Fault Diagnosis Strategy of Four-Switch Buck-Boost Converter Based on Support Vector Machines
- Resource Type
- Conference
- Authors
- Liu, Xueqi; Zhang, Xin; Hu, Kexin
- Source
- 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA) Industrial Electronics and Applications (ICIEA), 2023 IEEE 18th Conference on. :1186-1191 Aug, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Fault diagnosis
Extrapolation
Simulation
Search methods
Switches
Feature extraction
Four-switch Buck-Boost (FSBB)
support vector machines (SVM)
grid optimization
Fast Fourier Transform (FFT)
- Language
- ISSN
- 2158-2297
The four-switch Buck-Boost (FSBB) converter, with advantages such as bidirectional power transmission and low switch stress, has been widely applied in various fields including photovoltaic power generation and energy storage. This paper addresses the problem of the complex working mode of the FSBB converter and the small sample size of fault samples by using the support vector machines (SVM) model for fault diagnosis. By selecting signal acquisition nodes and using Fast Fourier Transform (FFT) to extract fault features, the improved grid search method is used to find the optimal parameters of the SVM model. Finally, the SVM model is used for multi-class recognition to achieve effective diagnosis of single-switch open circuit faults and double-switch open circuit faults of the FSBB converter under different modes. Simulation results show that this method has a high diagnostic accuracy and strong extrapolation ability.