PVeSight: Dimensionality reduction-based anomaly detection and visual analysis of photovoltaic strings

Abstract

Efficient and accurate detection of anomalies in photovoltaic (PV) strings is essential for ensuring the normal operation of PV power stations. Most existing studies focus on developing automated anomaly detection models based on temporal abnormalities in PV strings. However, since analyzing anomalies often requires domain knowledge, existing automated methods have significant limitations in assisting experts to understand the causes and impact of these anomalies. In close collaboration with domain experts, this work has summarized the specific user requirements for PV string anomaly detection and designed PVeSight, an interactive visual analysis system to help experts discover and analyze anomalies in PV strings. We use dimensionality reduction techniques to generate string pattern map. These maps are used for anomaly detection, classifying anomalies, comparative analysis between strings, and hierarchical analysis under inverters and combiner boxes. This helps experts trace the causes of anomalies and acquire valuable insights into anomalous PV strings. Through case studies and expert evaluation, we verified the usability and effectiveness of PVeSight for PV string anomaly detection.