光伏电站发电支路故障快速检测技术研究

Research on Rapid Detection Technology for Power Generation Branch Faults in Photovoltaic Power Stations

为了开发一套高效的光伏电站发电支路故障检测系统,以提升故障识别速度、准确性,及运维效率,通过搭建硬件平台,采用机器学习、故障专家系统及统计分析等手段来实施了系统整体联调与性能测试,根据测试数据,该系统在传感器数据采集、故障检测准确性、系统响应时间、数据传输稳定性及软件系统集成等方面均表现出色。具体而言,传感器准确采集了电压和电流数据;故障识别时间仅需1秒,告警方式包括声光等;系统平均响应时间为2.5秒;数据传输成功率达100%;与ETAP 22软件的集成实现了数据共享,故障分析准确率高达95%。为光伏电站的精细化运维管控提供了新的解决方案。

In order to develop an efficient fault detection system for the power generation branch of photovoltaic power plants to improve the speed,accuracy,and operation efficiency of fault recognition,a hardware platform was built,and machine learning,fault expert system,and statistical analysis were used to implement overall system debugging and performance testing.According to the test data,the system performed well in sensor data acquisition,fault detection accuracy,system response time,data transmission stability,and software system integration.Specifically,the sensor accurately collected voltage and current data;the fault recognition time only takes 1 second,and the alarm methods include sound and light;the average response time of the system is 2.5 seconds;the success rate of data transmission reaches 100%;the integration with ETAP 22 software has achieved data sharing and a fault analysis accuracy rate of up to 95%.This provides a new solution for the refined operation and control of photovoltaic power plants.