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Abstract:Nuisance faults are caused by weather events, which result in solar farms being disconnected from the electricity grid. This results in long stretches of downtime for troubleshooting as data are mined manually for possible fault causes, and consequently, cost thousands of dollars in lost revenue and maintenance. This paper proposes a novel fault detection technique to identify nuisance faults in solar farms. To initialize the design process, a weather model and solar farm model are designed to generate both training and testing data. Through an iterative design process, a fine tree model with a classification accuracy of 96.7% is developed. The proposed model is successfully implemented and tested in real-time through a server and web interface. The testbed is capable of streaming in data from a separate source, which emulates a supervisory control and data acquisition (SCADA) or weather station, then classifies the data in real-time and displays the output on another computer (which imitates an operator control room).Keywords: nuisance faults; photovoltaic farms; machine learning; fine tree classifier; supervisory control and data acquisition (SCADA)

Many of the neighbors say good riddance. The development that was once the centerpiece of southwest Houston's Westbury area has fallen into such sad disrepair, it now resembles an abandoned movie set that neighbors decry as a dangerous nuisance. A distinctive fountain that stood at the center of Italian-style piazza is now a busted reminder of what used to be a distinctively quaint community of upscale apartments built above boutique shops and restaurants. 2b1af7f3a8