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

Michael W. Hopwood, Lekha Patel and Thushara Gunda
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Michael W. Hopwood: Sandia National Laboratories, Albuquerque, NM 87123, USA
Lekha Patel: Sandia National Laboratories, Albuquerque, NM 87123, USA
Thushara Gunda: Sandia National Laboratories, Albuquerque, NM 87123, USA

Energies, 2022, vol. 15, issue 14, 1-12

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.

Keywords: photovoltaics; failure detection; hidden Markov model; unsupervised statistical learning; classification; operations and maintenance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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