Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

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.

Prognostics and Health Monitoring Methodologies and Approaches: A Review

“…The survey is supported by an extensive list for up-to-date references. The present survey is intended to supplement many excellent reviews that address the prognostics of devices of contemporary vital importance [85,86,87,88,89,90,91,92,93,94].…”

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

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A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

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