学术讲座：Condition Monitoring for Intelligent Operation and Maintenance of Wind Power Systems

讲座题目：Condition Monitoring for Intelligent Operation and Maintenance of Wind Power Systems

讲 座 人：Xiandong Ma （Lancaster University, UK）

讲座时间：2025年9月12日（周五）下午15:30-17:00

地    点：立功楼6-2报告厅

承办单位：机电工程学院

简    介：Xiandong Ma is a Reader in Power and Energy Systems in the School of Engineering at Lancaster University, UK. His research interests include intelligent condition monitoring and fault diagnosis of power and energy systems; condition-based operation and maintenance, modelling, optimization and control of smart grids and microgrids with distributed energy sources; machine learning and digital twin solutions. His work has been supported by UK EPSRC, Royal Society, Leverhulme Trust, European Commission and industry. He has published >180 papers in leading journals and conferences. He served Technical Program Chair for IEEE ICAC’18, General Conference Chair for IEEE ICAC’19 and President of the Chinese Automation and Computing Society in the UK (CACSUK) for 2019-2021. He is a Chartered Engineer (CEng), a Fellow of the Institution of Engineering and Technology (FIET) and a Fellow of the Higher Education Academy (FHEA). He has been ranked in the world's top 2% researchers since 2021 by Stanford University in terms of the citation impact of published work。

Abstract

The current situation concerning renewable energy production in the UK sees offshore wind generation reaching a level at which true economies of scale become reality. It is crucial that high O&M costs are reduced, and a reliable and predictable operation is ensured. This requires interdisciplinary research to develop new physical-knowledge based analytical models and data-driven condition-monitoring techniques to create new methods for asset performance management of wind power systems. Our research has been centred on the study of machine learning based data-driven models for effective condition monitoring using an intelligent and integrated approach. Through analysis of various signals, developing faults are diagnosed and prognosed well ahead of damage affecting the system. This presentation will describe the journey in the development of smart condition monitoring techniques from conception in an academic environment to practical deployments at Lancaster University.