Reactive Power Optimization Using Genetic Algorithm
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This research applies genetic algorithm for reactive power optimization in the IEEE 33-bus test system. Genetic algorithm is a heuristic optimization method that mimics natural evolutionary processes to find optimal solutions. In reactive power optimization, the algorithm adjusts the magnitude and placement of reactive power resources to minimize power system losses and enhance system efficiency and stability. The implementation typically involves coding chromosome representations of control variables (such as capacitor bank settings and transformer tap positions), fitness functions calculating power loss minimization, and genetic operators including selection, crossover, and mutation. The IEEE 33-node case study evaluates the algorithm's performance through convergence analysis and solution quality assessment, demonstrating its practical value in power system optimization applications.
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