Intelligent prediction of photovoltaic inverter failures for improved reliability and control in smart grid systems

The increasing demand for renewable energy sources necessitates advanced methods to ensure grid stability and operational efficiency. Smart grids present a viable solution by enabling the seamless integration of renewable energy systems, such as solar photovoltaic (PV) and other sources. However, the performance and reliability of such systems are critically dependent on the performance and functionality of power inverters (e.g., solar PV inverters). This paper proposes the application of several decision tree algorithms, which are traditional decision tree (TDT), iterative dichotomizer tree (IDT), C4.5 algorithm, and CART algorithm, for fault prediction and diagnostics in solar PV inverters. The training and prediction phases of the decision tree models employ both key inverter specifications, such as nominal operating ranges, abnormal readings, and fault conditions, alongside critical operational parameters, including voltage, current, temperature, conversion efficiency, power factor (PF), and total harmonic distortion (THD). Also, a cost-benefit analysis is made to consider the cost-effectiveness of predictive maintenance of solar PV inverters with the presence of significant economic benefits. A substantial cost advantage is shown, where 12,000 $/year in losses is saved with an investment of merely 4000 $/year in support of a predictive maintenance model with a first year ROI of 200%. Overall, the results of all decision tree models, implemented in MATLAB, confirm that both TDT algorithm and CART algorithm attain a high classification accuracy of about 95% with TDT possessing the shortest fault prediction time of about 50 ms. Markedly, the CART algorithm proves effective when it comes to dealing with an up to 90% missing values. Additionally, the analysis demonstrates a direct proportional relationship between voltage, current, temperature, and conversion efficiency, while revealing an inverse relationship between conversion efficiency and THD. The proposed algorithms for fault prediction hold significant potential for enhancing the reliability of solar PV systems integrated within smart grid frameworks.