Advanced Fault Diagnosis and Condition Monitoring Schemes for Solar PV Systems

Gawre, Suresh Kumar

doi:10.1007/978-981-19-0979-5_3

Cited by 8 publications

(2 citation statements)

References 105 publications

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“…New methods are needed to effectively quantify cost savings that could arise from predictive maintenance activities. However, solutions will only be successful if they go beyond looking at failure signatures for individual assets (current state of the art; [34]) to consider signatures at the site level (where data are often analyzed). The use of PI as the sole input into the PV-HMM reduces the data requirements for the analysis.…”

Section: Resultsmentioning

confidence: 99%

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

2022

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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.

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Section: Resultsmentioning

confidence: 99%

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

2022

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“…Much progress has been made in the study of PV systems, especially in terms of efficiency, cost, and obtaining maximum available power from PV cells. Nevertheless, PV systems are often subject to a variety of types of faults, which can seriously affect the safe operation and conversion efficiency of the systems [3]. The Energy Audit Report shows an annual energy loss of up to 18.9% due to PV system faults in the UK alone [4].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Yuan

Xiong

2022

Energies

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Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

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TinyML Model for Fault Classification of Photovoltaic Modules Based on Visible Images

Ksira,

Blasuttigh,

Mellit

et al. 2024

Lecture Notes in Networks and Systems

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Advanced Fault Diagnosis and Condition Monitoring Schemes for Solar PV Systems

Cited by 8 publications

References 105 publications

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

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

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

TinyML Model for Fault Classification of Photovoltaic Modules Based on Visible Images

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