This paper presents a new approach to fault location on distribution power lines. This approach uses an artificial neural network based learning algorithm and ClarkeConcbrdia transformation. The a&O components of line currents resulting from the Clarke-Concbrdia transformation are used to detect all types of fault. The neural network is trained to map the non-linear relationship existing in fault location equations.The proposed approaeh is able to identify and locate all different types of faults (single line to ground, double line to ground, line-to-line and three-phase short-circuit). This approach is subdivided into several main steps: -Data acquisition, corresponding on three-phase current signals; -Mathematical treatment by the Clarke-Conc6rdia transformation; -Fault identification, obtained by the analysis of fault and pre-fault data;-Fault location artificial neural network based learning algorithm. The fault position is presented as the output of the neural network on which, as the input, it was considered the eigenvalue of matrix representing transformed line current.Results are presented which shows the ellectiveness of the proposed algorithm for a correct fault location on distribution power system networks.
Teaching power system relaying is a fundamental issue in a power system high-level course.However, for an effective instruction of this topic an experience with real equipments can be considered as fundamental. To achieve this purpose, in this paper a new approach for the practical learning of power system relaying is presented. This consists of a computer-based testing system of relay-operating characteristic. Different relay types and developed specific software are also an important piece of the laboratory practice. Using this system it is possible to understand the performance and limitations of different protective relay systems and to test a real relay disoperation. The benefit of using this system is not available through traditional lectures and textbooks. ß
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.