Title :
Identification of Maximum Loadability Limit under security constraints using Genetic Algorithm
Author_Institution :
Electr. Eng. Dept., Nat. Inst. of Technol. Durgapur, Durgapur, India
Abstract :
New simple real coded Genetic Algorithm (GA) is developed to solve the Maximum Loadability Limit (MLL) problem. MLL problem is formulated as maximization problem. As handling of real coded power flow variables are easier than binary coding, real coding of GA parameters is applied. Novel formulas are developed to update power flow parameters considering corresponding power mismatches. Utilizing decoupling properties of power system, mutation is implemented. To provide diversity, new parent selection in crossover section is adopted. The developed method is applied for test systems of IEEE 14, 30, 57 and 118 bus. Showing characteristics and results, the effectiveness and efficiency is established.
Keywords :
genetic algorithms; load flow; power system parameter estimation; power system security; MLL problem; crossover section; genetic algorithm; maximization problem; maximum loadability limit identification; parent selection; power flow parameters; power mismatch; power system decoupling properties; real coded power flow variables; security constraint; Convergence; Genetic algorithms; Load flow; Loading; Reactive power; Security; Genetic Algorithm; Maximum Loadability Limit; decoupling property;
Conference_Titel :
System Science and Engineering (ICSSE), 2011 International Conference on
Conference_Location :
Macao
Print_ISBN :
978-1-61284-351-3
Electronic_ISBN :
978-1-61284-472-5
DOI :
10.1109/ICSSE.2011.5961905
