Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Abstract

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

pvOps: a Python package for empirical analysis of photovoltaic field data

“…For example, research efforts have demonstrated utility of natural language processing techniques (e.g., topic modeling) and survival analyses to support evaluation of patterns in O&M records (Gunda et al, 2020). Additional statistical methods, such as Hidden Markov Modeling, have also been successfully used to support classification of failures within production data (Hopwood, Patel, et al, 2022). These and other capabilities will continue to be added to the package to improve its utility for supporting empirical analyses of field data.…”

Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

“…The processing of the difference between the measurements and model predictions, used as fault indicators, was carried out through the application of an exponentially weighted moving average (EWMA) monitoring chart [6]. Another statistical approach is based on labeling faults at over 100 PV sites in the United States, by the use of machine learning (ML) algorithms and hidden Markov modeling (HMM), which allow the labeling of historical behaviors without a manual setting of threshold [11]. Chine et al proposed a model for the detection of faulty modules in a string, faulty string, faulty inverter and false alarm, a group of faults which include partial shading, the ageing of PVs and inverter MPPT errors.…”