Ngamroo, Issarachai

Preferred name

Ngamroo, Issarachai

Alternative Name

Ngamroo, I.

Main Affiliation

School of Engineering

issarachai.ng@kmitl.ac.th

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Robust pitch controller design in hybrid wind-diesel power generation system
(2008-09-23)
Cuk Supriyadi, A. N.
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Ngamroo, I.
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Kaitwanidvilai, S.
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Kunakorn, A.
;
Hashiguchi, T.
In this paper, the robust control design of pitch controller for frequency control in a hybrid wind-diesel power generation system is proposed. The structure of the pitch controller is a 1<sup>st</sup>-order lead-lag compensator. To take system uncertainties into account, the coprime factorization is applied in system modeling. To obtain the controller parameters, the performance and stability conditions of H<inf>∞</inf> loop shaping technique are used to formulate the optimization problem. The genetic algorithm is employed to solve the problem. Simulation studies show the frequency control effect and robustness of the proposed controller against system uncertainties in comparison with a variable structure pitch control. ©2008 IEEE.
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Coordinated SVC and AVR for robust voltage control in a hybrid wind-diesel system
(2010-12-01)
Vachirasricirikul, Sitthidet
;
Ngamroo, Issarachai
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Kaitwanidvilai, Somyot
This paper proposes a robust control of voltage fluctuation due to the variation of reactive loads in an isolated wind-diesel hybrid power system using Static Var Compensator (SVC) and Automatic Voltage Regulator (AVR). The structure of the voltage controller of SVC and AVR is the proportional integral (PI) controller with single input. In the system modeling, a normalized coprime factorization is applied to represent possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. Based on the H<inf>∞</inf> loop shaping, the performance and robust stability conditions of the control system are formulated as the optimization problem. The genetic algorithm is applied to solve an optimization problem and to achieve PI control parameters of SVC and AVR simultaneously. Simulation studies show the control effect and robustness of the proposed coordinated SVC and AVR. © 2010 Elsevier Ltd. All rights reserved.
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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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PMU-based system identification for wide area robust PSS design in interconnected power systems with wind farm
(2012-01-01)
Ngamroo, Issarachai
It is well known that the penetration of wind power in the smart power grids not only causes the power fluctuation problem, but also results in the system instability. To tackle this problem, a sophisticated design of robust power system stabilizer (PSS) based on system identification using multiple synchronized phasor measurement units (PMUs) is proposed. The small load fluctuation is applied to the system in order to generate the phasor data measured from multiple PMUs which are assumed to be located in the system. Applying the least square method, the phasor data are used to identify the coupled vibration model (CVM) which represents the dominant inter-area oscillation modes. The CVM is used to design the PSS which is a 2nd-order lead-lag compensator. To take system uncertainties such as variation of system parameters etc., in the CVM, the inverse additive perturbation model is applied. Based on an enhancement of the robust stability margin and damping effect, the PSS parameters optimization problem is formulated. The genetic algorithm is used to solve the problem and achieve the PSS parameters. The performance and robustness of the proposed PSS are evaluated in the IEEJ Western Japan 10 machine power system with wind farm in comparison with a conventional PSS. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.
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Design of robust SVC for voltage control in an isolated wind-diesel hybrid power system
(2008-10-06)
Vachirasricirikul, S.
;
Ngamroo, I.
;
Kaitwanidvilai, S.
This paper focuses on a new robust control design of Static Var Compensator (SVC) for voltage control in an isolated wind-diesel hybrid power system. The proposed method is based on the H<inf>∞</inf> loop shaping technique and genetic algorithm (GA). The structure of the controller is a proportional integral (PI) controller with single input. In the design, system uncertainties are modeled by a normalized coprime factorization. The performance and robust stability conditions of the designed system satisfying the H<inf>∞</inf> loop shaping are formulated as the objective function in the optimization problem. The GA is applied to solve an optimization problem and to achieve control parameters. Simulation studies show the effectiveness and robustness of the proposed method. © 2008 IEEE.
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Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints
(2008-04-01)
Pothiya, Saravuth
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Ngamroo, Issarachai
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Kongprawechnon, Waree
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches. © 2007 Elsevier Ltd. All rights reserved.
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Inverse additive perturbation-based optimization of robust PSS in an interconnected power system with wind farms
(2008-12-01)
Cuk Supriyadi, A. N.
;
Ngamroo, I.
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Kunakorn, A.
;
Dechanupaprittha, S.
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Watanabe, M.
This paper proposes a design of robust power system stabilizer (RPSS) based on inverse additive perturbation optimization in an interconnected power system with wind farms. In the design, system uncertainties are represented by the inverse additive model. The robust stability condition is used to form the optimization problem of PSS parameters. The structure of PSS is a conventional second-order lead-lag controller. The genetic algorithm is applied to solve the problem and achieve the PSS parameters. Simulation studies in the two-area four-machine system with wind farms confirm that the damping effect and robustness of the proposed PSS are superior to those of the compared PSS. © 2008 SICE.
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Analysis of voltage drop using transformer tap changer and placement of capacitor bank with genetic algorithm
(2025-12-01)
Siregar, Yulianta
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Saragi, Agus Kivander
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Ngamroo, Issarachai
The demand for electrical energy is increasing due to high economic growth and population. The impact is that electrical energy operates excessively to meet the required demand. Unbalanced loads, higher power losses on the line, and voltage drops that are higher than allowed are just a few of the issues that may result from this. Adding tap changers and capacitor banks is one method of improving the voltage profile and power losses. To conduct this study, tap changers and capacitor banks were added to the IEEE 33 bus network system. The value, capacity, and location of the tap changers and capacitor banks in the system were ascertained using the genetic algorithm (GA) approach. According to the simulation results, the voltage profile, which initially had 21 buses outside the IEEE standard limits, may be ideal by installing two tap changers and two capacitor banks. Additionally, reactive power losses decreased from 41.8 kVar to 93.3 kVar, and active power losses decreased from 202.7 kW to 130.7 kW, a decrease of 72 kW.
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Microgrid stabilization by superconducting magnetic energy storage with optimal energy capacity by genetic algorithm
(2012-01-01)
Ngamroo, Issarachai
In a microgrid with wind and photovoltaic power generations, the intermittent power from these sources may cause a large power fluctuation. Besides, when the severe fault occurs in the system, it may cause the transient power fluctuation. If the power fluctuation cannot be maintained in the acceptable range, the system stability may be deteriorated. To compensate for fast power fluctuation, a superconducting magnetic energy storage (SMES) can be applied. This paper proposes a new optimization technique of SMES power controller with optimal energy capacity. The power controller structure is the first-order lead-lag compensator. The optimization problem of power controller parameters, coil inductance and initial coil current is formulated based on an enhancement of system damping and a minimization of initial energy capacity of SMES. The genetic algorithm is applied to achieve all optimized parameters. Simulation results confirm the control effect of the SMES with optimized energy capacity against various disturbances. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

Ngamroo, Issarachai

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