Stream-computing Based Synchrophasor Applications for Power Grid

1. Authors Jagabondhu Hazra, Kaushik Das, Deva Seetharam, Amith Singhee IBM Research Presented by Anand Seetharam Stream-computing Based Synchrophasor Applications for Power Grid

2. Introduction Need for real-time situational awareness of the grid for stable, economic operation Increasing volatility from renewables Increasing energy usage Improved Sensing Technology Conventional sensors (e.g. Remote Terminal Units) in SCADA systems provide one measurement every 4-10 seconds Phasor Measurement Units (PMUs) could provide upto 120 phasor and frequency measurements per second PMUs provide more precise measurements with time stamp having microsecond accuracy Phasor measurements are time synchronized across national-scale grid via GPS clock: “synchrophasors” Potential for unprecedented real-time visibility into the grid state across a wide area (regional/national)

3. Phasor Measurement System Example (NASPInet) PMUs collect real-time data and through a communications system deliver the data from many PMUs to a local data concentrator, Phasor Data Concentraretor (PDC). Concentrated data are relayed on a wide-band, high-speed communications channel to a higher capability data concentrator sometimes called Super Phasor Data Concentrator (SPDC) SPDC feeds the consolidated data from all the PDCs into analytical applications such as a wide-area visualization, state estimator, stability assessment, alarming, etc. Super PDC (SPDC) Master DB Phasor Data Concentrator(PDC) Phasor Data Concentrator(PDC) GPS satellite Local DB Local DB PMU PMU PMU PMU PMU PMU PMU PMU PMU PMU

4. Real-Time Synchrophasor Applications Application examples Dynamic state estimation Voltage stability monitoring Oscillation monitoring Real-time grid stability control Requirements from application framework Low latency data processing: 100 ms – 1 s High data rates (throughput): 1000’s PMU x 120 /s Synchronization of data streams: Network jitter, different reporting rates Integration of analysis engines: State estimation, voltage stability, oscillation monitor Reconfigurable: Changes in grid Highly available: Configuration change, software upgrades Expandable: new data sources, new analytics

5. Stream Computing: A New Paradigm • Streaming Algorithms used to analyze massive amount of real time data 'Useful information' extracted in 'low memory' in 'low time' complexity Computations can be done in parallel to improve performance Applications - database, networking and machine learning Methods – sampling, sketches and clustering

6. Stream Computing: A New Paradigm Stream Computing Traditional Computing Historical fact finding with data-at-rest • Batch paradigm, pull model • Query-driven: submits queries to static data • Relies on Databases, Data Warehouses Real time analysis of data-in-motion Streaming data • Stream of structured or unstructured data-in-motion Stream Computing • Analytic operations on streaming data in real-time Data Queries Queries Results Data Queries Queries Queries Results Results Results Results Data Data Data

7. Programming Streams Application specified as a data flow graph Data streams (tuples of data) Operators (operations on these tuples of data) Operators are triggered by arrival of tuple on input port Subscription model IBM InfoSphere Streams derived from System S Stream Processing Core: execution engine SPADE: programming language and compiler Computing Node PE Container (PEC) Processing Element (PE)

8. Synchrophasors and Stream Computing Synchrophasor systems can take advantage of stream computing because High volume of data: too much to store and mine Data streaming by, faster than a database can handle Complex analytics: correlation from multiple sources and/or signals Time Sensitive: responses required in under a couple of hundred milliseconds especially for the control applications.

9. Proof of Concept Application - Real time voltage stability monitoring

10. Voltage Stability Voltage stability is the ability of a power system to maintain steady acceptable voltages at all buses in the system under normal operating conditions and after being subjected to a disturbance. Causes of voltage instability Disturbance During fault, angular difference between generators increases quickly which causes depressed voltages Motor stall When terminal voltage of a motor goes below 80% of nominal, motor torque falls below load torque and the motor slows to a standstill where it draws a large reactive current further depressing voltage and force nearby motors to stall. Reactive power deficiency Reactive power available to a portion of the grid falls below that required by customers, transmission lines, and transformers in that portion of the grid.

11. Voltage Instability – An important industry problem Power Blackouts caused by voltage instability • 1996 US west blackouts • 1997 Brazil blackout • 2003 US/Canada blackout • 2003 Italy blackout • 2003 South Sweden/Denmark • 2005 Moscow blackout • 2007 Colombia blackout Lessons learn from history • In general control devices are tuned under normal loading condition and hence are effective under normal condition • Most of the control devices do not perform satisfactorily during abnormal condition • Need for intelligent online monitoring and decision making tools Real time voltage stability monitoring and control using Synchrophasors with high end communication & middleware architectures could be effective in ensuring the voltage stability of the grid. “Voltage collapse is still the biggest single threat to the transmission system. It’s what keeps me awake at night.” -Phil Harris, PJM President and CEO, March 2004

12. Voltage stability index Voltage magnitudes, in general, do not give a good indication of proximity to voltage collapse Voltage stability index gives better idea about how far the current operating condition is from voltage collapse Operating point 1.0 Normal range 0.8 Stable 0.6 Where, NB Number of buses in the system Pi Active power injection at bus i Vi Voltage magnitude Voltage phase angle at bus I B Admittance matrix Critical voltage 0.4 Unstable 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Voltage stability index is given by: At Vcritical, value of stability index is 0.5 Fig. Character of a PV curve

13. zone1 2 7 8 9 3 G G 5 6 4 1 G Test Grid Generators : 3 Loads : 3 Tr. Lines : 6 Transformers : 3 PMUs : 9 PDCs : 3 SPDCs : 1 zone3 zone2 Fig. 9 bus system

14. SPADE application graph PDC1 PDC2 PDC3 SPDC Application PDCs

15. Real Time Stability Monitoring Gradual overloading by 20% Load buses 5, 6, 8 Fault initiated In the presence of a fault

16. Aggregation Experiments Communication network may degrade: application needs to gracefully adapt by reducing traffic Data prioritization Filter out phasors with V > Vth Data dropping Filter out phasors with insignificant change since last reading (phasor(t) ~= phasor(t-1)) |Vt – Vt-1| <= Vth AND |δt – δt-1| <= δth Data clustering “Compress” N phasors into k < N cluster centers Partial computations VSI calculation for a bus needs phasors for the bus and its neighbors Distribute VSI calculation among different nodes instead of transporting all phasors to one node

17. Aggregation Experiments: Accuracy Aggregation methods tested on the IEEE 14 bus grid Accuracy ~1-10% Data prioritization has worst accuracy Too much information lost Partial computation has best accuracy All phasors are used Data dropping shows state-dependent behavior Filtering depends on state evolution

18. Aggregation Experiments: Reduction in Traffic

19. Discussions Stream computing is a compelling framework for data collected from PMU - Highly parallelized and scalable - Data flow abstraction - Reconfigurability, expandability Streaming Algorithms can be used and tailored for smart grid applications. http://people.cs.umass.edu/~mcgregor/courses/CS711S12/index.html Data from PMU considered as flows; flow algorithms and packet inspection algorithms can be applied Fault detection techniques used in networks can be used in smart grids as well.

20. Performance Scalability in Streams Multi-level parallelism Across nodes Across PEs Across operator threads Compile-time data stream optimization Inter-node: network transport Inter-PEC (intra-node): shared memory between processes Inter-PE (intra-PEC): pointer passing between threads Inter-operator (intra-PE): direct function calls Compile-time operator-PE mapping Minimize inter-PE traffic Optimize processor utilization (not too low, not too high) Statistics collection driven compilation From [Amini et al, DM-SSP ’06] 700 PEs on 85 dual-core Xeon 3.06

21. Synchrophasor Application Needs and Streams Low latency Data transport and PE management is highly optimized High data rates High parallelism and data transport optimization Synchronization of data streams Inbuilt operators like Barriers, Join in InfoSphere Streams. Also custom operators. Integration of analysis engines Edge adaptors (operators) for network, file and pipe connections Analytics can be implemented/interfaced via primitive operators in C++/Java Reconfigurable Operators can have state-based behavior and state can be modified dynamically Expandable Subscription model allows dynamic operator additions/upgrades and stream addition/upgrades Fully dynamic application composition and re-composition possible New applications can dynamically subscribe to data from running applications Highly available Subscription model maintains PE independence: graceful fail-over

22. Features of Streams Stream-centric design Process, analyze as soon as available: no intermediate archiving Operator / data-flow graph level declarative programming model High abstraction level keeps things simple Hides complexities of infrastructure Data streaming manipulations: e.g., language support for data types and building block operations Application decomposition in a distributed computing environment: e.g., application layout, resource optimization Computing infrastructure and data transport: e.g., shipping data streams between operators, thread management C++, Java programming interface available Customized operators