MBO- S²-Serpentnet: A Novel Optimized Intelligent Fault Detection and Performance Monitoring Framework for Solar Energy Conversion Systems

P Karuppusamy; SathyaBama M; Mary A G Ezhil; R. Anuja

doi:10.55003/ETH.430310

Authors

P Karuppusamy Erode Sengunthar Engineering College, India
SathyaBama M Erode Sengunthar Engineering Collegel, India
Mary A G Ezhil Faculty of Electrical Engineering, Arunachala College of Engineering for Women, India
R. Anuja Faculty of Electrical Engineering, Arunachala College of Engineering for Women, India

DOI:

https://doi.org/10.55003/ETH.430310

Keywords:

PV system, S²-SeRpEntNet, MBO, RBFNN-MPPT, DC-DC boost converter

Abstract

A fault detection and monitoring mechanism is required to ensure reliable and consistent performance of a solar energy conversion system throughout its operational lifetime. The proposed framework uses RBFNN controller for the Maximum Power Point Tracking (MPPT) system combined with a DC-DC boost converter. The RBFNN controller allow for maximum energy extraction from PV systems with varying environmental conditions. An intelligent fault detection mechanism continuously monitor the electrical parameters of the PV module including voltage and current output to increase the dependability of the structure and provide a level of operational safety. A Deep Learning (DL) mechanism allow for optimising the network using Monarch Butterfly optimisation (MBO). The MBO process enhance the accuracy of fault classification of the proposed framework and the S²-SeRpEntNet enable robust identification of both early stages and critical stages of a fault during the lifetime of each fault. Preprocessing the data, performing feature extraction and normalizing are other steps that were taken to develop the accuracy of the predictions. The suggested system is implemented in MATLAB and achieved a higher accuracy of 0.99, precision of 0.98, Recall of 0.97 and F1-Score of 0.98. Hence, it deals with significantly enhanced energy efficiency, decreased power losses and improved long-term performance of solar energy conversion systems.

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