Photovoltaic anomaly data detection method based on clustering iForest

Under the dual carbon goals of carbon neutrality and emission peak, accelerating the development of new energy has become a major trend in China. Distributed photovoltaic has become the fastest-growing new energy development method in recent years. In the actual power generation process, due to sensor errors, failures and other reasons, the data collected from photovoltaic power plants usually contain some abnormal data. Abnormal operation data is not conducive to photovoltaic power generation grid connection, dispatching, power generation prediction and other business operations. This article uses photovoltaic power generation as a scenario, proposes a photovoltaic power generation abnormal data detection method based on Isolation Forest and Density Clustering. It can be used when the sample set is small and the sample set is non-convex. This method does not need to rely on empirical judgment to achieve good detection results. It is very suitable for the detection of photovoltaic abnormal data.