Results from a laboratory based decentralized mode-estimator application to show that decentralized architecture is more effective in detecting local, low-damped oscillations. Comparison between decentralized and centralized mode-estimator in the context of monitoring local forced oscillations.

1. Real-Time Testing of a Decentralized PMU Data-Based
Power Systems Mode Estimator
Ravi Shankar Singh*, Hossein Hooshyar**, Luigi Vanfretti***
*Eindhoven University of Technology (TUe) **KTH Royal Institute of Technology (KTH) *** Rensselaer Polytechnic Institute
r.singh1@tue.nl h.hoosh@gmail.com luigi.vanfretti@gmail.com
Introduction / Motivation Poster Presents
• Synchronized PMU measurements forms the backbone of wide area real-
time monitoring and control of power system networks.
• Near real-time applications using PMU data have been developed to monitor
power system oscillations and extract modal information.
• These applications function in centralized architecture where mode-
estimator acquires and processes data from all the PMUs.
• A different, decentralized architecture can improve performance of mode
estimation applications in context of local, low-damped oscillations.
• Results from a laboratory based decentralized mode-estimator
application to show that decentralized architecture is more effective in
detecting local, low-damped oscillations.
• Comparison between decentralized and centralized mode-estimator in
the context of monitoring local forced oscillations.
Test System
Conclusion
• Decentralized architecture provides a more local and high-resolution awareness in mode
estimation than the centralized architecture.
• In case of voltage magnitude signals, Decentralized architecture substantially improves the
accuracy of estimates for local oscillations. In case of voltage angle difference signals, the
decentralized architecture gives marginally more accurate results than the centralized
architecture.
Mode-Meter
Evaluation
of
Oscillation
Energy
ARMA based modified
Yule-Walker
Eigenvalue Realization
Algorithm (ERA)
Acquisition of System
Response
Detrending or High-Pass
Filtering
Low-Pass Filtering and
Downsampling
Mode Frequency
Mode 1 (inter-area) 0.41 Hz
Mode 2 (forced local) 1.70 Hz
Architectures
Centralized
Decentralized
• 4 PMUs placed at nodes 101, 814,
840 and 888 (PMU1, PMU2, PMU3
and PMU4 respectively).
• Inter-Area oscillation (mode) present
throughout the network. A low-level,
local oscillation was forced at node
888 in the LV section.
• In decentralized architecture,
Synchrophasor data is processed
separately for each PMU. Each
processor give mode-estimates
based on individual PMU data.
• Voltage Magnitude and Voltage
Angle difference Signals were used to
identify the modes.
Voltage Magnitude
Signals
Voltage Angle
Difference Signals
Less than set
threshold
Greater than set
threshold
Model of the Active Distribution
Network used as Test system
Results
• This work was supported in part by the FP7 IDE4L project funded by the European
Commission, the STandUp for Energy Collaboration Initiative and by Statnett SF, the
Norwegian TSO. Website: http://ide4l.eu/.
• Ravi Shankar Singh is currently supported by the European Union’s Horizon 2020
research and innovation program under the Marie Sklodowska-Curie grant agreement No
676042.
Acknowledgement
Modes resent in the Network
Centralized Architecture Decentralized Architecture (PMU 4)
𝜇 = 0.408 𝐻𝑧
𝜎 = 0.005 𝐻𝑧
𝜇 = 1.702 𝐻𝑧
𝜎 = 0.096 𝐻𝑧
𝜇 = 0.408 𝐻𝑧
𝜎 = 0.004 𝐻𝑧
𝜇 = 1.720 𝐻𝑧
𝜎 = 0.048 𝐻𝑧
𝜇 = 3.050 %
𝜎 = 2.280 %
𝜇 = 2.890 %
𝜎 = 3.050 %
𝜇 = 3.130 %
𝜎 = 0.800 %
𝜇 = 2.410 %
𝜎 = 5.880 %
𝜇 = 2.850 %
𝜎 = 2.280 %
𝜇 = 0.414 𝐻𝑧
𝜎 = 0.004 𝐻𝑧
𝜇 = 1.699 𝐻𝑧
𝜎 = 0.052 𝐻𝑧
𝜇 = 2.750 %
𝜎 = 5.050 %
𝜇 = 2.470 %
𝜎 = 1.540 %
𝜇 = 1.180 %
𝜎 = 0.550 %
𝜇 = 0.417 𝐻𝑧
𝜎 = 0.005 𝐻𝑧
𝜇 = 1.702 𝐻𝑧
𝜎 = 0.048 𝐻𝑧
Frequency Spectra (Normalized Signals)