高比例新能源发电功率波动异常自适应检测研究

Research on Adaptive Detection of Abnormal Power Fluctuations in High Proportion New Energy Generation

为提高新能源发电的可靠性,设计高比例新能源发电功率波动异常自适应检测方法。在光伏发电终端的太阳能电池上安装传感器,利用该传感器监测并采集太阳能电池的工作状态、电流、电压、温度等关键参数;根据采集的数据样本,引进随机森林分类器进行发电类型的自适应划分。在发电类型划分的基础上,从原始数据中提取出与功率波动相关的数据,结合离散化处理结果进行高比例新能源发电功率波动异常的自适应检测。选择某大型发电企业作为试点进行实验,实验结果证明,设计方法的检测结果与发电功率波动异常曲线适配度极高,证明该方法的检测结果精确度较高,可以实现对发电功率波动异常的自适应检测。

In order to improve the reliability of new energy generation,a high proportion of new energy generation power fluctuation anomaly adaptive detection method is designed.Install sensors on the solar cells of photovoltaic power generation terminals,and use these sensors to monitor and collect key parameters such as the working status,current,voltage,and temperature of the solar cells.Based on the collected data samples,a random forest classifier is introduced for adaptive classification of power generation types.Based on the classification of power generation types,extract data related to power fluctuations from the original data,and combine the discretization results for adaptive detection of high proportion new energy generation power fluctuation anomalies.We chose a large power generation enterprise as a pilot for the experiment,and the experimental results proved that the detection results of the designed method are highly compatible with the abnormal curve of power generation fluctuations.This proves that the detection results of the method have high accuracy and can achieve adaptive detection of abnormal power generation fluctuations.