Abstract
In order to improve the evaluation process of inverter open-circuit faults diagnosis in permanent magnet synchronous motor (PMSM) drives, this paper presents a diagnosis method based on current residuals and machine learning models. The machine learning models are introduced to make a comprehensive evaluation for the current residuals obtained from a state observer, instead of evaluating the residuals by comparing with thresholds. Meanwhile, fault diagnosis and location are conducted simultaneously by the machine learning models, which simplifies the diagnosis process. Besides, a sampling strategy is designed to implement the proposed scheme online. Experiments are carried out on a DSP based PMSM drive, and the effectiveness of the proposed method is verified.
Original language | English |
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Pages (from-to) | 180-189 |
Number of pages | 10 |
Journal | CES Transactions on Electrical Machines and Systems |
Volume | 4 |
Issue number | 3 |
DOIs | |
State | Published - 2020 |
Keywords
- Current residuals
- fault diagnosis
- inverter open-circuit
- machine learning