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Particle swarm optimization based optimal power flow for units with non-smooth fuel cost functions

机译:基于粒子群优化的具有非平滑燃料成本功能的机组的最佳功率流

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This paper presents a Particle Swarm Optimization (PSO) based algorithm for optimal flow with generating units having non-smooth fuel costs curves while statisfying the constraints such as generator capacity limits, power balance, line flow limits, bus voltages and transformer tap setting, The conventional loed flow and incorporation of the proposed method using PSO has been examined and tested for standard IEEE 30 bus system. The PSO method is demonstrated and compared with conventional OPF method and the intelligence heuristic algorithm such as genetic algorithm, evolutionary programming. The superiority of th method over other methods has been demonstrated on two test cases. From simulation results, it has been found that PSO method is highly competitve for its better general convergence performance.
机译:本文提出了一种基于粒子群优化(PSO)的算法,用于具有不平滑燃料成本曲线的发电机组的最佳流量,同时规定了诸如发电机容量限制,功率平衡,线路流量限制,母线电压和变压器抽头设置等约束条件。对于标准的IEEE 30总线系统,已经检查并测试了传统的泥浆流和采用PSO的建议方法的合并。对PSO方法进行了演示,并与传统的OPF方法以及遗传算法,进化规划等智能启发式算法进行了比较。在两个测试案例中证明了该方法相对于其他方法的优越性。从仿真结果可以看出,PSO方法具有更好的一般收敛性能,因此具有很高的竞争力。

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