Condition-based maintenance of wind power generation systems considering different turbine types and lead times

Abstract

Condition monitoring measurements, such as vibration data, acoustic emission data, oil analysis data, power voltage and current data, etc., can be obtained from wind turbine components and be utilised to evaluate and predict the health conditions of the components and the turbines. The objective of condition-based maintenance (CBM) is to optimise the predictive maintenance activities based on the condition monitoring and prediction information to minimise the overall costs of wind power generation systems. In existing work, all the wind turbines are assumed to be of the same type and the lead times of different components are assumed to be constant. This is not the case in many practical applications. In this paper, we develop a CBM approach for wind turbine systems considering different types of wind turbines in a wind farm and different lead times for different turbine components, which lead to more accurate modelling of CBM activities in actual wind farms. In the proposed CBM approach, we present a new CBM policy involving two design variables for each turbine type, a method for turbine failure probability evaluation considering different lead times and a CBM cost evaluation method. Numerical examples are provided to demonstrate the proposed CBM approach.

Wind power system reliability and maintenance optimization considering turbine and wind uncertainty

“…Tian et al proposed an approach for wind farm CBM considering turbines under continuous monitoring, where an artificial neural network (ANN)-based model was used for prediction and the failure probability thresholds were used for defining the CBM policies (Tian et al , 2011). The work was further extended to consider different turbine types and lead times (Ding et al , 2014). Heath condition and remaining useful life prediction are critical for effective CBM implementation (Ragab et al , 2017).…”