基于改进教与学算法的配电网无功优化

Reactive Power Optimization of Distribution Power System Based on Improved Teaching-Learning Optimization Algorithm

在解决配电网无功优化问题中,智能启发式算法得到了广泛应用,但仍存在一些不足。采用了教与学优化算法求解含分布式电源的配电网无功优化问题。教与学优化算法算法简单,取消了其他智能算法求解时需设定的控制参数,收敛速度快,收敛能力强。现将精英策略引入教与学算法,改进了该算法的搜索能力,提高了求解的稳定性。以有功网损最小为目标建立了无功优化模型,并基于改进的IEEE 33母线配电网系统进行仿真计算,结果验证了基于精英策略改进的教与学算法具有更强的收敛性和鲁棒性,能获得更好的优化结果,为配电网无功优化问题求解提供了一种新的方法和思路。

Reactive power optimization（RPO） of the distribution power system has received extensive attention, and intelligence heuristic algorithm has been widely applied, but there are still some shortcomings. This paper uses Teaching- Learning-Based Optimization（TLBO）to solve the reactive power optimization problem. TLBO is simple and has better convergence ability and quick convergence rate. More importantly, it does not need the control parameters which should be set in other intelligence algorithms. In this paper, the elitist strategy has been introduced in TLBO to further strengthen the searching ability and improve stability of the algorithm. The paper chooses the minimization of the active network loss as the optimization objective and builds the mathematical model of RPO problem. The improved IEEE33 bus system is adopted to test the algorithm. The results show that the Elitist Teaching- Learning Based Optimization （ETLBO） has higher robustness and convergence and better optimization results. The paper provides a new method and some ideas to solve the RPO problem of the distribution power system.