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Reactive Power Optimization Algorithm of Active Distribution Network Based on Improved Quantum Particle Swarm Optimization

机译:基于改进量子粒子群优化的主动分配网络无功优化算法

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摘要

The particle swarm optimization (PSO) algorithm is prone to local optimal position in the late iteration when solving the reactive power optimization problem of active distribution network. Aiming at the above problems, a reactive power optimization algorithm, improved quantum particle swarm optimization (IQPSO), is proposed in this paper. Based on quantum particle swarm optimization (QPSO) algorithm, the inverse point of worst particle position in the iteration process is introduced into the update formula of particle position, which makes the particle jump out of the local optimal position constraint in the iterative process. In this paper, an optimization model is established with the aim of minimum voltage deviation and system loss in a typical 34 node system, and the effectiveness of proposed method is verified by comparing with QPSO.
机译:在解决主动分配网络的无功功率优化问题时,粒子群优化(PSO)算法容易出现在后期迭代中的局部最佳位置。针对上述问题,本文提出了一种无功优化算法,改善量子粒子群优化(IQPSO)。基于量子粒子群优化(QPSO)算法,将迭代过程中最差粒子位置的逆点引入粒子位置的更新公式中,使得粒子跳出迭代过程中的局部最佳位置约束。本文通过典型34节点系统中的最小电压偏差和系统损耗建立了优化模型,通过与QPSO相比,验证了所提出的方法的有效性。

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