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Application of ACSA to solve single/multi objective OPF problem with FACTS devices

机译:ACSA在FACTS装置解决单/多目标OF问题中的应用

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This paper presents a multi-objective adaptive clonal selection algorithm (MOACSA) to minimise generation cost, transmission losses and voltage stability index (L-index) when voltage source converter (VSC) based flexible alternating current transmission systems (FACTS) devices are embedded in power systems. In this algorithm, a non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Further, a fuzzy based mechanism has been used to select best compromise solution from the Pareto set. Two types of VSC based FACTS devices such as static synchronous compensator (STATCOM) and static synchronous series compensator (SSSC) are considered and incorporated them as power injection models in multi-objective optimization problem. The proposed MOACSA has been tested on standard IEEE 30-bus test system with integration of these FACTS devices. The results are analyzed and compared with implementation of a standard nondominated sorting genetic algorithm-II(NSGA-II).
机译:本文提出了一种多目标自适应克隆选择算法(MOACSA),以便在将基于电压源转换器(VSC)的柔性交流输电系统(FACTS)装置嵌入其中时,将发电成本,输电损耗和电压稳定指数(L-index)降至最低。电力系统。在该算法中,非支配的排序和拥挤距离已用于查找和管理帕累托最优前沿。此外,基于模糊的机制已被用来从帕累托集合中选择最佳折衷解决方案。考虑了两种基于VSC的FACTS设备,例如静态同步补偿器(STATCOM)和静态同步串联补偿器(SSSC),并将它们作为功率注入模型并入多目标优化问题。拟议的MOACSA已在集成这些FACTS设备的标准IEEE 30总线测试系统上进行了测试。分析结果并将其与标准非主导排序遗传算法-II(NSGA-II)的实现进行比较。

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