Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

Navid, Qamar; Hassan, Ahmed; Fardoun, Abbas A.; Ramzan, Rashad; Alraeesi, Abdulrahman

doi:10.3390/su13041629

Cited by 17 publications

(6 citation statements)

References 138 publications

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“…This method has been used as a suitable method for research in most studies. Including research: (Guney, 2016), Solar power and application methods; (Holopainen et al, 2016), Feasibility studies of energy retrofits-case studies of Nearly Zero-Energy Building renovation; (Teimuri et al, 2017), Polymer Solar Cell: A New Instrument for Solar Energy Conversion to Electricity Energy; (Fatahi, 2018), Valuation of Solar Power Generating Potential in Iran Desert Areas, and finally, (Navid et al, 2021), Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review. The researchers mentioned confirmed the efficiency of the models used in the present study.…”

Section: Discussion and Validation Of Research Resultsmentioning

confidence: 99%

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Safarianzengir

Fatahi²,

Doumari

2022

Front. Energy Res.

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Attention to climatic and environmental potentials for exploitation and meeting human needs has been considered for many years. But the optimal and correct use of this potential requires accurate knowledge and subsequent careful planning. Today, the use of climate and productive energy from the environment is more of a concern for researchers. This investigation aims to appraise the feasibility of establishing solar thermal power plants (solar panels) by examining eight climatic and atmospheric parameters in the environment based on fuzzy logic in northwestern Iran. In this study, the weather datum of 22 Urban Climate Synoptic stations were prepared to evaluate the climatic conditions for the feasibility of establishing a thermal solar power plant for sustainable development using fuzzy logic in the north-west of Iran for each synoptic station during a statistical period of 31 years (1990–2020). This research used eight climatic parameters: average max temperature, average temperature, average min relative moisture, mean relative moisture, total monthly precipitation, and a daily average of Sunny hours, altitude, and wind speed in this systemic model. MATLAB software was used to combine the input data from fuzzy logic and ANFIS Adaptive Neural Network. The climate zoning map for establishing a thermal solar power plant with the scores obtained for each of the parameters and finally the final map was drawn using the ArcGIS. The results show that the fuzzy logic method showed a great variety of options for the establishment of solar thermal power plants using climatic parameters. In total, after obtaining the final rating for each of the 22 stations, Bonab station, with a coefficient score of (0.544–0.589), and Miyaneh station with a score (0.543–0.577), located in East Azarbaijan Province, in the north-west of Iran are appropriate for the establishment of a solar power plant. The Northwest of Iran is suitable to meet the needs of various energies, including domestic electricity, due to the potential stemming from its favorable climatic conditions climate; therefore, it is necessary to take steady steps to develop this important industry. Also, the accuracy of MATLAB and ArcGIS software with a high level of 0.96% confidence in finding a suitable place for the establishment of solar power stations was confirmed. According to the final results obtained from the integration and fuzzification of 8 climatic parameters and zoning of suitable and unsuitable areas for prioritization, Ardabil station with a score of 0.345% in the priority of inconvenient location, and Bonab station with a score of 0.589% in the priority of where the best places were for the establishment of solar power stations. Considering the findings of the present study, the central region and distant locales in mountainous areas were suitable places for the establishment of solar panels. The valuable results of the present study can be given more attention by the sectors that use a lot of sustainable energy. The results of this research in the field of sustainable energy production from the environment are more in the spotlight.

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Section: Discussion and Validation Of Research Resultsmentioning

confidence: 99%

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Safarianzengir

Fatahi²,

Doumari

2022

Front. Energy Res.

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“…Such methods use electrical signatures for fault detection in PV systems and they can be utilised for online monitoring and diagnostics, even for large-scale PV installations. One of the main challenges is the difficulty to locate the fault root cause [ 91 ] and the exact position of the problem/affected module [ 92 ].…”

Section: Failure Modes In Pv Systems and Existing Approachesmentioning

confidence: 99%

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Michail,

Livera,

Tziolis

et al. 2024

Heliyon

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“…Especially, due to the advantages of mobility, flexibility, programmability and large-area coverage, the unmanned aerial vehicles (UAVs) have been widely employed in the PV plant to capture moving images of various PV modules in the distributed power plants. Through intelligent image analysis, we can inspect specific PV faults in an efficient way, which is key to improve the operation and maintenance (O&M) level of the power plant ( Atsu et al, 2020 ; Chen et al, 2020 ; Abubakar et al, 2021 ; Navid et al, 2021 ; Cui et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

Zhang,

Ge,

Wang

et al. 2024

Front. Neurorobot.

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With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.

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Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

Cited by 17 publications

References 138 publications

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

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