Abstract: A human-machine collaborative discriminant method based on the random forest algorithm was proposed to diagnose distributed photovoltaic shadow occlusion. Key characteristic parameters, such as the current dispersion rate on the direct current side of the string, solar altitude angle, solar azimuth angle, and instantaneous power generation level of the power station, were constructed based on the analysis of the shadow occlusion mechanism and the conversion of inverter telemetry parameters. The random forest shadow occlusion diagnosis model was subsequently established. The parameters were optimized based on the grid search method and the K-fold cross-validation method, and the splitting method based on information gain was determined by comparing the accuracy with other machine learning algorithms, such as support vector machine, logistic regression, and decision tree. The random forest algorithm had obvious advantages in shadow occlusion diagnosis scenes. An expert system was combined to obtain the diagnosis position, and then the effectiveness of the method using the random forest algorithm based on information gain and an expert system was verified on site.

Key words: distributed photovoltaic, random forest, expert system, shadow occlusion diagnosis, analysis of shadow occlusion mechanism