Abstract
The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to complex uncertainties. With the large-scale penetration of DERs, traditional outage detection methods, which rely on customers making phone calls and smart meters' 'last gasp' signals, will have limited performance because 1) the renewable generators can supply powers after line outages, and 2) many urban grids are mesh and line outages do not affect power supply. To address these drawbacks, we propose a new data-driven outage monitoring approach based on the stochastic time series analysis with the newly available smart meter data utilized. Specifically, based on the power flow analysis, we prove that the statistical dependency of time-series voltage measurements has significant changes after line outages. Hence, we use the optimal change point detection theory to detect and localize line outages. As the existing change point detection methods require the post-outage voltage distribution, which is unknown in power systems, we propose a maximum likelihood method to learn the distribution parameters from the historical data. The proposed outage detection using estimated parameters also achieves the optimal performance. Simulation results show highly accurate outage identification in IEEE standard distribution test systems with and without DERs using real smart meter data.
Original language | English (US) |
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Title of host publication | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509019700 |
DOIs | |
State | Published - Dec 1 2016 |
Externally published | Yes |
Event | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Beijing, China Duration: Oct 16 2016 → Oct 20 2016 |
Other
Other | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 |
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Country/Territory | China |
City | Beijing |
Period | 10/16/16 → 10/20/16 |
ASJC Scopus subject areas
- Statistics, Probability and Uncertainty
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Statistics and Probability