Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan.
Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.
Sensors (Basel). 2019 Apr 8;19(7):1671. doi: 10.3390/s19071671.
Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.
在物联网 (IoT) 传感器中，面临的挑战是如何从收集的数据中挖掘出潜在有价值的信息，以支持决策。本文提出了一种基于机器学习的方法，用于预测风力涡轮机的长周期维护时间，以便在电力公司中进行有效的管理。长周期维护时间预测使电力公司能够尽可能有效地运行风力涡轮机，以最大化利润。从两个风电场的 31 台风力涡轮机中收集了包括运行数据、维护时间数据和事件代码在内的传感器数据。执行数据聚合以滤除一些错误并从数据中获取重要信息。然后，构建混合网络以基于卷积神经网络 (CNN) 和支持向量机 (SVM) 来训练预测模型。实验结果表明，所提出的方法的预测达到了高精度，这有助于提高风力涡轮机维护的效率。
