Ngamroo, Issarachai

Preferred name

Ngamroo, Issarachai

Alternative Name

Ngamroo, I.

Main Affiliation

School of Engineering

issarachai.ng@kmitl.ac.th

Now showing 1 - 10 of 19

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MPC-Based Virtual Energy Storage System Using PV and Air Conditioner to Emulate Virtual Inertia and Frequency Regulation of the Low-Inertia Microgrid
(2022-01-01)
Pahasa, Jonglak
;
Potejana, Potejanasak
;
Ngamroo, Issarachai
Grid-connected large-scale power converter-based intermittent renewable energy sources (RES) reduce system inertia, increase frequency fluctuation, and increase the rate of change of frequency (RoCoF). An energy storage system (ESS) is an indispensable component of a smart grid, and is used to overcome low-inertia problems. However, the capital and maintenance costs of ESS are high and high RoCoF events are less frequent in power systems. Therefore, the introduction of a virtual energy storage system (VESS) to provide the function of a conventional ESS for power system ancillary services is an innovative and cost-effective method. This study investigated a VESS using photovoltaic (PV) generators and inverter air conditioners (IACs) to provide virtual inertia and frequency regulation for a low-inertia microgrid. A model predictive control (MPC)-based VESS regulates indoor temperature, microgrid frequency, and RoCoF. The impact of parameter variation, that is, the microgrid frequency weight, indoor temperature weight, virtual inertia gain, and number of IACs, was studied and selected by considering the ability of the parameters to provide virtual inertia and frequency regulation. Finally, the efficiency and robustness of the proposed MPC-based VESS technique are compared with those of a conventional VESS. Simulation results revealed that the proposed MPC-based VESS can improve the virtual inertia, reduce the frequency deviation, and reduce the RoCoF of the studied microgrid. In addition, the proposed method is robust to variations in the system parameters.
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Adaptive Output Power Smoothing of Grid-Connected Hybrid Wind-Photovoltaic by SMES
(2020-10-16)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes an adaptive output power control of superconducting magnetic energy storage (SMES) to solve the output power fluctuation problem of the grid connected hybrid permanent magnet synchronous generators (PMSG) wind and photovoltaic generations (HWPV). By the power control of SMES, the adaptive output power reference is employed to achieve the desired output power of the HWPV while the variation of SMES coil current is regulated between the minimum and maximum limits. Study result shows that the proposed adaptive power control of SMES is able to alleviate the HWPV output power fluctuation, and maintain the SMES coil current in the acceptable range effectively.
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Improved Resilient Model Predictive Control for Enhanced Microgrid Virtual Inertia Emulation by Virtual Energy Storage System Under DoS Attacks
(2023-01-01)
Muangchuen, Satawat
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Pahasa, Jonglak
;
Ngamroo, Issarachai
The distributed control of a microgrid is fully dependent on advanced information and communication technologies that are sensitive to cyber-physical systems. Cyberattacks, such as denial-of-service (DoS) attacks, can cause unstable operation of low-inertia microgrids. This paper proposes enhanced microgrid virtual inertia control under DoS attacks using an improved resilient model predictive control (IRMPC)-based virtual energy storage system (VESS). IRMPC comprises an attack detector, an autoregressive (AR)-based signal estimator, and an MPC-based VESS controller. An attack detector was used to detect the DoS attacks. An AR-based signal estimator is then used to estimate the feedback data that are subjected to DoS attacks. The firefly algorithm was used to optimize the AR parameters. The effectiveness of the proposed IRMPC was compared with that of conventional model predictive control, conventional model predictive control-based VESS, and resilient model predictive control-based VESS. The simulation results revealed that under a DoS attack, the proposed IRMPC can successfully improve the microgrid virtual inertia emulation. Additionally, the proposed IRMPC has a performance effect over the compared techniques in terms of the reduction in RoCoF deviation and frequency deviation during normal situations, DoS attacks, and disconnection of wind turbine generation. The simulation results also confirmed that IRMPC is robust to microgrid parameter variations when compared to the other methods.
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Optimal least squares support vector machines for SMES controller design using wide area phasor measurements
(2012-07-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
In this paper, the least squares support vector machines (LS-SVMs) based design of superconducting magnetic energy storage (SMES) controller is proposed for wide area stability control. The LS-SVMs for SMES controllers are trained by local and inter-area data based on synchronized phasor measurements considering time delay. A large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. The LS-SVM parameters and the similarity threshold are optimized by a particle swarm optimization. Subsequently, the redundant data in the training set can be discarded while the reduced data are the optimal support vectors in the LS-SVM model. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a six-area West Japan interconnected power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under wide range of operating conditions in comparison to the conventional SMES. Copyright © 2011 John Wiley & Sons, Ltd.
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Least squares support vector machine for power system stabilizer design using wide area phasor measurements
(2011-07-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes a design method of power system stabilizer by a least squares support vector machine (LS-SVMPSS) for wide area stability control. Both local and inter-area data based on synchronized phasor measurements considering time delay are considered as the input features of the LS-SVMPSS. A large number of the training data sets of a multi-machine power system are reduced by the measurement of similarity between samples. Removing the redundant data in the training set not only improves the LS-SVMPSS performance but also decreases computation expense during the operation of LS-SVMPSS. The LS-SVMPSS parameters and the similarity threshold are optimized by a genetic algorithm. As a result, the redundant data in the training set can be discarded while the reduced data are the optimal support vectors in the LS-SVMPSS model. The LS-SVMPSS control signals can be adapted in real time by various operating conditions and different disturbances. The performance of the LS-SVMPSS is compared with the conventional PSS and the neural network-based PSS. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVMPSS is very robust to various disturbances under wide range of operating conditions in comparison to other PSSs. © 2011 ISSN.
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GA-based support vector machines for adaptive power system damping controller of SMES
(2010-07-30)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes the application of support vector machines (SVMs) to design of an adaptive power system damping controller for superconducting magnetic energy storage (SMES). A genetic algorithm is used to optimize the SVM parameters based on k-fold cross-validation. The SVMs for SMES controllers are trained by the data obtained from a multi-machine power system, and the optimal SVM parameters. The SVMs can be adapted by various operating conditions when the power system operates either inside or outside of the training set. Simulation results in a two-area four-machine power system demonstrate that the proposed SVMs for adaptive SMES is much superior to the conventional SMES controller with fixed parameters under various operating conditions and severe disturbances.
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Two-Stage Optimization Based on SOC Control of SMES Installed in Hybrid Wind/PV System for Stabilizing Voltage and Power Fluctuations
(2021-11-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes the two-stage optimization of superconducting magnetic energy storage (SMES) integrated into hybrid wind/photovoltaic (PV) generators considering the state of charge (SOC) control for stabilizing voltage and power fluctuations. The first stage aims to achieve the minimal coil inductance of SMES that guarantees the stored energy for system stabilization. In the second stage, the control parameters of SMES are optimized to keep the SOC at the desired level. As a result, the minimum coil inductance with sufficient stored energy of SMES for stabilizing system and regulating SOC at the target value can be obtained for entire period of operation. Study results in the distribution system with various loads ensure that the hybrid wind/PV with optimized internal SMES yields superior stabilizing performance in comparison with the SMES externally installed at the wind/PV terminal.
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Kernel principal component analysis for power quality problem classification
(2010-07-30)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes the application of kernel principal component analysis (KPCA) for power quality (PQ) problem classification. First, the features of PQ signal are extracted using wavelet-multiresolution analysis. Then, KPCA captures the dominant nonlinear properties of the extracted features by transforming to a high dimensional feature space. The dimension of extracted features produced by KPCA can be reduced without loss of information of the original features. Finally, support vector machines (SVMs) are used to classify the PQ problem using the dominant components of KPCA. Simulation results with six types of PQ problem demonstrate that the proposed KPCA-based SVMs provides the superior classification performance of PQ problem to the conventional SVMs.
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Fabrication of Metallic Nano-Ring Structures by Soft Stamping with the Thermal Uplifting Method
(2022-05-01)
Potejanasak, Potejana
;
Pahasa, Jonglak
;
Ngamroo, Issarachai
In this study, the unconventional microfabrication method by the combined processes of the chemical soft stamping technique with the thermal uplifting technique to fabricate metal nanoarrays on a glass plate is proposed and their feasibility verified. The gold micro-ring arrays on a quartz glass plate are realized by utilizing a chemical template with the thermal uplifting method. Their optical properties are studied experimentally. First, a plastic mold is made of a Biaxially Oriented Polyethylene Terephthalate (BOPET) via the hot embossing method. Then, the Methanal micropatterns are transferred onto an etched surface of a substrate via a soft stamping process with a BOPET mold. The gold thin film is coated onto the methanol patterned glass plate via the Ar+ sputter coating process. Finally, the metallic micro-ring structures are aggregated on a glass plate via the thermal uplifting technique. The LSPR optical properties as the extinction spectrums of the gold micro-ring structure arrays are investigated experimentally. It is confirmed that this method was able to fabricate plasmonic micro-ring arrays with low cost and high throughput.
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Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC
(2017-03-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model- based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

Ngamroo, Issarachai

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