Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri, Majdi; Trabelsi, Mohamed; Nounou, Hazem; Nounou, Mohamed

doi:10.1109/access.2021.3110947

Cited by 77 publications

(27 citation statements)

References 157 publications

(146 reference statements)

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“…The article emphasizes the necessity for solar PV panel to increase efficiency to meet climate targets. It also discusses cost-cutting, technological developments, and the necessity to prepare power systems for increased solar PV panel penetration [66]. Among the findings:…”

Section: The Benefits Of Fault Identification In Pv Panelsmentioning

confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

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Section: The Benefits Of Fault Identification In Pv Panelsmentioning

confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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“…Photovoltaic (PV) system reliability and durability have attracted considerable attention in recent years from the PV industry as well as investors, as PV defects and faults have become an essential factor that has been scientifically proven to reduce power generation from PV assets and cause complications. Previous research on PV fault detection and classification has greatly improved our understanding of PV partial shading conditions 1 , 2 , faulty power converters/inverters 3 , and dead-state battery storage 4 . Although we have gained some insight into the severity of hotspots 5 , cracks 6 , and potential induced degradation (PID) 7 , 8 , we are still far from understanding how severe the defects are.…”

Section: Introductionmentioning

confidence: 99%

Field study on the severity of photovoltaic potential induced degradation

Badran

Dhimish

2022

Sci Rep

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Photovoltaic (PV) systems can be affected by different types of defects, faults, and mismatching conditions. A severe problem in PV systems has arisen in the last couple of years, known as potential-induced degradation (PID). During the early installation stage of the PV system, the PID may not be noticed because it appears over time (months or years). As time passes, it becomes more apparent since the output power may drop dramatically. We studied PV modules over the course of three years that were affected by PID. An electroluminescent and thermal imaging technique helped discover the PID. PID appeared in PV modules after being in different fields for 4–8 months, resulting in a 27–39% drop in power. An anti-PID box was fitted during the second year of the PV operation to recover the PID. Accordingly, it has stabilized the power degradation, but it could not restore the performance of the affected PID modules as compared with healthy/non-PID modules.

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“…ML and in particular Deep Learning (DL) algorithms have been developed for FDD of other fault types in solar plants (Mellit, Tina, & Kalogirou, 2018;Haque, Bharath, Khan, Khan, & Jaffery, 2019;Mansouri, Trabelsi, Nounou, & Nounou, 2021;Pillai & Rajasekar, 2018;Triki-Lahiani, Abdelghani, & Slama-Belkhodja, 2018). However, only very few of those algorithms have reached commercial deployment.…”

Section: Introductionmentioning

confidence: 99%

Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants

Zgraggen¹,

Guo²,

Notaristefano³

et al. 2022

PHM_CONF

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One of the main challenges for fault detection in commercial fleets of machines is the lack of annotated data from the faulty condition. The use of supervised algorithms for anomaly detection or fault diagnosis is often unrealistic in this case. One approach to overcome this challenge is to augment the available normal data by generating synthetic anomalous data that represents faulty conditions. In this paper we apply this approach to the detection of faults in the tracking system of solar panels in utility-scale photovoltaic (PV) power plants. We develop a physical model in order to augment the training data for a deep convolutional neural network. We show that the physics informed learning algorithm is capable of detecting faults in an accurate and robust manner under diverse weather conditions, outperforming a purely data-driven approach. Developing and testing the algorithm with real operational data ensures its efficient deployment for PV power plants that are monitored at string level. This in turn enables the early detection of root causes for power losses, thereby contributing to the accelerated adoption of solar energy at utility scale.

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Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Cited by 77 publications

References 157 publications

An Effective Evaluation on Fault Detection in Solar Panels

An Effective Evaluation on Fault Detection in Solar Panels

Field study on the severity of photovoltaic potential induced degradation

Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants

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