Fault Diagnosis of Grounding Grid Based on Genetic Algorithm and Neural Network

Abstract:

The grounding grid of power plants and substations is an important device to ensure the safe and stable operation of electric power system. However, it is difficult to diagnose the fault of grounding grid using traditional method of identification. In recent years, the development of artificial neural network has provided effective ways to solve this problem. In this paper, neural network is used to diagnose fault of the grounding grid, because it has good learning and training characteristics, and performance of fault tolerance. It can search fault localization of the grounding grid. BP Algorithm has the advantage of the optimization accuracy, but there are some drawbacks, the majority of which is easy to fall into local minimum, slow convergence and cause oscillation effect. Genetic algorithm has a strong globe search capability, and can find the global optimal solution with high probability, so it can overcome the shortcomings of the BP algorithm using GA to complete the pre-search. This paper presents a hybrid training algorithm by GA combine with BP to optimize network. Simulation results show that the hybrid method has a fast convergence rate and high diagnostic accuracy for diagnosing the fault of grounding grid; it can be used in fault diagnosis of grounding grid effectively and reliably.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 4095-4099
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.4095
• Online since: September 2013
• Authors: Wei Qian, Mei Hong Qiu, Miao Wu, Xian Yue Shi
• Keywords: Fault Diagnosis, Genetic Algorithm (GA), Grounding Grid, Neural Network (NN)

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