Adaptive clonal selection algorithm for solving OPF problem with emission constraints

B. Rao, K. Vaisakh
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引用次数: 1

Abstract

This paper presents an artificial immune system (AIS) based adaptive clonal selection algorithm (ACSA) to solve combined economic emission dispatch (EED) problem of thermal units in power system. In this work different emission substances like NOX and SOX are considered along with power demand equality constraints and thermal unit operating limits. The clonal selection principle is one of the models used to incorporate the behaviour of the AIS. The biological principles like clone generation, proliferation and maturation are mimicked and incorporated into this algorithm. In order to find and manage the pareto optimal set a non dominated sorting technique and crowding distance measure have been used. The proposed multi-objective ACSA (MOACSA) method has been tested on two different test systems and the results are compared with other methods reported in literature.
求解带有发射约束的OPF问题的自适应克隆选择算法
提出了一种基于人工免疫系统(AIS)的自适应克隆选择算法(ACSA)来解决电力系统热力机组联合经济排放调度问题。在这项工作中,考虑了不同的排放物质,如NOX和SOX,以及电力需求相等约束和热机组运行限制。克隆选择原理是用于纳入AIS行为的模型之一。克隆产生、增殖和成熟等生物学原理被模拟并纳入该算法。为了寻找和管理pareto最优集,采用了非支配排序技术和拥挤距离测度。本文提出的多目标ACSA (MOACSA)方法在两个不同的测试系统上进行了测试,并与文献中报道的其他方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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