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Unit Commitment Solution Using Particle Swarm Optimisation (PSO)

机译:使用粒子群优化(PSO)的单位承诺解决方案

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Existing unit commitment methods have the problem of stopping at local optimum and slow convergence. So it is replaced by a new method known as Particle Swarm Optimisation (PSO) which is a biological method based on particle swarming. It consists of a group of particles moving towards optimal solution. Feasible solutions are obtained as particles move in feasible solution space rather than infeasible ones. Thus the method reduces computational time. In this paper PSO is applied to IEEE 30 bus test system with six generators so that fuel cost of each generator is reduced using PSO.
机译:现有的单元承诺方法具有在局部最优和缓慢收敛时停止的问题。因此,它被一种称为粒子群优化(PSO)的新方法所取代,该方法是一种基于粒子群的生物学方法。它由一组朝向最佳解的粒子组成。当粒子在可行的解决方案空间而不是不可行的解决方案空间中移动时,可获得可行的解决方案。因此,该方法减少了计算时间。本文将PSO应用于具有六台发电机的IEEE 30总线测试系统,以便使用PSO降低每台发电机的燃料成本。

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