Abstract
In the proposed work, the development of a condition monitoring system for a stand-alone photovoltaic (PV) system is presented. In order to meet the major global issues such as depletion of fossil fuels, increase in the greenhouse gas emissions, and the increase in operating and maintenance cost of electrical installations, there is a great need for highly reliable PV systems in different applications. The main aim of this work is to develop a condition monitoring system for identifying the failures in critical components of the stand-alone PV system. PV system consists of a PV panel, power electronic converter, and the rechargeable valve regulated lead acid (VRLA) battery. Faults in PV panel can be easily detected from the visual inspection and variation in V-I characteristics. The power electronic converter and VRLA battery are the most critical and life-limiting components of the stand-alone PV-based system and therefore their early failures need to be detected, which itself is a challenge. Early detection of precursors of failures in these components would allow their condition-based maintenance and provide sufficient time for the controlled shutdown of the PV system, thereby reducing the costs of outage time and repair of PV system in safety critical applications. In the proposed work, the condition monitoring of both VRLA battery and power electronic converter is presented in two sections. In Section 1, an intelligent scheme for predictive fault diagnosis in VRLA battery based on infrared (IR) thermal imaging and the fuzzy algorithm is presented for scheduling its preventive maintenance. IR images of pristine and aged VRLA batteries in uninterrupted power supply applications are acquired using an IR camera at different discharging cycles. Image processing of IR images is performed for the detection of faults. In order to intelligently classify the faults a fuzzy inference system (FIS) is developed. The proposed scheme for automatic diagnosis and classification of faults in VRLA battery is implemented using LabVIEW 2015 software. Based on the occurrence of major faults in VRLA battery, an alert signal is sent to intended users at both onsite and remote locations. In Section 2, the online technique for condition-based maintenance of power electronic converter due to parametric degradation of its high failure rate components is presented. The condition monitoring algorithms are implemented using the intelligent embeddable system for determining the real-time in-circuit degradation in failure signature parameters of the above components at accelerated aging conditions. The proposed real-time in-circuit embeddable condition monitoring and maintenance techniques will eliminate the need for expensive test bed hardware and measurement systems that were used in earlier studies.
Published Version
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