Improved PSO-SVM-Based Fault Diagnosis Algorithm for Wind Power Converter

Due to the complexity of the working environment of wind power generation systems, wind turbine power converters (WPC) can experience different types of faults. Traditional fault diagnosis methods suffer from issues such as the need for additional hardware, low accuracy, long execution time, and applicability only to small sample offline fault diagnosis. In order to address these problems, this article proposes a particle swarm optimization-based support vector machine (SVM) algorithm. The algorithm combines PSO algorithm, SVM algorithm, and moving average algorithm to effectively improve the robustness and accuracy of the fault diagnosis algorithm, while reducing the execution time and cost. This article selects three-phase current signals and bus voltage signals as fault diagnosis data, and then uses the moving average algorithm to process the fault data of the power converter, retaining the data features based on effectively smoothing the data. Finally, an improved particle swarm algorithm is used to construct a fault diagnosis model based on support vector machines for diagnosing open circuit faults in the power converter. In a dataset containing 9800 training samples and 4200 testing samples, the accuracy of the training samples is 98.898%, and the accuracy of the testing samples is 98.4524%. This effectively solves the problem of traditional SVM methods being only able to handle small batches of nonlinear datasets. Finally, this article compares the proposed fault diagnosis method with other types and similar types of fault diagnosis methods, verifying the effectiveness and superiority of the proposed approach.