A Hybrid machine‐learning method for oil‐immersed power transformer fault diagnosis

Xiaohui Yang, Wenkai Chen, Anyi Li, Chunsheng Yang

2020IEEJ Transactions on Electrical and Electronic Engineering27 citationsDOI

Abstract

This paper presents a hybrid machine‐learning method based on oil‐immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a Multi‐Verse Optimizer (MVO) algorithm. PNN is a radial basis function prefeedback neural network based on Bayesian decision theory. It has strong fault tolerance and has significant advantages in pattern classification. However, the performance of PNN is greatly affected by the hidden‐layer unit‐smoothing factor, and the classification result is affected. MVO is a metaheuristic algorithm with strong global convergence. Therefore, the smoothing factor of MVO‐optimized PNN (MVO‐PNN) can effectively improve the fault diagnosis ability. Recent studies have demonstrated the MVO algorithm. We utilize an experiment about the oil data in the power transformer in Jiangxi Province, China. The results show that MVO‐PNN can significantly improve the accuracy of power transformer fault classification and is more efficient than the Cuckoo search algorithm, Bat algorithm, Genetic Algorithm optimization, and other algorithms capabilities in some cases. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Topics & Concepts

Artificial neural networkCuckoo searchSmoothingTransformerProbabilistic neural networkArtificial intelligenceEngineeringComputer scienceMachine learningAlgorithmPattern recognition (psychology)Particle swarm optimizationTime delay neural networkVoltageComputer visionElectrical engineeringPower Transformer Diagnostics and InsulationHigh voltage insulation and dielectric phenomenaCurrency Recognition and Detection