Optimizing PV Array Performance: A2 LSTM for Anomaly Detection and Predictive Maintenance based on Machine Learning

Conference paper


Hamza, A., Ali, Z., Dudley, S. and Saleem, K. 2025. Optimizing PV Array Performance: A2 LSTM for Anomaly Detection and Predictive Maintenance based on Machine Learning. 2024 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE. https://doi.org/10.1109/ECCE55643.2024.10861733
AuthorsHamza, A., Ali, Z., Dudley, S. and Saleem, K.
TypeConference paper
Abstract

Photovoltaic (PV) energy is considered one of the most promising renewable sources. Detecting and monitoring faults in PV systems ensures optimal efficiency and prevents safety and hazards. Predictive maintenance (PdM) is the prominent anomaly prediction strategy that predicts health conditions with machine learning (ML) algorithms. However, existing algorithms overlook the importance of attribute consideration and fail to account for temporal dependence in final results. To address such issues, this paper proposes the implementation of Attribute Attention (A2)-based Long short-term memory (LSTM) for general PdM framework based on clustering and anomaly detection in PV array data. The A2-LSTM model is complemented by an unsupervised K Means clustering technique to identify patterns within the data. The attention mechanism in the attribute attention-based LSTM model is used to identify the most relevant attributes in PV array electrical data for each cluster, allowing the model to focus on the information that is most pertinent to predicting the behavior of the PV arrays within that cluster. The results indicate that the proposed model identified anomalies in the predicted data of the PV array more accurately. The proposed model could help the plant operator perform Remaining Useful Life (RUL) for PdM to carry out PV array maintenance. To the best of our knowledge, the A2 method is not been used for the PdM problem of PV plants.

Year2025
Conference2024 IEEE Energy Conversion Congress and Exposition (ECCE)
PublisherIEEE
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Online10 Feb 2025
Publication process dates
Completed24 Oct 2024
Deposited22 Apr 2025
Accepted21 May 2024
Journal citationpp. 1681-1688
ISSN2329-3748
2329-3721
Book title2024 IEEE Energy Conversion Congress and Exposition (ECCE)
ISBN979-8-3503-7606-7
979-8-3503-7605-0
979-8-3503-7607-4
Digital Object Identifier (DOI)https://doi.org/10.1109/ECCE55643.2024.10861733
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/10860271/proceeding
Copyright holder© 2025 IEEE
Additional information

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Accepted author manuscript
ECCE_Final_paper_submission__PdM_paper_.pdf
License: All rights reserved
File access level: Anyone

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