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发表于 2009-1-28 11:04:14
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Executive Summary ~ J* U4 h: j4 {8 X7 q
The risk of cascading outages in power systems manifests itself in a number of ways.
; k9 x* W! G4 oWith the advent of structured competitive power markets, and with the lack of needed
& }0 t3 p0 T8 m4 w" }; o) v7 uinvestment in the transmission grid, electric power systems are increasingly being
0 j% m' j/ o9 ?+ joperated close to their limits. Potential terrorist threats raise concerns about power system
4 Y. Y' N+ _. o! w! K5 a! q. [/ Ebeing placed in unforeseen operating conditions. Recent blackouts in North America and
D( h* s8 B6 F" Q4 ^, P9 k& \in Europe show that the risk of cascading events is real with the cost of the 2003 North1 i! T* q' a7 R$ B
American blackout estimated to be around $10 Billion. As the power system becomes7 k6 Y) N" Z' u5 M
more stressed there are a host of reasons (such as weak connections, unexpected events,: z4 {- j+ W. g6 X* M% o' X
hidden failures in protection system, and human errors) for the loss of stability eventually/ F; R, d# n" n9 i9 e8 B
leading to catastrophic failures.
! q2 D& W: }6 w" W/ s/ o" pWhen a power system is subjected to large disturbances control actions need to be taken
7 j; ~7 t1 B0 ^( mto steer the system away from severe consequences and to limit the extent of the2 u& Z' v5 L9 `) O/ O5 | R
disturbance. This is particular true if system is in an operating condition that makes it. H9 U5 f" _! p$ L0 _. i2 \
unusually vulnerable to catastrophic failure. In a previous PSERC project (S-19 that* E4 l7 e" L7 N$ G# i' o
ended in 2005), we developed novel algorithms for each of the following steps:4 F0 a, V0 s* Y1 I
1. Detection of major disturbances and protective relay operations leading to9 o; d N# }* O7 A$ F
cascading events. The detection algorithms improved capabilities of real-time
! x5 C) e6 A! P; i$ dfault detection and analysis to classify the impact of a fault towards initiating
8 K9 o. @$ t% q: k: ncascading outages.
3 l8 A4 g; M% y+ o) R5 o. G2 d2. Wide-area measurement based detection and remedial control actions. The+ s# d$ b/ w$ y* d
wide-area mitigation algorithms include methods for reliably extracting modal/ v% @* v' s: Y# t, S3 }3 D$ c% y
information on critical wide-area modes through real-time wide-area Phasor
; W( N( l' c! S d0 D) a# v. c6 XMeasurement Unit (PMU) measurements. They also provide specific control& h8 `) k, c/ u! o
actions to damp out the oscillations when problems are detected.
9 V& B& I6 ?8 ~8 I1 h3. Adaptive islanding with selective under-frequency load shedding. The
/ G G( h* J' B3 E+ d4 T i) i' e% Q$ kadaptive islanding algorithms suggest methods for controlled islanding of the
& r4 o- O: c. ~& }+ \system should mitigation strategies fail.
1 H: v+ K ^ Z9 o2 ]& a0 AThe algorithms were shown to be effective using realistic computer models of test power
9 ]& k+ E, G: Ksystems. In this project, we focused on prototype implementations of those algorithms at$ o$ q7 m u, K* n9 ?, W H% z3 R1 P; L
collaborating PSERC member utilities.8 W: l* Y, N' `( N
Volume I: Detection of major disturbances and protective relay operations leading
! E: w3 u4 y! Q$ o6 s; Y. l; L. fto cascading events (Lead: Mladen Kezunovic, Texas A&M University).
, l1 {5 X7 a/ o% F ~1 `, {/ s* GThis research volume proposes a system-wide monitoring and control solution intended
2 ?( {1 S3 @! ?; N6 ]" f5 yfor use at control centers. The solution includes steady state and dynamic analysis tools.' q, [/ L. n2 C' a" r: I+ j6 d5 T
The tools may be used to check system stability, find the vulnerable elements, and send
6 D3 Z( B( }* R2 ycommands to the local tools for initiating detailed monitoring.
) _( Z6 n9 ?! {% O( aThe local monitoring and control tools are intended for installation at substations. They- [5 z. f. t3 d! @' j0 T
have the capability for advanced fault analysis and relay monitoring using neural network; B5 s( l K; e+ q9 V, q2 H# K
ii: A+ r6 m( S Y1 c3 D
for fault detection and classification, synchronized sampling for fault location, and event
" Q: z6 K' k9 c- n. E- W P0 ^8 ~tree analysis for relay operation verification. The local monitoring and control tools can) }. S7 Z2 I' M+ G
characterize a disturbance and make a correction if there is an unintended relay operation.
( f- ^( P* {: d5 A0 _( cThis information can be sent to a system monitoring and control tool for better security
7 m$ O# `! b, \: ucontrol.
. P8 U i6 P& f* E hImplementation of the proposed algorithms requires assessment of data handling2 G9 \! _" r* Q0 n
requirements. Data handling includes obtaining, converting and storing data.
( I+ M/ c( I7 r/ n$ n. H. dConsideration must also be given to integrating data from various sources: Supervisory+ F- T9 G0 \, W( L& q) [1 ?
Control and Data Acquisition (SCADA) systems, Intelligent Electronic Devices, and
8 {* s$ |( v: ~! Sother add-on high speed data acquisition systems. Data from system simulation packages
7 v, `2 z/ `* ^is also needed to perform steady state analysis.
/ N9 @4 o2 M! ^& v% oSteady state and dynamic analysis cases were studied using a model of the entire Entergy
% U) n& Z) G3 j8 D# lsystem. Vulnerability Index and Margin Index for each bus, generator, and transmission+ O- m5 T8 t. T! B- N
line were calculated to identify the vulnerability and security margin information for each
" v( t+ D- E* D% \* v9 J, E; o! @individual power system element. A 500kV Extra High Voltage transmission line was
8 [3 K( a3 ]3 M3 F8 U5 ]1 Umodeled for evaluating local monitoring and analysis tools. The results indicate that
* _. d% J5 f$ h0 t% qwhen using adequate training sets and time synchronized data, the new algorithms are7 _" L) e4 z- s) T* U
quite accurate and provide assessment of system conditions during cascades that are not) u# E& J8 A4 R8 p8 l3 |
feasible with any of the existing techniques.
8 a8 f* t \5 e# Q+ p5 YVolume II: Wide-area measurement based detection and remedial control actions7 J! c. B* M- x1 Y' [" c+ S7 z
(Lead: Mani V. Venkatasubramanian, Washington State University)' o1 u. T# Y. }! n K
If persistent over an extended period of time (e.g., 30 minutes), poorly damped
- W; j0 x' _3 |4 goscillations in a power system can lead to permanent damage of expensive power system* b- U7 |4 o- `0 }$ m
equipment and pose power quality issues. Negatively-damped oscillations can be even
) C( @ O( F( K- _! O5 bmore problematic by resulting in sudden tripping of generators and/or widespread system
/ I! \# Q- h' y# l+ rblackout such as occurred in the August 10, 1996 western power system blackout.( i1 T' Q+ ]" J% p, t
In this research project, we designed, developed and implemented an Oscillation/ y' P' x' }% Z" ~& A' }
Monitoring System (OMS) that uses wide-area PMU measurements for automatically8 b2 O1 P1 m# a. g& T
monitoring for poorly damped and/or negatively-damped oscillatory modes. OMS
0 F! v' M5 [9 Zincludes two complementary engines that provide real-time modal analysis: 1) an
! y4 z6 U+ A* D( o0 G+ jautomatic Prony-type analysis of power system responses following routine events such# j! \7 G S/ }* x
as line tripping and generator outages; and 2) an engine for continuous estimation of
5 k5 X( D0 X+ l+ b9 Lpoorly damped mode frequencies and their damping ratios from routine ambient noise. E/ o8 l% }9 ^( k, v2 j p
PMU measurements. The algorithms were structured as a rule-based expert system for
' P, t1 U O) c- R/ p( @simplifying the Prony analysis of nonlinear PMU responses in real-time while avoiding
0 |7 W q. {8 b9 r! u5 G( J; ?false alarms and incorrect estimates. The OMS can issue operator alerts as well as initiate+ H0 C/ x, c5 r/ U- B
triggers to start appropriate control actions to improve damping of problematic oscillatory) P$ A- o( D* P/ u3 @
modes. A wide-area damping control strategy that uses Static VAR Compensators was/ m- Z+ N6 A& v+ e* S, \# w$ a
developed in the previous PSERC project. Another damping control strategy was
1 p7 a$ R' D. @( U2 {developed in this project. It uses HVDC modulation to improve the damping of inter-area
. f% B5 q0 k8 |5 D) ?$ n9 u! N9 ooscillatory modes.) t$ x; I/ u* C- {& @
iii
4 @1 p9 \3 G1 B7 R+ m# j0 oA prototype version of OMS has been integrated into real-time monitoring capability of) l, Q' F$ H5 g+ ^- ]: F8 A
the Phasor Data Concentrator at TVA. At present, it monitors PMU data from within* \ C, f+ j l3 e4 D
TVA for estimating the frequency, damping, and mode shape of oscillatory modes. We
, n0 t4 W1 i7 ~- n$ yare also collaborating with the Electric Power Group and Bonneville Power
. t0 S1 S) k% q) f3 }6 ~" c, `Administration (BPA) to implement a version of the Prony-type first engine of OMS at
# B: w9 o" b% u$ [/ xtwo PSERC companies: BPA and the California Independent System Operator. An offline
( o6 y) c4 D' i2 D8 }% g& a* Eversion of three Prony type algorithms from this project has been integrated into an
" R" n# [: [7 J: f: V3 Q' qevent analysis software package being developed by Electric Power Group Inc., and the O" g8 M) h/ D- M; v! P
package aimed at off-line modal analysis of PMU measurements is available to PSERC5 e" V6 S* n' @9 `. M* Y! j
members.
$ } n% _' Z1 L3 g5 l) Z: A+ j( fVolume III: Adaptive islanding with selective under-frequency load shedding (Lead:0 T& P( M& V) a6 n" E& N
Vijay Vittal, Arizona State University)
6 h+ l* d. g% V3 S7 [4 s$ K$ JThe main objective of this portion of the research project was to develop a fast and4 H* \: T8 l8 v+ g4 ~) o
accurate assessment tool to determine the timing of conducting controlled islanding( ~. A% R" R7 Q/ u0 @
scheme in real time for preventing cascading events that could lead to a large scale
/ p$ P' F. p; C1 k0 }9 z# q+ vblackout. This work demonstrated an event-initiated controlled islanding scheme using! d6 z0 b+ y( u4 M' x. R
phasor measurement units and decision trees to stabilize power systems following severe$ ?" h0 g" Q$ S0 S* y& v
contingencies. The demonstration was performed on a test system provided by Entergy.
7 u8 O, n, m/ J' Y8 P/ M( `& @! x X! SA control scheme for must be designed very carefully since the costs can be quite high
2 V; m/ \: o5 Yfor unneeded controlled separation operation and for failure to operate when needed.
7 s/ m8 o$ g! `/ D* nNormally, there are two major issues associated with the design of a controlled separation
$ M, F# m) x- y& Y1 `% \/ xscheme:
: f+ H/ D# r: `: ^& wi. Where to island? This issue mainly focuses on searching for the optimal cut set* V, u/ M, i5 G/ q3 v
in the system to satisfy certain constraints, such as (a) coherent generators should
% B3 I8 d9 P0 T- O2 gstay in one island and (b) the load/generation imbalance of each island should be1 M9 w9 \+ }( M* G7 d5 o
minimized. Much of the research effort of a previous PSERC project (S-19)
d2 W; g9 [) z4 s$ afocused on addressing this issue. Due to the large computational burden, y9 h/ C. ^6 y8 e' v8 V
associated with this aspect, the controlled separation cut set is usually obtained0 g8 D4 N2 J4 |% a5 e
offline.
+ s2 C5 }" j3 N) D f& P3 X7 Tii. When to island? The second issue is to accurately determine the timing of
( D9 z4 g! `7 D$ O9 U( Icontrolled separation. This is the main focus of this research. The objective was to- ]" E4 ?4 C G6 M1 l' k
develop an online transient stability prediction scheme to determine whether
- D7 N, z9 W8 n$ X1 B9 scertain contingencies can initiate severely unstable swings and cause cascading( Y, w+ H) e3 _1 _
events. If so, controlled separation should be initiated to prevent a large scale
, }* m; n* V1 A' }$ cblackout.0 l: T7 q% {0 s% b
Taking advantage of synchronized phasor measurements, we developed a decision tree
# G+ J7 Q8 S0 [based transient stability assessment scheme for online application. The synchronized
3 j2 ~# N+ L$ U1 w9 F: O2 \signals and high sampling frequency give PMUs the capability of observing different1 T) g$ n i J% Q- g B; G
states across the whole system in a common time frame with great accuracy. System2 L1 u" R8 z1 t' I
transient behaviors can also be accurately captured compared to the traditional SCADA U* I8 E" N2 o3 t' a5 g/ C8 N
system. The decision tree technique is an effective data mining tool to solve the
3 D' u* t: {% I$ `! D1 J8 L' jclassification problems in high data dimensions. It can be used to uncover critical system7 F! O4 g& A* p% _' G$ z1 T
attributes that contribute to an objective such as transient stability or voltage security. The0 k2 q: w5 J0 p, x( _
splitting rules and the corresponding thresholds in the decision trees help to build a
. Q# I, v$ K5 K4 y gnomogram in terms of these critical system parameters and guide system operators
: ~. b/ C1 Y4 D' ^effectively.7 I+ ^9 L. \& R
The project results indicate that out of step swings can be accurately predicted only a few
- W+ C# B4 U2 ]# z3 K' E7 `cycles after the initial disturbance using properly trained decision trees. This is much
; _2 Q" Y# c/ E2 w Vfaster than the traditional analysis method. The decision trees also identify transient
) |* c5 s; A0 qstability indicators that are good candidates for new PMU locations. With an effective
5 }' c E! v: v- o$ \controlled islanding strategy, the system can be stabilized faster with less amount of load
* Z& K, J3 b5 g9 Ito be shed than the uncontrolled separation case.- K! g1 p7 K" j. O7 p2 x$ a- w
We developed a software platform that implements the entire approach based on data
8 ?2 h9 n2 H6 Yprovides the DSATools suite of software from Powertech Labs. The approach also uses6 I4 W/ b2 z+ D+ L
commercially-available software called CART developed by Salford Systems for3 U0 X* \2 J' f' m
decision tree training and testing.* Y* |2 A/ i7 Z7 D! {
Next Steps to Advancing this Research
& Q( B6 f0 ^* v$ G4 t6 ~0 V2 ?" PAt Texas A&M University, future steps to implementing algorithms for the detecting,
v! _/ I3 e& U; S, Aclassifying, and mitigating cascading events require selection of a utility test set-up.
% h. ~* I# Z) h$ I* D4 aCurrently discussions with several utilities under auspices of DOE-CERTS, EPRI, and7 n8 r/ E; w9 [" V$ p* {
ERCOT are under way to determine whether such arrangements for the final testing work% Q/ m8 d: ~) ~* ?: \6 R# V, o5 ~) q
can be made.
7 r- l+ R6 h$ w& N7 [& [The Washington State University team will continue to work with PSERC utilities and, y1 h' G" I* n; ^1 t& T
software vendors for field implementations as well as testing, tuning and enhancement of: q, Z9 W; e; }0 V
OMS capabilities. Future research needs to focus on determining correct operator actions
; A5 U/ }# `7 z$ Y @7 a! T+ ?4 `" Kas well as automatic control actions to improve the damping of problematic oscillatory6 z: l9 v: {# M* A
modes when such problems are detected by OMS.
: v* R: T9 b, s5 y: x7 J+ v3 u) iAt Arizona State University, future steps to implementing the controlled islanding work
' T) i4 u6 s; a1 H+ `& Q7 \' h6 Jwould require testing and implementation at an electric utility. Some of this work is being
9 u( t0 l2 E5 R( u) f+ _) x! U0 x" hdone in a CERTS project and discussions are under way to consider implementation at a
& U. C5 i; P6 o/ z1 R fspecific company. |
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