DocumentCode :
107561
Title :
A Closed-Loop State Estimation Tool for MV Network Monitoring and Operation
Author :
Hayes, Barry P. ; Gruber, Jorn K. ; Prodanovic, Milan
Author_Institution :
Inst. IMDEA Energy, Madrid, Spain
Volume :
6
Issue :
4
fYear :
2015
fDate :
Jul-15
Firstpage :
2116
Lastpage :
2125
Abstract :
This paper discusses the design and simulation of an integrated load forecasting and state estimation tool for distribution system operations. A predictive database is created and applied to forecast future network states in order to allow short-term (e.g., hours/days ahead) planning to be carried out. The predictive database is based on adaptive nonlinear auto-regressive exogenous (NARX) load estimation and forecasting models, which are continuously updated using feedback from the state estimator. This creates a closed-loop information flow designed to continuously monitor and improve the system state estimation performance by updating and retraining models where appropriate. The aim of this methodology is to improve situational awareness and help to provide network operators with early warning of potential issues, in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition (SCADA) and smart meter measurements recorded from an actual MV distribution network.
Keywords :
SCADA systems; autoregressive moving average processes; closed loop systems; feedback; load forecasting; nonlinear estimation; power distribution planning; power system measurement; power system state estimation; smart meters; MV distribution network monitoring; SCADA; adaptive nonlinear autoregressive exogenous load estimation; closed loop information flow; closed loop state estimation; distribution system operation; feedback; integrated load forecasting model; medium voltage network; online measurement; power injection; predictive database; situational awareness; smart meter measurement; supervisory control and data acquisition; Decision support systems; Forecasting; Load modeling; Predictive models; State estimation; Weight measurement; Distributed energy management systems; distributed energy resources (DER); load forecasting; state estimation;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2014.2378035
Filename :
6995980
Link To Document :
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