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Data‐driven online distributed disturbance location for large‐scale power grids
by
Polunchenko, Aleksey
, Liu, Yilu
, Yang, Zekun
, Zhou, Ning
, Chen, Yu
in
Algorithms
/ B7230 Sensing devices and transducers
/ B8110 Power systems
/ B8150 Power system measurement and metering
/ change-point detection method
/ Data analysis
/ data-driven online distributed disturbance location
/ distributed sensors
/ disturbance source point
/ Disturbances
/ DODDL scheme
/ fault location
/ frequency disturbance recorders
/ geographic location
/ Geographical distribution
/ Geographical locations
/ grid nonhomogeneity
/ Homogeneity
/ Machine learning
/ Methods
/ power grids
/ power system faults
/ power system measurement
/ Sensors
/ singular spectrum analysis
/ smart power grids
/ Spectrum analysis
/ Time series
/ Topology
/ travelling-wave based scheme
2019
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Data‐driven online distributed disturbance location for large‐scale power grids
by
Polunchenko, Aleksey
, Liu, Yilu
, Yang, Zekun
, Zhou, Ning
, Chen, Yu
in
Algorithms
/ B7230 Sensing devices and transducers
/ B8110 Power systems
/ B8150 Power system measurement and metering
/ change-point detection method
/ Data analysis
/ data-driven online distributed disturbance location
/ distributed sensors
/ disturbance source point
/ Disturbances
/ DODDL scheme
/ fault location
/ frequency disturbance recorders
/ geographic location
/ Geographical distribution
/ Geographical locations
/ grid nonhomogeneity
/ Homogeneity
/ Machine learning
/ Methods
/ power grids
/ power system faults
/ power system measurement
/ Sensors
/ singular spectrum analysis
/ smart power grids
/ Spectrum analysis
/ Time series
/ Topology
/ travelling-wave based scheme
2019
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Data‐driven online distributed disturbance location for large‐scale power grids
by
Polunchenko, Aleksey
, Liu, Yilu
, Yang, Zekun
, Zhou, Ning
, Chen, Yu
in
Algorithms
/ B7230 Sensing devices and transducers
/ B8110 Power systems
/ B8150 Power system measurement and metering
/ change-point detection method
/ Data analysis
/ data-driven online distributed disturbance location
/ distributed sensors
/ disturbance source point
/ Disturbances
/ DODDL scheme
/ fault location
/ frequency disturbance recorders
/ geographic location
/ Geographical distribution
/ Geographical locations
/ grid nonhomogeneity
/ Homogeneity
/ Machine learning
/ Methods
/ power grids
/ power system faults
/ power system measurement
/ Sensors
/ singular spectrum analysis
/ smart power grids
/ Spectrum analysis
/ Time series
/ Topology
/ travelling-wave based scheme
2019
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Data‐driven online distributed disturbance location for large‐scale power grids
Journal Article
Data‐driven online distributed disturbance location for large‐scale power grids
2019
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Overview
Timely detecting disturbances and locating their sources are critical to the reliable operation of power grids. This capability enables operators to effectively diagnose disturbances over wide areas and earns time for remedial reactions. In this study, a travelling‐wave based scheme, namely data‐driven online distributed disturbance location (DODDL), is proposed to quickly detect disturbances and determine their geographic location in large‐scale power grids when the grids’ topology is not available. The proposed DODDL scheme consists of two function blocks: (i) a singular spectrum analysis‐based change‐point detection method, which can quickly detect disturbances and determine their arrival time at distributed sensors, and (ii) a novel temporal scanning algorithm, which can accurately determine the geographic location of the disturbance source point. Utilising field measurement data sets recorded by the frequency disturbance recorders from the frequency monitoring network, it is shown that the DODDL scheme is not only quicker and more robust to grid non‐homogeneity than existing approaches, but also can capture and locate more subtle and concealed disturbances.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
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