Abstract: Determining when to perform preventative maintenance is an important consideration for maximizing the revenue of a wind turbine. For example, performing preventative maintenance may be cheaper than replacing turbine components when they fail. When determining to perform preventative maintenance, a maintenance scheduler may consider multiple factors. These factors may include the probability of failure, the predicted price of energy, predicted wind power production, resource constraints, and the like. Specifically, the maintenance scheduler may predict the future values of these factors which are then integrated into a net present value (NPV) for each of the components. Based on the respective NPVs, the maintenance scheduler may determine which maintenance actions to perform and in what order.

Abstract: The invention relates to a decision support system (DSS, 1) for maintenance of renewable energy generators, such as wind turbine generator (WTG, 11). A forecasting module (FM, 21) outputs renewable power plant relevant parameters (PF) in a prediction window of time (TW), whereas an optimization module (OPT, 22) receives the relevant parameters (PF), and proposes a maintenance schedule (PROP-MAN) for the renewable power plant (WPP) in order to optimize the produced energy with respect to the demand in said predefined prediction window (TW). A renewable energy generator condition module (WT-CON, 23) outputs condition data into maintenance recommendations (REC-MAN) for one or more renewable energy generators. Finally, a renewable energy generator maintenance recommendation module (WTM, 24) is arranged to combine the proposed maintenance schedule and the maintenance recommendations into a final maintenance decision proposal (FIN-PROP-MAN).

Abstract: A service planning engine may predict the future wind speed at a wind farm as well as the future price of energy of the utility grid coupled to the wind turbines. Based on the predicted values, the service planning engine identifies times to perform maintenance when lost revenue is minimized—e.g., when the predicted price of energy is low. Furthermore, the service planning engine may calculate a service priority associated with the turbine to determine when to perform service. The service priority may be based on a status signal associated with the component to be serviced or the time window stipulated in the service contract for performing the maintenance. In one embodiment, the service planning engine also considers resource constraints such as labor costs (e.g., work schedules, overtime costs, availability of technicians with certain skills, availability of components, shipping costs, and the like).