Title :
A Fault Location Method for Active Distribution Network with Renewable Sources Based on BP Neural Network
Author :
Zhang Tong; Li Xianhong; Yu Haibin; Liu Jianchang; Zeng Peng; Sun Lanxiang
Author_Institution :
Key Lab. of Ind. Control Network &
Abstract :
This paper presents a neural network method to locate common fault exactly in a distribution power system (DPS) with renewable sources. The back propagation (BP) neural network method is applied to identify patterns of voltage and current measured from distribution branches. The input matrix of BP network consists of the voltage and current values, which can identify the accurate fault position. The fault location of a common short-circuit fault is analyzed thoroughly in an active distribution network (ADN) with the renewable power sources. Simulation results prove the feasibility and usefulness of the fault location method based on the BP neural network, wherein the fault location accuracy can reach 0.09%.
Keywords :
"Fault location","Circuit faults","Mathematical model","Neural networks","Voltage measurement","Power system stability"
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
Print_ISBN :
978-1-4799-8645-3
DOI :
10.1109/IHMSC.2015.194