Statistical and AI-based reliability assessment of photovoltaic inverters using statistical modeling and machine learning
2025, vol.17 , no.3, pp. 37-48
Article [2025-03-04]
This study presents a methodology for assessing the reliability of a photovoltaic (PV) inverter by combining classical statistical approaches and machine learning algorithms. The analysis is based on real SCADA data from a PV system and includes measured quantities such as voltages, currents, active power, temperature, cos(φ), frequency and registered alarm states. Statistical profiling of each numerical characteristic was performed by comparing probability distributions - normal, exponential and Weibull. To select the best distribution, the information criteria AIC and BIC were applied, as well as fit criteria such as Pearson's χ² and the Anderson–Darling test. Additionally, a reliability analysis was performed by modelling the time intervals between consecutive failures with a Weibull distribution, calculating the parameters shape (β), scale (η) and mean time to failure (MTTF), visualized by survival and failure functions.
machine learning, inverter, predictive maintenance, reliability, statistical analysis, SHAP
https://doi.org/10.59035/AGCX7287
Plamen Stanchev, Nikolay Hinov. Statistical and AI-based reliability assessment of photovoltaic inverters using statistical modeling and machine learning. International Journal on Information Technologies and Security, vol.17 , no.3, 2025, pp. 37-48. https://doi.org/10.59035/AGCX7287