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An optimization of superconducting coil installed in an hvdc-wind farm for alleviating power fluctuation and limiting fault current
(2019-03-01)
Ngamroo, Issarachai
The vital problems of a high-voltage direct current (HVdc) wind farm are the dc power fluctuation and dc faults. To tackle both problems simultaneously, the superconducting magnetic energy storage with fault current limiting capability (SMES-FCL), which is incorporated into the HVdc-wind farm system, is proposed. The SMES-FCL can be used to lessen the dc power fluctuation due to intermittent wind power and limit the fault current in the dc line. The optimal SMES coil and control parameters are tuned based on minimization of power and voltage fluctuations, and stored energy. Study results elucidate that the optimal SMES-FCL provides higher effect on power smoothing and fault current limiting than the non-optimal SMES-FCL against dc fault and wind power levels.
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Optimal fuzzy gain scheduling of PID controller of superconducting magnetic energy storage for power system stabilization
(2013-05-21)
Chaiyatham, Theerawut
;
Ngamroo, Issarachai
It is well known that the proportional-integral-derivative (PID) can be applied to solve practical control problems effectively. However, in the face of the high system nonlinearity, the PID controller with fixed parameters may fail to provide satisfactory control performance. To enhance the PID control effect, a new design of the fuzzy gain scheduling of PID controller (FGS-PID) is presented in this paper. The proposed technique is applied to design FGS-PID controllers of superconducting magnetic energy storage (SMES) for power system stabilization. Without trial and error, the scale factors, membership functions and control rules of the FGS-PID controller are automatically tuned by a bee colony optimization. With the optimal FGS-PID controller, the PID parameters can be adjusted automatically according to various system operating conditions. As a result, the high robustness of the FGS-PID controller can be expected. Simulation study confirms that the stabilizing effect and robustness of the proposed SMES with an optimal FGS-PID controller are much superior to those of the SMES with an optimal PID controller. © 2013 ICIC International.
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A self-tuning PID-based SMES controller by optimal fuzzy gain scheduling for stabilization of inter-area power system oscillation
(2013-01-16)
Chaiyatham, Theerawut
;
Ngamroo, Issarachai
Generally, the PID (Proportional-Integral-Derivative) controller with fixed parameters may fail to provide satisfactory performance when the system nonlinearity is high. To augment the PID control effect, the optimal fuzzy gain scheduling for a self-tuning PID controller (FGS-PID) is presented in this paper. The proposed technique is applied to design an FGS-PID controller of superconducting magnetic energy storage (SMES) for stabilization of inter-area power system oscillation. Without trial and error, the scale factors, membership functions and control rules of the FGS-PID controller are automatically tuned by a bee colony optimization. With the optimal FGS-PID controller, the PID parameters can be adjusted automatically according to various system operating conditions. As a result, the high robustness of the FGS-PID controller can be expected. Simulation study confirms that the stabilizing effect and robustness of the proposed SMES with an optimal FGS-PID controller are much superior to those of the SMES with an optimal PID controller. © 2013 ICIC International.
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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.
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A heuristic training-based least squares support vector machines for power system stabilization by SMES
(2011-10-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES. © 2011 Elsevier Ltd. All rights reserved.
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Simultaneous optimization of SMES coil size and control parameters for robust power system stabilization
(2011-06-01)
Ngamroo, Issarachai
As the coil size is the heart of superconducting magnetic energy storage (SMES), the simultaneous optimization of coil size and control parameters of SMES for robust power system stabilization is proposed. The structure of active and reactive power controllers of SMES is the practical first-order lead/lag compensator. To handle system uncertainties such as various generating and loading conditions, unpredictable network structures etc., the multiplicative uncertainty model is embedded in the system modeling. As a result, the optimization problem of SMES coil size and controller parameters based on the enhancement of system damping and robust stability margin against system uncertainties can be formulated. Solving the problem by a particle swarm optimization, both optimal coil size and controller parameters are obtained simultaneously and automatically. Simulation study in the West Japan six-area interconnected power system with two SMES units confirms the superior robustness and damping performance of the proposed SMES controller with an optimal coil size under various situations in comparison with the conventional SMES controller. © 2011 IEEE.
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A robust centralized SMES controller design based on WAMS considering system and communication delay uncertainties
(2011-04-01)
Saejia, Mongkol
;
Ngamroo, Issarachai
It is well known that the communication delay due to the phasor measurement in wide area monitoring system (WAMS) as well as various system operating conditions such as heavy line flows and unpredictable network structures, may deteriorate the wide-area stabilizing control effect. To overcome this problem, the inverse input and output multiplicative model is proposed to represent unstructured uncertainties due to system operations and communication delay in the robust centralized damping controller design of superconducting magnetic energy storage (SMES) based on WAMS. The structure of centralized controller for SMES is the practical 1st-order lead/lag compensator. To automatically tune the control parameters, the optimization problem based on the enhancement of damping performance and system robust stability margin is achieved by particle swarm optimization. Simulation studies in the West Japan six-area interconnected system confirm that the proposed robust SMES centralized controller is superior to the conventional SMES centralized controller in terms of damping performance and robustness against system and time delay uncertainties. © 2010 Elsevier B.V.
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An optimization of robust SMES with specified structure H∞ controller for power system stabilization considering superconducting magnetic coil size
(2011-01-01)
Ngamroo, Issarachai
Even the superconducting magnetic energy storage (SMES) is the smart stabilizing device in electric power systems, the installation cost of SMES is very high. Especially, the superconducting magnetic coil size which is the critical part of SMES, must be well designed. On the contrary, various system operating conditions result in system uncertainties. The power controller of SMES designed without taking such uncertainties into account, may fail to stabilize the system. By considering both coil size and system uncertainties, this paper copes with the optimization of robust SMES controller. No need of exact mathematic equations, the normalized coprime factorization is applied to model system uncertainties. Based on the normalized integral square error index of inter-area rotor angle difference and specified structured H <inf>∞</inf> loop shaping optimization, the robust SMES controller with the smallest coil size, can be achieved by the genetic algorithm. The robustness of the proposed SMES with the smallest coil size can be confirmed by simulation study. © 2010 Elsevier Ltd. All rights reserved.
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Wide-area SMES controller design using least-squares support vector machines
(2010-12-01)
Pahasa, Jonglak
;
Ngamroo, Issarachai
This paper presents an adaptive wide-area damping controller of superconducting magnetic energy storage (SMES) using the least squares support vector machine (LS-SVM). The LS-SVM for SMES controllers are trained using wide-area control signal obtained from synchronized phasor measurements considering time delay. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine 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.
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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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