| Autor: |
Poblete, Pablo, Pizarro, German, Droguett, Gabriel, Nunez, Felipe, Judge, Paul D., Pereda, Javier |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Power Electronics; Sep2022, Vol. 37 Issue 9, p10306-10318, 13p |
| Abstrakt: |
Modular multilevel converters (MMCs) have become one of the most popular power converters for medium/high-power transmission systems and motor drive applications. Standard control schemes for MMCs use a voltage measurement per submodule (SM) to balance the capacitor voltages and govern the MMC. Consequently, the control system requires a significant amount of sensors and the effective communication of sensitive data under relevant electromagnetic interference (EMI), impacting the reliability and cost of the MMC. This work presents a distributed neural network (DNN) observer inspired by a general predictor-corrector structure for estimating the capacitor voltages at each SM. The proposed observer predicts each SM capacitor voltage using a standard average model. Then, each prediction is corrected and denoised by a neural network of reduced computational complexity. As a result, the proposed observer reduces the number of required voltage sensors per arm to only one and filters the high-frequency noise without noticeable delay in the estimated SM capacitor voltages for both transient and steady-state operations. Experiments conducted in a three-phase MMC with 24 SMs confirm the effectiveness of the proposed DNN observer. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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