双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调...双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调节功率一定时,以负荷节点电压与环网两端馈线出力极限为约束条件,分别以UPFC输出的有功功率和视在功率最小为目标,提出了潮流控制策略。仿真结果表明2种优化控制策略能够满足配电环网潮流调度要求,且兼顾了UPFC应用的经济性。展开更多
In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combinat...In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combination of tabu search （TS） algorithm and artificial neural network （ANN） in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemen- ted in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm （GA） and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.展开更多
文摘双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调节功率一定时,以负荷节点电压与环网两端馈线出力极限为约束条件,分别以UPFC输出的有功功率和视在功率最小为目标,提出了潮流控制策略。仿真结果表明2种优化控制策略能够满足配电环网潮流调度要求,且兼顾了UPFC应用的经济性。
文摘In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combination of tabu search （TS） algorithm and artificial neural network （ANN） in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemen- ted in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm （GA） and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.
