Polmai, Sompob

Preferred name

Polmai, Sompob

Alternative Name

Polmai, S.

Main Affiliation

School of Engineering

sompob.po@kmitl.ac.th

Now showing 1 - 10 of 27

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Stabilization of power system using improved virtual inertia of virtual synchronous generator
(2022-01-01)
Poungdokmai, Aditap
;
Polmai, Sompob
The renewable energy source (RES) based distributed generation (DG) has grown up rapidly that causing a reduction inertia and instability of power system. To support the increased RES, grid connected inverters require improved control schemes. Therefore, Virtual synchronous generator is getting attention. VSG is a control scheme applied for distributed generating (DG) that controls virtual rotational synchronous generator and enhances stability of power system. However, VSG has some issues when grid frequency fluctuations occur, which can cause the steady-state active power output deviation. It can be solved by analyzing closed loop transfer function of power output. The steady-state power deviation is caused by damping coefficient in the swing equation of the VSG. Thus, power deviation is decreased by set damping coefficients to zero and adding differential compensation to control dynamic characteristic instead of damping coefficients. By adding differential compensation, we can control dynamic characteristics without steady-state power output deviation in weak grid.
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Experiment on fault current limiting by a single-phase bridge type fault current limiter with DC and AC reactors
(2010-12-01)
Sujjapan, Noppadol
;
Polmai, Sompob
This paper presents description of configuration and operation principle of a solid-state bridge-type SSFCL with DC and AC reactors. In this configuration the short-circuit limitation takes effect immediately after the fault by DC and AC reactor. The DC reactor is separated from the fault circuit after a half cycle period leaving only AC reactor for steady-state current limitation. This SSFCL is planned for used in microgrid for reliability enhancement and improve power quality at the point of common coupling. The laboratory-scale single phase model of a SSFCL is built for evaluation and validation. The experimental results are compared with the simulation result for confirmation.
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Generalized regression networks for partial discharge classification
(2016-01-18)
Pattanadech, N.
;
Nimsanong, P.
;
Potivejkul, S.
;
Yuthagowith, P.
;
Polmai, S.
This document represents a partial discharge (PD) classification by using Generalized Regression Networks (GRNN) model. Two PD classification models, GRNN1 with 11 input variables and GRNN2 with 3 selected derived statistic parameters, were investigated for classification of PD signals into 5 patterns, corona at high voltage side in air, corona at low voltage side in air, corona at high voltage side in mineral oil, corona at low voltage side in mineral oil and surface discharge in mineral oil. The conventional PD measurement was performed for measuring PD signals of the artificial PD models. The statistical parameters of the PD signals such as skewness, kurtosis, asymmetry, cross correlation and so on were calculated from the developed computer program. Then, 60% of the experimented data was used as a training data for the developed PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. It was found that the GRNN1 model can classify PD patterns better than GRNN2 model. The accuracy for PD classification of GRNN1 model was 100% while the accuracy of GRNN2 model was 97.5% of 40 testing data.
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Improvement of output dynamic performance of an average current mode controlled buck converter with a parallel controller
(2010-09-17)
Chrin, P.
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Trakuldit, S.
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Polmai, S.
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Bunlaksananusorn, C.
This paper presents a parallel voltage controller to improve the output dynamic performance of a buck converter with Average Current Mode Control (ACMC). The parallel controller is a high-order controller synthesized from parallel connection of the two basic controllers. It is proposed to compensate the control-to-output transfer function of the converter, which has multiple poles and zeros. Up until now, the task is commonly performed by the two-pole one-zero controller, which is clearly not adequate and has somewhat limited the dynamic performance of the converter. In the paper, the design of parallel controller is illustrated with a 5V/2V ACMC buck converter. Experimental and simulated results that show the improvement of output voltage response to a step load change are presented. © 2010 IEEE.
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Online Parameters Identification for an IPMSM Based on Extended Kalman Filter
(2024-01-01)
Treebubpha, Khanaphot
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Polmai, Sompob
;
Kittiratsatcha, Supat
This paper introduces an online identification of d-q axis apparent inductance in an interior permanent magnet synchronous machines (IPMSM) using extended Kalman filter (EKF). The primary objective is to achieve accurate tracking of apparent inductances, which are nonlinear and varies under different operating conditions. The apparent inductances are crucial for calculating electromagnetic torque. A nonlinear IPMSM model based on finite element analysis (FEA) is employed for simulations. The effectiveness of the proposed approach is demonstrated through MATLAB/Simulink simulations. To verify the efficiency of real-world application, two machines with different conditions were investigated under various operating scenarios, with the results compared to steady state analysis to ensure the accuracy of the proposed estimation method.
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Low voltage series arc fault detection using rogowski coil
(2018-08-13)
Sritrai, Eakburin
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Kittiratsatcha, Supat
;
Polmai, Sompob
This paper presents series arc fault detection in low voltage ac circuit using Rogowski coil d/dt sensor. Series arc faults are simulated with various types of loads and current levels to understand the characteristic of arcing signal pattern and level. The Rogowski coil is designed to be able to detect high frequency di/dt component of the arc signal. The experimental results clearly show that by detecting the high frequency component of the di/dt, series arc fault condition can be reliably detected.
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Experiment on distributed cooperative control with multi-agent system for a single-phase microgrid
(2015-08-17)
Leng, Darith
;
Polmai, Sompob
In a microgrid, electric generation is based on combination of small generators and renewable resources such as solar and wind. The operating conditions of the microgrid are dynamically altered by both the load and the intermittent nature of the renewable resources. To maintain the voltage and frequency stability of such microgrid, this paper presents distributed cooperative control of a microgrid using multi-agent system. The multi-agent system is implemented by using Mobile-C based on Client-Server architecture, and Ch and Embedded Ch as the agents run time. Serial communication is implemented for data exchange between agents and microgrid elements. To validate the effectiveness of the proposed control system, two scenarios have been experimented. The experimental results demonstrate the generator connecting/disconnecting and load shedding ability of the microgrid.
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A Transformerless Multi-Cell Solid-State Fault Current Limiter for Medium Voltage Power System
(2018-10-22)
Techama, Pantarote
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Polmai, Sompob
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Bunlaksananusorn, Chanin
This paper proposes a transformerless solid-state fault current limiter composed of multiple cells of single-phase bridge controlled rectifier with DC reactor and an AC reactor connected in parallel with cascaded rectifier cells performing current limiting in steady state. The proposed fault current limiter can be used in medium-voltage power system without power transformer so the system is more compact. With modular design of each rectifier cell, the construction and maintenance of the system could also be simplified. In this paper, the circuit designs, the computer simulation and the construction of the solid-state fault current limiter prototype are presented. The simulation and experimental results show that the proposed fault current limiter can operate properly.
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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
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Polmai, Sompob
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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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Partial discharge classification using probabilistic neural network model
(2016-01-18)
Pattanadech, N.
;
Nimsanong, P.
;
Potivejkul, S.
;
Yuthagowith, P.
;
Polmai, S.
The aim of this paper is to propose the probabilistic neural network (PNN) model for classification partial discharge (PD) patterns, which comprised of corona discharge at high voltage side and at low voltage side in air, corona discharge at high voltage side and at low voltage side in mineral oil and surface discharge in mineral oil. Partial discharge signals were investigated by conventional method according to IEC60270. Independent parameters such as skewness, kurtosis, asymmetry, and cross correlation of the Φ-q-n PD patterns were analyzed. The PNN PD classification model was constructed. Moreover, the principal component analysis (PCA) was utilized to reduce the input dimension of the developed PD classification model. After that, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used for evaluation the performance of the designed PD classification models. Effects of spread parameters and input neuron numbers on the PD classification performance were examined. It was found that the first four score variable was appropriate to be used to construct the designed PNN model with the optimal spread value of 1.2. The proposed PD classification model can classify PD types with the accuracy of 100% of 40 tested data.

Polmai, Sompob

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