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Comparative Optimization Analysis of Ramp Rate Constriction Factor Based PSO and Electro Magnetism Based PSO for Economic Load Dispatch in Electric Power System

机译:电力系统经济负荷分配的基于斜率压缩因子的PSO与基于电磁的PSO的比较优化分析

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摘要

In this modern era, Economic Load Dispatch (ELD) is one of the essential issues of apprehension in power system process and scheduling. The cost function for each unit in ELD problems is resolved by using mathematical or analytical methods in conservative computation. So, the certain ELD problem is signified as a non linear optimization problem having both equality and inequality constraint that cannot be directly solved by conservative analytical techniques. Subsequently, in order to solve this ELD problems evolution of soft computing methods and many other prominent approaches have been successfully implemented such as numerical method, genetic algorithm, evolutionary programming, neural network tactics, particle swarm optimization and some other hybrid methods. Here Ramp Rate constriction facor based PSO algorithm and Electro magnetism based PSO algorithm are proposed and applied for solving ELD problem in this paper. These two PSO techniques are employed in the form of Transmission line losses, valve point loading effect, ramp rate limits and prohibited zones constraints were used to make the results of ELD problem as reasonably approximate as possible in the comparative analysis form. The comparison results which are obtained from RRCPSO and EMPSO are compared with to other heuristic methods like Genetic algorithm (GA) & Taboo search (GATS) method, Artificial intelligence (AI) method, Bacterial Foraging Optimization (BFO) method, Dance Bee Colony Optimization (DBCO) method etc.
机译:在这个现代时代,经济负荷分配(ELD)是电力系统过程和调度中令人担忧的重要问题之一。 ELD问题中每个单元的成本函数通过在保守计算中使用数学或分析方法来解决。因此,将某个ELD问题表示为具有等式和不等式约束的非线性优化问题,无法通过保守的分析技术直接解决。随后,为了解决该ELD问题,软计算方法和许多其他重要方法的成功实现已成功实现,例如数值方法,遗传算法,进化规划,神经网络策略,粒子群优化和其他一些混合方法。本文提出了基于斜坡速率约束的PSO算法和基于电磁的PSO算法,并将其应用于解决ELD问题。这两种PSO技术以传输线损耗,阀点负载效应,斜率限制和禁止区域约束的形式使用,以使ELD问题的结果在比较分析形式中尽可能合理地近似。将从RRCPSO和EMPSO获得的比较结果与其他启发式方法进行比较,例如遗传算法(GA)和禁忌搜索(GATS)方法,人工智能(AI)方法,细菌觅食优化(BFO)方法,舞蜂殖民地优化(DBCO)方法等

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