Stream-computing Based Synchrophasor Applications for Power Grid

© 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


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)

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.


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

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


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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)


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.


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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


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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


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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


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SPADE application graph

PDCs SPDC Application

PD

C1

PD

C2

PD

C3


15

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


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


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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


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.


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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


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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


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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

Documents

Stream-computing Based Synchrophasor Applications for Power Grid