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首页> 外文期刊>Electric power systems research >Considering uncertainty in the multi-objective stochastic capacitor allocation problem using a novel self adaptive modification approach
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Considering uncertainty in the multi-objective stochastic capacitor allocation problem using a novel self adaptive modification approach

机译：使用新型自适应修改方法考虑多目标随机电容器分配问题的不确定性

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摘要

This paper suggests a new stochastic method based on point estimate method (PEM) to consider the uncertainty effects in the optimal capacitor placement problem. The proposed stochastic method will capture the uncertainty associated with the forecast errors of active and reactive loads as well as the cost function coefficients, concurrently. The objective functions to be investigated are the (1) active power losses, (2) voltage deviation and (3) total cost. Investigating the capacitor placement problem in the proposed stochastic framework will convert it to a complex, nonlinear, discrete multi-objective optimization problem which requires a powerful optimization tool to escape from the local optimal. In this regard, a novel self adaptive modification approach based on Honey Bee Mating Optimization (HBMO) algorithm is proposed to enhance the total ability of the algorithm effectively. During the optimization process, the proposed algorithm will find a set of Pareto optimal solutions which are stored in an external memory called repository. In addition, a fuzzy based clustering technique is used to control the size of the repository in the pre-determined values. The feasibility and effectiveness of the proposed method are assessed through 2 standard IEEE test systems.
机译：本文提出了一种基于点估计法（PEM）的新的随机方法，以考虑不确定性影响最佳电容器放置问题。所提出的随机方法将同时捕获与有功和无功负载的预测误差以及成本函数系数相关的不确定性。要研究的目标函数是（1）有功功率损耗，（2）电压偏差和（3）总成本。在提出的随机框架中研究电容器放置问题会将其转换为复杂的，非线性的，离散的多目标优化问题，这需要一个功能强大的优化工具来摆脱局部最优。为此，提出了一种基于蜜蜂交配优化（HBMO）算法的新型自适应修改方法，以有效地提高算法的整体能力。在优化过程中，提出的算法将找到一组Pareto最优解，这些解存储在称为存储库的外部存储器中。另外，基于模糊的聚类技术被用于以预定值来控制存储库的大小。通过2个标准IEEE测试系统评估了该方法的可行性和有效性。

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