Pradeep Jangir, P. Manoharan, Sundaram B. Pandya, R. Sowmya
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引用次数: 5
Abstract
This paper recommends a new Many-Objective Teaching-Learning-Based Optimizer (MaOTLBO) to handle the Many-Objective Optimal Power Flow (MaO-OPF) problem of modern complex power systems while meeting different operating constraints. A reference point-based mechanism is utilized in the basic version of Teacher Learning-Based Optimizer (TLBO) to formulate the MaOTLBO algorithm and directly applied to DTLZ test benchmark functions with 5, 7, 10-objectives and IEEE-30 bus power system with six different objective functions, namely the minimization of the voltage magnitude deviation, total fuel cost, voltage stability indicator, total emission, active power loss, and reactive power loss. The results obtained from the MaOTLBO optimizer are compared with the well-known standard many-objective algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) and Non-Dominated Sorting Genetic Algorithm-version-III (NSGA-III) presented in the literature. The results show the ability of the proposed MaOTLBO to solve the MaO-OPF problem in terms of convergence, coverage, and well-Spread Pareto optimal solutions. The experimental outcomes indicate that the suggested MaOTLBO gives improved individual output and compromised solutions than MOEA/D-DRA and NSGA-III algorithms.
针对现代复杂电力系统在满足不同运行约束条件下的多目标最优潮流问题,提出了一种新的多目标教学优化器(MaOTLBO)。在基于教师学习的优化器(TLBO)基础版中,利用基于参考点的机制制定了MaOTLBO算法,并直接应用于具有5、7、10个目标的DTLZ测试基准函数和具有电压幅值偏差最小、总燃油成本最小、电压稳定指标最小、总排放最小、有功损耗最小、无功损耗最小六个不同目标函数的IEEE-30母线电力系统。将MaOTLBO优化器得到的结果与文献中提出的基于动态资源分配分解的多目标进化算法(MOEA/D-DRA)和非支配排序遗传算法-版本- iii (NSGA-III)等知名标准多目标算法进行了比较。实验结果表明,与MOEA/D-DRA和NSGA-III算法相比,所提出的MaOTLBO算法具有更好的个体输出和折衷解。