Inverter open circuit fault diagnosis strategy based on KNN algorithm

Inverters play a crucial role in renewable energy generation system. Once a fault occurs, it will cause damage to equipment and even threaten personal safety. Therefore, accurate inverter fault diagnosis algorithm is crucial. This paper proposes a new inverter open circuit fault diagnosis strategy based on k-Nearest Neighbor (KNN) algorithm to address the problems of complexity, high computational resource consumption, and inapplicability to embedded control system in existing fault diagnosis algorithms. Firstly, the inverter sampling data is preprocessed, and the moving average method is used to process the data, reducing the amount of data and improving the calculation speed. Pearson thermal maps are used to reduce the dimension of the data and extract fault features. Then, the sampled data obtained from preprocessing is used to train the fault diagnosis strategy based on KNN algorithm. Finally, the trained model was validated using a test set, and the results showed that the proposed algorithm occupies less computational resources and has high fault diagnosis accuracy, making it suitable for embedded control system.