Load and risk based maintenance management of wind turbines
Wind power has proven to be an important source of renewable energy in the modern electric power systems. Low profit margins due to falling electricity prices and high maintenance costs, over the past few years, have led to a focus on research in the area of maintenance management of wind turbines. The main aim of maintenance management is to find the optimal balance between Preventive Maintenance (PM) and Corrective Maintenance (CM), such that the overall life cycle cost of the asset is minimized. This thesis proposes a maintenance management framework called Self Evolving Maintenance Scheduler (SEMS), which provides guidelines for improving reliability and optimizing maintenance of wind turbines, by focusing on critical components.
The thesis introduces an Artificial Intelligence (AI) based condition monitoring method, which uses Artificial Neural Network (ANN) models together with Supervisory Control And Data Acquisition (SCADA) data for the early detection of failures in wind turbine components. The procedure for creating robust and reliable ANN models for condition monitoring applications is presented. The ANN based Condition Monitoring System (CMS) procedure focuses on issues like the selection of configuration of ANN models, the filtering of SCADA data for the selection of correct data set for ANN model training, and an approach to overcome the issue of randomness in the training of ANN models. Furthermore, an anomaly detection approach, which ensures an accuracy of 99% in the anomaly detection process is presented. The ANN based condition monitoring method is validated through case studies using real data from wind turbines of different types and ratings. The results from the case studies indicate that the ANN based CMS method can detect a failure in the wind turbine gearbox components as early as three months before the replacement of the damaged component is required. An early information about an impending failure can then be utilized for optimizing the maintenance schedule in order to avoid expensive unscheduled corrective maintenance.
The final part of the thesis presents a mathematical optimization model, called the Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC), for optimal maintenance decision making. The PMSPIC model provides an Age Based Preventive Maintenance (ABPM) schedule, which gives an initial estimate of the number of replacements, and an optimal ABPM schedule for the critical components during the life of the wind turbine, based on the failure rate models created using the historical failure times. Modifications in the PMSPIC model are presented, which enable an update of the maintenance decisions following an indication of deterioration from the CMS, providing a Condition Based Preventive Maintenance (CBPM) schedule. A hypothetical but realistic case study utilizing the Proportional Hazards Model (PHM) and output from the ANN based CMS method, is presented. The results from the case study demonstrate the possibility of updating the maintenance decisions in continuous time considering the changing conditions of the damaged components. Unlike the previously published mathematical models for maintenance optimization, the PMSPIC based scheduler provides an optimal decision considering the effect of an early replacement of the damaged component on the entire lives of all the critical components in the wind turbine system.
condition monitoring system (CMS)
supervisory control and data acquisition (SCADA)
Artificial neural network (ANN)
life cycle cost
EB, Hörsalsvägen 11, 41296 Gothenburg.
Opponent: Prof. Miguel A. Sanz Bobi, Intelligent Systems Research Group, Comillas Pontifical University, Madrid, Spain