Assessment of Harmonic Mitigation in V/f Drive of Induction Motor Using an ANN-Based Hybrid Power Filter for a Wheat Flour Mill

Krishnasamy, Bhuvaneswari; Ashok, Kavithamani

doi:10.3390/pr10061191

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Adaptive algorithms of electric drive control systems are actively developed by many scientists. The main tools are fuzzy logic algorithms (Sun et al, 2009), genetic algorithms (Ustun & Demirtas, 2009), and neural network algorithms (ANN algorithms) (Krishnasamy & Ashok, 2022), which provide adjustment of regulators coefficients. A large part of scientists conducts research on an automated electric drive with a scalar control system.…”

Section: Algorithmic Approaches To Improve the Electromechanical Perf...mentioning

confidence: 99%

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Zhukovskiy

Vasilev

Korolev

et al. 2022

Indonesian J. Sci. Technol

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The article was devoted to the study of an automated electric drive with a scalar closed-loop speed control system. Severe duty and operating modes of electric drive determine the actual service life. Wear of the induction motor, as a key link of the electric drive, was associated with deviation from nominal parameters. The deviation of parameters of the induction motor equivalent circuit determined the resultant change of characteristics. The parameters of the equivalent circuit determined the accuracy of the adjustment of regulators and optimal algorithms in the control system of the electric drive. In continuous operation modes the possibility of auto-tuning regulators, which requires stopping or no-load mode, was excluded. The paper considered the influence of the magnetization circuit mutual inductance value of the induction motor on the behavior of the electric drive control system. Evaluation of the behavior of the scalar closed-loop speed control system was performed on the basic energy (power factor, efficiency factor) and mechanical (speed, electromagnetic torque) characteristics of the electric drive.

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Section: Algorithmic Approaches To Improve the Electromechanical Perf...mentioning

confidence: 99%

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Zhukovskiy

Vasilev

Korolev

et al. 2022

Indonesian J. Sci. Technol

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Observer-based robust servomechanism control of a three-phase induction motor drive

Friday,

Shehu,

Olarinoye

et al. 2024

Journal of Electrical Systems and Inf Technol

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Induction motors (IMs) are widely used in industrial applications, but harmonic distortion in IM drives can lead to inefficiencies and reduced lifespan. Traditional control strategies and filtering methods have limitations in mitigating lower-order harmonics, particularly the 5th harmonic, which can generate reverse torque and increase overheating risk. Existing solutions often struggle to fully address these issues, especially in high-power applications with dynamic loading conditions. This paper proposes a control strategy to reduce voltage and current harmonics in a Z-source inverter (ZSI)-fed IM drive. The system incorporates an observer-based output feedback robust servomechanism controller, designed using Linear Quadratic Regulator (LQR) and Linear Matrix Inequality (LMI) techniques. It ensures closed-loop stability, precise tracking regulation, and effective disturbance rejection, while also featuring a speed adaptation mechanism for sensorless operation. Simulation results demonstrate significant harmonic mitigation, with the controller achieving 1.01% lower voltage THD and 1.03% lower current THD compared to conventional approaches. This improvement aligns with industry standards and outperforms several existing techniques. The proposed method contributes a comprehensive solution for harmonic mitigation in high-power IM drives, applicable to various industrial applications requiring high-quality power and precise speed control.

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Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Chahine

2024

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Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.

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Assessment of Harmonic Mitigation in V/f Drive of Induction Motor Using an ANN-Based Hybrid Power Filter for a Wheat Flour Mill

Cited by 3 publications

References 33 publications

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Observer-based robust servomechanism control of a three-phase induction motor drive

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

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