ArticleScientific reports2024
Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hybrid fuzzy C-means and deep learning framework for intelligent fault classification in solar PV systems.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence-based fault classification on photovoltaic plants using a low-cost open-source IoT system.Scientific reports · 2025Article
- Diagnosis of misalignment faults using the DTCWT-RCMFDE and LSSVM algorithms.Scientific reports · 2025Article
- Assessment of compressive strength of eco-concrete reinforced using machine learning tools.Scientific reports · 2025Article
- Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria.Scientific reports · 2025Article
- Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques.Scientific reports · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis. The CNN input consists of two-dimensional scalograms generated using Continuous Wavelet Transform (CWT). The proposed diagnosis technique demonstrated superior performance compared to benchmark architectures, namely MobileNet, NASNetMobile, and InceptionV3, achieving higher test accuracies and lower losses on binary and multi-fault classification tasks on balanced, unbalanced, and noisy datasets. Further, a quantitative comparison is conducted with similar recent studies. The obtained results indicate good performance and high reliability of the proposed fault diagnosis method.
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