Abstract
Cooperative Co-evolutionary Approach (CCA) is a new architecture of evolutionary computation. Based on CCA, the paper proposes a new method for reactive power optimization problem in power system, which is non-convex, non-linear, discrete, and usually with a large number of control variables. According to the decomposition-coordination principle, the reactive power optimization problem is decomposed into a number of sub-problems, which is optimized by a single evolutionary algorithm population. The populations interact with each other through a common system model and co-evolve and result in the continuous evolution of the whole system. The reactive power optimization problem is solved when the co-evolutionary process ends. Simulation results show that compared with conventional Genetic Algorithm (GA), CCA not only can obtain better optimal results, but also has better convergence property. CCA reduce the over-long computational time of GA and is more suitable for solving large-scale optimization problems.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, J., Wang, W., Wang, X., Chen, H., Wang, X. (2006). Cooperative Co-evolutionary Approach Applied in Reactive Power Optimization of Power System. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_85
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DOI: https://doi.org/10.1007/11881070_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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