Application of tap-changer fault diagnosis of transformer based on AGA-BP neural network

doi:10.16731/j.cnki.1671-3133.2017.04.003

Abstract

Abstract: The On-Load Tap Changer(OLTC)of transformer has a complex nonlinear relationship between the mechanical fault symptom and fault type,and the traditional BP neural network is used to diagnose with low accuracy,slowing convergence rate and easy to fall into local minimum value and so on.An Adaptive Genetic Algorithm(AGA) is proposed to optimize the BP neural network fault diagnosis.The weights and thresholds of BP neural networks based on adaptive genetic algorithm optimization,and then the optimized BP neural network is applied to the OLTC mechanical fault diagnosis.The simulation results show that the fault diagnosis model of BP neural network optimized by AGA algorithm is superior to the traditional BP neural network method.It can effectively improve the accuracy and speed of the mechanical fault diagnosis of OLTC.

Key words: transformer tap changer, BP neural network, genetic algorithm , fault diagnosis

CLC Number:

TP183

Wang Fuzhong, Shi Xiuli. Application of tap-changer fault diagnosis of transformer based on AGA-BP neural network[J]. Modern Manufacturing Engineering, 2017, 439(4): 11-16.

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