Pseudo-parallel genetic algorithm for reactive power optimization
Zi-Hao Wang, Xuanhua Yin, Zheng Zhang, Jun Yang
Abstract
Zi-Hao Wang, Xuanhua Yin, Zheng Zhang, Jun Yang
Abstract
In this paper, static voltage stability is considered in reactive optimization via the minimum singular value of the Jacobian matrix of converged power flow, and a pseudo-parallel genetic algorithm is introduced to find the global optimal results and avoid premature of conventional simple genetic algorithm. Two simple test systems are employed to verify the effectiveness of the proposed model and algorithm. Simulations results show that both operational and economical performances of test power systems are improved after optimization, and either the optimal results or the convergent characteristics of the proposed algorithm are superior to those of the simple genetic algorithm.
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In this paper, static voltage stability is considered in reactive optimization via the minimum singular value of the Jacobian matrix of converged power flow, and a pseudo-parallel genetic algorithm is introduced to find the global optimal results and avoid premature of conventional simple genetic algorithm. Two simple test systems are employed to verify the effectiveness of the proposed model and algorithm. Simulations results show that both operational and economical performances of test power systems are improved after optimization, and either the optimal results or the convergent characteristics of the proposed algorithm are superior to those of the simple genetic algorithm.
Key concepts: Jacobian matrix and determinant, Genetic algorithm, AC power, Computer science, Simple (philosophy), Meta-optimization, Algorithm, Mathematical optimization