AN INTELLIGENT AND COMPREHENSIVE METHOD FOR FAULT LOCATION IN TRANSMISSION LINES
Fault Location (FL) in Transmission Lines (TLs) is an essential function to ensure continuity of service in Electric Power Systems (EPS). In general, a complete FL scheme is formed by two previously steps, that is Fault Detection (FD) and Fault Classification (FC). Conventional methods for FL may present some limitations, such as the use of current signals, high computational cost, dependency on communication links, performance loss against different systems or fault characteristics. The objective of this thesis is to propose a complete and reliable FL method, addressing some limitations aforementioned. For this purpose, the FD and FC were designed by using Euclidian distance, while the FL was developed using Independent Component Analysis (ICA). To improve the reliability of the proposed method for FL, an intelligent Disturbance Classification (DC) based on Convolutional Neural Network (CNN) was also developed.
The proposed methods for FD, FC, FL and DC were all evaluated against different EPS and fault characteristics (in PSCAD), always presenting good results and advantages when comparing to conventional methods. Moreover, a comparison considering different possibilities for implementing the DC method is presented, proving that ICA is the best option to achieve an accurate and robust performance.