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Article

Application of Neural Networks to Estimate Common Mode (CM) Model Impedance Parameters in AC Drives

Tokyo Metropolitan Institute of Technology, Department of Electronic Systems Engineering, 6-6 Asahigaoka Hino-Shi, 19 1-0065, Tokyo, Japan
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Math. Comput. Appl. 2003, 8(2), 225-233; https://doi.org/10.3390/mca8020225
Published: 1 August 2003

Abstract

Predicting and solving EMI emissions related to Common Mode (CM) ones in inverter (including pulse with modulation (PWM) inverter)-induction motor drive systems requires various frequency-dependent effects to be considered. Thus, the application of filter circuitry to improve the performance per size and prevent dependency on some parameters of the motor for Electro-Magnetic Interference (EMl) filter is considered. The major problems are power line frequency rejection and the compensation of the feedback loop, which is influenced by the wide-ranging utility impedance. Thus, the proposed method allows another way to determine Common Mode (CM) impedance components as a beginning way and sample on the application stage of EMI filter to prevent the EMI noise applying to the conducted electromagnetic emissions generally caused by power supply firstly, then secondly to Common Mode (CM) path of the motor drive system.
Keywords: EMC/EMI; AC Drives; Neural networks; Common Mode (CM) impedance EMC/EMI; AC Drives; Neural networks; Common Mode (CM) impedance

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MDPI and ACS Style

Gulez, K.; Mutoh, N. Application of Neural Networks to Estimate Common Mode (CM) Model Impedance Parameters in AC Drives. Math. Comput. Appl. 2003, 8, 225-233. https://doi.org/10.3390/mca8020225

AMA Style

Gulez K, Mutoh N. Application of Neural Networks to Estimate Common Mode (CM) Model Impedance Parameters in AC Drives. Mathematical and Computational Applications. 2003; 8(2):225-233. https://doi.org/10.3390/mca8020225

Chicago/Turabian Style

Gulez, Kayhan, and Nobuyoshi Mutoh. 2003. "Application of Neural Networks to Estimate Common Mode (CM) Model Impedance Parameters in AC Drives" Mathematical and Computational Applications 8, no. 2: 225-233. https://doi.org/10.3390/mca8020225

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