Abstract
Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.
Highlights
The problems associated with global warming have led the scientific community to search for new sources of energy, with renewable resources emerging as a promising alternative to conventional energy from fossil fuels
Studies describing the architecture of machine learning (ML) models, classification methods, as well as Artificial intelligence (AI) techniques applied to early detection of failures in wind turbine (WT); Studies detailing which WT components are most frequently submitted to condition monitoring (CM) techniques; Studies detailing the variables acquired by Supervisory Control and Data Acquisition (SCADA) that are most often used in the training of ML models; Studies using SCADA system variables for CM and WTs early fault detection
The technique used which included ML, fuzzy logic, Bayesian networks, data mining, artificial neural networks (ANNs), artificial vision, support vector machines (SVMs), generative adversarial nets (GANs), decision tree, deep learning (DL), extreme learning machine (ELM), Gaussian process, random forest, etc.; Computer tools used in training WT fault prediction models which included MATLAB®, Python®, R® and alike, such as LIBSVM, Azure Machine Learning Studio, and LabVIEW® ; Prediction model evaluation metricswhich included R-square, mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and confusion matrix
Summary
The problems associated with global warming have led the scientific community to search for new sources of energy, with renewable resources emerging as a promising alternative to conventional energy from fossil fuels. The scientific literature has proposed high costs of operation and maintenance (O&M) activities. 35% of of the the total total cost cost of of power power generation These cost percentages could be increased by recurring failures in the different components. (CMSs) are are used used to to provide provide data data on on different different components components of of aa based on different types of information (parameters or signals) obtained from many types. Using appropriate intervals of 1 to 10 min using their SCADA systems [14], generating rich historical data. An SLR is performed with the aim of assessing the methods and algorithms most commonly in CM and predicting WTs failures from SCADA data. The proposed methodology for the SLR used in CM and predicting WTs failures from SCADA data.
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