PMU-based protection for grid operation applications Wide Area Monitoring platform and controlled islanding

Transcription

1 DELFT UNIVERSITY OF TECHNOLOGY Department of Intelligent Electrical Power Grids PMU-based protection for grid operation applications Wide Area Monitoring platform and controlled islanding Marjan Popov Delft University of Technology Faculty of EEMCS Delft, The Netherlands M. 1

2 Project objectives The focus of this project is on the design of a new closedloop coordinated corrective control scheme, which can be applied to mitigate instabilities, cascading outages and catastrophic blackouts in existing and future networks. PMU Supported Frequency-Based Corrective Control of Future Power Systems Control Actions 2

3 Phasor Measurement Units PMUs provide Synchronized, Wide-Area Measurements for Electric Power Systems in Real-time. PMUs provide measurements of Magnitudes and Angles, Frequency and the Rate of Change of Frequency across the power system. Measurements are provided up to 50 shots per second. Each measurement is time tagged with a GPS time precision. In Control Centers all measurements are time aligned and processed in advanced applications. 3

4 Wide Area Measurement Systems PMUs can be used for various applications like: State estimation Online dynamic security assessment Oscillation monitoring Adaptive protection & control PMUs can be used in advanced services which can reconfigure the power system operation in order to avoid large blackouts. 4

5 Real-time Wide Area Monitoring platform WAMS platform for TenneT TSO Main features: Real-Time PMU Measurements Monitoring Line Current Load Chart PMU Alert Monitor Vertical Grid Load 6

6 Overview: Controlled islanding Motivation is to limit propagation of disturbances/cascades In worst case only a small part of the network can be lost Islands are resynchronised after disturbance is over Islands should include slow-coherent generator groups Strong internal connections inside the groups A practical criterion to enhance transient stability Some other constraints Good real-time network observability (e.g. with WAMS) is required Minimisation of power imbalance within islands Open issues Islanding necessity assessment ( When to island? ) Some sort of instability prediction 7

7 Overview: Controlled islanding Usual sequence of events Source: Quirós-Tortós, J., Sánchez-García, R., Brodzki, J., Bialek, J., Terzija, V. (2015): Constrained spectral clustering-based methodology for intentional controlled islanding of large-scale power systems. In IET Generation, Transmission & Distribution 9 (1), pp

8 16-generator test system System data: 3 areas (A, B, C) 16 generators 66 buses 28 transformers 51 TLs Source: Gonzalez-Longatt, F. M.; Rueda, J. L. (2014): PowerFactory applications for power system analysis. Cham: Springer (Power systems) 9

9 System instability: Stage 0 Stage 0 1 Transformer (690 MVA) is on maintenance 10

10 System instability: Stage 1 Stage 0 1 Transformer (690 MVA) is on maintenance Stage 1 TL A-B with 800 MVA flowing from A to B trips 2 nd transformer gets overloaded 11

11 System instability: Stage 2 Stage 0 1 Transformer (690 MVA) is on maintenance Stage 1 TL A-B with 800 MVA flowing from A to B trips 2 nd transformer gets overloaded Stage 2 2 nd Transformer (690 MVA) trips Overload of other components Unstable voltage, angle instability 12

12 System instability: Stage 2 Generator speeds Bus voltages - Generators in area A - Generators in area B - Buses in Area C - Generators in area C Some generators in Area C swing together with Area A! 13

13 Controlled islanding procedure A possible implementation: Power flows in network branches are sampled with a small time step Network power flow graph is updated with this information Buses are vertices Branch elements are edges with weights equal to their power flows Coherency of generator buses is tracked in real-time using the PMUs This is a separate algorithm for tracking of coherent generator groups Partition the power flow graph to obtain balanced islands Coherent generator buses should be specified for this partitioning as well Latest data before the triggering event are used to calculate the splitting The resulting splitting is therefore adaptive to the actual situation 14

14 Controlled islanding: Stage ICI Stage ICI Calculate system splitting boundary in max. 1 2 s after the initiating event. Also consider gen. coherency. 3 islands obtained No angle instability after islanding No significant overloads Some voltages are low, but isolated in the smallest island unhealthy island 15

15 Controlled islanding: Stage ICI Generator angles Generator speeds - Generators in area A - Generators in area B - Generators in area C Bus voltages - Buses in sick island 16

16 Comparison Elements Case 1 ICI case Generators tripped 11 3 Unsupplied load 57,8% 20,5% 17

17 Conclusions ICI is the last resort measure to rescue the grid from a large blackout When separation is unavoidable, try to find the best splitting boundary ICI utilizes WAMS (e.g. PMU) data Fast update of network state (e.g. power flows) for near real-time observability ICI uses the actual state of the system before the triggering event to determine the splitting boundary Resulting splitting transient is usually smooth Islands are quite balanced ICI is able to adaptively separate sick coherent generator group from the rest of the system Blackout is limited near to its minimum The topic is subject of research, so not all the answers are known so far 18

18 Conclusions Fast and reliable ICT network plays an important role when providing real-time WAMS observability. Sufficient bandwidth, low latency, negligible jitter. Latest ICT trends dictate migration towards Multiprotocol Label Switching (MPLS) protocol stack. MPLS provide services which are more cost-effective and efficient. Due to continuous WAMS traffic growth it is endorsed to re-evaluate the existing ICT infrastructures and mark possible bottlenecks. Emulation and benchmarking of an existing ICT network with dedicated software solutions saves money and time. 19

19 This work is performed within the frame of the URSES and financed by NWO in collaboration with TenneT Prof. M. Popov Dipl-Ing. Matija Naglic Dipl-Ing. Ilya Tyuryukanov Prof. Mart van der Meijden 20