Genetic algorithmbased preventiveAnd corrective control approachesFor power system securityEnhancement

Abstract

The ability of a power system to withstand the occurrence of newlinecontingencies like outage of transmission line or generator without any newlineuntoward incident is called the security of the system Action must be taken if newlinethe operating state of the power system is found to be insecure Security newlineConstrained Optimal Power Flow SCOPF is the main tool used in the newlineenergy control centers to enhance the security of the system newlineSeveral optimization techniques such as linear programming nonlinear newlineprogramming and the integer programming method have been used for newlinesolving the SCOPF problem But these conventional optimization techniques newlinehave the common weakness of requiring a differentiable objective function newlineconvergence to local optima and difficulty in dealing with discrete variables newlineIn order to overcome these difficulties evolutionary computation techniques newlinelike the Genetic Algorithm GA have been proposed to solve the SCOPF newlineproblem This thesis proposes an improved GA to solve the security newlineconstrained optimal power flow problem In the proposed algorithm newlinecontinuous variables are represented as floating point numbers and discrete newlinevariables are represented as integers This type of representation has a number newlineof advantages over binary coding The efficiency of the GA is increased as newlinethere is no need to convert the solution variables to the binary type Moreover newlineless memory is required Further crossover and mutation operators which can newlinedirectly operate on the mixed string are proposed newline newline