PSS with SVC Damping Controllers Coordinated Design and Real-Time Implementation in Multi-Machine Power System Using Advanced Adaptive PSO

2013 ◽  
Vol 14 (5) ◽  
pp. 487-498
Author(s):  
Rajendraprasad Narne ◽  
P.C. Panda
Keyword(s):  
Power System ◽  
Real Time ◽  
Optimization Problem ◽  
Test System ◽  
Swarm Optimization ◽  
Damping Controllers ◽  
Control Schemes ◽  
Controller Performance ◽  
Adaptive Pso ◽  
System Stabilizer

Abstract This article proposed coordinated tuning and real-time implementation of power system stabilizer (PSS) with static var compensator (SVC) in multi-machine power system. The design of proposed coordinated damping controller is formulated as an optimization problem, and the controller gains are optimized instantaneously using advanced adaptive particle swarm optimization. Here, PSS with SVC installed in multi-machine system is examined. The coordinated tuning among the damping controllers is performed on the non-linear power system dynamic model. Finally, the proposed coordinated controller performance is discussed with time-domain simulations. Different loading conditions are employed on the test system to test the robustness of proposed coordinate controller, and the simulation results are compared with four different control schemes. To validate the proposed controller, the test power system is also implemented on real-time (OPAL-RT) simulator, and acceptable results are reported for its verifications.

scholarly journals Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2471
Author(s):  
Tommaso Bradde ◽  
Samuel Chevalier ◽  
Marco De Stefano ◽  
Stefano Grivet-Talocia ◽  
Luca Daniel
Keyword(s):  
Dynamical Systems ◽  
Power System ◽  
System Dynamics ◽  
Real Time ◽  
Initial Conditions ◽  
Test System ◽  
Initial State ◽  
Power System Dynamics ◽  
Vector Fitting ◽  
Time Modeling

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system.

scholarly journals Optimal Location and Design of TCSC controller For Improvement of Stability

2011 ◽  
pp. 210-215
Author(s):  
Swathi Kommamuri ◽  
P. Sureshbabu
Keyword(s):  
Power System ◽  
Optimization Problem ◽  
Low Frequency ◽  
System Stability ◽  
Simulation Environment ◽  
Swarm Optimization ◽  
Stability Performance ◽  
Low Frequency Oscillations ◽  
Simulation Results ◽  
The Stability

Power system stability improvement by a coordinate Design ofThyristor Controlled Series Compensator (TCSC) controller is addressed in this paper.Particle Swarm Optimization (PSO) technique is employed for optimization of the parameterconstrained nonlinear optimization problem implemented in a simulation environment. The proposed controllers are tested on a weakly connected power system. The non-linear simulation results are presented. The eigenvalue analysis and simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system by efficient damping of low frequency oscillations under various disturbances.

Speaker-Independent Malay Syllable Recognition Using Singular and Modular Neural Networks

2012 ◽  
Author(s):  
Hua Nong Ting ◽  
Jasmy Yunus ◽  
Sheikh Hussain Shaikh Salleh
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

Designing Power System Stabilizer for Multimachine Power System Using Neuro-Fuzzy Algorithm

2012 ◽  
Author(s):  
M.F. Othman ◽  
M. Mahfouf ◽  
D.A. Linkens
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

Desain Power System Stabilizer Berbasis Fuzzy dan Particle Swarm Optimization

2019 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Tamaji
Keyword(s):  
Particle Swarm Optimization ◽  
Feedback Control ◽  
Power System ◽  
State Feedback ◽  
The State ◽  
State Feedback Control ◽  
Power System Stabilizer ◽  
Feedback Gain ◽  
Swarm Optimization ◽  
System Stabilizer

One important factor to produce  a qualified electricity is the stability of the system.  An unstable system resulted  an undamped oscilation of system, and the stable system can damp the oscilation quickly. Therefore, it is necessary to apply  a stability device to a power system and it is called a Power System Stabilizer (PSS). One of stability design is a feedback control design. Here, in this research, the state feedback control are designed for Single Machine Infinite Bus (SMIB) . The SMIB model is non linear therefore the feedback control can’t be designed directly. Some researchers do linearize the system before design the feedback control.  In this research, a nonlinear model of SMIB is build in a state space form. Subsequently, a fuzzification Takagi-Sugeno is applied. The state feedback controls are applied to design the control of SMIB fuzzy system, a state feedback gain is determined using method Routh Hurwitz. The determining the parameter of state feedback gain influence the performance of SMIB. Therefore, it is important to determine the suitable parameter such that the SMIB has the optimal performance. The Particle Swarm Optimization (PSO) is applied to optimaze the performance of SMIB. In these research, it is compared the performance of SMIB by applying between Routh Hurwitz, fuzzy Routh Hurwitz, PSO fuzzy Routh Hurwitz for state feedback control. The simulation result show that Performance of SMIB using The PSO Fuzzy Routh  Hurwitz state feedback can improve the performance of SMIB, but the performance of Efd become oscillate and this method influence by the chosen parameter.

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