Application of Apriori fault detection algorithm in provincial photovoltaic station digital monitoring platform

When implementing the digital monitoring platform for a provincial photovoltaic substation digital monitoring platform, fault detection is a key link. A reasonable and scientific fault detection model can enhance the platform’s sensitivity and provide timely warnings for faults. However, the current fault monitoring models for photovoltaic power plants have the problem of overly relying on a large number of fault samples. In view of this, this study introduces the Apriori algorithm that does not rely on sample data and the density-based clustering algorithm, and combines the two. The improved Apriori algorithm and the density-based clustering algorithm were used to mine and cluster the sample data. Based on this, a fault detection model for photovoltaic power plants was constructed. This research aims to establish a more accurate fault detection model, improve monitoring systems, repair faults in a timely manner, and reduce economic losses for owners and power plants. The research algorithm’s accuracy is 93.55%, and its recall rate is 96.67%, both higher than those of the unmodified model. The research model’s average F1 value is 97.53%, which is significantly higher than that of support vector machine and decision tree models. The performance superiority of fault detection models has been verified, demonstrating the research model’s applicability in fault detection of photovoltaic power plants. It further improves the monitoring system of the digital monitoring platform for provincial photovoltaic stations and also provides theoretical and data support for fault detection in power stations.