Genetic Algorithm Based Parameters Identification for Power Transformer Thermal Overload Protection

2001 ◽  
pp. 308-311
Author(s):  
V. Galdi ◽  
L. Ippolito ◽  
A. Piccolo ◽  
A. Vaccaro
Keyword(s):  
Genetic Algorithm ◽  
Power Transformer ◽  
Parameters Identification ◽  
Overload Protection

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

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