Determination of Fault Location in Transmission Lines with Image Processing and Artificial Neural Networks

• URL: http://arxiv.org/abs/2102.11073v1
• Date: Mon, 22 Feb 2021 14:43:13 GMT
• Title: Determination of Fault Location in Transmission Lines with Image Processing and Artificial Neural Networks
• Authors: Serkan Budak and Bahadir Akbal
• Abstract summary: Artificial neural network (ANN) has been used in order to accurately locate short circuit faults in different grounding systems in transmission lines. The transmission line model is made in the PSCAD-EMTDC simulation program.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In order to transmit electrical energy in a continuous and quality manner, it is necessary to control it from the point of production to the point of consumption. Therefore, protection of transmission and distribution lines is essential at every stage from production to consumption. The main function of the protection relays in electrical installations should be deactivated as soon as possible in the event of short circuits in the system. The most important part of the system is energy transmission lines and distance protection relays that protect these lines. An accurate error location technique is required to make fast and efficient work. Transformer neutral point grounding in transmission lines affects the operation of the zero component current during the single phase to ground short circuit failure of a power system. Considering the relationship between the grounding system and protection systems, an appropriate grounding choice should be made. Artificial neural network (ANN) has been used in order to accurately locate short circuit faults in different grounding systems in transmission lines. Compared with support vector machines (SVM) for testing inside ANN The transmission line model is made in the PSCAD-EMTDC simulation program. Data sets were created by recording the image of the impedance change of the R-X impedance diagram of the distance protection relay in short circuit faults created in different grounding systems. The related focal points in the images are given as an introduction to different ANN models using feature extraction and image processing techniques and the ANN model with the highest fault location estimation accuracy was chosen.

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