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首页> 外文期刊>Neural computing & applications >Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems
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Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems

机译:改进了混沌的随机分数态搜索算法,以实现分配系统分布式发电机的位置,尺寸和数量的最佳确定

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摘要

In the power system operation, the reduction of the power loss in distribution systems has significance in the reduction of operating cost. In this paper, a novel chaotic stochastic fractal search (CSFS) method is implemented for determining the optimal siting, sizing, and number of distributed generation (DG) units in distribution systems. The objective of the optimal DG placement problem is to minimize the power loss in distribution systems subject to the constraints such as power balance, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. The proposed CSFS method improves the performance of the original SFS by integrating chaotic maps into it. On the other hand, ten chaotic maps are utilized to replace the random scheme of the original SFS to enhance its performance in terms of accuracy of solution and convergence speed, corresponding to ten chaotic variants of the SFS where variant being chosen is the best chaotic variant regarding search performance. For solving the problem, the CSFS is implemented to simultaneously find the optimal siting and sizing of DG units and the optimal number of DG units will be obtained via comparing optimal results from different numbers of DG in the problem. The proposed method is tested on the IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the CSFS are verified by comparing to those from the original SFS and other methods in the literature. The result comparisons indicate that the proposed CSFS method can obtain higher quality solutions than the original SFS version and many other methods in the literature for the considered cases of the test systems. Moreover, the incorporation of chaos theory allows performing the search process at higher speeds. Therefore, the proposed CSFS method can be a very promising method for solving the problem of optimal placement of DG units in distribution systems.
机译:在电力系统操作中,分配系统中功率损耗的降低在减少运营成本方面具有重要意义。在本文中,实施了一种新的混沌随机分形搜索(CSFS)方法,用于确定分配系统中的最佳选址,尺寸和分布式发电(DG)单元的最佳选址和数量。最佳DG放置问题的目的是最小化经受受电力平衡,总线电压限制,DG容量限制,电流限制和DG穿透限制的限制的分配系统中的功率损失。所提出的CSFS方法通过将混沌映射集成到其中,提高了原始SFS的性能。另一方面,10个混沌映射用于更换原始SFS的随机方案,以提高其在溶液和收敛速度的精度方面的性能,对应于所选择的变体的SF的10个混沌变体是最好的混沌变体关于搜索性能。为了解决问题,实现CSF以同时找到DG单位的最佳选址和尺寸,并且通过在问题中的不同数量的DG中比较最佳结果来获得最佳的DG单元。该方法在IEEE 33总线,69总线和118母线径向分配系统上进行测试。通过与文献中的原始SFS和其他方法的比较来验证来自CSF的所得结果。结果比较表明,所提出的CSFS方法可以获得比原始SFS版本和文献中的许多其他方法获得更高质量的解决方案,以便考虑测试系统的情况。此外,Chaos理论的融合允许以更高的速度执行搜索过程。因此,所提出的CSFS方法可以是解决分配系统中DG单位的最佳位置问题的非常有希望的方法。

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