Hybrid processing of SCADA and synchronized phasor measurements for tracking network state

Transcription

1 IEEE PES General Meeting, Denver, USA, July Hybrid processing of SCADA and synchronized phasor measurements for tracking network state Boris Alcaide-Moreno Claudio Fuerte-Esquivel Universidad Michoacana de San Nicolás de Hidalgo Mexico Mevludin Glavic Thierry Van Cutsem University of Liège Belgium

2 2 Tracking state estimation: objective Track the changes in network state of concern: complex bus voltages not the dynamics of components connected to the network executed at much higher rate than state-of-the-art state estimators period of execution T r 1 second to provide better information to EMS applications e.g. tracking voltage stability to increase situational awareness especially after a (large) disturbance

3 3 Tracking state estimation: context Can be traced back to the 70 s when only SCADA measurements were available worth being revisited, considering the availability of synchronized phasor measurements only a limited number of PMUs are assumed to be available PMU configurations are far from ensuring full network observability situation expected to prevail for some time in many power systems even with a rich PMU configuration, SCADA measurements will still be providing useful information, which it is of interest to exploit methods are needed to take maximum benefit from: SCADA measurements: received every 2-5 seconds synchrophasor measurements: several tens of samples per second

4 4 Continuation of previous work M. Glavic, T. Van Cutsem, Reconstructing and Tracking Network State from Limited Number of Synchrophasor Measurements, IEEE Trans. Power Syst., vol. 28, no. 2, pp , May 2013 observability restored by bus power injection pseudo-measurements M. Glavic, T. Van Cutsem, Tracking network state from combined SCADA and synchronized phasor measurements, Proc. IREP Symposium, Rethymnon (Greece), Aug Available on IEEEXplore SCADA and synchrophasor measurements combined this presentation

5 5 Principle of proposed Tracking State Estimator (TSE) Every T r seconds, the TSE processes in the least-square sense: the most recent synchronized phasor measurements bus voltage and branch currents the SCADA measurements received since the last TSE execution older SCADA measurements not used to decrease the time skew effect thus, only a fraction of all SCADA measurements is used the recursively predicted values of all SCADA measurements used as pseudo-measurements to restore observability obtained from a time series analysis using the results of the recent TSE executions zero bus injections treated as equality constraints

6 Principle of proposed TSE 6

7 7 Measurement model : state vector : real and imaginary parts of bus voltages At time : estimate using : the subset of available SCADA measurements : the latest synchrophasor measurements : the predicted SCADA measurements : zero injections treated as linear equality constraints

8 8 Weighted least-square formulation solved by Hachtel s augmented matrix method iterations initialized with computed at previous time

9 9 Main steps of TSE similarity with (extended) Kalman filter but prediction on SCADA measurements instead of state to avoid resorting to a transition model for the bus voltages (very difficult to obtain in practice)

10 10 SCADA measurement prediction Predictor : Single Exponential Smoothing Holt s Linear method : etc.

11 11 Test system SCADA measurements within a given substation : collected with delays in the range [ ] s transmitted to control center every 2 5 s depending on the substation received with transmission delays in the range [ ] s Two multi-channel PMUs 2 bus voltages 5 branch currents 10 pairs of zero injections

12 12 Scenario System evolution fault cleared by opening line driven by load tap changers and overexcitation limiters (not known by TSE) Scenario # 1: long-term voltage instability Scenario # 2: same stabilized by undervoltage load shedding (not known by TSE) TSE executed every T r = 0.5 s topology updated model without distribution transformers 200 MW shed 100 MW shed

13 Tracked vs. exact voltage evolution - scenario # 1 13

14 Tracked vs. exact voltage evolution - scenario # 2 14

15 15 Standard deviations and accuracy indices SCADA power flow measurements : current synchrophasor measurements : predicted SCADA (pseudo-)measurements : Mean Absolute Percentage Error : exact estimated Mean Absolute Error : total number of TSE executions total number of buses

16 16 Standard deviations and accuracy indices Tuning of factor K based on unstable scenario (large deviations of operating point) zero-order prediction in TSE K varied over a wide range until minimum MAPE is found : K=3.1 Tuning of and in Holt s linear method (prediction of SCADA measurements) varied until minimum MAPE is found : = 0.6 and = 0.5

17 17 Detailed assessment of TSE accuracy s=500 Monte-Carlo simulations with : random noise on each (SCADA and synchrophasor) measurement random SCADA measurement transmission delays in [ ] s T successive TSE executions (every T r = 0.5 s) for the j-th quantity with a SCADA measurement : averaging over samples averaging over time

18 18 Detailed assessment of TSE accuracy filtering capability in spite of system transients

19 19 Conclusion Repeated least-square state estimation using measurements as and when they are received replaces dynamic model of system by prediction of SCADA measurements handled as pseudo-measurements solving the unobservability problem network state evolution after a major disturbance can be tracked sudden (unknown) changes in operating point are tracked with a short delay due to non-synchronized SCADA measurements can filter measurement noise as confirmed by Monte-Carlo simulations bridges the gap between standard static and full dynamic state estimation scalable: accommodates progressively richer PMU configurations

20 20 Conclusion TSE could be executed as often as every 0.1 s for a wide range of slower phenomena monitored from a control center, TSE could contribute to better situational awareness provides inputs to applications requiring full network state e.g. long-term voltage stability, slow interarea oscillations, thermal overloads and cascading effects, etc. to track short-term angle or voltage dynamics, a richer PMU configuration and a true dynamic state estimator are required ongoing work : bad data analysis (and discrimination from system changes) exploitation of time-tagged SCADA measurements improved pseudo-measurement covariance determination.

21 21 Thank you for your attention! For more information:

22 22 Hachtel s augmented matrix method reduced set of eqs. stemming from 1st-order optimality conditions subject to: zero injections measurement residual complex bus voltages in rectangular coordinates 22 Hachtel s augmented matrix