Power System Control & Protection Lab

BD SMART

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

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Performer                : Texas A&M Engineering Experiment Station

Partners                    : OSIsoft LLC; Quanta Technology, LLC; Temple University

Title                            : Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

Federal Funds          : $1,000,000

Cost Share                : $696,560

Total Project Value: $1,696,560

Summary                  : The project utilizes Big Data Analytics (BDA) to automate monitoring of synchrophasor recordings. This improves assessing events that may affect power system resilience. The BDA is used to automatically extract knowledge leading to event analysis, classification and prediction, all used at different stages of the grid resilience assessment: operations, operations planning, and planning. The project’s techniques are based on their past work performed at the Texas A&M Engineering Experiment Station (TEES) on automated classification of faults, location of faults and instability detection using neural network and machine learning classifiers and predictors, and the latest innovations in BDA techniques developed by Temple University. The team engages Quanta Technology experts experienced in the utility interaction to interpret the PMU data files to be utilized in the process. They facilitate industry feedback leading to the development of metrics for evaluation of the proposed solution. Additionally, the project proposes a novel solution for predicting future events based on historical PMU data by extracting the sets of precursors and analyzing the development of PMU observed disturbances over time.

Read more: https://today.tamu.edu/2019/05/24/big-data-analytics-could-reduce-power-grid-outages/

References

Conferences:

  1. Kezunovic, Z. Obradovic, Y. Hu, “Use of Machine Learning on PMU Data for Transmission System Fault Analysis,” CIGRE General Session, Paper # 191, Paris, France, August 2022.
  2. Kezunovic, Z. Obradovic, Y. Hu, “Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data,” CIGRE General Session, Paper # 224, Paris, France, August 2022.
  3. Dokic, R. Baembitov, A. A. Hai, Z. Cheng, Y. Hu, M. Kezunovic and Z. Obradovic., “Machine Learning Using a Simple Feature for Detecting Multiple Types of Events from PMU Data,” in 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), 24-26 May 2022 2022, pp. 1-6, doi: 10.1109/SGSMA51733.2022.9806000.
  4. Cheng, Y. Hu, Z. Obradovic, M. Kezunovic, “Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?” 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), 24-26 May 2022 2022, doi: 10.1109/SGSMA51733.2022.9806010.
  5. M. Mohamed, M. Kezunovic, J. Lusher, J.C. Liu, and J. Ren, “The use of digital twin for timing intrusion detection in synchrophasor systems,” IEEE PES GM, Denver, USA, July 2022
  6. Alqudah, M. Pavlovski, T. Dokic, M. Kezunovic, Y. Hu, Z. Obradovic, “Convolution-based Fault Detection Utilizing Timeseries Synchrophasor Data from Phasor Measurement Units,” IEEE Transactions on Power Systems, 2022, DOI: 10.1109/TPWRS.2021.3135336.
  7. Fast fundamental frequency event detection: R. Baembitov, T. Dokic, M. Kezunovic and Z. Obradovic, “Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset Using Big Data Analytics,” HICSS-54 Conference, Hawaii, USA, January 2021.
  8. Fast event detection: T. Dokic, R. Baembitov, A. Abdel Hai, Z. Cheng, Y. Hu, M. Kezunovic, Z. Obradovic, “Efficient Event Detection in Big PMU Data using Machine Learning Method with Single Rectangle Area Feature,” journal paper (submitted to Elsevier).
  9. M. K. Alqudah, M. Pavlovski, T. Dokic, M. Kezunovic, Y. Hu and Z. Obradovic, “Convolution-based Fault Detection Utilizing Timeseries Synchrophasor Data from Phasor Measurement Units,” in IEEE Transactions on Power Systems, DOI: 10.1109/TPWRS.2021.3135336.
  10. M. Pavlovski, M. Alqudah, T. Dokic, A. A. Hai, M. Kezunovic and Z. Obradovic, “Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements,” in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-13, 2021, Art no. 2514813, DOI: 10.1109/TIM.2021.3115583.
  11. A. Abdel Hai, T. Dokic, M. Pavlovski, M. Alqudah, M. Kezunovic, Z. Obradovic, “Transfer Learning for Event Detection from PMU Measurements with Scarce Labels” journal paper (to be submitted to IEEE Access).
  12. H. Otudi, T. Dokic, T. Mohamed, Y. Hu, M. Kezunovic, Z. Obradovic, “Line Faults Classification Using Machine Learning on Three Phases Voltages Extracted from Large Dataset of PMU Measurements”,HICSS-55 Conference, Hawaii, USA, January 2022.

Journal Papers:

  1. M. Alqudah, M. Kezunovic, Z. Obradovic, “Automated Power System Fault Prediction and Precursor Discovery Using Multi-modal Data” in IEEE Access, vol. 11, pp. 7283-7296, December 2022, doi: 10.1109/ACCESS.2022.3233219
  2. M. Alqudah, M. Pavlovski, T. Dokic, M. Kezunovic, Y. Hu, Z. Obradovic, “Convolution-based Fault Detection Utilizing Timeseries Synchrophasor Data from Phasor Measurement Units,” in IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3434-3442, September 2022, DOI:  10.1109/TPWRS.2021.3135336
  3. M. Pavlovski, M. Alqudah, T. Dokic, A. Abdel Hai, M. Kezunovic, Z. Obradovic, “ Hierarchical Convolutional Neural Networks for Event Classification on PMU Data,” in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-13, 2021, Art no. 2514813, September 2021, DOI: 10.1109/TIM.2021.3115583
  4. A. Abdel Hai, T. Dokic, M. Pavlovski, T. Mohamed, D. Saranovic, M. Alqudah, M. Kezunovic, Z. Obradovic, “Transfer Learning for Event Detection from PMU Measurements with Scarce Labels” in IEEE Access, Vol. 9, pp. 127420 – 127432, 10 September 2021, DOI: 10.1109/ACCESS.2021.3111727.