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With the aim of solving the problems arising from the low efficiency and low accuracy of fault classification of wind power towers and turbine equipment (referred to as wind power systems for short) using artificial data analysis, this paper takes the operational data for wind power systems as the research object and proposes an improved K-means weighted dynamic clustering fault classification algorithm (DT clustering). First, historical and asymmetrical operational data from wind power systems were pre-processed to construct the data time series matrix and establish the fault classification model; second, the linear approximate constrained optimization algorithm and multiple regression algorithm were combined to build the model parameter optimization model. Finally, the comparative analysis of various algorithms showed the superiority of this algorithm, and the effectiveness of this model should be proved in practical applications.