Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management. Issue 10 (26th November 2021)

• Record Type: Journal Article
• Authors: Black, Innes Murdo; Richmond, Mark; Kolios, Athanasios
• Is Part Of: International journal of sustainable energy. Volume 40:Issue 10(2021)
• Page Start: 923
• Page End: 946
• Publication Date: 2021-11-26
• Subjects: Condition monitoring -- SCADA -- artificial intelligence -- machine-learning -- supervised learning; Solar energy -- Periodicals; Renewable energy sources -- Periodicals; 621.4705
• DOI: 10.1080/14786451.2021.1890736
• Languages: English
• ISSNs: 1478-6451

Abstract

ABSTRACT: Information is key. Offshore wind farms are installed with supervisory control and data acquisition systems (SCADA) gathering valuable information. Determining the precise condition of an asset is essential on achieving the expected operational lifetime and efficiency. Equipment fault detection is necessary to achieve this. This paper presents a systematic literature review of machine learning methods applied to condition monitoring systems, using both vibration information and SCADA data together. Starting with conventional methods using vibration models, such as Fast-Fourier transforms to five prominent supervised learning regression models; Artificial neural network, support vector regression, Bayesian network, random forest and K-nearest neighbour. This review specifically looks at how conventional vibration data can be combined with SCADA data to determine the assets condition.