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Comprehensive Library of Photovoltaic Functions on Python for Academic and Educational Purposes

Description
This comprehensive library of photovoltaic functions (PVSimLib) is an attempt to help the photovoltaics community to solve one of its long-lasting problems, the lack of a simple, flexible and comprehensive tool that can be used for photovoltaic calculations. The library contains a collection of useful functions and detailed examples that…

This comprehensive library of photovoltaic functions (PVSimLib) is an attempt to help the photovoltaics community to solve one of its long-lasting problems, the lack of a simple, flexible and comprehensive tool that can be used for photovoltaic calculations. The library contains a collection of useful functions and detailed examples that will show the user how to take advantage of the resources present in this library. The results will show how in combination with other Python libraries (Matplotlib), this library becomes a powerful tool for anyone involved in solar power.
ContributorsReguera, Pedro (Author) / Honsberg, Christiana (Thesis advisor) / King, Richard (Committee member) / Bowden, Stuart (Committee member) / Arizona State University (Publisher)
Created2018
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Active Learning for Incipient Fault Detection

Description
Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault which is very criticalin an electrical cable but very difficult…

Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault which is very criticalin an electrical cable but very difficult to spot is incipient fault. These momentary faults are observed for very short periods however, if it persists, this would lead to consequences like insulation wear-off and even, power outages. With the advent of machine learning in the power systems fraternity, this paper also uses a new and updated Active Learning algorithm to detect incipient fault data from a simulated test case. The objective of the paper is to detect the fault data accurately using this new and precise method. For purposes of data collection and training of the model, MATLAB Simulink and Python programming has been used respectively.
ContributorsGhosh, Kinjal (Author) / Weng, Yang (Thesis advisor) / Pal, Anamitra (Committee member) / Hedman, Mojdeh K (Committee member) / Arizona State University (Publisher)
Created2023