Modified differential evolution optimization algorithm for multi-constraint optimal power flow

M. Nayak, K. R. Krishnanand, P. Rout
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引用次数: 25

Abstract

In this paper presents an algorithm for solving optimal power flow problem through the application of a modified differential evolution algorithm(MDE). The objective of an optimal Power Flow(OPF) is to find steady state operation point which minimizes total generating unit (thermal) fuel cost and total load bus voltage deviation from a specified point while maintaining an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, transformer taps, output of various compensating devices and power flow of transmission lines. Differential Evolution (DE) is one of evolutionary algorithms, which has been used in many optimization problems due to its simplicity and efficiency. The proposed MDE is in the framework of differential evolution owning new mutation operator and selection mechanism. To test the efficacy of the algorithm, it is applied to IEEE 30-bus power system with two different objective functions. The optimal power flow results obtained using MDE are compared with other evolutionary methods. The simulation results reveal that the MDE optimization technique provides better results compared to other methods recently published in the literature as demonstrated by simulation results.
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多约束最优潮流的改进差分进化优化算法
本文提出了一种应用改进的差分进化算法求解最优潮流问题的算法。最优潮流(OPF)的目标是找到稳态工作点,使发电机组(热)总燃料成本和总负载母线电压偏离最小,同时在发电机实功率和无功输出、母线电压、变压器分接、各种补偿装置的输出和输电线路的功率流的限制方面保持可接受的系统性能。差分进化算法是进化算法中的一种,以其简单、高效的特点被广泛应用于优化问题中。该模型在差分进化的框架内,具有新的变异算子和选择机制。为了验证该算法的有效性,将其应用于具有两种不同目标函数的IEEE 30总线电力系统。比较了采用MDE方法得到的最优潮流结果。仿真结果表明,MDE优化技术与文献中其他方法相比具有更好的优化效果。
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