Health evaluation method of oilfield photovoltaic inverter based on Analytic Hierarchy Process-Extreme Gradient Boosting

The degree of equipment health refers to its ability to consistently fulfill the predetermined function within specified time and conditions, serving as a quantitative indicator for the condition of the equipment or its components. In recent years, there has been a growing research interest in utilizing machine learning methods to develop equipment health evaluation models. Therefore, in response to the health evaluation needs for photovoltaic power generation equipment proposed by oil fields, this paper presents an evaluation method AHP-XGBoost that combines analytic hierarchy process and XGBoost. Initially, an enhanced AHP was employed to establish the index system for evaluating equipment health degree and calculate the weight of each evaluation index. Subsequently, the weighted evaluation indices were used as input parameters for training an XGBoost model that meets accuracy requirements. Finally, experimental verification was conducted using actual data from photovoltaic inverter equipment in the oilfield. The results demonstrate that compared with other models, the proposed XGBoost model achieves a satisfactory evaluation effect with a coefficient of determination (R2) reaching 0.983.