基于智能系统的光伏发电场故障检测研究

Research on fault detection of photovoltaic power plants based on intelligent systems

模糊推理引擎进而评估这些模糊信息,依据模糊规则库中的规则来确定是否存在潜在故障。该方案提供了光伏场在正常运行时的瞬时发电量的估计。然后,将估计功率与实际功率进行比较,若功率之间的差值超过阈值,则生成警报信号,其中模糊规则系统TSK-FRBS已在正常运行期间将光伏电站模拟器收集的数据进行了培训,通过再现正常和故障条件,在模拟框架中进行测试。结果表明,即使引入噪声数据,该系统也能识别90%以上的故障情况。

The fuzzy inference engine evaluates these fuzzy information and determines whether there is a potential fault based on the rules in the fuzzy rule library.This scheme provides an estimate of the instantaneous power generation of the photovoltaic field during normal operation.Then,the estimated power is compared with the actual power.If the difference between the powers exceeds the threshold,an alarm signal is generated.TSK-FRBS has trained the data collected by the photovoltaic power plant simulator during normal operation and tested it in the simulation framework by reproducing normal and fault conditions.The results show that even with the introduction of noisy data,the system can still recognize more than 90%of fault situations.