混沌模拟树木生长算法在无功优化中的应用

Chaotic trees growth simulation algorithm for reactive power optimization

针对模拟树木生长算法在求解大规模电力系统无功优化问题中存在的收敛稳定性差、难以找到全局最优解的缺点,提出了新的无功优化算法——混沌模拟树木生长算法(CTGSA)。该方法利用混沌优化所具有的对初值敏感性和遍历性的特点,在模拟树木生长寻优过程中引入混沌移民操作来改善生长点集中可行解的质量、增加可行解的多样性,从而提高算法的收敛稳定性和寻优精度。将该算法应用于IEEE 30节点系统,结果表明该算法具有较强的全局优化能力和收敛稳定性。

As trees growth simulation algorithm （TGSA） has the disadvantage of bad convergence stability and it is difficult to find the global optimal solution in solving large scale reactive power optimization problem, a new reactive power optimization algorithm called chaotic trees growth simulation algorithm （CTGSA） is presented by using the characters that chaos optimization algorithm is sensitive to initial value and has ergodicity. An operator called chaos immigrant is introduced to the process of TGSA to improve the quality of feasible solutions in the growing point collection and keep the diversity of feasible solutions, by which the new algorithm has better convergence stability and optimization precision. When the algorithm is used for IEEE 30-bus system, the results show that the algorithm has strongly global optimization ability and convergence stability.