Abstract
This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain.
Highlights
This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines
Recent developments in sensors and signal processing systems, big data management, machine learning (ML) and improvements in computational capabilities have opened-up opportunities for integrated and in-depth Condition monitoring (CM) analytics, where different types of data can facilitate informed, reliable, cost-effective and robust decisionmaking in CM
Empirical Mode Decomposition (EMD) is another important timefrequency method used for signal decomposition that finds Intrinsic Mode Functions (IMFs) of different frequencies that sum to obtain the original signal
Summary
CM of wind turbine is an integral part of O&M, where operations include the management, monitoring and high-level onshore control of the wind farm site, while maintenance covers interventions required to upkeep the installation. The ML model selection step is significant as it is the core functionality that learns from past data and generalizes into the future Such models have been used for different tasks, including classification, regression, anomaly detection, synthesis and sampling, imputation of missing values, denoising, density estimation and many others [16]. SVMs are often used in fault detection and CM [22], [23], and for complex data sets in general [25] They perform linear/non-linear classification or regression by finding decision boundary hyperplanes that best separate classes of instances, i.e. by leaving the widest possible margin to the instances closest to the margin (see Fig. 4).
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