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Authors Jagabondhu Hazra, Kaushik Das, Deva Seetharam, Amith Singhee IBM Research Presented by Anand Seetharam. Stream-computing Based Synchrophasor Applications for Power Grid. Introduction. Need for real-time situational awareness of the grid for stable, economic operation - PowerPoint PPT Presentation
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© 2011 IBM Corporation
Stream-computing Based Synchrophasor Applications for Power Grid
Authors Jagabondhu Hazra, Kaushik Das, Deva Seetharam, Amith Singhee
IBM Research
Presented by Anand Seetharam
© 2011 IBM Corporation
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)
© 2011 IBM Corporation
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Phasor Measurement System Example (NASPInet)
Phasor Data Concentrator
(PDC)
Phasor Data Concentrator
(PDC)
Super PDC
(SPDC)
Local DB Local DB
Master DB
PMU PMU
PMU
PMU
PMU
PMU
PMU
PMU
PMU
PMU
GPS satellite
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.
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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Stream Computing: A New Paradigm
Real time analysis of data-in-motionStreaming data Stream of structured or unstructured data-in-motion
Stream Computing Analytic operations on streaming data in real-time
Historical fact finding with data-at-rest
Batch paradigm, pull model Query-driven: submits queries to static data Relies on Databases, Data Warehouses
Traditional Computing Stream Computing
Queries Data ResultsQueries Data ResultsQueries Data ResultsQueries Data Results Data Queries Results
© 2011 IBM Corporation
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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
Processing Element (PE)
Computing Node
PE Container (PEC)
© 2011 IBM Corporation
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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.
© 2011 IBM Corporation
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Proof of Concept Application - Real time voltage stability monitoring
© 2011 IBM Corporation
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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.
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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
Fig. Character of a PV curve
Voltage stability index is given by:
Where,NB Number of buses in the systemPi Active power injection at bus iVi Voltage magnitude Voltage phase angle at bus IB Admittance matrix
At Vcritical, value of stability index is 0.5
Normal range
0.2
0.4
0.6
0.8
1.0
0.0
Stable
Unstable
Operating point
argM inP
argM inV
/R SV E
/R RMAXP P0.0 0.2 0.4 0.6 0.8 1.0
Critical voltage
© 2011 IBM Corporation
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Test Grid
zone1
G G
G
1
2 7 8 9 3
5 6
4
zone2
zone3
PDC1 PDC2 PDC3
PMUs 2, 5, 7 1, 4, 6 3, 8, 9
Generators : 3
Loads : 3
Tr. Lines : 6
Transformers : 3
PMUs : 9
PDCs : 3
SPDCs : 1
Fig. 9 bus system
© 2011 IBM Corporation
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SPADE application graph
PDCs SPDC Application
PD
C1
PD
C2
PD
C3
© 2011 IBM Corporation
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Real Time Stability MonitoringGradual overloading by 20%
In the presence of a fault
G G
G
1
2 7 8 9 3
5 6
4
G G
G
1
2 7 8 9 3
5 6
4
Load buses 5, 6, 8
Fault initiated
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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Aggregation Experiments: Reduction in Traffic
© 2011 IBM Corporation
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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.
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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
© 2011 IBM Corporation
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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
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