to estimate the lightning-related risk of failure of power system apparatus

Abstract

Abstract This paper proposes a new and fast wavelet network based method for estimating the risk of failure caused by lightning overvoltages in arrester protected networks. First, failure risks obtained by simulations are used as the training data for training the wavelet network. The trained wavelet network is then used for accurate and fast estimating of the lightning-related risk of failure of power system apparatus for all possible conditions. The accuracy of the proposed method has been tested and verified under various conditions in the 230 kV network of Sistan–Baluchestan. Performance of the new method has also been compared with several existing methods under same conditions, and the test results show better accuracy of the proposed method. The proposed method not only does not have the restriction of conventional methods, but also it does not have the limitations associated with traditional neural networks based algorithms such as convergence to local optimum points, over-fit and/or under-fit problems. The main contribution of the paper is an accurate (due to proper selection of the training data set based on the k-fold cross validation technique and using wavelet network for estimation), fast (mean calculation time for the network risk of failure computation is 54 s) and simple wavelet network-based algorithm (as compared to the conventional algorithms) for estimating the lightning-related risk of failure of power system apparatus.