Phase Identification of Smart Meters by Clustering Voltage - - PowerPoint PPT Presentation

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Phase Identification of Smart Meters by Clustering Voltage - - PowerPoint PPT Presentation

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Phase Identification of Smart Meters by Clustering Voltage Measurements Frdric OLIVIER Antonio SUTERA Pierre GEURTS Raphal FONTENEAU Damien ERNST PSCC 2018 University of Lige, Belgium Dublin Introduction Distribution transformer

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Phase Identification of Smart Meters by Clustering Voltage Measurements

Frédéric OLIVIER Antonio SUTERA Pierre GEURTS Raphaël FONTENEAU Damien ERNST University of Liège, Belgium PSCC 2018 Dublin

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Introduction

M 1 M 2 M 3 M 4 M 5 M 6 M 7 M 8 M 9 M 10 M 11 M 12 M 15 M 16 M 17 M 18 M 19 M 20 M 21 M 22 M 23 M 24 M 25 M 26 M 27 M 28 M 29 M 30 M 31

Network node Smart meter Electrical line Distribution transformer

2 PSCC 2018

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The phase identification problem ? ?

𝑏 𝑐 𝑑 𝑜 Network 𝑠

&

𝑠

'

𝑡' 𝑢' 𝑡& 𝑢& 𝑠

*

Cluster 𝒟& Cluster 𝒟' Cluster 𝒟*

3 PSCC 2018

𝑏 𝑐 𝑑 𝑜 Smart meter Smart meter 𝑠 𝑡 𝑢 𝑠 𝑜 𝑜

𝑁-& 𝑁.& 𝑁/& 𝑁-*

Measurements

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PSCC 2018

Why is the phase information important? What are the existing solutions? What is the algorithm we propose? Performance and discussions

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PSCC 2018

Why is the phase information important? What are the existing solutions? What is the algorithm we propose? Performance and discussions

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The phase identification is important

If you are a DSO If you are a researcher

6 PSCC 2018

Change connection phase of the customer Reduce the imablance in the network Increase the hosting capacity

Phase-to-neutral active and reactive power measurements Three-phase power flow Comparison between measured and simulated voltages

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PSCC 2018

Why is the phase information important? What are the existing solutions? What is the algorithm we propose? Performance and discussions

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Manual

Existing solutions

Automatic

8 PSCC 2018

Phase identifiers Smat meters with PLC k-means clustering Graph theory

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Contributions

  • 1. Novel algorithm

1. Using the underlying structure of the network 2. Using the advantages of both graph theory and correlation 3. Identifying the measurements that should be linked together and cluster them.

  • 2. Performance compared to those of a constrained k-means

clustering

  • 3. Tested on real measurements from a distribution network in

Belgium, in a variety of settings.

9 PSCC 2018

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PSCC 2018

Why is the phase information important? What are the existing solutions? What is the algorithm we propose? Performance and discussions

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  • Distance between two voltage measurements:
  • Pearson’s correlation

𝑒 𝑁1,𝑁3 = 1 − 𝑄𝐷 𝑁1, 𝑁3 , ∀𝑚, 𝑗 ∈ ℐ

  • Distance between a voltage measurement and a cluster

Δ 𝒟@, 𝑁3 = min

1∈𝒟D 𝑒(𝑁1, 𝑁3)

Distances

11 PSCC 2018

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Reference algorithm Constrained k-means Clustering

12 PSCC 2018

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Proposed algorithm Constrained Multi-tree Clustering

13 PSCC 2018

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PSCC 2018

Why is the phase information important? What are the existing solutions? What is the algorithm we propose? Performance and discussions

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Test system

  • Belgium LV distribution network
  • 5 feeders, star configuration 400 V
  • 79 three-phase smart meters
  • 2 single phase smart meters
  • Average phase-to-neutral voltage

measurements every minute

15 PSCC 2018

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Results for the test sets Discussions on the selection of the root

0,2 0,4 0,6 0,8 1

CKM CMT

Transformer House

PSCC 2018

Performance measure The ratio between the measurements correctly identifiedand the total number of measurements

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Influence of the voltage-averaging period

PSCC 2018

CMT CKM

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Influence of ratio single-phase – three-phase smart meters

PSCC 2018

CKM CMT

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Influence of the number of smart meters

PSCC 2018

CKM CMT

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Conclusion

  • Novel method to identify the phases of smart meters in LV

distribution networks

  • Clustering the voltage measurements using graph theory and the

correlation between measurements

  • A root smart meter as input upon which the clustering process is

done

  • Better performance than Constrainted k-means clustering

PSCC 2018

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Future works

  • Use this novel method to infer network topology.
  • Test the algorithm on measurements from other network

configurations, such as

1. 3-phase 4-wire with grounded neutral, 2. 3-phase 3-wire (3x230V) and no ground.

21 PSCC 2018

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Phase Identification of Smart Meters by Clustering Voltage Measurements

Contact: Frédéric OLIVIER Montefiore Institute (B28) University of Liège, Belgium frederic.olivier@uliege.be PSCC 2018 Dublin