Predicting lightning outages of transmission lines using generalized regression neural network

Lightning is the major cause of transmission line outages, which can result in large area blackouts of power systems. One effective method to prevent catastrophic consequences is to predict lightning outages before they occur. The abundance of recorded lightning and lightning outage data in power system makes it possible to predict lightning outages of transmission lines. This paper proposes an artificially intelligent algorithm using general regression neural networks (GRNN) to predict lightning outages of transmission lines. First, the data that can be obtained from the operation and management system of a power company are analyzed, and the features that can be used as input parameters of GRNN are extracted. The prediction model based on GRNN is then built to perform lightning outage prediction. Finally, the effectiveness of the proposed method is validated by comparing it with (Back Propagation) BP and (Radial Basis Function) RBF neural networks using actual lightning data and lightning outage data. The simulation results show that the proposed method provides much better prediction performance. © 2019 Elsevier B.V.