TY - GEN
T1 - Classification of PD faults using features extraction and K-means clustering techniques
AU - Kumar, Haresh
AU - Shafiq, Muhammad
AU - Hussain, Ghulam Amjad
AU - Kumpulainen, Lauri
AU - Kauhaniemi, Kimmo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/26
Y1 - 2020/10/26
N2 - Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.
AB - Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.
KW - Classification
KW - Dimensionality reduction techniques
KW - Features extraction
KW - K-means clustering
KW - Medium voltage
KW - Partial discharge
UR - http://www.scopus.com/inward/record.url?scp=85097338700&partnerID=8YFLogxK
U2 - 10.1109/ISGT-Europe47291.2020.9248984
DO - 10.1109/ISGT-Europe47291.2020.9248984
M3 - Conference contribution
AN - SCOPUS:85097338700
T3 - IEEE PES Innovative Smart Grid Technologies Conference Europe
SP - 919
EP - 923
BT - Proceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
PB - IEEE Computer Society
T2 - 10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Y2 - 26 October 2020 through 28 October 2020
ER -