Synchrophasor Applications for Distribution Networks Enhancing T&D Operation and Information Exchange

Transcription

1 Synchrophasor Applications for Distribution Networks Enhancing T&D Operation and Information Exchange Luigi Vanfretti, Hossein Hooshyar, Farhan Mahmood, Ravi Shankar Singh, Ali Bidadfar, Narender Singh, and Mehdi Monadi Presented by Vedran Perić

2 SLIDE 2 Laboratory Implemented Architecture PMU PMU Synchrophasor Data (C ) Synchrophasor Data (C ) PDC IEC Gateway IEEE C (TCP/IP) WAN IEC (UDP/IP) IEEE C (TCP/IP) Processed Data Graphs DMS Data parser Data processing and extracting required components Synchrophasor Data (C ) Monitoring functions PMU

3 SLIDE 3 Applications Providing Dynamic Information

4 SLIDE 4 Steady State Model Synthesis (SSMS) Methodology V 1 a <δ 1 a V 2 a <δ 2 a I 1 a <φ 1 a 3 I 2 a <φ 2 a V 1 b <δ 1 b 3 V 2 b <δ 2 b Concept a a ( )( ) V Ðd - V Ð d = R + jx I Ðj - I Ðj a a a a a a a a I b b 1 <φ 1 V c c 1 <δ 1 I c c 1 <φ 1 PMU1 Any feeder configuration with an arbitrary combination of load and DG I b b 2 <φ 2 V c c 2 <δ 2 I c c 2 <φ 2 PMU2 R a & X a a a ( ) V Ð d = V Ðd - R + jx I Ðj a a a a a a V 1 a <δ 1 a V 1 b <δ 1 b V 1 c <δ 1 c I 1 a <φ 1 a I 1 b <φ 1 b I 1 c <φ 1 c R a R b R c X a X b X c I0 a <φ0 a V 0 a <δ 0 a V 0 b <δ 0 b I0 b <φ0 b V 0 c <δ 0 c I0 c <φ0 c R a R b R c X a X b X c I 2 a <φ 2a I 2 b <φ 2b I 2 c <φ 2 c V 2 a <δ 2 a V 2 b <δ 2 b V 2 c <δ 2 c V 0 a & δ 0 a Assumption: R1a=R2a=R3a & X1a=X2a=X3a a a ( ) E Ð d = V Ðd - R + jx I Ðj a a a a a a The reduced steady state equivalent model R a X a R b X b R c X c E a <δ a E b <δ b E c <δ c E a & δ a

5 Grid model is simulated in real-time SLIDE 5 SSMS Algorithm Opal-RT Simulator Reconfigurable data buffer SSMS LabVIEW Application Filtered PMU data KF Algorithm Estimated Equivalent Model HIL RT Simulation Setup Node 1 Node 2 Phase Unwrapping V1 I1 data buffer Kalman Filter LabVIEW Application Raw PMU Data PDC Stream NI-cRIO PMU1 Current Amplifier V2 I2 S 3 DK real-time data mediator SEL-PDC 5073 NI-cRIO PMU2 Current Amplifier

6 Transmission SLIDE 6 Illustration Example: Model synthesis of sample active distribution network Event 1: A lateral MV feeder disconnects at Node 834 at t = 40 s Event 2: A wind farm generation of 1 MW (0.2 p.u.) disconnects at Node 854 at t = 70 s. EPS Static load Dynamic load Voltage regulator Wind farm RBTS RBTS Legend Capacitor bank Circuit breaker FOP Recloser PMU node IEEE 34 - Bus Test Feeder PMU PMU Event2 (Wind farm disconnects) IEEE 34 - Bus Test Feeder Distribution PMU PMU Event1 (Feeder disconnects) Reduced Model Event1 (Feeder disconnects) R (p.u.) X (p.u.) Event1 Event2 Event1 Event E a ( p.u.) δ a ( rad) Event1 Event2 Event1 Event Sample power Sample system network network with synthesized model Estimated parameters of equivalent model

7 SLIDE 7 Illustration Example: Model synthesis of sample active distribution network True phasors vs Reproduced phasors at PMU 1 and PMU 2 Va (p.u.) e 1 = p.u. e 2 = p.u. e 3 = p.u Va (p.u.) e 1 = p.u. e 2 = p.u. e 3 = p.u. true reproduced Ia (p.u.) e 1 = p.u. e 2 = p.u. e 3 = p.u. true reproduced Ia (p.u.) e 2 = p.u. e 3 = p.u. e 1 = p.u a (rad) (rad) a e 2 = rad. e 1 = rad. e 3 = rad e 2 = rad. e 3 = rad. e 1 = rad Time (sec) a (rad) a (rad) e e 1 = rad. 2 = rad. e 3 = rad e 1 = rad. e 1 =0.016 rad. e 1 =0.017 rad Time (sec) True phasors vs Reproduced phasors at PMU 1 True phasors vs Reproduced phasors at PMU 2 The end-to-end TVE is less than 3%. Submitted to IEEE Transaction on Power Delivery (2 nd review): F. Mahmood, H.Hooshyar, L. Vanfretti, Real-time Reduced Steady State Model Synthesis of Active Distribution Networks Using PMU Measurements

8 SLIDE 8 Dynamic Model Synthesis of Distribution System TSO Data process and analysis Data process and analysis Calculation of stability indices Data process and analysis Decentralized Centralized Architecture Ambient Data Analysis (for stochastic variations) Ringdown Data Analysis (for transients) Data curation, fusion and extraction of steady state component

9 SLIDE 9 Centralized vs decentralized architecture (local mode visibility)

10 SLIDE 10 Centralized vs decentralized architecture (better observability) Decentralized Mode Estimation Dynamic Stability Indices More information to appear in IEEE PES General Meeting 2016 in Boston: R. S. Singh, M. Baudette, H.Hooshyar, L. Vanfretti, In Silico Testing of a Decentralized PMU Data-Based Power Systems Mode Estimator

11 SLIDE 11 Voltage Stability Analysis in Distribution Networks Computation of stability indicators based on real-time measurements and equivalent models. By samplings from PMU1 and PMU2, three different equivalent models are developed 11

12 SLIDE 12 Three different PV curves are calculated from the three models. The voltage stability and instability indices are calculated from these models to indicate the contributions of two networks on the voltage stability. 12

13 Transmission SLIDE 13 LABVIEW Application Real-time simulations results for aggregated load (LV network) seen from PMU22: A. all distributed generations inside MV network are connected B. all distributed generations inside MV network are disconnected Distribution A B SLIDE 13

14 SLIDE 14 Feeder Dynamic Rating Application for Active Distribution Network Using Synchrophasor Dynamic line rating (DLR) is a way to optimize the ampacity of transmission and distribution lines by measuring the effects of weather and actual line current. IEEE 738 Standard: Can be used to calculate conductor line Ampacity and Conductor temperature if conductor material properties, ambient weather conditions and actual line current is known GPS Time Source State Change Equation: Relates two different states of an overhead conductor. The values of conductor temperature and corresponding parameters in one state can be used to estimate the conductor temperature in another state. Kalman filter: A filtering technique that uses a series of measurements observed over time to produce a more precise estimate. Gives a more precise estimate of conductor temperature.

15 HIL Setup SLIDE 15

16 SLIDE 16 LabVIEW application and sample results Static rating Event 1 Event 2 Event 1: Outage of a line. Event 2: Outage of a wind generation (more power t be drawn from the grid).

17 SLIDE 17 Adaptive Auto-Reclosing (AAR) The proposed AAR scheme is capable to: Firstly, in the case of single-phase faults discern between temporary and permanent faults. This is done by considering the voltage phasor of the opened-phase (shown below). Secondly, in case of temporary single-phase faults, recloses when the secondary arc extinguishes (when TVD backs close to zero). Thirdly, in the case of permanent or non-detectable faults, reclose only when the healthy part of the network is able to tolerate another reclosing attempt. In this case the Stability and Thermal indices are calculated based on the PMU s outputs. Voltage (V) Permanent 1-phase fault Voltage (V) Temp. 1-phase fault Secondary arc Arc extinguished Time(s) Time (s)

18 Results SLIDE 18 The TVD index during and after the fault: The voltage of the faulted phase and the CB status: x Opend phase voltage(v o ) CB Status (1:close) TVD (%) Arc extinction Voltage (V) Fault Detected Arc extinction Arc extinction detected 0 Threshold (5%) Time(s) Time (s) Fault occurrence Before the fault occurrence the TVD is close to zero. After fault, its value goes up dramatically. Finally, after the arc extinction TVD backs to zero. The proposed method send the close command after the arc extinction.

19 SLIDE 19 Proposed Method and Implementation Issuing the reclosing command Providing information on different considerations: For 1-phase faults, whether the fault cleared. For 3-phase faults, whether the system operating point is far enough from the stability margins to tolerate another fault (in case the fault is not cleared). For 1-phase and 3-phase faults, whether the conductors are cooled down.

20 Thank You