A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

Abstract

The analysis of reliable studies helps to identify the credibility, scope, and limitations of various techniques for condition monitoring of a wind turbine (WT) system’s design and development to reduce the operation and maintenance (O&M) costs of the WT. In this study, recent advancements in data-driven models for condition monitoring and predictive maintenance of wind turbines’ critical components (e.g., bearing, gearbox, generator, blade pitch) are reviewed. We categorize these models according to data-driven procedures, such as data descriptions, data pre-processing, feature extraction and selection, model selection (classification, regression), validation, and decision making. Our findings after reviewing extensive relevant articles suggest that (a) SCADA (supervisory control and data acquisition) data are widely used as they are available at low cost and are extremely practical (due to the 10 min averaging time), but their use is in some sense nonspecific. (b) Unstructured data and pre-processing remain a significant challenge and consume a significant time of whole machine learning model development. (c) The trade-off between the complexity of the vibration analysis and the applicability of the results deserves further development, especially with regards to drivetrain faults. (d) Most of the proposed techniques focus on gearbox and bearings, and there is a need to apply these models to other wind turbine components. We explain these findings in detail and conclude with a discussion of the main areas for future work in this domain.

Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm

“…The GPR's optimal solution is a maximum likelihood estimate, which involves nonlinear optimization. when its common solution is a great likelihood estimate that includes nonlinear optimization 4 …”

“…The traditional method of waiting for system failure before replacing failed parts results in long downtime and puts enormous pressure on maintenance staff and standby parts inventory, increasing operating costs and ultimately affecting the Levelized Cost of Energy (LCoE) 2 . The cost of operation and maintenance (O&M) is a significant portion of the total annual wind turbine cost, with O&M accounting for 20%–25% of the LCoE 3,4 . To address this issue, improving the reliability of key components and predicting failures by improving maintenance strategies are currently the main ways to reduce O&M costs and lower the LCoE of wind power.…”

“…2 The cost of operation and maintenance (O&M) is a significant portion of the total annual wind turbine cost, with O&M accounting for 20%-25% of the LCoE. 3,4 To address this issue, improving the reliability of key components and predicting failures by improving maintenance strategies are currently the main ways to reduce O&M costs and lower the LCoE of wind power.…”

A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights

“…The most recent improvements in data‐driven models for condition monitoring and preventive maintenance of important wind turbine components were examined by Pandit et al. [40]. They noted that while generators, which are more crucial for wind turbine failures, are understudied, the health status research on hydraulic blade pitch is still in its infancy.…”

Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance

“…The logistics and supply chain management of OWF maintenance have a fundamental impact on their availability and, as a result, profitability. The issues of servicing WTs and OWF maintenance, in general, are currently very often discussed in the literature on the subject [13][14][15][16]. Decision-making, including the management of spare-part inventory, the purchase or lease of consumables, the outsourcing of repair services, the organization and planning of maintenance tasks, and the determination of vessel routes, as well as the selection of OWF maintenance strategies are the most frequently addressed areas of research.…”