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

https://www.mdpi.com/1996-1073/13/12/3132/pdf

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

An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case

Tidal current turbine, as an effective technical means to contribute to all around the world achieving targets for reducing greenhouse gas emissions and the production of renewable energy, has obtained great concern. It was established that more than 94 TWh per year could be generated in the developable sea area. Many types of tidal current turbine structures and control methods have been proposed, however, the possible increase in power generation must consider parameters such as reliability, cost, and complexity of the control system. The paper presents a review on configuration and control methods of tidal current turbines. The configuration and control methods of tidal current turbines are classified, described and compared, especially for horizontal axis tidal current turbines. Horizontal axis tidal current turbine is more widely used, due to high energy conversion efficiency. Different configuration and corresponding control methods have their advantages and disadvantages, which affect efficiency, maintenance requirements, and the cost of electricity from tidal current.

Review on configuration and control methods of tidal current turbines

Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation

Digital Twin technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning, 5G/6G, cloud computing, and Internet of Things, Digital Twin has been moving progressively from concept to practice. In this paper, a Digital Twin framework based on cloud computing and deep learning for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different sub-models including mathematical, finite element, and machine learning ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using deep learning algorithms, with high accuracy of 92%.

/pdf/cloud-based-digital-twinning-for-structural-health-2ug9rsr5e0.pdf

Cloud-based digital twinning for structural health monitoring using deep learning

A novel wind turbine condition monitoring method based on cloud computing

Experimental and numerical investigation of effects of residual stress and its release on fatigue strength of typical FPSO-unit welded joint

Effects of welding residual stresses on the vibration fatigue life of a ship's shock absorption support

Neutral-point-clamped (NPC) three-level converter has been widely used in wind power generation systems. However, traditional NPC three-level converter often suffers excessive voltage fluctuation at the neutral point of DC-link capacitors. This problem may leads to switching devices damage and increases additional harmonics in output voltage of the converter. To solve this problem, a compensating scheme based on a current compensation method is proposed. The results show that the proposed scheme can alleviate considerably voltage fluctuation. Consequently, it is effective and can also enhance the reliability of the converter.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/el.2016.4702

Liangbi Li

Papers

A novel wind turbine condition monitoring method based on cloud computing

Experimental and numerical investigation of effects of residual stress and its release on fatigue strength of typical FPSO-unit welded joint

Effects of welding residual stresses on the vibration fatigue life of a ship's shock absorption support

Neutral-point voltage balancing in wind power NPC three-level converter

Power performance and dynamic responses of an integrated system with a semi-submersible wind turbine and four torus-shaped wave energy converters