基于改进教与学优化算法的配电网重构

正常运行工况下的配电网重构能降低配电网损耗。近年来新兴的教与学算法具有自有参数少、简单易懂、收敛迅速等优点，十分适合多目标、多约束的配电网重构优化问题求解。以网损最小和开关操作次数为目标，并考虑运行经济成本，将教与学算法引入到配电网优化重构中，对基本教与学算法中的教学因子进行了自适应改进，给出了算法的编码策略、迭代过程中“学生”信息的修改原则。PG&E 69节点系统以及一个实际城区配电网的优化重构仿真结果表明所提改进算法的有效性。

Reconfiguration of distribution network based on improved teaching-learning-based optimization algorithm

The reconfiguration of distribution network can reduce the loss of positive power in normal condition. There are many optimization algorithms for the reconfiguration of distribution network. As an emerging algorithm, Teaching-Learning-Based Optimization (TLBO) Algorithm has some advantages. For example, the algorithm has less algorithm-specific parameters, and is easy to understand and converge quickly. So TLBO is very suitable for the reconfiguration of distribution networks which is a multi-objective and multi-constraint problem. This paper chooses the minimization of the net loss and the switch operation times as the objectives, considers the costs of operations, and introduces TLBO into the research of distribution network reconfiguration. It elaborates on the coding strategy and the principle of modification of individual during iterations and self-adaptive modification of teaching factor in TLBO algorithm. The simulation results of PG&E 69 and a practical distribution network of a city validate the effectiveness of the improved TLBO proposed. This work is supported by Research Fund for the Doctoral Program of Higher Education of China (No. 20110141110032).