An open circuit fault identification method of inverter based on FDA-KNN

Xueli Shen; Xiaowei Gu

doi:10.1117/12.2674718

2 May 2023 An open circuit fault identification method of inverter based on FDA-KNN

Xueli Shen, Xiaowei Gu

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422R (2023) https://doi.org/10.1117/12.2674718
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

The fault of inverters seriously affects individuals' lives, and researchers have become increasingly interested in its method of fault identification. In view of the similarity of three-phase current data of inverter open circuit fault and the low classification accuracy caused by the slight change of three-phase current data measured for distinct open circuit faults, the FDA-KNN method is proposed. Moreover, the method extracts fault characteristics from the three-phase current value of the inverter prior to label discrimination. In the first place, fisher linear discrimination (FDA) is adopted to separate normal data from fault data to extract fault data features, and the subsequently K-nearest neighbor algorithm (KNN) is employed to label faults and identify the open circuit fault type of inverter. The results demonstrate that this method is straightforward to implement and comprehend. It solves the issue of fault classification for similar inverter three-phase current data with greater precision than conventional methods.

Citation Download Citation

Xueli Shen and Xiaowei Gu "An open circuit fault identification method of inverter based on FDA-KNN", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422R (2 May 2023); https://doi.org/10.1117/12.2674718

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KEYWORDS

Field effect transistors

Matrices

Feature extraction

Vector spaces

Image classification

Data modeling

Diagnostics

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