A Novel Capacitance Estimation Method of Modular Multilevel Converters for Motor Drives Using Recurrent Neural Networks with Long Short-Term Memory

Mehdi Syed Musadiq and Dong-Myung Lee ()
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Mehdi Syed Musadiq: School of Electronic and Electrical Engineering, Hongik University, Seoul 04066, Republic of Korea
Dong-Myung Lee: School of Electronic and Electrical Engineering, Hongik University, Seoul 04066, Republic of Korea

Energies, 2024, vol. 17, issue 22, 1-17

Abstract: Accurate estimation of submodule capacitance in modular multilevel converters (MMCs) is essential for optimal performance and reliability, particularly in motor drive applications such as permanent magnet synchronous motor (PMSM) drives. This paper presents a novel approach utilizing recurrent neural networks with long short-term memory (RNN–LSTM) to precisely estimate capacitance in MMC-based PMSM drives. By leveraging simulation data from MATLAB, the LSTM neural network is trained to predict capacitance based on voltage, current, and their temporal variations. The proposed LSTM architecture effectively captures the dynamic behavior of MMCs in PMSM drives, providing high-precision capacitance estimates. The results demonstrate significant improvements in estimation accuracy, validated through mean squared error (MSE) metrics and comparative analysis of actual versus estimated capacitance. The method’s robustness is further confirmed under varying operating conditions, highlighting its practical utility for real-time applications in power electronic systems.

Keywords: capacitance estimation; control optimization; long short-term memory; LSTM; modular multilevel converter; MMC; motor drive; power electronic systems; recurrent neural networks; RNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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