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This paper presents a high gain modified three-port dual boost single-ended primary-inductor converter (SEPIC) converter operated hybrid photovoltaic (PV)/battery electric vehicle to improve the power transfer capability and efficiency of the power conversion stage. The proposed three-port dual boost high gain SEPIC converter accepts wide voltage range of input then the conventional single stage SEPIC converter. The three-winding high frequency coupling transformer integrates the two input sources and step-up the input voltage. The theoretical and experimental analyses are presented to validate the performance of the proposed modified SEPIC converter in boosting the wide range of input voltage. A 6 kW, 12 to 600 V of experimental model has been developed and tested. The efficiency obtained from the designed model is around 94.11%. K E Y W O R D SDC-DC power conversion, high boost dual input SEPIC converter, hybrid electric vehicle (HEV), hybrid PV/battery, PV array, three-port converter, voltage source inverter (VSI)

Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

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Photovoltaic (PV) solar energy can only be economical if the PV module operates reliably for 25–30 years under field conditions. The PV module and it overall reliability can be radically affected by faults during the manufacturing process, in real field conditions, transportation, and installation. So, there is a need for diagnosing defects in PV modules to improve their reliability. Operating temperature plays the key role for improving the efficiency of PV panels. The temperature within the PV cell unevenly increases because of such defects in the cell. As such, it is very important to monitor the temperature and temperature distribution in PV panels in order to locate such defects. Infrared thermography (IRT) plays a major role in predictive and preventive maintenance of PV panels and can determine the severity of the problem. This article investigates the delamination, snail trails, and bubbled faults of PV panels using digital thermal image analysis and their feature extraction. Real time experiments were conducted, and the test results are presented in this article. Thermal images of panels are captured using a (FLIR) T420bx® thermal imager. The thermal images of panels are analyzed by segmenting the image using the k-means clustering algorithm. Histogram statistical features such as mean, standard deviation, variance, entropy, skew, and kurtosis are extracted from the segmented thermal image. Based on these features, the defects in PV panels are identified with reasonable accuracy.