计算机科学
断层(地质)
人工神经网络
专家系统
数据挖掘
电力系统
人工智能
反向传播
功率(物理)
图层(电子)
机器学习
地质学
物理
地震学
有机化学
化学
量子力学
作者
Ding Ma,Yanchun Liang,Xiaoshe Zhao,Renchu Guan,Xiaohu Shi
标识
DOI:10.1016/j.engappai.2012.03.017
摘要
Fault diagnosis and assessment is a crucial and difficult problem for power system. Back propagation neural network expert system (BPES) is an often used method in fault diagnosis. However, with the layer numbers increasing, BPES becomes time consuming and even hard to converge. To solve this problem, we divide the whole networks into many sub-BP groups within a short depth and then propose a novel Multi-BP expert system (MBPES) based method for power system fault diagnosis. We use two real power system data sets to test the effectiveness of MBPES. Experimental results show that MBPES obtains higher accuracy than two commonly used methods.
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