Data-driven fault detection in renewable energy systems using hybrid machine learning techniques
A. GOPALAKRUSHNA, Neeraja B, K. VISALA, Kiran Kumar KOMMINENI, Gokul TALELE, Umakant Bhaskar GOHATRE
Abstract. The explosive growth of grid-connected renewable energy systems (RES) has increased the complexity of the operation of the modern power infrastructure, making the detection of the faults reliably an inevitable condition of the stable functioning and safety. Traditional single-algorithm and rule-based monitoring systems are not sufficiently flexible or discriminatory to distinguish between the many varieties of faults that occur in photovoltaic (PV) arrays, wind turbines, battery management systems, and grid-tie inverters. The paper suggests a new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner. Publicly available SCADA and lab bench data were used to create a curated multi-source dataset of 8,400 labelled samples to represent five operational states. The proposed framework achieved an accuracy of 97.8, a macro-averaged F1-score of 97.1, and a Matthews Correlation Coefficient (MCC) of 0.972, outperforming all the compared baseline methods at least by 3.3 percentage points. The findings verify the effectiveness of the hybrid stacking paradigm in identifying faults in real-time and multiple classes in heterogeneous renewable energy settings.
Keywords
Renewable Energy Systems, Fault Detection, Machine Learning, CNN-BiLSTM, XGBoost, Stacking Ensemble, Photovoltaic, SCADA
Published online 8/15/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: A. GOPALAKRUSHNA, Neeraja B, K. VISALA, Kiran Kumar KOMMINENI, Gokul TALELE, Umakant Bhaskar GOHATRE, Data-driven fault detection in renewable energy systems using hybrid machine learning techniques, Materials Research Proceedings, Vol. 71, pp 1-8, 2026
DOI: https://doi.org/10.21741/97816449042312-1
The article was published as article 1 of the book AI-Driven Innovations in Mechanical, Manufacturing, and Energy Engineering
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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