Abstract
Current control of grid-connected converters may result in harmonic instability when grid impedance changes. To prevent this issue, current controller parameters can be tuned adaptively according to different short-circuit ratios (SCRs). It is thus important to estimate the grid impedance in real-time. Unlike traditional FFT-based impedance measurement methods, a more efficient estimation approach based on neural networks is proposed in this paper. This method does not require a fixed and relatively long sampling window, making it possible for real-time impedance measurement. Further, a step-by-step design method of the feedforward neural network (FNN) used for grid impedance estimation is developed. Time-domain simulation results validate the effectiveness of the approach. Based on the designed FNN, adaptive current control is implemented and verified through simulation.
| Original language | English |
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| Title of host publication | IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Publication date | 2022 |
| ISBN (Electronic) | 9781665480253 |
| DOIs | |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022 |
Conference
| Conference | 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 |
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| Country/Territory | Belgium |
| City | Brussels |
| Period | 17/10/2022 → 20/10/2022 |
| Series | IECON Proceedings (Industrial Electronics Conference) |
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| Volume | 2022-October |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- adaptive current control
- Feedforward neural network
- grid impedance estimation