基于DIgSILENT的分布式光伏短路电流自动化监测系统  

Distributed Photovoltaic Short-circuit Current Automatic Monitoring System Based on DIgSILENT

作　　者：赵晶 庞怡君 管春伟 崔艳昭 ZHAO Jing;PANG Yijun;GUAN Chunwei;CUI Yanzhao(State Grid Shandong Electric Power Company Qingdao Power Supply Company,Qingdao 266002,China)

机构地区：[1]国网山东省电力公司青岛供电公司,青岛266002

出　　处：《自动化与仪表》2024年第12期65-69,共5页Automation & Instrumentation

摘　　要：分布式光伏系统短路故障分析过程中,采用简化电路模型法完成短路电流计算,忽略了光伏系统的动态特性和非线性行为,得出的监测结果误差较大。因此,提出基于DIgSILENT的分布式光伏短路电流自动化监测系统。引入完全自适应噪声集合经验模态分解算法,得到干扰抑制后的信号序列。基于鲸鱼优化算法改进神经网络构建短路故障识别模型,得到短路电流自动化监测结果。测试结果表明,该系统输出的短路电流自动化监测结果RMSE值始终小于0.1,满足电流监测精度要求。In the process of analyzing short-circuit faults in distributed photovoltaic systems,the simplified circuit model method is used to calculate the short-circuit current,ignoring the dynamic characteristics and nonlinear behavior of the photovoltaic system,resulting in significant errors in the monitoring results.Therefore,a distributed photovoltaic short-circuit current automatic monitoring system based on DIgSILENT is proposed.Introduce a fully adaptive noise set empirical mode decomposition algorithm to obtain the signal sequence after interference suppression.A neural network improved based on whale optimization algorithm is used to construct a short-circuit fault recognition model.The test results show that the RMSE value of the short-circuit current automatic monitoring output by the system is always less than 0.1,which meets the accuracy requirements of current monitoring.

关 键 词：DIGSILENT 分布式光伏 短路电流 改进神经网络 自动化在线监测 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]