Identification of Abnormal PV Output Power Based on kernel Density Estimation and Consistency Method

Abstract

The accuracy of PV output power is essential for the performance improvement of PV electricity station. Facing the fact that the original dataset of PV output power contains a lot number of abnormal data, an identification method is proposed based on kernel density estimation. First, the main factors that can influence the output power of PV system are recognized, i.e. solar radiation and module temperature. And then, the kernel density estimation method is applied to derive the joint distribution function of PV output power, solar irradiance and module temperature. Finally, according to the narrowest interval principal, the rational range of PV output power is given. With the real operational data from a PV power station in Qionghai, Hainan, the superiority of the proposed method identifying the abnormal PV output power in terms of precision, effectiveness and practicability is demonstrated by comparing it with the traditional 3-$\sigma$ method.