Detection of Distributed Photovoltaic Output Anomalies Based on BIRCH and Gaussian Cloud Model

[Objective] The detection of output anomalies in distributed photovoltaics can effectively reduce operational costs， which are important for enhancing the efficiency of distributed photovoltaic power generation. However， owing to the cost and geographical constraints of distributed photovoltaic systems， the high-precision irradiance data required by conventional anomaly detection methods， including mean comparison algorithms， quartile methods， and neural networks， are often unavailable. This limitation renders traditional anomaly-identification approaches， which depend on the linear correlation between photovoltaic output power and irradiance， largely inapplicable to distributed photovoltaic systems. [Methods] Based on this， a method for detecting output anomalies in distributed photovoltaics using the balanced iterative reducing and clustering using hierarchies （BIRCH） method and Gaussian cloud model is proposed. First， a method based on the BIRCH algorithm is used to identify typical photovoltaic output curves by extracting the operational indicators of distributed PV systems and categorizing scenarios based on season and weather. Second， an approach which generates Gaussian clouds from the overall PV data distributions is introduced and it compares real-time output clouds with typical output clouds to detect anomalies in the distributed PV outputs. [Results] A verification analysis is performed using a distributed photovoltaic system in a specific region of Hangzhou as a case study. The results demonstrate that the proposed algorithm effectively identifies typical photovoltaic output curves and accurately detects output anomalies in distributed photovoltaics. [Conclusions] The scene-set segmentation method can effectively eliminate the dependence of distributed photovoltaic anomaly detection on precise irradiance data. The Gaussian cloud model can effectively detect power output anomalies， with the detected anomalies having clear parametric characteristics， thereby providing a valuable reference for subsequent related research.