School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
School of Electrical Engineering, Southwest Jiaotong University-University of Leeds Joint School, Chengdu 610031, China.
Sensors (Basel). 2022 Feb 15;22(4):1516. doi: 10.3390/s22041516.
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.
为降低风力涡轮机（WT）的维护成本，WT 健康监测引起了广泛关注，并且已经提出了不同的方法。然而，大多数现有的 WT 温度监测方法忽略了这样一个事实，即各种风况会直接影响 WT 的内部温度，例如主轴承温度。本文分析了风况对 WT 温度监测的影响。为了降低这些影响，本文还提出了一种新的 WT 温度监测解决方案。与现有解决方案相比，所提出的解决方案具有两个优点：（1）应用风况聚类（WCC），然后为每种风况建立正常涡轮机行为模型；（2）通过改进的遗传算法（IGA）优化极限学习机（ELM），以避免由于风况变化的不规则性和初始系数的随机性而导致的局部最小值。实际 SCADA 数据的案例验证了所提出解决方案的有效性和优势。
