2004Unpublished venueRequires access

Pseudo-parallel genetic algorithm for reactive power optimization

Zi-Hao Wang, Xuanhua Yin, Zheng Zhang, Jun Yang

Open publisher page 13 citations

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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What this paper is about

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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OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Jacobian matrix and determinant, Genetic algorithm, AC power, Computer science, Simple (philosophy), Meta-optimization, Algorithm, Mathematical optimization

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