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Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

13 pagesPublished: September 27, 2019

Abstract

Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.
For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

Keyphrases: Classification, detection, electrical energy, fuel energy, Localization, Machine Learning (ML), non-renewable sources, solar energy

In: Quan Yuan, Yan Shi, Les Miller, Gordon Lee, Gongzhu Hu and Takaaki Goto (editors). Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering, vol 63, pages 21--33

Links:
BibTeX entry
@inproceedings{CAINE2019:Machine_Learning_as_an,
  author    = {Masoud Alajmi and Sultan Aljahdali and Sultan Alsaheel and Mohammed Fattah and Mohammed Alshehri},
  title     = {Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays},
  booktitle = {Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering},
  editor    = {Quan Yuan and Yan Shi and Les Miller and Gordon Lee and Gongzhu Hu and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {63},
  pages     = {21--33},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/JTbl},
  doi       = {10.29007/34bz}}
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