Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Power Electronics, 2022, 37, (9), pp. 10306-10318
- Issue Date:
- 2022-09-01
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Filename | Description | Size | |||
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Distributed_Neural_Network_Observer_for_Submodule_Capacitor_Voltage_Estimation_in_Modular_Multilevel_Converters.pdf | 4.81 MB |
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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.
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