scholarly journals Power System Stabilizer Driven by an Adaptive Fuzzy Set for Better Dynamic Performance

10.14311/810 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
H. F. Soliman ◽  
A.-F. Attia ◽  
M. Hellal ◽  
M. A. L. Badr
Keyword(s):  
Power System ◽  
Fuzzy Set ◽  
Fuzzy Logic Controller ◽  
Dynamic Performance ◽  
Reference Value ◽  
Rate Of Change ◽  
Power System Stabilizer ◽  
Computational Time ◽  
Adaptive Fuzzy ◽  
System Stabilizer

This paper presents a novel application of a fuzzy logic controller (FLC) driven by an adaptive fuzzy set (AFS) for a power system stabilizer (PSS).The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS). Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced. 

scholarly journals Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

10.14311/1518 ◽  
2012 ◽  
Vol 52 (2) ◽  
Author(s):  
Y. A. Al-Turki ◽  
A.-F. Attia ◽  
H. F. Soliman
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Set ◽  
Fuzzy Logic Controller ◽  
Fuzzy Model ◽  
Rate Of Change ◽  
Computational Time ◽  
Adaptive Fuzzy ◽  
Wide Range ◽  
Adaptive Fuzzy Logic

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.

Robust adaptive fuzzy logic power system stabilizer

2009 ◽  
Vol 36 (10) ◽  
pp. 12104-12112 ◽  
Author(s):  
T. Hussein ◽  
M.S. Saad ◽  
A.L. Elshafei ◽  
A. Bahgat
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Power System Stabilizer ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic ◽  
Robust Adaptive ◽  
System Stabilizer

scholarly journals Dynamic Stability and Analysis of SMIB system with FLC Based PSS including Load Damping Parameter Sensitivity

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Jagadeesh Pothula ◽  
Ch. Durga Prasad ◽  
M. Sai Veerraju
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Dynamic Analysis ◽  
Single Machine ◽  
Fuzzy Logic Controller ◽  
Parameter Sensitivity ◽  
Power System Stabilizer ◽  
Pid Controllers ◽  
Damping Parameter ◽  
System Stabilizer

This paper studies Dynamic Analysis and Stability of Single machine connected to infinite bus (SMIB) with power system stabilizer (PSS) in presence of Fuzzy logic controller (FLC) including load damping parameter sensitivity. Here PSS is modeled using fuzzy logic controller and the response is compared with the responses of the system in presence of conventional PI, PID controllers including load damping parameters sensitivity. In case of FLC based PSS the responses are compared different load damping parameters. Matlab-Simulink is used to test the results.

Optimal Design of Modified Power System Stabilizer Using Multi Objective Based Bio Inspired Algorithms

2018 ◽  
Vol 7 (4) ◽  
pp. 17-55 ◽  
Author(s):  
Dasu Butti ◽  
Siva Kumar Mangipudi ◽  
Srinivasarao Rayapudi
Keyword(s):  
Power System ◽  
Dynamic Performance ◽  
Operating Conditions ◽  
Power System Stabilizer ◽  
Value Analysis ◽  
Multi Objective ◽  
De Algorithm ◽  
System Data ◽  
Bus Voltage ◽  
System Stabilizer

In this article, a multi objective and a novel objective based Power System Stabilizer (PSS) design is proposed for a modified Heffron - Philiphs model (MHP) using bio inspired algorithms. A conventional Heffron – Philphs (CHP) model is developed by taking infinite bus voltage as reference, whereas MHP model is developed by taking transformer high voltage bus voltage as reference, which makes independent of external system data for the PSS design. PSS parameters are optimized using differential evolution (DE) algorithm and Firefly (FF) algorithm to obtain better dynamic response. The proposed method is tested on various operating conditions under different typical disturbances to test efficacy and robustness. Simulation results prove that better dynamic performance is obtained with the proposed stabilizers over the fixed gain stabilizers. This method of tuning would become a better alternative to conventional stabilizers as conventional stabilizers require retuning of parameters mostly when operating condition changes, which is a time-consuming process and laborious. Eigen value analysis is also done to prove the efficacy of the proposed method over the conventional methods.

Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer

2010 ◽  
Vol 80 (12) ◽  
pp. 1428-1436 ◽  
Author(s):  
T. Hussein ◽  
M.S. Saad ◽  
A.L. Elshafei ◽  
A. Bahgat
Keyword(s):  
Power System ◽  
Power System Stabilizer ◽  
Adaptive Fuzzy ◽  
System Stabilizer
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