Experimental design and genetic algorithm optimization of a fuzzy-logic supervisor for embedded electrical power systems

2013 ◽  
Vol 91 ◽  
pp. 91-107 ◽  
Author(s):  
Stefan Breban ◽  
Christophe Saudemont ◽  
Sébastien Vieillard ◽  
Benoît Robyns
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Experimental Design ◽  
Power Systems ◽  
Electrical Power ◽  
Algorithm Optimization ◽  
Electrical Power Systems ◽  
Genetic Algorithm Optimization

scholarly journals Comparative performance analysis of hybrid differential evolution and pattern search technique for frequency control of the electric power system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pratap Chandra Pradhan ◽  
Rabindra Kumar Sahu ◽  
Sidhartha Panda
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Power System ◽  
Differential Evolution ◽  
Frequency Control ◽  
Electrical Power ◽  
Pattern Search ◽  
Thermal System ◽  
Electrical Power Systems ◽  
Suggested Approach

AbstractIn the current situation, operation and control of power system is a greater challenge. The most significant situation in power system control is load frequency control. In the present work, a hybrid differential evolution and pattern search (hDE-PS) method has been suggested for frequency regulation of electrical power systems. Fractional-order proportional integral derivative (FOPID) controller is implemented for design and analysis purpose. The suggested control method has been applied for two electrical power systems model, i.e., 2-area diverse source power system with/without HVDC linkage and 2-area thermal system. The performances of the suggested controller have been evaluated with PID and optimal controller. The simulation results indicate that system performances are enhanced with the suggested approach for identical structure. Robustness of the suggested approach has been analyzed by variation in random load and the system parameters. The suggested method (hDE-PS tuned FOPID) is further investigated with a 2-area thermal system. The performance of the recommended approach is analyzed by equating the results with other newly available approaches, like Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO), hybrid BFOA and PSO (hBFOA-PSO), multi-objective Non-dominated Sorting Genetic Algorithm (NSGA)-II and Firefly Algorithm for the similar structure.

Sign in / Sign up

Export Citation Format

Share Document