基于改进TLBO算法的输电网规划

输电网规划问题维数高、变量多以及约束条件复杂,导致问题难于求解。本文采用新型的智能算法教与学算法(TLBO)对问题进行求解。教与学算法具有收敛速度快、设置参数少的优点,但在求解时容易陷入局部最优解。本文通过加入自主学习环节和反思环节以及自适应扰动策略,提高算法寻找全局最优解的能力,使其适应大规模输电网规划问题的求解。采用目标函数为线路投资费用、网损费用、过负荷费用之和的输电网规划模型,通过在Garver-6节点系统和IEEE-18节点系统中的计算,验证了该算法可以正确有效地解决输电网规划问题。

Transmission network planning on improved teaching-learning-based optimization algorithm

The transmission network planning problem has high dimensionality, many variables and complex constraints, which makes the problem difficult to solve. This paper uses a new intelligent algorithm teaching-learning-based optimization (TLBO) to solve the problem. The proposed teaching-learning optimization algorithm has the advantages of fast convergence speed and few setting parameters, but it is easy to fall into the local optimal solution when solving. By adding the independent learning link and reflection link and the adaptive disturbance strategy, this paper improves the ability of the algorithm to find the global optimal solution, and adapts it to the solution of large-scale transmission network planning problem. The transmission network planning model with the objective function is the sum of line investment cost, network loss cost and overload cost. Through the calculation in the Garver-6 node system and the IEEE-18 node system, the correctness and effectiveness of the algorithm applied to the transmission network planning are verified.