Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrad...Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrade existing systems or to install new systems. Therefore, planning of new installations and determination of best operating conditions of existing systems need power flow analysis. In this way, cost/benefit ratio for both suppliers and customers is maintained. This research involves the design and power flow analysis of IEEE-14 bus system. Newton Raphson method is applied for better efficiency and reduced computational time. Simulation analysis is conducted in ETAP software because of its excessive used in real life systems.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Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrade existing systems or to install new systems. Therefore, planning of new installations and determination of best operating conditions of existing systems need power flow analysis. In this way, cost/benefit ratio for both suppliers and customers is maintained. This research involves the design and power flow analysis of IEEE-14 bus system. Newton Raphson method is applied for better efficiency and reduced computational time. Simulation analysis is conducted in ETAP software because of its excessive used in real life systems.
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.
