Abstract
The dimension of control variables in dynamic reactive power optimization would increase rapidly with the enlargement of power system. A two-phase optimization method was proposed in this paper to confine the regulation times of control equipments. In the first phase, a static optimal model solved by the improved genetic algorithm was established for each time-interval to find several optimal states for the second phase optimization. In the second phase, dynamic programming and genetic algorithm were used to determine the shortest transition path of states to meet the restrictions of regulation times. Furthermore, this method can implement parallel computing easily to be suitable for online applications. The results of test system show the proposed method has a good performance in convergence speed and global optimization.
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Zhang, Bh. et al. (2009). Two-Phase Dynamic Reactive Power Optimization Based on Improved Genetic Algorithm. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_67
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DOI: https://doi.org/10.1007/978-3-642-01510-6_67
Publisher Name: Springer, Berlin, Heidelberg
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