Title: Genetic Programming Hyper-heuristic for Evolving a Maintenance Policy for Wind Farms
Time: 13:00 to 14:00, Wednesday, April.3, 2024
Place: F303, School of Mechanical Engineering
Host: PAN Ershun, Professor (Department of Industrial Engineering and Management)

Biography
Dr Wenjuan Zhang is an Associate Professor in the ISMA (Information System Management & Analytics) group at Warwick Business School, Warwick University in the UK. Her current research interests include optimization, forecasting and decision making; key areas of application: infrastructure asset management, reliability, and maintenance optimisation.  Dr Zhang has worked on over 20 research and consultancy projects from a wide range of clients from industry, commerce and the public sector, as well as working collaboratively with the wider academic community. The ESTEEM project with London Underground was awarded the IET Innovation Award by the Institution of Engineering and Technology, in the Asset Management category. 

Abstract
Reducing the cost of operating and maintaining wind farms is essential for the economic viability of this renewable energy source. This study applies hyper-heuristics to design a maintenance policy that prescribes the best maintenance action in every possible situation. Genetic programming is used to construct a priority function that determines what maintenance activities to conduct, and the sequence of maintenance activities if there are not enough resources to do all of them simultaneously. The priority function may take into account the health condition of the target turbine and its components, the characteristics of the corresponding maintenance work, the workload of the maintenance crew, the working condition of the whole wind farm, the current inventory level, and stochastic wind conditions. Empirical results using a simulation model of the wind farm demonstrate that the proposed model can construct maintenance dispatching rules that perform well both in training and test scenarios, which shows the practicability of the approach. The results also show the advantage of the proposed maintenance policy over several previous maintenance strategies. Furthermore, the importance of taking the weather condition and inventory conditions is also proved.