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Multi-objective Optimization of Current Limiting Scheme Considering Constraint Conditions

机译:考虑约束条件的电流限制方案的多目标优化

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The multi-objective optimization of current limiting scheme for power system is a complex, nonconvex and nonlinear problem and it is more complex when considering constraint conditions. Aiming to minimize total investments and short-circuit capacity margin under constraint conditions, a multi-objective optimization model of current limiting scheme based on the fast and elitist non-dominated sorting in genetic algorithms is presented. In order to increase the iterative rate, penalty coefficient is changed from zero to the maximum during the iterative process. Moreover, the influence on impedance matrix caused by common current limiting measure is analyzed, and the sensitivity relation between current limiting measure and node self-impedance is proposed. The conditions when the algorithm is used in multi-objective optimization of current limiting scheme are discussed. Then, case studies performed on IEEE 39-node network indicate the effectiveness of the proposed model and method in simulation results.
机译:电力系统电流限制方案的多目标优化是复杂,非凸起和非线性问题,在考虑约束条件时更复杂。旨在最大限度地减少约束条件下的总投资和短路容量余量,提出了一种基于遗传算法中快速和精英非主导分类的电流限制方案的多目标优化模型。为了提高迭代速率,在迭代过程中,惩罚系数将从零变为最大值。此外,分析了对公共限制措施引起的阻抗矩阵的影响,提出了限制测量与节点自阻抗之间的灵敏度关系。讨论了算法在多目标优化中使用电流限制方案的条件。然后,在IEEE 39节点网络上执行的案例研究表明所提出的模型和方法在仿真结果中的有效性。

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