TY - JOUR
T1 - Analyses of different approaches for detecting, classifying and locating faults in a three-terminal VSC-HVDC system
AU - Arita Torres, Julio
AU - dos Santos, Ricardo Caneloi
AU - Yang, Qingqing
AU - Li, Jianwei
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/2
Y1 - 2022/2
N2 - This paper presents three intelligent approaches for fault detection, classification and location in a three-terminal VSC-HVDC system. The proposed approaches are all based on ANNs, although each one has a specific pre-processing step to process the DC current samples. The approaches were implemented in Matlab, while the adopted three-terminal VSC-HVDC system was modelled in PSCAD. A large number of fault cases differing with respect to fault location, fault type and fault resistance were specifically generated to develop, test and compare the solutions presented here. The observed results encourage the application of the proposed approaches in practical situations, as a good performance was verified regardless the fault case characteristics. In terms of fault detection and classification functions the response time is always within 2 ms, while for fault location function the average accuracy is 1.7%.
AB - This paper presents three intelligent approaches for fault detection, classification and location in a three-terminal VSC-HVDC system. The proposed approaches are all based on ANNs, although each one has a specific pre-processing step to process the DC current samples. The approaches were implemented in Matlab, while the adopted three-terminal VSC-HVDC system was modelled in PSCAD. A large number of fault cases differing with respect to fault location, fault type and fault resistance were specifically generated to develop, test and compare the solutions presented here. The observed results encourage the application of the proposed approaches in practical situations, as a good performance was verified regardless the fault case characteristics. In terms of fault detection and classification functions the response time is always within 2 ms, while for fault location function the average accuracy is 1.7%.
KW - Artificial neural networks
KW - Discrete wavelet transform
KW - Fault detection
KW - Fault location
KW - HVDC protection
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85118725409&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2021.107514
DO - 10.1016/j.ijepes.2021.107514
M3 - Article
AN - SCOPUS:85118725409
SN - 0142-0615
VL - 135
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 107514
ER -