Suwanngam, Warachart

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Implementation and Optimization of Two Degree of Freedom H∞ Loop Shaping Control for Average Current Mode Control Buck Converter using Genetic Algorithm
(2021-01-01)
Kaitwanidvilai, Somyot
;
Phurahong, Nuttapon
;
Suwan-Ngam, Warachart
In this paper, we focus on the implementation and optimization of two degree of freedom (2DOF) H∞ loop shaping control for the DC-DC buck converter. The output voltage is controlled using the current control mode called the average current mode control (ACMC). The technique using the fixed structure robust controller technique, as well as genetic algorithm (GA), is applied, resulting in the reduction of the controller order and optimal parameter for the robust proportional integral (PI) controller. In this paper, the performance of the proposed controller is compared with those using the conventional 2DOF H∞ loop shaping controller and other techniques. According to both simulation and experimental results, the robust controller designed by the proposed technique is simple, low order, and practical, yet still retains both performance and robustness.
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System Implementation for the Soft Start Operation of a Doubly-Fed Induction Motor
(2024-10-01)
Chaimanekorn, Knapoj
;
Suwan-Ngam, Warachart
This research aims at system implementation for the soft start operation of a doubly-fed induction motor (DFIM) based on stator flux vector control. In this paper, the theory is briefly discussed. The simulation is performed using PLECS software to validate the hypothesis. A 5 kW wound rotor induction motor (WRIM) mechanically coupled with a simulated load is setup for the experiment. The STM32F407 microcontroller is applied to control the experimental system. Moreover, the Modbus protocol is applied for communication between the microcontroller and the computer using RS485 standard. The problem about rotor angle correction before enabling the MSC, which does not appear in the simulation, is seriously discussed. The experimental results do substantiate the proposed method and can be practically applied to the real system.
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Implementation of two-output three-level series-resonant inverter for induction melting application
(2018-01-12)
Meesrisuk, Watanyu
;
Jangwanitlert, Anuwat
;
Suwan-Ngam, Warachart
This study aims to present a two-output three-level series-resonant inverter together with an implementation and experiments for an induction melting application. A three-level circuit is applied for each branch of inverter in order to decrease the voltage stress on switching devices. A phase-shifted pulse-width modulation (PSPWM) with variable frequency control is applied for providing and maintaining a zero-voltage switching condition over a wide load range. In addition, the PSPWM could provide symmetrical output voltage and current waveforms that result in decreasing the THD<inf>v</inf> and THD<inf>i</inf> of output waveforms and harmonic losses of high-frequency transformer cores comparing to other PWM schemes. Finally, the prototype was designed, built, and tested to verify the consistency between the experimental results and theories of the proposed inverter.
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A Small Deep Learning Model for Fault Detection of a Broken Rotor Bar of an Induction Motor
(2024-01-01)
Taweewat, Pat
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Suwan-Ngam, Warachart
;
Songsuwankit, Kanoknuch
;
Konghuayrob, Poom
In this paper, we present an investigation of a small deep learning model applied to the detection of a broken rotor bar of an induction motor. The motor current spectrum analysis is the base method for fault detection. This proposed method focuses on the analysis of the modification of the input vector and model configuration. This method was implemented and it showed that the feature length and size of the model are reduced compared with the existing method. The experimental results showed that only feature extraction using the spectral-based method and limit range of its coefficient are adequate to provide accuracy of small deep learning comparable to that of the parallel-layer deep learning model. Likewise, at the same accuracy level, based on the deep learning model, a shorter sampling duration than that required by the reference model is needed.
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Stator Flux Estimation Based on a Kalman Filter for Stator Flux Vector Control of a Doubly-Fed Induction Motor
(2025-06-01)
Kodchaporn, Jirawat
;
Suwan-Ngam, Warachart
Reliable knowledge of the stator flux vector is essential for stator flux vector control of a doubly-fed induction motor (DFIM); therefore, reliable flux estimation is required. Several flux observers have been proposed while the Kalman filter is well known as an optimal state estimator for linear systems. In this paper, a Kalman-filter-based stator flux observer is developed and combined with a double second-order generalized integrator phase-locked loop (DSOGI-PLL) to estimate the flux angular speed and position under non-ideal grid conditions. This work is validated through computer simulation in PLECS software. The CMSIS-DSP library is adopted to perform matrix operations of the Kalman filter. Simulations were performed under different operating conditions, demonstrating that the proposed approach successfully estimates the stator flux vector of the DFIM.
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Analysis of a Doubly-Fed Induction Motor for Soft Start Operation Based on Stator Flux Vector Control
(2024-10-01)
Suwan-Ngam, Warachart
This paper aims at analysis of an operation of a doubly-fed induction motor (DFIM). The paper mainly focuses on the soft start method for the DFIM based on stator flux vector control and the conventional topology without either circuit reconfiguration or change in the control methods. Likewise, the details about reactive power control are also discussed. With the proposed method, the DFIM can start from standstill to any speed by controlling the machine-side converter (MSC). The computer simulation is performed to a 5 kW wound rotor induction motor using PLECS software. The simulation results have successfully validated that the DFIM can operate from standstill to any speed with only a single circuit configuration and a control scheme.

Suwanngam, Warachart

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