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A hybrid evolutionary algorithm for economic load dispatch problem considering transmission losses and various operational constraints

机译:考虑传输损失和各种操作约束的经济负荷调度问题的混合进化算法

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Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Economic Dispatch (ED) is also one of the optimization problem. ED is the process of determining optimal output of available number of electric power generating stations in order to meet total system load, at a minimum possible cost while serving power to the public in a robust and reliable manner satisfying physical and operational constraints. Scarcity of energy resources, ever growing production cost of generation and increased load demand, there is a need to optimize the economic dispatch problem. In this research work, a hybrid technique proposed to solve non-linear ED problem named as Hybrid Particle Swarm Optimization with Gravitational Search Algorithm (HPSO-GSA) considering/neglecting valve point effects, prohibited operating zones, ramp rate limits and transmission losses. In order to evaluate the performance of the proposed Hybrid PSO-GSA algorithm has been tested on different generating unit test systems with different constraints and defined load demands. The simulation results of proposed algorithm are in comparison with the techniques in literature proves the efficiency and effectiveness of the proposed algorithm.
机译:优化是一种数学技术,涉及在一些可行区域中找到最大值或最小值的数学技术。没有业务或行业,不参与解决优化问题。各种优化技术竞争最佳解决方案。经济派遣(ED)也是优化问题之一。 ED是确定可用数量的发电站的最佳输出的过程,以便以最小的可能成本满足总系统负载,同时以令人稳健且可靠的方式为公众提供满足物理和操作约束的方式。能源资源稀缺,越来越多的生产成本和增加负荷需求,有必要优化经济派遣问题。在这项研究工作中,提出了一种用引力搜索算法(HPSO-GSA)命名为混合粒子群优化的非线性ED问题,考虑/忽略阀点效应,禁止操作区域,斜坡率限制和传输损耗。为了评估所提出的混合PSO-GSA算法的性能已经在具有不同约束和定义负载需求的不同产生单元测试系统上进行了测试。所提出的算法的仿真结果与文献中的技术相比,证明了所提出的算法的效率和有效性。

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