Lidong Zhang

By Tieliu Jiang, Shengwen Wang, Lidong Zhang and Zhongbin Zhang

Direct heating of wind turbine is a form of heating production using wind energy. Because of its low requirements for wind quality, relatively simple device structure and high heating efficiency, the wind turbine-driven heating devices can take the place of traditional fossil energy for winter heating and achieve the purpose of reducing carbon emissions to a certain extent. Through the heater experimental platform constructed at Northeast Electric Power University, it is concluded that the liquid stirring heater can operate at 7 m/s, however; the efficiency is extremely low in the low-temperature environment. The permanent magnet eddy current heater must operate at the wind speed above 13 m/s, but it also can operate normally under the low-temperature condition. Combining the advantages and disadvantages of the two experimental devices and setting the vertical axis wind generator as the original motor, the heating efficiency of the two types of heating devices are analyzed under different working conditions, and then the adaptability of the wind turbine and the heating efficiency of the heating device are also studied.

Part of the book: Rotating Machines

By Lidong Zhang, Yimin Xu and Yuze Zhao

The global wind energy business has grown considerably in recent years. Wind energy has a bright future as a major component of the renewable energy sector. However, one of the major barriers to the growth of wind energy is the freezing of wind turbine blades. The major solution to overcome the aforementioned problem will be to foresee wind turbine ice using existing anti-icing technologies. As a result, improving wind turbine ice prediction technology can assist wind farms in achieving more precise operation scheduling, avoiding needless shutdowns, and increasing power generation efficiency. Traditional wind turbine icing prediction methods have problems such as misjudgment and omission, while machine learning algorithms have higher accuracy and precision. Because of the rapid advancement of deep learning technology, machine learning algorithms have become an important tool for predicting wind turbine icing. However, in real applications, machine learning algorithms still face obstacles and limits such as inadequate data and poor model interpretability, which require additional study and refinement. This chapter discusses the application of machine learning algorithms in wind turbine icing prediction, provides a comprehensive description of the applicability and accuracy of various machine learning algorithms in wind turbine icing prediction, and summarizes the applications and advantages.

Part of the book: Wind Turbine Icing