Integration of Synchro-Phasor Measurements in Power Systems State Estimation for Enhanced Power System Reliability

2. Integration of Synchro-Phasor Measurements in Power Systems State Estimation for Enhanced Power System Reliability Hassan Ghoudjehbaklou, Ph.D.– Open Systems International, Inc. Gary Roskos – Open Systems International, Inc.

3. Agenda • PMUs and the Smart Grid • PMUs and the State Estimation (SE) • Effect of PMU in Observability • Enhancing Solutions for Unobservable Islands • Certification Tests for SE PMU • Conclusions – How PMUs Can Help?

4. Applications of PMUs (Wikipedia) • Power system automation, as in smart grids • Load shedding and other load control techniques such as demand response mechanisms to manage a power system. (i.e. Directing power where it is needed in real-time) • Increase the reliability of the power grid by detecting faults early, allowing for isolation of operative system, and the prevention of power outages. • Increase power quality by precise analysis and automated correction of sources of system degradation. • Wide Area measurement and control, in very wide area super grids, regional transmission networks, and local distribution grids.

5. PMUs and the Smart Grid Phasor Measurement Units (PMUs) Provide Synchronized, Wide-Area Power Measurements • PMUs provide the Magnitude and Angle of all power measurements at all grid locations simultaneously • Measurements are available as frequentlyas 30 times each second

6. PMUs and the Smart Grid Thoughtful PMU deployment is a key element to Smart Grid development at the transmission level, accomplishing these Smart Grid goals: Increased System Reliability • High Quality, Real-time Data • Advanced Analysis, Optimization and Controls • Aggregate transmission operations and planning • Enhance grid security and stability (reliability) • Manage losses and congestion • Enhanced Communications Infrastructure and Data Security and Security , Efficiency

7. Current Events and Challenges PMU Technology is Now Widely Available: • PMU Devices are Readily Available From Multiple Vendors • Open Connectivity/Interoperability via IEEE Standards • GPS and Communications Equipment is Affordable and Hardened for Substation Use • Utility Communications Infrastructure is Improving Daily

8. Current Events and Challenges Multiple Active Pilot Projects and Research Programs are in Place • Basic Research in Massive-Volume, Real-Time Data Processing and Dynamic Applications • Basic Research in New Approaches to Grid Stability • Strategic Deployment • Post-event analysis • Model verification • Data integrity and visualization

9. Current Events and Challenges Implementation Hurdles • Device Deployment ( ) • Communications Infrastructure Deployment ( ) • Application Development - “Chicken and Egg” Problem • Can’t Justify Applications without Data • Can’t Justify Data Collection without Applications

10. PMU Project Lab SEL 5076 Sychrowave SW OSI monarchEMS System SEL3306 PDC

11. OSI PMU Implementation OpenPMU: • Brings PMU Data Directly to EMS • Initially, Utilize EMS Development/QA System for Testing and Comparisons

12. Visualization Tools

13. OSI PMU Implementation Early Lessons Learned (Learning) • IEEE Standards Revisions • Communications: Security/Redundancy/Failover • Visualization Tool Improvements • Troubleshooting

14. OSI PMU Implementation SRP Research with Arizona State University • Optimal PMU positioning in electric power system – based on achieving maximum State Estimation improvement (Prof. Heydt, Vittal) • Synchrophasor technology in validation of T-line impedance parameters (Prof. Tylavsky) • Decision tree assisted online Security Assessment using PMU measurements (Prof. Vittal) • Generator dynamic parameters validation (Prof. Heydt)

15. PMU Implementation Current and Future SRP PMU Uses • Instantaneous State of the Electric System View • Enhanced State Estimation (Measurement) • Operator Visualization • Black Start Visibility • Line Impedance Derivation • Disturbance Post-analysis • Island Phase Angle Studies

16. PMU Implementation PMU Observations • Will be the Most Important Measuring Device in Transmission System Monitoring and Control • Will Revolutionize Power Systems Monitoring and Control • Gradual Migration Towards Full PMU Implementation for the Transmission Grid • For Full Potential, a PMU System Must Have Communication Infrastructure Support Including Coverage and Speed to Match Streaming PMU Measurements • WECC Synchronized Phasor Network (DMWG & WAMTF) • NASPInet

17. OSI Application Development OSI is Working to Bring PMU Data into the EMS Environment to Meet Several Goals, Including: • Ease of Implementation • Solution Accuracy • Input Data • System Models • Solution Speed • Increased Observability • Development of Enhanced Visualization Tools • Situational Awareness • Development of Enhanced Dynamic Analysis Tools • Take advantage of a reduced solution cycle

18. OSI Application Development Short-term Enhancements: • Enhanced Communications Security • Enhanced Fail-over Capabilities • Enhanced Visualization Tools Current OSI PMU-Specific Development: • Enhanced Data Access • Optimized Hybrid State Estimation • Advanced Data Archive/Historian Capabilities • Enhanced Dynamic Stability Analysis and Control • More Real-time and Historical Visualization Tools • Next-generation Data Security Tools -

19. PMU Deployment Strategies Limited Deployment • Measurement and Model Improvement • Both sides of a variable device (Phase-Shifter, LTC, DC Line, etc.) • Measurement or Visibility Problem Areas • Interconnections Large-Scale Deployments • Start at Highest Voltages • Cover 500kV, then 345kV, etc. • Grow Contiguous PMU Measurement Areas • Start at one end and work toward the other

20. PMU Deployment Strategies Long-Term Goals • High-Quality, Sub-second State and Model Measurement • System state measured, not estimated • System parameters measured, not calculated • Dynamic events detectable • Add Applications to Capitalize on New Paradigm

21. PMUs and the State Estimation (SE) Effects on: • Observability • Solution accuracy for observable islands and boundaries • Bad data detection • Solution accuracy for the unobservable islands

22. Topological Observability • Step 1: Determine the measurement islands. All islands with PMUs will have the same group/island number • Step 2: Reduce the effect of bad angle measurements (Use Median of the angles) • Step 3: All Branches within a measurement islands will have observable flows

23. Topological Observability • Step 4: Enlarge the observable islands using n-1 rule recursively • Step 5: If voltage/angle of both sides of a branch are measured, add its calculated flows as pseudo measurement, for added stability and accuracy • Step 6: Change unobservable islands to observables, if all injections are measured or at most one injection is not measured

24. PMU measurements added to model studied by P. Katsilas, et. al. (2003) PMU Voltage/Angle Measurements Injection Measurement Flow Measurement

25. Actual Flows

26. SE FLOWS (NO PMU)

27. SE Flows (W/ PMU)

28. Selection of Reference Angles for SE (No PMUs) Electric Island 1 Electric Island 2 U2 O2 O1 U2 U3 Main Observable Island O1 U1 U1 O2 Main Observable Island O3 U4

29. Selection of Reference Angles for SE (No PMUs) • Action • In Flat start, initial angles are set to zero • Convergence • Good convergence of SE for Observable islands • Poor convergence for unobservable islands • Accuracy of the SE solution • Good for inner observable island • Poor for close to boundaries • Worst for unobservable islands

30. Selection of Reference Angles for SE With PMUs Electric Island 1 Electric Island 2 U2 O2 O1 U2 U3 Main Observable Island O1 U1 U1 O2 Main Observable Island O3 U4

31. Selection of Reference Angles for SE With PMUs • Action • In Flat start, initial angles of the observable islands are set to the Median angles of all PMUs of that island. Initial angles of unobservable islands are set to zero. • Convergence • Good convergence of SE for Observable islands • Poor convergence for unobservable islands • Accuracy of the SE solution • Good for inner observable island • Poor for close to boundaries • Worst for unobservable islands

32. Heuristic Selection of Reference Angles for SE With PMUs Electric Island 1 Electric Island 2 U2 O2 O1 U2 U3 Main Observable Island O1 U1 U1 O2 Main Observable Island O3 U4 Selection of PMU Based Reference Angle for SE

33. Heuristic Selection of Reference Angles for SE With PMUs • Action • In Flat start, initial angles of the observable islands are set to the Median angles of all PMUs of that island. Initial angles of unobservable islands are set to angle reference of the electrical island. • Convergence • Good convergence of SE for Observable islands • Better chance of convergence for unobservable islands • Accuracy of the SE solution • Good for observable island • Good for close to boundaries • Good for unobservable islands (depends on schedules)

34. PMU SE Certification Databases • Following slides present results for series of tests for Phasor Measurement Units (PMU) implementation in State Estimation (SE). Four different databases are considered for this study: • IEEE-14 (Power Flow solution as PMU Measurements) • Large Customer no. 1 (With actual PMU measurements) • Larger Customer no. 2 (No PMU Measurements)

35. PMU SE Certification Test 1 • Test 1 – Verify Observability and solvability of the PMU SE with only Phase angle and Voltage Magnitude Measurements at all buses with no other measurements. Compare the results with only bus injection measurements or only branch flow measurements. • Action Summary – All tests completed with solution matching within the tolerances • Conclusion – When all measurements are good, phase angles and voltage magnitudes provide good observability and accurate solution (This fact has been reported by other researchers as well.)

36. PMU SE Certification Test 2 • Test2 – Introduce some bad angle measurements to the cases with all phase angle and voltage magnitude measurements. Note the effect on the solution quality and convergence. • Action Summary – Initially some tests completed and bad angles detected. Later Median angle enhancement was employed for the reference angle of the measurement islands. That made all cases converge, when only few angles were bad. • Conclusion – SE solution is very susceptible to bad angle measurements (As reported by other researchers) and some heuristics should be deployed.

37. PMU SE Certification Test 3 • Test3 – Use databases with PMU measurements for the existing large customers (if the large customer does not have PMU, introduce some PMUs in the model and use phase angles from a Power Flow solution as measurement.) Verify Convergence of PMU SE. • Action Summary – Initially some tests completed when phase angles where small. Later with enhancement for large angles, all cases converged, when all angles where good. Using the enhancement of Power Flow for PMU, all cases converged and good results were obtained for the unobservable as well as observable islands. • Conclusion – Classical SE and PF need to be enhanced to handle both large and bad angle measurements..

38. PMU SE Certification Test 4 • Test4 – Verify that adding phase angle and voltage magnitude measurements actually changes observable islands. • Action Summary – To observe any change in the observable island the PMU measurements need to be close to the boundaries in the unobservable islands. • Conclusion – Not all PMUs directly impact the quality of the solution of the network. Some have more effect than the others.

39. Conclusions • How PMUs can help SE. • Provides redundant measurement that could enhance observability and improve quality of the solution for the observable island. • Provides angle reference for measurement islands that enhances stability and accuracy of the solution for the unobservable island.

40. Conclusions • What Enhancements are needed for PMU SE? • Enhancing Observability algorithm for PMU measurements. • Good selection of PMU phase angles for measurements islands. • Improved heuristics for handling unobservable islands. • What Other improvements are possible for PMU SE? • Model verification (parameter estimation). • Real-time State Estimation of a critical sub-network. • Enhanced Visualizations.

41. Questions?