Title: Fault diagnosis and location method for electrical power supply and distribution of buildings

Authors: Jundong Fu; Tianhang Leng

Addresses: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China

Abstract: This paper presents a new fault diagnosis and location method for electrical power supply and distribution of buildings using Bayesian and wavelet neural network (WNN). Aiming at the complex trunk-type power supply and distribution structure of buildings, wavelet transform (WT) is adopted to process the current, voltage and phase data of each branch to extract the features that can distinguish faults effectively. And the fault diagnosis model based on Bayesian network is established by the above features. In order to improve the accuracy of WNN in fault location in buildings, a WNN method optimised by dragonfly algorithm (DA) is proposed to obtain better thresholds and weights, which are utilised to enhance the prediction ability. A simulation study was made with MATLAB/Simulink to verify the performance of the proposed method on a power supply and distribution model.

Keywords: fault diagnosis; location method; electrical power supply and distribution of buildings; wavelet transform; Bayesian network; wavelet neural network; WNN; dragonfly algorithm.

DOI: 10.1504/IJSPM.2020.110176

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.4, pp.333 - 342

Received: 27 Feb 2019
Accepted: 21 Jun 2019
Published online: 08 Oct 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article