Interval State Estimation for Low-Voltage Distribution Systems Based on Smart Meter Data

The real-time monitoring and control of distribution systems are an essential step toward the future smart grid. This paper presents an interval state estimation model for low-voltage (LV) systems based on smart meter data provided by electrical customers. The formulation is based on a two-level app...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2019-09, Vol.68 (9), p.3090-3099
Main Authors: Huang, Manyun, Wei, Zhinong, Pau, Marco, Ponci, Ferdinanda, Sun, Guoqiang
Format: Article
Language:English
Subjects:
Computer simulation
Distribution grids
Electric potential
Extreme value theory
Extreme values
interval state estimation (SE)
low voltage (LV) system
Measurement errors
Measurement uncertainty
Measuring instruments
Microprocessors
Monitoring
Probability distribution
Smart grid
smart meter
Smart meters
State estimation
Voltage
Voltage measurement
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Summary:The real-time monitoring and control of distribution systems are an essential step toward the future smart grid. This paper presents an interval state estimation model for low-voltage (LV) systems based on smart meter data provided by electrical customers. The formulation is based on a two-level approach, constituted of a nodal processor and a system state estimator. The nodal processor filters the smart meter data and provides a smaller amount of clean processed data to the upper level system state estimator. The system state estimator, based on the extreme value theory, provides the maximum and minimum values of the system states. To obtain narrow interval images, a scaling parameter is used to consider the fact that the errors of downstream smart meters are rarely at the maximum values simultaneously. The presented approach allows removing the assumption on the particular probability distribution of the smart meter measurement errors. Simulations are carried out on a sample LV network to show the characteristics and performance of the proposed approach.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2018.2877549