Intelligent fault diagnosis of power transmission line using fuzzy logic and artificial neural network

Abstract

In the industrial sector, transmission lines are an important part of the electrical grid. Thus it is important to protect it from all the different faults that may occur as soon as possible to supply the electric power continuously. This paper presents a modern solutions and a comparative study of fault detection and identification in electrical transmission lines using artificial neural network (ANN) compare to the fuzzy logic. Faults in transmission line of various types have been created using simulation model. An intelligent monitoring system (IFD: Intelligent Fault Diagnosis) was used at both ends of a 230 kV overhead transmission line, voltage and current measurements exploited as indicator data for this system. Both approaches were found to be robust, accurate and reliable to detect the fault when it occurs, to determine the fault type short circuit or opening of a power line (open circuit), to locate the fault and to determine which phase was faulted.

Detection and identification of fault in transmission lines based on ANN

“…Under typical conditions, the neural network output remains at 0 as specified in the target matrix, the neural network output is set to 1 until the fault is fixed after it forms at a particular time instant. Figure (6) and figure (7) illustrate the single line to ground fault (b-g), (c-g) respectively, Figure (8) to figure (10)…”

“…Khaled, Eouati, et al [8] The author present a comparative study between identification and fault detection in electrical transmission lines using ANN and fuzzy. Simulation models are used to create various types of faults in transmission lines for testing purposes.…”