ISSN: 1813-0410

Author : M. Umran, Haider

The Faults Detection and Categorization for a Part of Iraqi Electricity Power Transmission Grid Based on Neural Network Techniques

Haider M. Umran; Mazin A. Hameed

journal of kerbala university, 2016, Volume 12, Issue 2, Pages 190-200

Modern power systems are characterized by the instant isolation of faults especially in the high voltage transmission lines. Therefore, the faults detection and categorization are very important to get a stable power system, and keep on integrity of the electrical equipment.
This paper focuses on using the Artificial Neural Networks (ANNs) as a technique of Artificial Intelligence to detect and categorize faults of the Basra-Karbala transmission line. The measurements of three phase voltages and current of the power system are used as input signals to the proposed design of neural networks. All types of faults that may occur in the studied transmission line are taken into account, while the high accuracy of the detection and categorization of faults are considered in the design. The adopted radial power system which is used as the case study, consists: main power station is connected with a substation across the transmission lines, transformers, bas bars, circuit breakers, and loads. The proposed design includes three ANNs instead of one to reduce the complexity and minimize response time. The performance of the studied power system and the operation results of ANNs are assessed by simulation.