An Integrated Maintenance Scheduling of a Wind Energy System Minimizing Economic Losses

The world is faced with a continuous challenge of climate change and global warming as a result of excess carbon emission due to the traditional method of generating electricity from fossil fuels. As measures to curb this challenge, re-searchers explored into renewable energy resources which provide clean and hazard-free energy. Wind as one of the fast-evolving sources requires a lot of attention in generating and sustaining the wind system to ensure reliability and customer satisfaction. In this context, this paper develops a model that forecasts wind energy production by artificial neural network (ANN) method. An integrated model for optimizing the production and maintenance planning cost was developed to minimize economic as well as the production losses that satisfy random demand. Our developed algorithm also determines the minimal number of preventive maintenances to be performed on the turbine thereby evaluating the eco-nomic losses associated with the total production lifecycle