An Intelligent Stability Prediction Method of Grid-Connected Inverter Considering Time-Varying Parameters
- Resource Type
- Periodical
- Authors
- Qiu, Y.; Wang, Y.; Tian, Y.; Chen, Z.
- Source
- IEEE Transactions on Industry Applications IEEE Trans. on Ind. Applicat. Industry Applications, IEEE Transactions on. 60(2):3685-3697 Apr, 2024
- Subject
- Power, Energy and Industry Applications
Signal Processing and Analysis
Fields, Waves and Electromagnetics
Components, Circuits, Devices and Systems
Power system stability
Inverters
Circuit stability
Stability criteria
Impedance
Real-time systems
Predictive models
Grid-connected inverter
intelligent
RBFNN
stability
stability region
time-varying parameter
- Language
- ISSN
- 0093-9994
1939-9367
This paper presents an intelligent stability prediction method for high-frequency oscillation of grid-connected inverter considering time-varying parameters of power grid and inverter. A data-based analysis method based on radial basis function neural network (RBFNN) is first developed to identify and predict time-varying parameters of grid and inverter. Then, the oscillation characteristic represented by physical model is combined to predict real-time stability of grid-connected inverter. Furthermore, the stability prediction criterion is developed according to real-time parameter identification and physical model. Simulation and experimental results are given to validate the proposed intelligent stability prediction method. The proposed method is able to predict time-varying stability region and stability margin of grid-connected inverter considering parameters variation, which thus improves the self-learning capability and adaptivity of grid-connected inverter system.