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Author

Long D. Ha

Bio: Long D. Ha is an academic researcher from Commissariat à l'énergie atomique et aux énergies alternatives. The author has contributed to research in topics: Orthophoto. The author has an hindex of 1, co-authored 1 publications receiving 44 citations.
Topics: Orthophoto

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors proposed two different techniques for advanced inspection mapping of PV plants; aerial triangulation and terrestrial georeferencing, which were tested in two grid-connected PV systems, of a total installed power of 70.2 KWp.

78 citations


Cited by
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Journal ArticleDOI
TL;DR: The types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA).
Abstract: Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

308 citations

Journal ArticleDOI
TL;DR: In this paper, a review of the photovoltaic systems, where the design, operation and maintenance are the key points of these systems, is presented. But, the authors do not focus on the operation of the PV systems.

195 citations

Journal ArticleDOI
TL;DR: A novel method for faults detection in photovoltaic panels employing a thermographic camera embedded in an unmanned aerial vehicle and two novels region-based convolutional neural networks are unified to generate a robust detection structure is proposed.

104 citations

Journal ArticleDOI
TL;DR: This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles that significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions.
Abstract: The efficient condition monitoring and accurate module defect detection in large-scale photovoltaic (PV) farms demand for novel inspection method and analysis tools. This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles. The convolutional neural network is used in the machine learning process to classify various forms of module defects. Such a supervised learning process can extract a range of deep features of operating PV modules. It significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions. The proposed algorithmic solution is extensively evaluated from different aspects, and the numerical result clearly demonstrates its effectiveness for efficient defect detection of PV modules.

97 citations

Journal ArticleDOI
TL;DR: Results demonstrated the superiority of the MC-NFC over the ANN-classifier and suggest that further improvements in terms of classification accuracy can be achieved by the proposed classification algorithm; furthermore faults can be also considered for discrimination.

81 citations