Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

Roy, Sukanta; Tufail, Shahid; Tariq, Mohd; Sarwat, Arif

doi:10.1109/tr.2024.3359540

Title: Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

Journal Article · 13 March 2024 · IEEE Transactions on Reliability

DOI: https://doi.org/10.1109/tr.2024.3359540 · OSTI ID:2406992

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Florida International University, Miami, FL (United States); Florida International University
Florida International University, Miami, FL (United States)

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

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Research Organization:: University of Maryland, College Park, MD (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0009349

OSTI ID:: 2406992

Journal Information:: IEEE Transactions on Reliability, Journal Name: IEEE Transactions on Reliability Journal Issue: 3 Vol. 73; ISSN 0018-9529

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Title: Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

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