Quantum Machine Learning based Wind Turbine Condition Monitoring - State of the Art and Future Prospects

Aug 2, 2026

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Wind energy grows rapidly worldwide, and reliable wind turbine operation depends on accurate condition monitoring and fault diagnosis. Conventional machine-learning approaches face obstacles including complex feature extraction, limited generalization on large, high-dimensional datasets, and high computational cost. Quantum computing enables quantum machine learning to combine quantum and ML advantages, potentially exceeding classical performance. This review surveys state-of-the-art ML-based CM, quantum computing fundamentals, and QML methods for feature extraction, fault detection, regression, and predictive maintenance. It compares accuracy with optimized classical methods, discusses practical challenges, and outlines future research directions to improve reliability and efficiency of wind power systems.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

AccessFree to download

Introduction

Wind energy growth and turbine importance

Environmental challenges for reliable operation

Evolution of condition monitoring methods

Sensor and data processing advances

Quantum machine learning for wind turbine condition monitoring

Quantum Machine Learning based Wind Turbine Condition Monitoring: State of the Art and Future Prospects Zhefeng Zhang, Yueqi Wu, Xiandong Ma∗ School of Engineering, Lancaster University, Lancaster, LA1 4YW, United Kingdom Abstract Wind energy, as a popular renewable resource, has gained extensive development and application in recent decades. Effective condition monitoring and fault diagnosis are crucial for ensuring the reliable operation of wind turbines. While conventional machine learning methods have been widely used in wind turbine condition monitoring, these approaches often face challenges such as complex feature extraction, limited model generalization, and high computational costs when dealing with large-scale, high-dimensional, and complex datasets. The emergence of quantum computing has opened up a new paradigm of machine learning algorithms. Quantum machine learning combines the advantages of quantum computing and machine learning, with the potential to surpass classical computational capabilities. This paper firstly reviews applications and limitations of the state-of-the-art machine learning-based condition monitoring techniques for wind turbines. It then reviews the fundamentals of quantum computing, quantum machine learning algorithms and their applications, covering quantum-based feature extraction, classification and regression for fault detection and the use of quantum neural networks for predictive maintenance. Through comparison, it is observed that quantum machine learning methods, even without extensive optimization, can achieve accuracy levels comparable to those of optimized conventional machine learning approaches. The challenges of applying quantum machine learning are also addressed, along with the future research and development prospects. The objective of this review is to fill a gap in the published literature by providing a new paradigm approach for wind turbine condition monitoring. By promoting quantum machine learning in this field, the reliability and efficiency of wind power systems are ultimately sought to be enhanced. Keywords: Condition Monitoring (CM), Wind Turbine (WT), Machine Learning (ML), Deep Learning (DL), Quantum Machine Learning (QML), Fault Detection, Fault Diagnosis, Fault Prognosis Nomenclature ALWM-ResNet Loss-Weighted Meta-Residual Network ANFIS Adaptive Neuro-Fuzzy Inference System CBAN Convolutional Block Attention Module CG-CNN Correlation-Graph-CNN CWT Continuous Wavelet Transform DAN Deep Adaptive Networks DeepFedWT Federated Deep Learning Framework ∗ Corresponding author Email address: [xiandong.ma@lancaster.ac.uk](xiandong.ma@lancaster.ac.uk) (Xiandong Ma) DRDN Deep Residual Deformable Network DTL Deep Transfer Learning GAT Graph Attention Network GL Graph Learning HA Hybrid Attention IPCA Incremental Principal Component Analysis LMMD Local Maximum Mean Discrepancy MAML Model-Agnostic Meta-Learning MC Multi-Channel MCA Multi-Channel Attention MDA Mixture Discriminant Analysis MIP-YOLO Multivariate Information Perception You Look Only Once MSCNN Multi-Scale Convolutional Neural Network MSRAN Multi-Scale Residual Attention Network MSTFAN Multidirectional Spatial-Temporal Feature Aggregation Networks PCC Pearson Correlation Coefficient SETCN-MVC Spectrum-Embedded Temporal Convolutional Network Multivariate Coefficient of Variation SMOTE Synthetic Minority Oversampling Technique STAGNN Spatial–Temporal Autocorrelation Graph Neural Network WPT Wavelet Packet Transformation 1. Introduction Wind energy, as a clean and renewable source, has gained widespread attention globally due to its environmental and economic benefits. According to the Global Wind Energy Council, by the end of 2023, the global installed wind power capacity exceeded 906 GW, showing a significant growth compared to the past decades [1] . Figure 1 shows a continuing trend of growth in installed wind power capacity. As the key part in wind energy convers

FAQs

Why is wind turbine condition monitoring critical?

It ensures stable and reliable turbine operation, reducing operation and maintenance costs and supporting equipment lifetime by detecting and diagnosing faults early.

What limitations affect conventional machine learning in wind turbine monitoring?

Conventional methods often struggle with complex feature extraction, limited model generalization on large-scale high-dimensional data, and high computational cost.

How does quantum machine learning help with fault detection and predictive maintenance?

Quantum machine learning leverages quantum-based feature extraction and quantum algorithms for classification/regression, and it can use quantum neural networks to support predictive maintenance.

Aug 2, 2026

Upload by Riley