An optimized GRNN-enabled approach for power transformer fault diagnosis

This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real-world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS-CSA-GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN-based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.