Challenges in Evaluating Smart Grid Solutions: Metric, Testing, Certification, Modeling and Simulation, Testbeds

Transcription

1 Challenges in Evaluating Smart Grid Solutions: Metric, Testing, Certification, Modeling and Simulation, Testbeds Breakout Session: Synchrophasor Systems, PMUs & PDCs, GPS Glauco Taranto, COPPE/UFRJ, Brazil, Session Chair Le Xie, TAMU, Session Co-Chair

2 Breakout Session: Synchrophasor Systems, PMUs & PDCs, GPS Topics: Evaluation of Synchrophasor Applications Evaluation of Data Analytics Evaluation of Synchrophasor Infrastructure

3 Outline Evaluation of Synchrophasor Applications - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

4 PMU Phasor Measurement Units (Synchrophasors) First experiments back to the seventies The pioneers: Profs. Phadke and Thorp In Brazil: MedFasee Project UFSC (2003) Technology for synchronization Loran-C (in the past khz) GPS (presently 2 MHz) Applications Monitoring (low sampling rate) M-Class IEEE Std C Protection and Control (high sampling rate) P-Class

5 Outline Evaluation of Synchrophasor Applications: - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

6 COPPE/UFRJ Background with PMU Sweden - Royal Institute of Technology Italy Application on Voltage Instability Detection Uruguay Application on Out-of-Step Protection Brazil Analysis of Steady-State Current Transformer Errors in the Accuracy of PMUs

7 The MedFasee Project 22 Low-voltage PMUs at University Sites

8 The Voltage Instability Awareness Application Developed in collaboration with CESI (Italy) Being tested with actual data BRAZIL NORWAY Norway Sweden Hasle 8

9 Outline Evaluation of Synchrophasor Applications: - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

10 Project Information 10

11 Research Topics for Collaborations 11

12 Streamlined collaboration will provide enhanced laboratory features (marked in dots) 12

13 Outline Evaluation of Synchrophasor Applications: - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of PMU Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

14 Challenges in Real-time PMU Processing 1. Tennessee Valley Authority (TVA) 120 PMUs produces 36GB data per day [4]. 2. One phasor data concentrator (PDC) collecting data from PMUs of 20 measurements each at 30 Hz requires over 50 GB/day storage [5]. 3. State-of-the-art: primarily post-event analysis 4. Online application for oscillation monitoring: 10+ seconds delay [4] N. Dahal, R. King, and V. Madani, Online dimension reduction of synchrophasor data, [5] M. Patel, S. Aivaliotis, E. Ellen et al., Real-time application of synchrophasors for improving reliability,

15 ERCOT BPA Percentage Percentage (%) (%) Percentage Percentage (%) (%) Percentage Percentage (%) (%) Percentage Percentage (%) (%) Dimensionality Reduction -- PCA (a) Cumulative Variance for Bus Frequency in Texas Data Number of PCs Number of PCs (b) Cumulative Variance for Voltage Magnitude V m in Texas Data (a) Cumulative Variance for Bus Frequency in Texas Data (b) Cumulative Variance for Voltage Magnitude V m in Texas Data (a) Cumulative Variance for Bus Number Frequency of PCs in Oscillation Data (a) Cumulative Variance for Bus Number Frequency of PCs in Oscillation Data Number of PCs Number of PCs (b) Cumulative Variance for Voltage Magnitude V m in Oscillation Data (b) Cumulative Variance for Voltage Magnitude V m in Oscillation Data Number of PCs Number of PCs PJM PSS/E Percentage (%) (%) Percentage (%) (%) Percentage (%) (%) Number of PCs Number of PCs Cumulative variance for bus frequency and voltage magnitude data Le Xie (a) Cumulative Variance for Bus Frequency in PJM (a) Cumulative Variance for Bus Frequency in PJM (b) Cumulative Variance for Voltage Magnitude in PJM (b) Cumulative Variance for Voltage Magnitude in PJM (a) Cumulative Variance Number of PCs Number for Bus of PCs Frequency in PSS/E Data.005 (a) Cumulative Variance for Bus Frequency in PSS/E Data Number of PCs Number of PCs Percentage (%) (%) (b) Cumulative Variance for Voltage Magnitude V m in PSS/E Data (b) Cumulative Variance for Voltage Magnitude V m in PSS/E Data Number of PCs Number of PCs 15

16 Scatter Plot of PMU ERCOT BPA Normal Condition Abnormal Condition Back to Normal Condition Le Xie

17 Early Event Detection Algorithm Local PDC Early Event Detection Synchrophasor Data Dimensionality Reduction : Phasor measurement unit PDC: Phasor data concentrator Corporate PDC Local PDC Local PDC Data Storage Data Storage : Raw measured PMU data : Preprocessed PMU data Early Event Detection Algorithm Adaptive Training PCA-based Dimensionality Reduction PMU Measurement Ysfdfffa t Covariance Matrix ga Reorder va N Eigenvalues Select fa m PCs, ggagga m N Project jfj Y in m-d Space Define Base Matrix ags i Calculate sh v n N C Y Y B 0 Robust Online Monitoring Online Detection Approximate grffs y t Approximation error gfsgf et i YES Event indicator grss t i Event Detected! Alert to System Operators i t? ˆ i T Update t t0 NO T up YES? t t 1 NO Theoretically justified using linear dynamical system theory [6]. [6] L. Xie, Y. Chen, P. R. Kumar, Dimensionality reduction of synchrophasor data: linearized analysis and online applications, IEEE Trans. Power Systems, Nov Le Xie

18 Outline Evaluation of Synchrophasor Applications: - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

19 Synchrophasor lifecycle New Tools are needed Synchrophasors Certification Field commissioning Periodic maintenance testing Detecting abnormalities Troubleshooting 19

20 Outline Evaluation of Synchrophasor Applications: - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications Evaluation of Data Analytics - Example: Collaboration between A&M researchers on PMU data handling Evaluation of Synchrophasor Infrastructure - Example: Collaboration between A&M, WSU, UIUC and GaTech on lifecycle management tools

21 PMU Certification Lead: Mladen Kezunovic, Texas A&M University

22 End-to-end tests Lead: Mladen Kezunovic, Texas A&M University

23 Application Integration test of gold PMU for In-service tests Lead: Mladen Kezunovic, Texas A&M University

24 Discussion What kind of applications that will enhance the resiliency of the system should be addressed? Do we have a killer application of synchrophasor systems for smart grid solution? Do we have adequate tools for lifecycle management? What are they and why we need them? How do we evaluate synchrophasor solutions using the proposed evaluation tools?

25 THANKS! FYI