Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-14, pub-electronic 2021-09-20Publication status: PublishedModern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Overall equipment effectiveness as a metric for assessing operational losses in wind farms : a critical review of literature

To become more competitive, less dependent on financial support and more attractive for investors, wind energy needs to reduce its final cost of energy. According to Levelized Cost of Energy, there are two ways to achieve this goal, by reducing costs or increasing production. Overall Equipment Effectiveness (OEE) is a widely used metric in manufacturing systems, supporting operators to enhance productivity by reducing operational losses. Therefore, this study aims to perform a qualitative literature review of the main operational losses following the OEE metric, namely availability, performance and quality, adjusting it to wind energy systems. Introduction of this metric can be a valuable tool towards an integrated indicator linking production and losses, allowing comparison between assets deployed in different settings