Fault modelling and detection in power generation, transmission and distribution systems. Issue 16 (1st December 2015)

• Record Type: Journal Article
• Authors: Ali, Asghar; Khan, Abdul Qayyum; Hussain, Babar; Raza, Muhammad Taskeen; Arif, Muhammad
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 16(2015)
• Page Start: 2782
• Page End: 2791
• Publication Date: 2015-12-01
• Subjects: power transmission faults -- power distribution faults -- power generation faults -- fault diagnosis -- power transmission reliability -- power distribution reliability -- power generation reliability -- observers -- power filters -- synchronous generators -- electric actuators; power generation system fault detection model -- power transmission system fault detection model -- power distribution system fault detection model -- fault diagnosis scheme -- power generating unit -- PGU reliability -- power transmission and distribution network -- power T&DN -- observer‐based fault detection filter -- observer‐based FDF -- synchronous generator -- single machine infinite bus power system -- sensor fault -- actuator fault; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2014.1023
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The study discusses a model‐based fault diagnosis scheme for detection of faults in power systems. The fault models in power generating units (PGUs) and transmission and distribution networks (T&DN) have been derived in state space form. An observer‐based fault detection filter (FDF) has been used to detect these faults. Observer‐based FDF is used because of its simplicity, reliability and ease of implementation. A single machine infinite bus power system is considered wherein a synchronous generator is connected to a T&DN through a bus system. Actuator and sensor faults in PGU, and symmetric faults in T&DN have been modelled and simulated in Matlab/Simulink. The simulation results show that a fault, whenever occurs in the aforementioned parts of power systems, is detected using the knowledge of the faults models proposed in this work.