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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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Heuristic optimization based-fixed structure robust H∞ loop shaping controller design with automatic weights selection of controllable distributed generations for Microgrid stabilization
(2012-01-01)
Vachirasricirikul, Sitthidet
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Ngamroo, Issarachai
In the microgrid with wind and solar power generations, the power fluctuation from such intermittent sources is an inevitable problem. To handle such situation, the distributed generations (DG) with controllable power i.e., electrolyzer (ES) and microturbine (MT) can be applied. This paper proposes a heuristic optimization based-fixed structure robust H<inf>∞</inf> loop shaping controller design with automatic weights selection of controllable DGs for microgrid stabilization. To guarantee the system robust stability margin, the normalized coprime factorization is applied to represent unstructured uncertainties. The proportional integral (PI) is selected as the controller structure of ES and MT. For comparison purpose, the particle swarm optimization (PSO) and genetic algorithm (GA) are applied to optimize the PI parameters based on the H<inf>∞</inf> loop shaping design. Simulation results show that the PSO-based control design is superior to GA-based controller design in terms of computation efficiency, robustness against system uncertainties and stabilizing effect. © 2012 Praise Worthy Prize S.r.l. - All right reserved.
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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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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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Least squares support vector machine for power system stabilizer design using wide area phasor measurements
(2011-07-01)
Pahasa, Jonglak
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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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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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Coordinated SVC and AVR for robust voltage control in a hybrid wind-diesel system
(2010-12-01)
Vachirasricirikul, Sitthidet
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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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GA-based support vector machines for adaptive power system damping controller of SMES
(2010-07-30)
Pahasa, Jonglak
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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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Ant colony optimisation for economic dispatch problem with non-smooth cost functions
(2010-06-01)
Pothiya, Saravuth
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Ngamroo, Issarachai
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Kongprawechnon, Waree
This paper presents a novel and efficient optimisation approach based on the ant colony optimisation (ACO) for solving the economic dispatch (ED) problem with non-smooth cost functions. In order to improve the performance of ACO algorithm, three additional techniques, i.e. priority list, variable reduction, and zoom feature are presented. To show its efficiency and effectiveness, the proposed ACO is applied to two types of ED problems with non-smooth cost functions. Firstly, the ED problem with valve-point loading effects consists of 13 and 40 generating units. Secondly, the ED problem considering the multiple fuels consists of 10 units. Additionally, the results of the proposed ACO are compared with those of the conventional heuristic approaches. The experimental results show that the proposed ACO approach is comparatively capable of obtaining higher quality solution and faster computational time. © 2009 Elsevier Ltd. All rights reserved.
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Robust frequency control of wind-diesel hybrid power system using superconducting magnetic energy storage
(2009-04-23)
Ngamroo, Issarachai
In an isolated wind-diesel hybrid power system, the variable power consumptions as well as the intermittent wind power may cause a large fluctuation of system frequency. If the system frequency can not be controlled and kept in the acceptable range, the system may lose stability. To reduce system frequency fluctuation, a superconducting magnetic energy storage (SMES) which is able to supply and absorb active power quickly, can be applied. In addition, variation of system parameters, unpredictable power demands and fluctuating wind power etc., cause various uncertainties in the system. A SMES controller which is designed without considering such uncertainties may lose control effect. To enhance the robustness of SMES controller, this paper focuses on a new robust control design of SMES for frequency control in a wind-diesel system. The coprime factorization is used to represent the unstructured uncertainties in a system modeling. The structure of a SMES controller is the practical first-order lead-lag compensator. To tune the controller parameters, the optimization problem is formulated based on loop shaping technique. The genetic algorithm is applied to solve the problem and achieve the control parameters. Simulation results confirm the high robustness of the proposed SMES controller with small power capacity against various disturbances and system uncertainties in comparison with SMES in the previous research. © 2009 The Berkeley Electronic Press. All rights reserved.

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