Abstract
To reduce maintenance costs of wind turbines (WTs), WT health monitoring has attracted wide attention, and different methods have been proposed. However, most existing WT temperature monitoring methods ignore the fact that various wind conditions can directly affect internal temperature of WT, such as main bearing temperature. This paper analyzes the effects of wind conditions on WT temperature monitoring. To reduce these effects, this paper also proposes a novel WT temperature monitoring solution. Compared with existing solutions, the proposed solution has two advantages: (1) wind condition clustering (WCC) is applied and then a normal turbine behavior model is built for each wind condition; (2) extreme learning machine (ELM) is optimized by an improved genetic algorithm (IGA) to avoid local minimum due to the irregularity of wind condition change and the randomness of initial coefficients. Cases of real SCADA data validate the effectiveness and advantages of the proposed solution.
Highlights
Since wind energy is renewable and pollution-free, many governments cite wind energy as a primary future energy source [1]
wind turbines (WTs) are exposed to harsh conditions all year round, and the variability of wind conditions can affect WT monitoring directly
The data-driven model may fall into a local minimum due to the irregularity of wind condition change and the randomness of initial weights and bias, which can affect monitoring accuracy
Summary
Since wind energy is renewable and pollution-free, many governments cite wind energy as a primary future energy source [1]. The wind speed of more than 12.5 m/s can keep for 1 day If this period is in the learning set, the intelligent algorithm may determine that the wind speed has little effect on the WT’s internal temperature. If the learning set includes one (or more) periods like that, the intelligent algorithm may determine that the wind speed has a strong impact on the WT’s internal temperature Both situations arise due to the irregularity of wind condition. Considering the effects of wind condition on the WT’s internal temperature, a WCC scheme using k-means algorithm is proposed. This is a divide-and-conquer strategy and can enable the building of models in different wind conditions, which can improve the reliability and accuracy of fault detection.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
