Abstract
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.
Original language | English |
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Journal | I E E E Transactions on Industry Applications |
Volume | 60 |
Issue number | 2 |
ISSN | 0093-9994 |
Publication status | Published - Mar 2024 |
Keywords
- Intelligent
- stability
- grid-connected inverter
- RBFNN
- time-varying parameter