Data-Driven Reliability Assessment of PV Inverters Using SCADA Measurements

This paper presents a case study framework for the operational reliability monitoring of a grid-connected photovoltaic (PV) inverter using SCADA measurements collected during February–April 2025. The workflow combines correlation-based drift analysis, probabilistic outputs from established machine learning models (XGBoost and LSTM), and temporal consistency modeled through a hidden Markov model (HMM). The resulting evidence is summarized into two interpretable composite indicators: a Health Index (HI), intended to capture short-term deviations, and a Reliability Score (RS), intended to provide a smoother reliability-oriented summary over time. A time-aware evaluation protocol is employed to reduce temporal leakage and to assess predictive utility under rare-event conditions, complemented by baseline comparisons and sensitivity checks for key thresholds and modeling settings. Within the analyzed dataset, the results suggest that HI is responsive to transient disturbances, while RS supports trend monitoring and maintenance prioritization by consolidating multiple weak signals into a consistent operational view. The proposed indicators are positioned as data-driven risk summaries for decision support rather than direct physical measures of deviation patterns. Generalization to other inverters and sites requires further validation on longer horizons and with additional operational/maintenance records.