International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
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Predictive Maintenance in Renewable Energy: A Machine Learning Approach Gaurav Vishwas Chaudhari1, Priyanka Balkrishna Sapte2 ---------------------------------------------------------------------***--------------------------------------------------------------------this knowledge, operators can take proactive measures to Abstract - The fast-growing adoption of renewable energy
avoid potential critical failures and allow for better decision making, extending the asset life and reducing operational costs overall [5], [7].
assessments has created an urgency for streamlined and smart maintenance solutions. Conventional human inspection or appeasing overdue maintenance can wreak havoc on renewable energy resources assets, like wind turbines and solar photovoltaic (PV) systems, to operate reliably and over their intended lifespan. This article assesses the potential of machine learning (ML) techniques to guide predictive maintenance, using datasets to show the feasibility of building predictive maintenance models for solar and wind farms. Our exploratory evaluation of historical data and realtime sensor data from renewable energy assessments attempts to inform and compare supervised ML such as Random Forest, Support Vector Machines, and Gradient Boosting, as well as Long Short-Term Memory (LSTM) networks. We utilized public datasets such as the NREL Wind Turbine SCADA dataset and sandia National Laboratories PV fault detection dataset to evaluate supervised machine learning models along with a deep learning model. We find that ML will generally improve early-stage fault detection and optimization of the PDP, thereby lowering service downtimes and costs.
Wind turbines and photovoltaic (PV) systems all produce massive amounts of data through, supervisory control and data acquisition (SCADA), command/control, and sensor systems and networks. These production data provide an excellent foundation for building intelligent models that learn from behaviors in the past to help predict the future happening of anomalies [1]. This work seeks and investigates how ML techniques can potentially address the challenge documentation and evidence in predictive. We will assess several models, take the opportunity of comparing their effectiveness and learn of successful implementation practices. Our aim is to emphasize how strategies based on data-primitive maintenance principles can serve as a value add to the reliability and economic feasibility of renewable energy projects [8].
2. Literature Review
Key Words: Predictive Maintenance, Renewable Energy,
Over the past decade, machine learning applications in the field of predictive maintenance for renewable energy has been extensively researched. Various studies have examined various machine learning models and their ability to detect and predict faults in wind and solar applications.
Machine Learning, Wind Turbines, Photovoltaic (PV) Systems, Random Forest, Support Vector Machines (SVM), Gradient Boosting, Long Short-Term Memory (LSTM), SCADA Data, Fault Detection, Operational Efficiency, Energy Loss, Maintenance Costs.
For wind turbines, Kusiak and Li [1] were early users of datadriven fault prediction models. They used neural networks and support vector machines (SVM) based on supervisory control and data acquisition (SCADA) data and showed the ability to notice faults in gearboxes and generators. More recently, Zhang et al. [2] used Convolutional Neural Networks (CNNs) to detect blade cracks from image data, demonstrating how computer vision techniques are now incorporated into maintenance diagnostics.
1.INTRODUCTION The change in the world toward sustainable energy has led to the rapid adoption of renewable energy systems, particularly solar and wind. Because the scale and complexity of the systems grow there is a greater need for maintenance strategies to maintain operational performance and limit downtime. Most renewable energy project operations rely on pre-scheduled inspections or reactive maintenance. These past maintenance operations are expensive and cannot prevent unforeseen breakdowns which would otherwise affect energy generation and risk safety [6].
Also, Liu et al. [3] researched Long Short-Term Memory (LSTM) networks to capture temporal dependencies in SCADA data. They indicated high accuracy with predicting anomalies several hours in advance. The implications for their research were the benefits of sequence-aware models for early warning systems.
Predictive maintenance, through artificial intelligence (AI) and machine learning (ML) provides a new way. Predictive maintenance through statistically based ML can use historical and currently observed operational data, in conjunction with sensor data/measurements adopted into equipment, to highlight indications of faults or declines in performance. With
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In solar photovoltaic (PV) systems, Kumar and Mishra [4] created random forest models for classifying inverter faults based on electrical sensor data. They achieved high
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