Deep Fault Analysis and Subset Selection in Solar Power Grids

TL;DR

This paper introduces a deep learning system for fault prediction and optimal generator selection in solar power grids, aiming to improve reliability and reduce costs in developing countries.

Contribution

It presents a novel deep learning approach for fault analysis and subset selection in solar power systems, addressing reliability and cost issues.

Findings

High accuracy in fault prediction

Effective generator subset selection

Potential for real-world application in developing countries

Abstract

Non-availability of reliable and sustainable electric power is a major problem in the developing world. Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others. There also exists various other issues like mis-commitment of power, absence of intelligent fault analysis, congestion, etc. In this paper, we propose a novel deep learning-based system for predicting faults and selecting power generators optimally so as to reduce costs and ensure higher reliability in solar power systems. The results are highly encouraging and they suggest that the approaches proposed in this paper have the potential to be applied successfully in the developing world.