Performance study of power system stabilizer of the form of state feedback control with state estimator

2015 ◽  
Author(s):  
Mou Das Mahapatra ◽  
Jayati Dey ◽  
Saradindu Ghosh
Keyword(s):  
Feedback Control ◽  
Power System ◽  
State Feedback ◽  
State Feedback Control ◽  
Power System Stabilizer ◽  
Performance Study ◽  
State Estimator ◽  
System Stabilizer

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.

Desain Power System Stabilizer Berbasis Fuzzy dan Particle Swarm Optimization

2019 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Tamaji 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 qualified electricity is the stability of the system.  An unstable system resulted in 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 is 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. 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 optimize the performance of SMIB. In this research, it is compared to the performance of SMIB by applying between Routh Hurwitz, fuzzy Routh Hurwitz, PSO fuzzy Routh Hurwitz for state feedback control. The simulation result shows 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 influenced by the chosen parameter

scholarly journals State-feedback control with a full-state estimator for a cart-inverted pendulum system

2018 ◽  
Vol 7 (4.44) ◽  
pp. 203
Author(s):  
Indrazno Siradjuddin ◽  
Zakiyah Amalia ◽  
Erfan Rohadi ◽  
Budhy Setiawan ◽  
Awan Setiawan ◽  
...  
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
Inverted Pendulum ◽  
System Modeling ◽  
The State ◽  
State Feedback Control ◽  
Underactuated System ◽  
State Estimator ◽  
Pendulum System ◽  
Inverted Pendulum System

A Cart Inverted Pendulum System is an unstable, nonlinear and underactuated system. This makes a cart inverted pendulum system used as a benchmark for testing many control method. A cart must occupy the desired position and the angle of the pendulum must be in an equilibrium point. System modeling of a cart inverted pendulum is important for controlling this system, but modeling using assumptions from state-feedback control is not completely valid. To minimize unmeasured state variables, state estimators need to be designed. In this paper, the state estimator is designed to complete the state-feedback control to control the cart inverted pendulum system. The mathematical model of the cart inverted pendulum system is obtained by using the Lagrange equation which is then changed in the state space form. Mathematical models of motors and mechanical transmissions are also included in the cart inverted pendulum system modeling so that it can reduce errors in a real-time application. The state gain control parameter is obtained by selecting the weighting matrix in the Linear Quadratic Regulator (LQR) method, then added with the Leuenberger observer gain that obtained by the pole placement method on the state estimator. Simulation is done to determine the system performance. The simulation results show that the proposed method can stabilize the cart inverted pendulum system on the cart position and the desired pendulum angle. 

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