Chary G Veera Bhadra, Rosalina K Mercy
Department of Electrical and Electronics Engineering, VFSTR deemed to be University, Vadlamudi, Guntur, A.P, 522213, India.
Heliyon. 2023 Feb 14;9(3):e13387. doi: 10.1016/j.heliyon.2023.e13387. eCollection 2023 Mar.
This paper proposed an offset measured least regression based ALO to solve ORPD and ELD problems of IEEE 57 bus system designed with different transmission line models. These two problems are highly non-linear and non-convex defiance optimization of problem. The solution of ALO depends on exploration and exploitation if the difference between local and global variables is large, therefore chance to miss the best optimal solution. The weighted elitism phase of the algorithm gives diversified results because exploration is more biased toward elite particles. Which is due to decreasing of random walk to achieve the convergence characteristics. The proposed LSR-EALO can balance both exploration and exploitation, which improves the solution of optimization problem. Simulation is performed with proposed method on different IEEE 57 bus power system models, such as the positive sequence, 3-Phase PI, and distributed CP transmission lines based power systems, and lumped PI lines based low voltage hardware model (LVHM). In this paper, the ORPD problem was used to describe control variables like generator voltage, tap changers of transformers, and switching of capacitor banks subjected to power loss minimization function. Also, described voltage deviation and voltage stability index. Similarly, the ELD was described the active power allocation among generators to meet the sum of load demand and losses in the systems at minimum fuel cost function. And in depth analysis of the optimization results shows accuracy of control variables in ORPD and ELD problems. Also, the effectiveness of proposed method was also verified by comparing results with other meta heuristic algorithms.
本文提出了一种基于偏移测量最小回归的自适应学习优化算法（ALO），以解决采用不同输电线路模型设计的IEEE 57节点系统的最优潮流（ORPD）和经济负荷调度（ELD）问题。这两个问题都是高度非线性和非凸的挑战性优化问题。如果局部变量和全局变量之间的差异很大，ALO的解决方案取决于探索和利用，因此有可能错过最佳最优解。该算法的加权精英阶段给出了多样化的结果，因为探索更倾向于精英粒子。这是由于随机游走的减少以实现收敛特性。所提出的基于最小二乘回归的增强型自适应学习优化算法（LSR - EALO）可以平衡探索和利用，从而改善优化问题的解。使用所提出的方法对不同的IEEE 57节点电力系统模型进行了仿真，例如基于正序、三相PI和分布式CP输电线路的电力系统，以及基于集总PI线路的低压硬件模型（LVHM）。在本文中，ORPD问题用于描述诸如发电机电压、变压器分接头变换器和电容器组切换等控制变量，以实现功率损耗最小化函数。此外，还描述了电压偏差和电压稳定指标。同样，ELD描述了发电机之间的有功功率分配，以满足系统中负荷需求和损耗之和的最小燃料成本函数。对优化结果的深入分析表明了ORPD和ELD问题中控制变量的准确性。此外，通过将结果与其他元启发式算法进行比较，也验证了所提方法的有效性。
