Power system transmission line fault diagnosis based on combined data analytics
Title | Power system transmission line fault diagnosis based on combined data analytics |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Wu, H., Liu, J., Liu, Y., Qiu, G., Taylor, G. A. |
Conference Name | 2017 IEEE Power Energy Society General Meeting |
ISBN Number | 978-1-5386-2212-4 |
Keywords | Bayes methods, Bayesian modelling, Circuit faults, Combined data analytics, Fault classification, fault diagnosis, Human Behavior, human factor, human factors, Metrics, multiple fault diagnosis, power system reliability, power transmission lines, pubcrawl, reliability, resilience, Resiliency |
Abstract | As a consequence of the recent development of situational awareness technologies for smart grids, the gathering and analysis of data from multiple sources offer a significant opportunity for enhanced fault diagnosis. In order to achieve improved accuracy for both fault detection and classification, a novel combined data analytics technique is presented and demonstrated in this paper. The proposed technique is based on a segmented approach to Bayesian modelling that provides probabilistic graphical representations of both electrical power and data communication networks. In this manner, the reliability of both the data communication and electrical power networks are considered in order to improve overall power system transmission line fault diagnosis. |
URL | https://ieeexplore.ieee.org/document/8274635 |
DOI | 10.1109/PESGM.2017.8274635 |
Citation Key | wu_power_2017 |