slides - Power Systems Engineering Research Center

Report
RJT WORKSHOP: The Past, Present and Future of
the Power Grid
Cornell University
August 8, 2012
James Thorp
Phasor (Steinmetz 1897)
Phaser (Star Trek 1966)
2012
2007
2002
1997
Citations by year
1992
 In 1976-77 a few years after Bob came to Cornell I
took a sabbatical leave at AEP in NY. I worked on a
digital relaying project aimed at replacing analog
relays with new microprocessor based digital relays.
 Thanks to Moore’s law it turned out to be a good
decision on my part.
 Ed Schweitzer was a professor at Washington State
who formed a company (SEL). He claims to have sold
400,000 digital relays. He has his own jet. Prof Yang
at NCEPU formed a company in China to make PMUs.
He is a billionaire.
 In 1983 we published a paper [1] that introduced the
terms PMU and Synchrophasor. Google Scholar has
33,620 total citations with 20,400 in the last 4 years.
ISI reports the majority of citations to PMUs in last 5
years. The majority of citations to my work have
been since I turned 70
 The DOE Smart Grid Investment Grant (SGIG) is
investing in ~ 850 PMUs
 Synchrophasors
use
GPS
time
synchronized sampling to get phasors on
a common reference. The phase angle
between Maine and Florida can be
measured, for example.
 Used for monitoring
 State estimation
 Reconstruction of events
 Bob put me on the Data Adequacy
Working Group for the 2003 blackout
San Diego Blackout vs. 2003
 Control WAMS (wide area measurement
systems)
 Almost all control was local
 Load frequency an exception
(frequency is universal)
 Control of Inter-area oscillations is popular
today
 Protection
 Reduce relay involvement in cascading events
 Back up projection is the typical culprit
 (three primary, a backup, and a backup to the backup)
 In the digital substation it was obvious that all
sampling of voltages and currents should be
synchronous. Could share current samples between
transformer, bus, and line protection processors for
backup. A sampling pulse was distributed in the
substation.
 It was also clear that there was an advantage to
sampling synchronously at the other end of the line.
 Attempts at Ping-Pong to determine delays, use of
GOES satellite signals. None successful. GPS was the
answer.
 Digital relaying depended on sampling voltages and
currents at a nominal rate of n times a 60Hz cycle
q
Samples
Imaginary
• Introduction to phasors - Steinmetz
q
Real
t=0
• The starting time defines the phase angle of the phasor.
• This is arbitrary.
• However, differences between phase angles are
independent of the starting time.
• Motivation for synchronization
Substation A
Substation B
At different locations
By synchronizing the sampling processes for
different signals - which may be hundreds of miles
apart, it is possible to put their phasors on the same
phasor diagram. Angle differences are correct
 “In 1893, Charles Proteus Steinmetz presented a paper on
simplified mathematical description of the waveforms of
alternating electricity. Steinmetz called his representation a
phasor. With the invention of phasor measurement units
(PMU) in 1988 by Dr. Arun G. Phadke and Dr. James S.
Thorp at Virginia Tech, Steinmetz’s technique of phasor
calculation evolved into the calculation of real time phasor
measurements that are synchronized to an absolute time
reference provided by the Global Positioning System. Early
prototypes of the PMU were built at Virginia Tech, and
Macrodyne built the first PMU (model 1690) in 1992.”
 It is argued [2] that the investment in improving
monitoring of the high voltage transmission
network represents the most cost-effective
category of smart grid investment.
 Jim McIntosh Director of Grid Operations, CAISO
said in a JASONS Workshop in 2010 in La Jolla
that the stimulus PMUs being installed in
California would save California from $200M to
$300M a year. Run the system closer to limits
 Most vendors of control center software allow
PMU data to be integrated with conventional
SCADA data
From Wikipedia
[2] Paul L Joskow,
Creating a Smarter US
Electricity Grid MIT
Center For Energy and
Environmental Policy
Research, Oct 2011
 “PMUs
could
improve
the
performance of energy management
systems by providing real-time data
to determine system state faster and
more
accurately
than
current
estimation tools. A more extensive
deployment of PMUs is required to
make this possible”.
 “Automatic control action based on
real-time data from a wide-area
network of PMUs represents a major
change in system operations. Today
such system are limited in number
and capability. Significant research
in control algorithms and improved
confidence in the reliability and
accuracy of PMU data is needed to
make such control more prevalent.”
Presented at National Press Club.
The second question was whether
the federal government could
regulate the flow of electricity since
there was no electricity when the
constitution was written.
 Too few PMUs. “Transmission 101” [4]
estimates
there
are
15,700
transmission substations in the US. A
PNNL study [5] has 48,000 nodes.
That’s Transmission not EHV but even
1,000 PMUs is probably below the
threshold
 We are interested in state estimation
and control with measurements of less
than 2% of the states
 Lack of confidence in the reliability and
accuracy of PMU by some (reason for
standards) Would you write a paper
with a fix for a 1985 computer
problem?
 Persistent concerns about PMU
locations and latency.
NIST, Virginia Tech, Georgia
Tech, and Texas A&M are
testing PMUs
 DOE Demonstration Project:
Dynamic
State
estimation
of
Dominion Virginia Power 500 kV
network at 1/30 sec interval.
Linear, three phase, PMU only
measurements communicated by a
Sonnet network to control center
Provide measurements for control
applications with latency of < 30ms,
i.e. appropriate for bandwidths of 5
or 6 Hz.
 Emphasis on renewable energy
implies more energy storage.
 Energy storage is a new potential
control means added to the power
electronic arsenal
Linear since only PMU
measurements of
voltage and current
Time Aug 6, 2012
“Utilities now receive
updates on
transmission lines 30
times a sec instead of
every two seconds”
Substation PMU Deployment
Strategy
PDC phasor data concentrator – take data with the same
time-tag make one vector with one time tag
Arbiter Clock
Dual-Use Line Relays
60 messages
per second
IEEE C37.118
...
IRIG-B Coax.
SEL 421-4/5
SEL 421-4/5
2. PMU shall provide current
measurements for all lines and
transformers connected at 500kV.
3. PMU shall stream synchrophasors
to SEL-3373 at the rate of 60
messages per second.
SEL 3373
SEL 421-4/5
...
Substation Critical Switch
GPS
Antenna
Monitoring Requirements:
1. PMU shall provide voltage
measurements for every 500kV bus
element that can be isolated.
or
EPG ePDC
Concentrated Data:
Down-sampled to 30 messages per second
IEEE C37.118
to
SOC
Juniper
Firewall
Cisco
Router
SEL 487E
Stand-Alone PMU
SEL Dual-Use Line Relay/PMU also give time tagged
breaker status
4. For every current and voltage, the
following synchrophasors shall be
measured:
i. A-phase
ii. B-phase
iii. C-phase
iv. Positive Sequence (calculated)
5. Where dual primary line relays
exist, both relays shall provide
synchrophasors.
6. Synchrophasor data shall be
archived locally within the SEL-3373
(substation phasor data concentrator)
7. Down-sampled synchrophasors
shall be streamed to central data
concentrator at rate of 30 messages
per second.
 China has decreed that there will
be a PMU in every high voltage
substation. Much cheaper than
retrofitting. They now have ~2000
 China’s growth rate is such that
fixed
frequency
PSSs
are
unworkable
 The demand has a doubling time
of ~ 7.5 years, they are
commissioning a 1000MW plant a
week.
 Frequency of modes change
rapidly.
 They designed a WAMS Based Widearea Coordinated Modulation Control of
Multi-infeed HVDC assuming they knew
the system. When a mode is observed
the operator inserts the system which
does a real-time Prony to determine the
frequency of the wave. Then the system
adjusts parameters in the controller to
match the frequency and the loop is
closed. The operator removes the
controller after the mode is successfully
damped [18]
Increase the damping of inter-area oscillation
in CSG
Control Unit
GZ
PMU
signal
YN
GZ
GX
2
~ 10 cycles
1.5
Control Applied
1
0.5
0
Control
signals
-0.5
-1
-1.5
-2
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
control
server
Not continuously adaptive control but a
control that is engaged by an operator
like a SIPs system and observes and
adjusts before acting. SIPs as we know
them do not change parameters before
engaging. Adaptive SIPs? (SIPS system
integrity protection system)
From observations of the actual event the
system selects parameters of the control
or even the controller structure from a
large predetermined list and then
engages. At least one author has labeled
things like this as “enumeration based
robust control”
Perhaps “Synchrophasor Aided Gain
Scheduling”?
900 papers in gain scheduling in power
systems but many are for a power plant
and none mentions WAMs
15
 Create a large data base of situations
(~contingencies).
 Augment with actual archived PMU data.
 Use Data Mining with lots of options to find out
what works, Done off-line (cancer researchers
are succeeding in finding DNA markers for
various cancers with data bases of millions of
cases)
 We have used simulation to create a data base
with more than 15,000 cases for a 4000+bus
model.[9] Hydro Quebec has used 60,000
cases
 Use data mining to select the scheduling
variables. Give CART* the option of using
many possible scheduling variables. CART
will select the best. There are solvable
technical issues with complex measurements
in CART
*CART Classification And Regression Trees. There is also a
BART which is Bayesian Additive Regression Trees
16
 University contributions in
adding PMUs to existing
estimators
 Large number of papers in
Control
 Improve PSSs with a few
remote PMU measurements
 Robust control applied to
reduced order models with full
state feedback.
Software used to model large,
real system > 10,000 buses does
not (cannot) find eigenvalues or A
matrices.
How do you control something
with no model? MIT finding 2
 Universities have made contributions in
finding locations for PMUs (there are
never enough so you must put the few
in the best places)
 Best locations for:
 observability (measure local voltage
and line currents-learn ~4 voltages
from 1 PMU – only need PMU at ~
1/3 of the buses)
 Redundancy to loss of PMUs or line
opening or topology changes…
 Phased installation-optimum per
year
 Techniques
 Topological – enumerating trees,
 binary integer programming,
 data mining
Unfortunately the utilities ignore these
results and use their own criteria ($)
Synchrophasor Detectives
 Just as new telescopes (Hubble) or new microscopes
(STEM) have shown us thing we did not predict, wide
area - time synchronized - measurements have produced
a few surprises.
 The next two slides are from Mack Grady UT Austin (with
his permission) The title comes from his second slide
Wind Country
McDonald
Observatory
400 Miles
Load- Dallas
“Central
ERCOT”
Austin UT monitor
at Austin Energy
Harris substation
You must become a
synchrophasor detective
 On Feb. 26, 2008, a short circuit in a Miami
electric power substation and an operator's
error gave managers of the nation's electrical
grids a glimpse of an uneasy future. The
events triggered a chain reaction of power
plant and transmission line outages in the
state, unleashing sharp swings in voltages
and power frequency that blacked out power
for nearly 1 million customers in southern and
central Florida for up to four hours.
 A video depicting the Florida incident's
rippling spread has been created by Virginia
Polytechnic Institute and State University's
electrical
and
computer
engineering
department, which caught the disturbance on
its first-generation grid frequency monitoring
network. Some grid executives have
downloaded the video on their laptops as a
kind of horror flick for engineers of what could
happen.
Adjusting balance of security-dependability
One of the five DOE Demonstration projects
What terminal?
What measurements?
PMU data
System State
Assessment
PMU data
Supervisory
signals
Determination of triggering logic
Performance evaluation
See detail
below
Critical System
Locations
Supervisory
signal
Pacific Gas & Electric
Southern California Edison
Three parallel 500Kv lines
between Midway and Vincent
path 25
JST
Relay 1
OR
Relay 2
VOTE
Relay 3
AND
2 out of 3
is voting
Adjustment of DependabilitySecurity balance under
stressed system conditions.
Voting Scheme
• Adaptive Voting Scheme with three relays.
• State of the System
– Stressed Security = Vote
– Safe Dependability = Don’t Vote
Adaptive
Voting
Scheme
• We are NOT changing relay settings neither during,
before or after a fault.
25 JST
Data Mining: CART*
Proposed Decision Tree:
– Nodes = 7.
– Probability of correctly distinguishing 1 and a 0: 0.99%.
• Splitting Nodes
• Terminal Nodes
PMU placement
Vote Decision
Utility demand for .9999.
We studied 15000 rare events, half of which would have
caused a major disturbance. By voting we reduced the
number of major disturbances from ~7,500 to 75
*Classification And Regression Trees
26
JST
PMU Placement:
Line Current
27
JST
PMU
TESLA
LOSBANOS
ROUND MT
LOSBANOS
MIDWAY
TESLA
N.GILA
IMPRLVLY
VINCENT
TRACY
TESLA
LUGO
LUGO
VINCENT
IMPLVLY
METCALF
MOSSLAND
LUGO
MOHAVE
MALIN
ROUND MT
Decision Trees -Location of PMUs and logic for
PMU inputs to real-time, discrete-event control [5]
Predicting cascading events [6]
voltage security [7-8]
transient stability [9-10]
detection of islanding [11]
processing post disturbance records [12]
security assessment [13-14]
adaptive security dependability of relays [14]
Proposed
NASPI’s Planning and Implementation Task Team (PITT) has made base
lining of phase angle differences their highest priority.
Set angle thresholds and operator alerts
Estimation of line flows and voltages after an outage
Correlate PMU data with GIC monitoring
VAR issues –return to Pilot Points with PMUs and CART
Equipment monitoring and asset management
At 60 times a second can monitor I2t in transformers due to faults
 There is a more limited history of data
mining of archived PMU data. Fifteen
months of PMU data of 54 angle
differences has also been subjected to
statistical analysis to detect abnormal
power system behavior using software
developed for NASA by PNNL [15-17].
 [16] presents results using state
estimator data sampled every few
minutes. Part of [16] is to identify data
as atypical [17] rather than typical.
 Plans to expand this by WECC using
OSIsoft. They will ultimately archive
150,000 measurements per second
 More PMUs
 All high voltage buses>100kV
~50,000 substations
 Operator alarms and
alerts
 Oscillation
 Angles
 Voltages
 More adaptive relays
 Possibly universal
 Coordinated SIPs
 WECC has >100. One went
wrong in San Diego blackout
 Control
 Inter-area oscillations
 Out of step
SIPs System Integrity Protection
Schemes
Remedial Action Schemes
Special Protection Schemes
Control actuated by protection: If one
of two parallel lines trips shed load
and reduce generation
Given we are likely to have to make due with the existing
wires and towers as AC transmission lines for decades, PMU
technology, advances in computing, and communications offer
the best hope for a Smart Transmission Grid.
Returning to the MIT Findings
 PMUs could improve the performance of energy
management systems by providing real-time data to
determine system state faster and more accurately than
current estimation tools.
 Automatic control action based on real-time data from a
wide-area network of PMUs represents a major change in
system operations. Significant research in control algorithms
and improved confidence in the reliability and accuracy of
PMU data is needed to make such control more prevalent.”
Terry Boston, President and CEO of PJM is reported to have
said ”running the nation’s power grid isn’t rocket science- it’s
harder”
Thank You
References
[1] A.G. Phadke, J. S. Thorp and M. G. Adamiak, "A New Measurement Technique for Tracking Voltage Phasors, Local System Frequency and
Rate of Change of Frequency," IEEE Trans on PAS, PAS 102, vol. 5, 1025-1038, May 1983.
[2] The Future of the Electric Grid, An MIT Interdisciplinary Study, December 2011
[3] Paul L Joskow, Creating a Smarter US Electricity Grid, CEEPR WP 2011-021, MIT Center For Energy and Environmental Policy Research,
Oct 2011
[4] Silverstein, A., “Transmission 101”, ECEP Transmission Technologies Workshop, April, 2011
[5] S. M. Rovnyak, C. W. Taylor, and J. S. Thorp, “Performance index and classifier approaches to real-time, discrete-event control,” Control
Engineering Practice, vol. 5, no. 1, pp. 91–99, 1997.
[6] Kamwa I.; Samantaray, S.R.; Joos, G., “On the Accuracy versus Transparency Trade-Off of Data-Mining Models for Fast-Response PMUBased Catastrophe Predictors”, IEEE Transactions on Smart Grid, Vol.: 1, Issue: 2, pp.: 144 – 158, 2010.
[7] Nuqui, R.F.; Phadke, A.G.; Schulz, R.P.; Bhatt, N., “Fast on-line voltage security monitoring using synchronized phasor measurements and
decision trees”, IEEE PES Winter Meeting, 2001. Vol.: 3, pp.: 1347 – 1352, 2001.
[8] Ruisheng Diao; Kai Sun; Vittal, V.; O'Keefe, R.J.; Richardson, M.R.; Bhatt, N.; Stradford, D.; Sarawgi, S.K., “Decision Tree-Based Online
Voltage Security Assessment Using PMU Measurements”, IEEE Transactions on Power systems Vol.: 24 , Issue: 2, pp.: 832 – 8, 2009.
[9] Sun, K., Likhate, S., Vittal, V, Mandal, S, “An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision
Trees”, Trans Power Systems, Vol. 22, No 4 2007 pp. 1935-1943
[10] Jan Ma; Makarov, Y.V.; Miller, C.H.; Nguyen, T.B.; “Use multi-dimensional ellipsoid to monitor dynamic behavior of power systems
based on PMU measurement”, IEEE PES General Meeting, Conversion and Delivery of Electrical Energy in the 21st Century, pp.: 1 – 8, 2008
[11] Rui Sun; Zhongyu Wu; Centeno, V.A, “Power system islanding detection & identification using topology approach and decision tree”
IEEE PES General Meeting, pp.: 1 – 6, 2011
[12] Kamwa, I.; Samantaray, S.R.; Joos, G., “Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area PostDisturbance Records”, IEEE Transactions on Power
systems, Vol.: 24, Issue: 1, pp.: 258 – 270, 2009.
[13] Zhiyong Li; Weilin Wu,”Phasor Measurements-Aided Decision Trees for Power System Security Assessment”, ICIC '09. Second
International Conference on Information and Computing Science, Vol.: 1, pp.: 358 – 361, 2009.
[14] Bernabeu, E.E.; Thorp, J.S.; Centeno, V., “Methodology for a Security/Dependability Adaptive Protection Scheme Based on Data
Mining”, IEEE Transactions on Power Delivery, Vol: 27, Issue: 1, pp 104 – 111, 2012.
[15] Ferryman, TA, and Amidan, BG. 2010. “Statistical Analysis of Abnormal Electric Power Grid Behavior” 2010 Hawaii International
Conference on System Sciences
[16] Ferryman, TA, and Amidan, BG,. “Investigation of Phase Angle Differences Using Statistical Analysis of Real World State Estimator Data,”
2012 Hawaii International Conference on System Sciences
[17] Amidan, B.G. and Ferryman, T.A., “ Atypical Event and Typical Pattern Detection within Complex Systems”, IEEEAC paper #1200, version
3 Dec 9, 2004
[18] Lu, Chao; Wu, Xiaochen; Wu, Jingtao; Li, Peng; Han, Yingduo; Li, Licheng ,”Implementations and Experiences of Wide-Area HVDC
Damping Control in China Southern Power Grid” PESGM2012

similar documents