Paper Info
Title
Real-time stability enhancement based on neural fuzzy networks and genetic algorithms
Abstract
This papers presents an adaptive power system stabilizers (PSS) using an adaptive network based fuzzy inference systems (ANFIS) and the second version of non-dominated sorting genetic algorithms (NSGA-II). Firstly genetic algorithms are used to tune stabilizer parameters on a wide range of loading conditions to create a data base. Two eigenvalue-based objective functions are considered to place the closed-loop system eigenvalues in the D-shape sector. Then, ANFIS is employed to establish the relationship between these operating points and the corresponding stabilizer parameters. The proposed stabilizer has been tested by performing non linear simulations and eigenvalue analysis using single machine infinite bus (SMIB) model. The results show the effectiveness and the robustness of the proposed stabilizer to provide efficient damping in real-time.
Year
DOI
Venue
2013
10.1109/SSD.2013.6564060
Systems, Signals & Devices
Keywords
DocType
ISBN
closed loop systems,damping,eigenvalues and eigenfunctions,fuzzy neural nets,fuzzy reasoning,genetic algorithms,power system simulation,power system stability,real-time systems,sorting,anfis,d-shape sector,nsga-ii,pss,smib model,adaptive network based fuzzy inference systems,adaptive power system stabilizers,closed-loop system eigenvalues,eigenvalue analysis,eigenvalue-based objective functions,loading conditions,neural fuzzy networks,nondominated sorting genetic algorithms,nonlinear simulations,real-time damping,real-time stability enhancement,single machine infinite bus model,stabilizer parameter tuning,real time systems,generators,mathematical model
Conference
978-1-4673-6458-4
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Farah, A.111.03
Guesmi, T.200.34
Abdallah, H.H.300.34
Ouali, A.400.34