Research on Reactive Power Optimization Based on Immunity Genetic Algorithm.
ABSTRACT This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive
power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and
adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the
multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist
are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter
of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE
30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than
current known algorithms.
Conference Proceeding: Optimal reactive power dispatch using an adaptive genetic algorithm[show abstract] [hide abstract]
ABSTRACT: This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, p<sub>c</sub> and p<sub>m</sub>, are varied depending on the fitness values of the solutions and the normalised fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on the IEEE 30-bus power systemGenetic Algorithms in Engineering Systems: Innovations and Applications, 1997. GALESIA 97. Second International Conference On (Conf. Publ. No. 446); 10/1997
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ABSTRACT: This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers/banks still remain as a difficult one. The genetic algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple paths and treat integer problems naturally. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities. Although this method is not as fast as sophisticated traditional methods, the concept is quite promising and useful in the coming computer ageIEEE Transactions on Power Systems 06/1994; · 2.92 Impact Factor
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ABSTRACT: This paper presents an improved simple genetic algorithm developed for reactive power system planning. Successive linear programming is used to solve operational optimization sub-problems. A new population selection and generation method which makes the use of Benders' cut is presented in this paper. It is desirable to find the optimal solution in few iterations, especially in some test cases where the optimal results are expected to be obtained easily. However, the simple genetic algorithm has failed in finding the solution except through an extensive number of iterations. Different population generation and crossover methods are also tested and discussed. The method has been tested for 6 bus and 30 bus power systems to show its effectiveness. Further improvement for the method is also discussed.IEEE Transactions on Power Systems 12/1995; · 2.92 Impact Factor