An Adaptive Fisher Discriminant Analysis Framework for Intelligent Fault Detection in Overhead AC Transmission Networks

Considering its critical role in guaranteeing a continuous power supply, preventing damage to system components, and reducing outage time, fault detection is essential in AC transmission lines. Voltage drops and current surges can be caused by any transmission line problem. One of the most crucial functions of AC power transmission line protection systems is early defect detection, which needs to be completed with accuracy and speedily. A suggested method for fault identification in AC transmission lines utilizing Adaptive Fisher Discriminant Analysis (AFDA), one of the efficient fault detection and diagnostic methods, is presented in this research. The suggested method does not use signal processing techniques; instead, current signals are obtained straight from the measurement point at the transmission line's sending end. This technique can detect faults in a variety of scenarios, such as shifts in the location of faults and variations in network load. The PSCAD program was used to simulate the system to implement the various situations, and the MATLAB program was used to develop and test the suggested method. The results showed that the method used in this paper is more effective and faster in detecting faults, with a time of up to 1 millisecond.