Zhihong Chen

Artificial neural network approach to single-ended fault locator for transmission lines

This paper describes the application of an artificial neural network-based algorithm to the single-ended fault location of transmission lines using voltage and current data. From the fault location equations, similar to the conventional approach, this method selects phasors of prefault and superimposed voltages and currents from all phases of the transmission line as inputs of the artificial neural network. The outputs of the neural network are the fault position and the fault resistance. With its function approximation ability, the neural network is trained to map the nonlinear relationship existing in the fault location equations with the distributed parameter line model. It can get both fast speed and high accuracy. The influence of the remote-end infeed on neural network structure is studied. A comparison with the conventional method has been done. It is shown that the neural network-based method can adapt itself to big variations of source impedances at the remote terminal. Finally, when the remote source impedances vary in small ranges, the structure of the artificial neural network has been optimized by the pruning method.

An artificial neural network based real-time fault locator for transmission lines

This paper describes the application of an artificial neural network-based algorithm to the single-ended fault location of transmission lines using voltage and current data. From the fault location equations, similar to the conventional approach, this method selects phasors of prefault and superimposed voltages and currents from all phases of the transmission line as inputs of the artificial neural network. The outputs of the neural network are the fault position and the fault resistance. With its function approximation ability, the neural network is trained to map the nonlinear relationship existing in the fault location equations with the distributed parameter line model. It can get both fast speed and high accuracy. The influence of the remote-end infeed on neural network structure is studied. A comparison with the conventional method has been done. It is shown that the neural network-based method can adapt itself to big variations of source impedances at the remote terminal. Finally, when the remote source impedances vary in small ranges, the structure of artificial neural network has been optimized by the pruning method.