基于EKF算法的分布式光伏发电异常数据排查技术

分布式光伏发电运营过程中，会因设备故障、仪表测量误差、用户个人行为等干扰因素导致运营系统采集异常数据，因此需要对异常数据进行排查，有助于保障光伏发电用户数据库的准确性和可靠性，并识别存在运营问题的分布式光伏用户。基于上述原因，本文针对分布式光伏系统的特殊性，提出一种光伏发电运营系统异常数据排查技术，结合温度、辐射量、纬度、时令等环境因素，采用中心复合设计方法，通过有限的数据量建立较为精确的发电量数学模型，采用扩展卡尔曼滤波算法对采集到的发电量进行修正，从而排查和消除异常数据。该方法能够实现发电数据的预测，能够快速、可靠地排查异常数据，文中给出了排查原理和具体的实现过程，最后通过实验结果证明了所提出技术的有效性。

Abnormal data inspection technology of photovoltaic power generation based on EKF algorithm

During the operation of distributed photovoltaic power generation, abnormal data collected by the operating system would be caused by interference factors, such as equipment failure, meter measurement error, user personal behavior and so on. Therefore troubleshooting abnormal data helps to ensure the accuracy and reliability of the PV generation user database and identify distributed PV users with operational problems. For the above reasons, this paper proposes an abnormal data inspection technology for photovoltaic power generation operation system. Environmental factors such as temperature, radiation, latitude, and seasonality are taken into consideration. The mathematical model of power generation is established by the central composite design method. The accumulated power generation is corrected by the extended Kalman filter algorithm. Thus abnormal data can be checked and eliminated. The proposed method can realize the prediction of power generation data. It can check abnormal data quickly and reliably. In this paper, the principle of investigation and the specific implementation process are discussed. Finally the effectiveness of the proposed technique is proved by experimental results.