Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

被引:0
|
作者
Liu, Meng [1 ]
Liu, Xuan [1 ]
Han, Xueying [1 ]
机构
[1] Baoding Tech Coll Elect Power, State Grid Jibei Elect Power Co Ltd, Skills Training Ctr, Baoding 071051, Peoples R China
来源
关键词
WOA; DE; distribution network; monitoring; power system; fault location; LOCATION;
D O I
10.3233/IDT-240720
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.
引用
收藏
页码:1763 / 1774
页数:12
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