TY - GEN
T1 - Impedance Tracking and Estimation using Power Line Communications
AU - Liang, Dong
AU - Guo, Huashan
AU - Zheng, Tao
N1 - Funding Information:
This work was financially supported by National Natural Science Foundation of China (Gran: 51707154). Shaanxi Key R&D Program (2018 ZDXM-GY-169)
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/16
Y1 - 2019/4/16
N2 - The broadband properties of impedance in power line communication (PLC) networks would enable smart PLC systems to implement fault detection and channel health monitoring in smart grid. In this paper, a novel real-time impedance estimation method for power line communication networks is proposed. We study the relationship between the channel frequency response (CFR) and the impedance behavior, CFR can be treated as the already known variable which is normally calculated by channel estimation algorithms in PLC devices, and find that the variation of certain key factor of the CFR curves including the frequency characteristics and values of Peak-Valley Difference and so on, could be used to tracking the impedance. A VMD (Variational Mode Decomposition) algorithm is used to obtain the useful frequency properties information in CFR waveform and machine learning method is used to estimate the parameters. Proposed impedance estimation method is verified by the computer simulations and the results show that it is a feasible solution for impedance estimation without any additional hardware and costs.
AB - The broadband properties of impedance in power line communication (PLC) networks would enable smart PLC systems to implement fault detection and channel health monitoring in smart grid. In this paper, a novel real-time impedance estimation method for power line communication networks is proposed. We study the relationship between the channel frequency response (CFR) and the impedance behavior, CFR can be treated as the already known variable which is normally calculated by channel estimation algorithms in PLC devices, and find that the variation of certain key factor of the CFR curves including the frequency characteristics and values of Peak-Valley Difference and so on, could be used to tracking the impedance. A VMD (Variational Mode Decomposition) algorithm is used to obtain the useful frequency properties information in CFR waveform and machine learning method is used to estimate the parameters. Proposed impedance estimation method is verified by the computer simulations and the results show that it is a feasible solution for impedance estimation without any additional hardware and costs.
KW - Variational Mode Decomposition
KW - impedance estimation
KW - machine learning
KW - power line communication
UR - http://www.scopus.com/inward/record.url?scp=85065390651&partnerID=8YFLogxK
U2 - 10.1109/ISPLC.2019.8693256
DO - 10.1109/ISPLC.2019.8693256
M3 - Conference contribution
AN - SCOPUS:85065390651
T3 - Proceedings of the 2019 IEEE International Symposium on Power Line Communications and its Applications, ISPLC 2019
SP - 6
EP - 11
BT - Proceedings of the 2019 IEEE International Symposium on Power Line Communications and its Applications, ISPLC 2019
A2 - Maga, Dusan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Symposium on Power Line Communications and its Applications, ISPLC 2019
Y2 - 3 April 2019 through 5 April 2019
ER -
