Kittiratsatcha, Supat

Preferred name

Alternative Name

Kittiratsatcha, S.

Main Affiliation

School of Engineering

supat.ki@kmitl.ac.th

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Analysis and experimental setup of a switched reluctance generator for maximum output power
(2014-01-01)
Thongprasri, P.
;
Kittiratsatcha, S.
Output power of a switched reluctance generator (SRG) in single pulse mode operation depends on control variables which consist of dc bus voltage, shaft speed, and excitation angles (turn-on and turn-off angles). This paper presents a model using equations to express maximum output power in terms of the control variables. The proposed analytical model is applied from a nonlinear magnetization curve that is used to mathematically demonstrate relationship of the control variables and to find the optimal control variables of the SRG for maximum output power. The validity of the analytical model has been confirmed by experimental results. © 2014 Praise Worthy Prize S.r.l. - All rights reserved.
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Analysis of control variables to maximize output power for switched reluctance generators in single pulse mode operation
(2016-10-01)
Thongprasri, Pairote
;
Kittiratsatcha, Supat
This paper presents an analytical modeling method of optimal control variables to maximize the output power for switched reluctance generators (SRGs) in single pulse mode operation. A method to obtain the phase current equation used to determine the optimal control variables is proposed. The phase current equation is derived from the phase voltage equation in combination with the inductance model. The inductance model proposed in this paper is applied from the flux linkage function. The characteristics of the phase current and the energy conversion relations are analyzed to determine the optimal phase current shape. The analytical results indicate that the optimal shape can be generated when the SRG is controlled with the optimal control variables. The optimal shape is used for analysis based on the phase current equation to determine the optimal control variables. An 8/6 SRG experimental setup is used to validate the proposed method. The optimal control variables obtained from the proposed method are used to control the SRG. Based on the experimental results, the SRG can produce the maximum output power.
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Analysis of a switched-reluctance generator for maximum energy conversion
(2017-01-01)
Wongguokoon, Supakit
;
Kittiratsatcha, Supat
An effective analytical expression for maximum energy conversion in terms of DC-bus voltage, shaft speed, and turn-on and turn-off angles of a switched-reluctance generator (SRG) based on self-excitation mode and single-pulse operation is successfully presented. The proposed analytical model, which can describe the nonlinearity of the magnetic characteristics of an SRG with sufficient accuracy, is the key to derive the relation between maximum energy conversion and the aforementioned control variables. The optimal ratio of DC-bus voltage to shaft speed and excitation angle are proposed. Simulation and experimental results are provided to validate the analysis.
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Optimal excitation angles of a switched reluctance generator for maximum output power
(2014-01-01)
Thongprasri, Pairote
;
Kittiratsatcha, Supat
This paper investigates the optimal values of turn-on and turn-off angles, and ratio of flux linkage at turn-off angle and peak phase current positions of optimal control for accomplishing maximum output power in an 8/6 Switched Reluctance Generator (8/6 SRG). Phase current waveform is analyzed to determine optimal excitation angles (optimal turn-on and turn-off angles) of the SRG for maximum output power which is applied from a nonlinear magnetization curve in terms of control variables (dc bus voltage, shaft speed, and excitation angles). The optimal excitation angles in single pulse mode of operation are proposed via the analytical model. Simulated and experimental results have verified the accuracy of the analytical model.
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Modeling of a switched reluctance generator using cubic spline coefficients on the phase flux linkage, inductance and torque equations
(2015-01-01)
Kerdtuad, Paiwan
;
Kittiratsatcha, Supat
This paper presents a dynamic modeling method for a switched reluctance generator (SRG) by which cubic spline coefficients are applied to the phase flux linkage, phase inductance and electromagnetic torque equations. To obtain the cubic spline coefficients, the flux linkage data of the SRG are first determined by a finite element analysis (FEA) prior to fitting into a third order polynomial equation to derive the curve fitting flux linkage data. In addition, the accuracy of the curve fitting data is verified by comparing with the FEA flux linkage data. Then, the cubic spline coefficients are applied to the proposed dynamic model of the SRG to simulate the machine behaviors. The simulations were carried out in a single pulse mode with fixed conduction angles at a rotation speed lower than, equal to and higher than a based speed of 6000 rpm. This research also presents the experimental results of an 8/6 SRG based on a TMS320F2812 DSP drive system, including the phase voltage, dc-link voltage, phase current, dcload current waveforms, as well as the output power-speed characteristics. The simulation and experimental results are compared to verify the accuracy of the proposed model.
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A Standalone Real-Time State of Charge Estimator Using the Long Short-Term Memory Artificial Neural Network
(2024-01-01)
Praisan, Akkarawat
;
Polmai, Sompob
;
Kittiratsatcha, Supat
Precise monitoring of the State-of-Charge (SoC) ensures optimal Lithium-ion (Li-ion) battery utilization, avoiding overcharging and deep discharging. Furthermore, it contributes to maximizing battery enhancement and longevity. However, a Li-ion battery's behavior is non-linear and varies under several conditions, making direct SoC measurement impossible. Currently, machine learning techniques are extensively used to estimate SoC; unfortunately, these models are typically executed in software only and cannot be realistically implemented in hardware. Consequently, this article proposes an approach for estimating SoC using a Recurrent Neural Network (RNN) with an improved Long Short-Term Memory (LSTM) cell. The proposed network was trained and tested on MATLAB using datasets of driving profiles for Electric Vehicles (EVs), which included battery voltage, current, and temperature while charging and discharging under various temperature conditions. Afterwards, the trained model was transferred to STM32-Cube-AI for validation and deployment of the proposed network for hardware implementation. Thanks to the STM32 microcontroller's performance, the network operates smoothly on a microcontroller and can estimate the SoC accurately and rapidly. Although Li-ion batteries have different characteristics at each temperature condition, the LSTM can predict the remaining SoC for each time step within a millisecond. Moreover, the experimental results show the proposed approach's quick convergence to the ground truth, substantiated by Root-Mean-Square-Errors (RMSEs) of around 4.5%, 2%, and 1.9% at 0 °C, 25 °C, and 45 °C, respectively. As a result, the proposed LSTM can be practically implemented for a real-time application with accuracy.
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Accurate Assessment of Moisture Content and Degree of Polymerization in Power Transformers via Dielectric Response Sensing
(2023-10-01)
Kunakorn, Anantawat
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Pramualsingha, Sarawuth
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Yutthagowith, Peerawut
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Nimsanong, Phethai
;
Kittiratsatcha, Supat
Power transformers are essential apparatuses used to transfer electrical energy from one voltage-level circuit to another. For reliable systems, preventive maintenance of the transformers is required to ensure good services of all mechanical, electrical, and insulation parts. Oil-immersed paper is most often used for transformer insulation. To ensure such good insulation performance and for assessing insulation conditions, advanced transformer sensing, monitoring, and effective assessment techniques are required. This paper introduces an effective technique for assessing the insulation conditions in power transformers, which are crucial for ensuring reliable energy transfer. The method utilizes advanced transformer sensing and monitoring, focusing on oil-immersed paper insulation commonly used in transformers. The technique employs dielectric response sensing, obtained from frequency-domain spectroscopy tests, to estimate degrees of polymerization (DP) and percentages of moisture content (PMCs) in the oil-immersed paper insulation. These parameters are well-known indicators of insulation performance. The approach is based on the weighted k-nearest neighbor regression, using a database of dielectric loss factors at low frequency and oil conductivities. To overcome limited data availability, linear interpolation and extrapolation techniques are applied to enlarge the database. Experimental verification and comparison with a previously developed method demonstrate the proposed technique’s superiority in accuracy and complexity. The maximum deviations of DP and PMC in the validation cases are 6.2% and 18.7%, respectively. In addition, to evaluate the validity of our proposed method in the case of a real power transformer, a comparative analysis of the DP and PMC values determined by the proposed method with those obtained through a previously developed and complicated approach was performed. The predicted results indicate that the DP and PMC values of the oil-immersed insulation fall within the ranges of 800 to 1000 and 1.5 to 2.0, respectively, which agree with the results determined by the complicated approach and closely align with real conditions. By offering a reliable and advanced means of assessing insulation conditions, this technique contributes to the preventive maintenance and overall efficiency of power transformers.
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A New Tapped Inductor Buck Converter with Large Step-Down Voltage Conversion
(2023-01-01)
Trakuldit, Siripan
;
Bunlaksananusorn, Chanin
;
Kittiratsatcha, Supat
This paper presents a new DC-DC converter with large step-down voltage conversion. The proposed converter is obtained by cascading an input circuit of a Quadratic Buck Converter (QBC) to a Tapped Inductor Buck Converter (TIBC). It uses only one active switch and provides a wider voltage conversion range than other well-known step-down converters. In the paper, the operation of the proposed converter is described. Circuit analysis is performed to derive the voltage gain and key current and voltage equations. The prototype circuit operating with the input voltage of 150 V, the output voltage of 5 V, the load current of 10 A and the switching frequency of 100 kHz is implemented. Experimental results show that the prototype converter exhibits good output voltage regulation and achieves the 30-to-1 voltage step-down operation with a maximum efficiency of 82%. In addition, measurement results confirm that the converter operation is consistent with the theoretical analysis.
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Output power control using artificial neural network for switched reluctance generator
(2021-01-01)
Kittiratsatcha, Supat
;
Kerdtuad, Paiwan
;
Bunlaksananusorn, Chanin
We propose an output power control of a variable-speed switched reluctance generator (SRG) by implementing an artificial neural network (ANN) in the control loop. In the high-speed operation with single pulse mode, the phase current waveform, and subsequently, the output power, depend on the conduction angles. The conduction angles, i.e., the turn-on and turn-off angles, can be determined by the proposed method using an ANN. A dynamic model of an SRG with eight stator poles and six rotor poles is used for simulation to obtain the output power profiles, which subsequently become the ANN training data. The inputs of the ANN are the reference value of the output power and the rotor speeds, while the outputs of the ANN are the turn-off and turn-on angles. The control algorithm is implemented by integrating the trained data into the dynamic model using MATLAB. The experimental setup of the SRG is implemented using a digital signal processor (DSP) to control the two-switches-per-phase drive system, which includes highly accurate phase current and dc-link voltage sensor circuits. The trained biases and weights of the ANN are also coded in the DSP. To validate the proposed method, comparisons are made between simulation and experimental results.
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Auxiliary Power Supply System with Parallel-Connected DC–AC Inverters for Low-Floor Light Rail Vehicle
(2023-07-01)
Kerdtuad, Paiwan
;
Chaiamarit, Kunjana
;
Kittiratsatcha, Supat
This research proposes a roof-mounted auxiliary power supply (APS) system for 600 VDC low-floor light rail vehicles (LRVs). The proposed APS system consists of five parallel-connected dc–ac inverter modules (modules 1–5). Inverter modules 1 and 2 are three-phase dc–ac inverters for the compressor motors of the air conditioning system, and inverter modules 3 and 4 are three-phase dc–ac inverters for the air pump motors of the air supply system. Inverter module 5 is a single-phase dc–ac inverter for the 220 VAC power supply of onboard electric loads. Simulations and experiments were carried out under variable load torques and output frequencies for modules 1–4 and under full and no resistive loads for module 5. The measured total input current and total input power of the proposed APS system under the full-load condition are 114.36 A and 68.84 kW. The total efficiency of the proposed APS system (modules 1–5) is 97.05%. The proposed APS system is suitable for 600 VDC low-floor LRVs. The novelty of this research lies in the use of five parallel-connected inverter modules, as opposed to the three-phase output transformer or isolated dc–dc converter in the early and conventional APS systems. Specifically, the proposed APS system requires neither a three-phase output transformer nor an isolated dc–dc converter.

Kittiratsatcha, Supat

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