Coordination of synergetic excitation controller and SVC damping controller using particle swarm optimization

Ademoye, Taoridi; Feliachi, A.; Karimi, Ali

doi:10.1109/pes.2011.6039428

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…In this study, firstly a brief description of gas turbine performance is presented. Then, a rebuilt SIMULINK model of a low power gas turbine based on a previous research by [4]. Is briefly presented.…”

Section: Review Of Related Workmentioning

confidence: 99%

Development of System Identification Fault in Gas Turbine Using Neural Network

O¹

2016

IJAREEIE

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System identification is an important way of investigating and understanding the world around. Identification is a process of deriving a mathematical model of a predefined part of the world, using observations. There are several different approaches of system identification, and these approaches utilize different forms of knowledge about the system In this research, a SIMULINK based Neural Tool has been developed for analysis and design of multivariable neural based control systems. This tool has been applied to the control of a high purity distillation column of a gas plant. The proposed control scheme offers an optimal response for both theoretical and practical challenges posed in process control task, in particular when both, the quality improvement of gas products and the operation efficiency in economical terms are considered. The code consisted of various training functions, different number of neurons as well as a variety of transfer (activation) functions for hidden and output layers of the network. It was shown that the optimal model for a two-layer network with MLP structure, consisted of 25 neurons in its hidden layer and used trainlmas its training function, as well as tansigand logsidas its transfer functions for the hidden and output layers. It was also observed that trainlmhas a superior performance in terms of minimum MSE, compared with each of the other training functions. The resulting model could predict performance of the system with high accuracy.

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Section: Review Of Related Workmentioning

confidence: 99%

Development of System Identification Fault in Gas Turbine Using Neural Network

O¹

2016

IJAREEIE

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“…Synergetic control design techniques have been applied and tested to give better performance than conventional power system stabilizers [33,47,48]. In [33], the technique was employed to design a nonlinear control for a Single Machine Infinite Bus (SMIB) system.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Section: Reinforcement Learningmentioning

confidence: 99%

“…In [48], decentralized synergetic controllers are coordinated with SVC-damping controllers. PSO technique is employed in both [47,48] to obtain optimal value for each of the controller's parameters. In this paper, PSO is chosen to select optimal values of the controller parameters and RL is added so as to improve the performance of the controllers for a wide range of the system's operating conditions.…”

Section: Reinforcement Learningmentioning

confidence: 99%

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Decentralized Synergetic Control of Power Systems

Ademoye¹

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“…For designing the controller, the main design issue is choosing suitable manifold. In [26], [47], a polynomial differential equation for the error is used for making small, slowly varying tracking errors decay. The controller is based on approach used by Slotine and…”

Section: Synergetic Controller Using Classical Modelmentioning

confidence: 99%

Robust Control Design for Non-Linear Loads in AC-DC System

Fazal¹

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Electric power system is complex interconnected system that is subjected to rapid change in its structure. Addition of new equipment, operation close to stability limits and variation in load in summer and winter are some factors that make power system unstable and introduce electro-mechanical oscillations in the system. Electro-mechanical oscillations of small magnitude often persist for long periods of time and restrict the power transfer capacity. These oscillations can further increase with the variation in load and system operating points. To stabilize the power system different control strategies are used to damp the oscillations. In the past, the dynamic stability of power system was modeled using linearized modeling of power system at operating points. Linear eigenvalue controllers damp the oscillation to some extent but are not robust for disturbance and variation in system operating points and therefore can render system unstable. Non-linear controller design has found extensive application in robotics for stabilizing system. Synergetic Controller based on Synergetic Control Theory, is a non-linear controller that forces the system towards global stability and has extensively been used in power electronics. In this research, a Single Machine Infinite Bus (SMIB) with HVDC link is considered for comparing the performance of linear and non-linear controllers. The test system has Static Var Compensator (SVC) connected on load bus. Reactive modulation is performed through SVC to damp oscillations in the system. The Synergetic Controller is designed for full transient model and the classical model of the synchronous machine. The performance of both non-linear controller and linear controller is compared for different disturbance and variation in system parameters. Results show the robustness of non-linear controller which damps the oscillations effectively. I would like to especially thank my adviser Dr. Muhammad Akram Choudhry for his guidance, support and encouragement throughout this research and my Master's program. He has impacted into my life an immense deal of knowledge and without him, this work would not have been accomplished. I am very grateful to Dr. Ali Feliachi for his advice, support and guidance. I would like to also thank Dr. Vinod Kulathumani for his time and guidance. Also, I appreciate all the faculty members who have taught, encouraged and guided me in special and different ways. Special thanks goes to my parents and my family members. Due to their immense support I am able to complete this work.

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Coordination of synergetic excitation controller and SVC damping controller using particle swarm optimization

Cited by 9 publications

References 7 publications

Development of System Identification Fault in Gas Turbine Using Neural Network

Development of System Identification Fault in Gas Turbine Using Neural Network

Decentralized Synergetic Control of Power Systems

Robust Control Design for Non-Linear Loads in AC-DC System

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