Abstract
This paper presents a remote fault detection and identification system for transmission lines, permitting elimination, or at least minimizing, the use of maintenance methods employed by power utilities, as a result of its technical potential and capability to reduce operational costs. It consists of a data acquisition system, capable of acquiring and storing high frequency signals present in the TLs. The signals, after their storage, are treated and identified through signal processing techniques such as digital filters and neural networks. This system has been installed in the reception terminal of one power line carrier system, in an electric power utility in Brazil. Many fault-simulating tests were carried out in the transmission line for pattern definition. Therefore, it is possible to develop an algorithm capable of identifying any potential transmission line fault. With the results obtained in this first part of the research, and with the continuity of the project, new signals will be obtained, identified and trained by the neural network.
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