Health Status Evaluation for PV Inverter Based on Enhanced MPdist-HMM-K-means Models

The performance and dependability of photovoltaic (PV) power systems depend heavily on the condition of the inverters. As the solar industry develops quickly, the inverter, a vital component, becomes even more important in guaranteeing the effective conversion of solar energy. However, basic data weighting or classification evaluation is frequently used in traditional health state evaluation methods, which makes it challenging to accurately portray the true state of the device. The MPdist-HMM-K-means health state evaluation method is presented in this paper. It integrates PV inverter environmental data with operational performance data, uses the MPdist algorithm to measure the similarity of time-series data under various conditions, builds a virtual health factor model of PV inverters, uses the Hidden Markov Model (HMM) to extract potential health states, and uses K-means clustering to classify and refine the hidden states. The results of the experiment demonstrate that the MPdist-HMM-K-means model can identify the process by which the instantaneous health state of PV inverters changes.