A Self-learning Model to Detect Icing Alarm Based on Multi-agents for Wind Park

Ting-shun LI, Ze-san LIU, Yuan-yi XIA


In the middle to high latitudes, the wind turbine often encounters icing on the blade surface in winter, which can not only lower the power energy generation, but also sometimes damages the wind turbine, increasing maintain cost. To improve wind turbine blade icing prediction accuracy, since every wind turbine in wind park is installed different place and has different performance, it’s impossible for each wind turbine to use one model to detect blade icing. According to the characteristics of various types wind turbine in wind park, this paper provides a multi-agents model to improve the accuracy of detecting wind turbine blade icing alarm based on machine learning. Every agent is self-learned from recent historic data of some type wind turbine in wind period by machine learning algorithm, hence different agent must have different characteristic of the wind turbine type in different circumstance so that the agent can better predict the blade icing. When each agent could detect icing better, multi-agent to detect icing in wind park would improve more precise.


Blade Icing Alarm, Multi-agent, Self-learning, Logistic Regression


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