Failure Mode and Effects Analysis of CIGS Thin Film PV Modules Using Thermography Analysis and IV Measurements

Ibrahim, Khaled H.; Eltuhamy, Reham A.; Rady, Mohamed; Mahmoud, Haitham A.

doi:10.15866/irecon.v9i1.19350

Cited by 1 publication

(5 citation statements)

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“…IRT imaging and I-V measurements were conducted three times for each module to reduce measurement uncertainty. In [ 3 , 38 ], the present authors have published a detailed set of IR pictures and I-V curves. Figure 2 shows an example of IRT image and I-V curve for a PV module with Type-B fault.…”

Section: Collection Of Input Datasetsmentioning

confidence: 99%

“…The process of features extraction was carried out using the MATLAB platform. Detailed description of these features is reported in [ 3 , 38 ]. The ratio of operating voltage, current, power, fill factor, and efficiency are all features of the I-V characteristic curve.…”

Section: Features Extraction Techniquesmentioning

confidence: 99%

“…The mean, standard deviation, skewness, and kurtosis are all statistical properties of an IRT image. The capability of employing I-V measurements and statistical aspects of IR images for CIGS module fault classification is discussed in [ 3 ]. Statistical features and I-V measurements are used to generate a generic classification matrix for fault identification and diagnosis.…”

Section: Features Extraction Techniquesmentioning

confidence: 99%

“…A general classification matrix (GCM) was proposed in [ 3 , 38 ] for classification of faults. It has been shown that mathematical parameter-based feature extraction exhibited a higher capability in classification than statistical feature extraction and electrical measurement-based feature extraction.…”

Section: Anfis Fault Classification Techniquementioning

confidence: 99%

“…Several techniques have been proposed for fault detection and diagnosis in PV modules; examples of these techniques include visual examination, infrared thermography (IRT), electroluminescence (EL), photoluminescence (PL), and measurements of static characteristics [ 2 ]. Based on the previously mentioned PV fault detection techniques, different types of PV module defects have been addressed, such as cracks, delamination, burn marks, potential-induced degradation (PID), soiling, snail trails, hotspots, faulty interconnections, back sheet defects, corrosion, and shunts [ 3 , 4 , 5 ]. The continuous, safe, reliable, and effective functioning of PV systems depends on the development of automatic diagnosis and classification techniques for PV monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme

Eltuhamy

Rady

Almatrafi

et al. 2023

Sensors

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The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module’s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.

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Section: Collection Of Input Datasetsmentioning

confidence: 99%

Section: Features Extraction Techniquesmentioning

confidence: 99%

Section: Features Extraction Techniquesmentioning

confidence: 99%

Section: Anfis Fault Classification Techniquementioning

confidence: 99%