University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 8 4 2018 01 21 Utilizing a Combination of Prony Analysis and Singular Value Decomposition for Intelligent Fault Locating in Bipolar High Voltage Direct Current Transmission Lines Utilizing a Combination of Prony Analysis and Singular Value Decomposition for Intelligent Fault Locating in Bipolar High Voltage Direct Current Transmission Lines 31 44 22530 10.22108/isee.2018.105962.1063 FA Mohammad Farshad Assistant Professor, Department of Electrical Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran Journal Article 2017 08 16 High voltage direct current (HVDC) transmission lines can be used to transfer bulk power over long distances. Accurate estimation of fault location in these transmission lines is very essential to speed up the maintenance operations. This paper presents a new approach for intelligent fault locating in bipolar HVDC transmission lines using the pattern recognition techniques and the machine learning algorithms. In the proposed approach, using a combination of Prony analysis (PA) and the singular value decomposition (SVD), some useful features are extracted from the post-fault voltage signals measured at one the line terminals. Then, a pre-trained generalized regression neural network (GRNN) receives the extracted features and estimates the corresponding fault location. Tests conducted on a sample bipolar system show that the proposed fault locator has accurate and appropriate performance despite changes in fault location, fault resistance, and pre-fault current. The obtained average percentage fault location errors for the positive-pole-to-ground (PG), positive-pole-to-negative-pole (PN), and positive-pole-to-negative-pole-to-ground (PNG) faults in the sample system under study are 0.264%, 0.287%, and 0.225%, respectively. High voltage direct current (HVDC) transmission lines can be used to transfer bulk power over long distances. Accurate estimation of fault location in these transmission lines is very essential to speed up the maintenance operations. This paper presents a new approach for intelligent fault locating in bipolar HVDC transmission lines using the pattern recognition techniques and the machine learning algorithms. In the proposed approach, using a combination of Prony analysis (PA) and the singular value decomposition (SVD), some useful features are extracted from the post-fault voltage signals measured at one the line terminals. Then, a pre-trained generalized regression neural network (GRNN) receives the extracted features and estimates the corresponding fault location. Tests conducted on a sample bipolar system show that the proposed fault locator has accurate and appropriate performance despite changes in fault location, fault resistance, and pre-fault current. The obtained average percentage fault location errors for the positive-pole-to-ground (PG), positive-pole-to-negative-pole (PN), and positive-pole-to-negative-pole-to-ground (PNG) faults in the sample system under study are 0.264%, 0.287%, and 0.225%, respectively. https://isee.ui.ac.ir/article_22530_6a950f2ec55b2aaf01bf50b9f4c833e8.pdf