Abstract
Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.
Highlights
With more attention paid to the energy security, ecological environment, climate change and other issues, to accelerate the development of wind power has become the consensus of the international community to promote the transformation of energy development, to cope with global climate change and concerted action
The development of computer science and artificial intelligence technology has greatly promoted the practical application of intelligent fault diagnosis technology
Based on the expert knowledge of wind turbine fault diagnosis, this paper designs a wind turbine fault diagnosis expert system based on production rules
Summary
With more attention paid to the energy security, ecological environment, climate change and other issues, to accelerate the development of wind power has become the consensus of the international community to promote the transformation of energy development, to cope with global climate change and concerted action. This paper applies a rule based expert system [7] on wind turbine fault diagnosis, in order to make timely, accurate diagnosis for wind turbine and provide timely solutions. Choose the C# language in the Visual Studio 2013 platform, use ADO.NET technology to access the database to establish the expert system In this way, it can meet the requirements of rapid response to wind turbine fault diagnosis, and prevent the emergence of human error, the valuable experience of experts can be retained to prevent lost, too.
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