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发表于 2009-1-28 11:04:14
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Executive Summary; k. j' U% O2 _9 i4 r; G. \ m
The risk of cascading outages in power systems manifests itself in a number of ways.
( b2 h6 ^; D/ I* ~0 F* p) S& PWith the advent of structured competitive power markets, and with the lack of needed' [' Q( ?; n$ |2 ~! |
investment in the transmission grid, electric power systems are increasingly being
1 @5 A3 H1 |4 Foperated close to their limits. Potential terrorist threats raise concerns about power system
, o; F ^ N# h8 M# W! H, Dbeing placed in unforeseen operating conditions. Recent blackouts in North America and
" h2 l" h. [! c4 m0 H8 m: M7 v) c l6 Pin Europe show that the risk of cascading events is real with the cost of the 2003 North
0 [; E! a" K' E! AAmerican blackout estimated to be around $10 Billion. As the power system becomes
( Q! G% T+ |( L* f( I+ K, L% Ymore stressed there are a host of reasons (such as weak connections, unexpected events,8 y: u) X) G8 Q! O* h" i
hidden failures in protection system, and human errors) for the loss of stability eventually
; G. U* j% {; t9 fleading to catastrophic failures.% Z% D2 _/ t, Y) F; G
When a power system is subjected to large disturbances control actions need to be taken% Y& F# @, Y; H+ n% T" N# p {
to steer the system away from severe consequences and to limit the extent of the
- y$ {! L2 M! I d7 {2 M9 Ldisturbance. This is particular true if system is in an operating condition that makes it
; j" M9 w1 V! f1 Q: p9 f7 ounusually vulnerable to catastrophic failure. In a previous PSERC project (S-19 that
$ X, t& n" j O- x, }( a% E' ?ended in 2005), we developed novel algorithms for each of the following steps:
5 G0 ] U5 A; P! A7 g1 o1. Detection of major disturbances and protective relay operations leading to
|4 K s! ?- G2 i" i2 Rcascading events. The detection algorithms improved capabilities of real-time* v9 o4 M% H; b4 P$ M
fault detection and analysis to classify the impact of a fault towards initiating
) Z9 G- M- B* W/ X( D# U) ecascading outages.. ^6 V8 N) O2 q- m0 c
2. Wide-area measurement based detection and remedial control actions. The q; ?3 }" \3 m) Z0 @2 \4 B
wide-area mitigation algorithms include methods for reliably extracting modal
/ A4 a! H1 l Y( g" O! Binformation on critical wide-area modes through real-time wide-area Phasor0 h6 t3 u' s$ n( Y$ m
Measurement Unit (PMU) measurements. They also provide specific control, S5 V# N9 _ t- k( [
actions to damp out the oscillations when problems are detected./ ^/ C1 {2 r, o. e! }5 s C, }: n4 k0 f
3. Adaptive islanding with selective under-frequency load shedding. The
; X: d% b D& n% @adaptive islanding algorithms suggest methods for controlled islanding of the
, f! d9 {* O* n) csystem should mitigation strategies fail." ]* N5 ~" Y. o6 ~' D
The algorithms were shown to be effective using realistic computer models of test power
5 W; k1 c# _+ l& b; |3 Rsystems. In this project, we focused on prototype implementations of those algorithms at
% c% _( z0 q# U5 `collaborating PSERC member utilities.
+ N6 t" a4 h7 r2 cVolume I: Detection of major disturbances and protective relay operations leading5 n" r: C, U; ^8 F5 B% F8 w. M) Z; @
to cascading events (Lead: Mladen Kezunovic, Texas A&M University).
& i# }! Z! ~( `' ?2 pThis research volume proposes a system-wide monitoring and control solution intended
$ z2 ^! U$ x! F; \. nfor use at control centers. The solution includes steady state and dynamic analysis tools.
( z) {, C( q: F9 { @( bThe tools may be used to check system stability, find the vulnerable elements, and send
( n7 ^' w1 f; O( q) [9 Vcommands to the local tools for initiating detailed monitoring.) i; S j8 E" t5 ^
The local monitoring and control tools are intended for installation at substations. They- K( l: P1 e% M3 G0 I+ P
have the capability for advanced fault analysis and relay monitoring using neural network
H! m& ?: A k3 oii+ m6 x. }: H1 M" E9 y- K& a
for fault detection and classification, synchronized sampling for fault location, and event6 P) [; f j- X. m. E
tree analysis for relay operation verification. The local monitoring and control tools can; v3 _+ R% w, q' `6 D; B
characterize a disturbance and make a correction if there is an unintended relay operation.
$ N/ y. T7 @% |This information can be sent to a system monitoring and control tool for better security
6 I2 n( Z* F2 E5 l9 scontrol.
% k" i+ N* \ q$ |Implementation of the proposed algorithms requires assessment of data handling
- U- C1 T5 j) D. Prequirements. Data handling includes obtaining, converting and storing data.2 C3 r" j$ I" b' p) D- p
Consideration must also be given to integrating data from various sources: Supervisory2 r% y* W. d$ \
Control and Data Acquisition (SCADA) systems, Intelligent Electronic Devices, and
/ w0 G% G8 i. s4 O# cother add-on high speed data acquisition systems. Data from system simulation packages2 u% o4 j9 d" E/ X b' o
is also needed to perform steady state analysis.
. i8 _3 d: o5 Y8 L; vSteady state and dynamic analysis cases were studied using a model of the entire Entergy' z4 c% s' O0 g( a
system. Vulnerability Index and Margin Index for each bus, generator, and transmission' C7 h" @' }! w% b c4 g
line were calculated to identify the vulnerability and security margin information for each
. F w5 `- L& X1 y8 u( V& Kindividual power system element. A 500kV Extra High Voltage transmission line was- ?8 n' H( k; ~* u% G0 s [
modeled for evaluating local monitoring and analysis tools. The results indicate that
6 ]; d9 Q3 x7 y1 Q) e4 k7 Bwhen using adequate training sets and time synchronized data, the new algorithms are
% N: E8 x0 T1 e- Wquite accurate and provide assessment of system conditions during cascades that are not
( `* \8 r2 D& g8 e" f. Z) Qfeasible with any of the existing techniques.
- b v( |6 [: |8 OVolume II: Wide-area measurement based detection and remedial control actions p* t" r3 h# E8 y1 m5 u' C
(Lead: Mani V. Venkatasubramanian, Washington State University): q3 B" `6 t2 [
If persistent over an extended period of time (e.g., 30 minutes), poorly damped l) p4 ?" E& W' V- _/ ~( X
oscillations in a power system can lead to permanent damage of expensive power system
8 D$ J9 y0 _% Aequipment and pose power quality issues. Negatively-damped oscillations can be even
4 F/ P0 z/ g! i. R- I. Amore problematic by resulting in sudden tripping of generators and/or widespread system9 e; L- I+ f9 V8 c7 o* @
blackout such as occurred in the August 10, 1996 western power system blackout.
( ]3 c+ p1 r+ L1 eIn this research project, we designed, developed and implemented an Oscillation, \! ]5 ]: Y5 T+ r8 g1 A
Monitoring System (OMS) that uses wide-area PMU measurements for automatically
' j% C1 O7 j0 Hmonitoring for poorly damped and/or negatively-damped oscillatory modes. OMS
! b* t% [$ n& ], u2 p( ^0 z/ Iincludes two complementary engines that provide real-time modal analysis: 1) an
; d0 D6 E; L; F" q/ R7 e( wautomatic Prony-type analysis of power system responses following routine events such
h) m* @. j9 Bas line tripping and generator outages; and 2) an engine for continuous estimation of0 }) {2 `: Q; G. a, H3 x
poorly damped mode frequencies and their damping ratios from routine ambient noise
2 \( y/ C* C) j% }PMU measurements. The algorithms were structured as a rule-based expert system for
1 |6 q) a/ u& U8 k: D% s) esimplifying the Prony analysis of nonlinear PMU responses in real-time while avoiding0 v# b: O7 ?+ _% ^2 X
false alarms and incorrect estimates. The OMS can issue operator alerts as well as initiate; }4 h N: T& \6 C: f0 s b9 s
triggers to start appropriate control actions to improve damping of problematic oscillatory
B! d5 T1 E, O+ W3 L) n6 z/ N/ Hmodes. A wide-area damping control strategy that uses Static VAR Compensators was
( W. Y& B! p. o2 f) Adeveloped in the previous PSERC project. Another damping control strategy was
& e6 m' c' Z- L* ]) Kdeveloped in this project. It uses HVDC modulation to improve the damping of inter-area% `- ~+ {' M! S! [3 Y* y2 C
oscillatory modes.
" S/ L+ u8 t- Z$ xiii
2 |' o6 v1 t0 uA prototype version of OMS has been integrated into real-time monitoring capability of0 D z, Q1 l; T1 ^5 U9 X* S
the Phasor Data Concentrator at TVA. At present, it monitors PMU data from within
+ _! G5 a6 J8 ~% VTVA for estimating the frequency, damping, and mode shape of oscillatory modes. We
4 o/ X) @" q2 S& xare also collaborating with the Electric Power Group and Bonneville Power3 d0 D8 x- p U
Administration (BPA) to implement a version of the Prony-type first engine of OMS at
* l% k3 E- F: |) i; c8 Btwo PSERC companies: BPA and the California Independent System Operator. An offline
$ M: z. O) ^1 t3 z: ~% b: J# Oversion of three Prony type algorithms from this project has been integrated into an6 P: E Z( M5 e
event analysis software package being developed by Electric Power Group Inc., and the4 q$ [) O' E2 o [* ^) o
package aimed at off-line modal analysis of PMU measurements is available to PSERC; @) {+ S( l. d" w! a: V$ F
members.# z# P/ s$ j' M. [& A
Volume III: Adaptive islanding with selective under-frequency load shedding (Lead:
/ _& Y @/ W+ WVijay Vittal, Arizona State University), w" x3 W' B b5 g) @
The main objective of this portion of the research project was to develop a fast and
& |/ a% k- M& a$ d% [1 Y. w: T! r+ F# uaccurate assessment tool to determine the timing of conducting controlled islanding' x+ u) K1 Z: |+ ]; M/ B
scheme in real time for preventing cascading events that could lead to a large scale
. b: W& i, s+ r# L* s Fblackout. This work demonstrated an event-initiated controlled islanding scheme using2 D+ y7 }6 e; q# l+ L3 w7 B
phasor measurement units and decision trees to stabilize power systems following severe
4 ]1 v- s+ T. Z: K5 Wcontingencies. The demonstration was performed on a test system provided by Entergy.& K9 {% |5 ? ?2 ^/ E
A control scheme for must be designed very carefully since the costs can be quite high
* s( z8 o3 _% ?6 S5 `for unneeded controlled separation operation and for failure to operate when needed.8 _! f3 U/ z! }8 W) \
Normally, there are two major issues associated with the design of a controlled separation$ ^$ h% y9 {& z8 U2 e
scheme:
" G0 C, }* J$ D8 t5 d8 z0 Hi. Where to island? This issue mainly focuses on searching for the optimal cut set
; e X/ u$ W5 W" `+ O d! Oin the system to satisfy certain constraints, such as (a) coherent generators should ~* b- m. K1 w; E8 X6 T! i
stay in one island and (b) the load/generation imbalance of each island should be* N8 M9 [; D1 X' P
minimized. Much of the research effort of a previous PSERC project (S-19)/ @, F: K6 D0 E: A1 j7 ~& I
focused on addressing this issue. Due to the large computational burden
: Y, h0 n) h+ ?, U; Eassociated with this aspect, the controlled separation cut set is usually obtained
) @% s- x! y/ ?3 w( poffline.
$ B0 D/ t4 H$ j2 q% ~$ ]" U# Pii. When to island? The second issue is to accurately determine the timing of
2 m; J: B# {6 L9 o; bcontrolled separation. This is the main focus of this research. The objective was to
, V! g$ Q0 y6 Z: B$ }) M' Rdevelop an online transient stability prediction scheme to determine whether
/ Z/ y2 h ?; j. j1 U Acertain contingencies can initiate severely unstable swings and cause cascading! Q' _1 K5 [5 M2 w6 s3 U
events. If so, controlled separation should be initiated to prevent a large scale
9 ~+ `; p# Z w. L$ [blackout.
3 i/ f& c) O! `3 }' J$ ATaking advantage of synchronized phasor measurements, we developed a decision tree; ], `: J4 \ J) E% M# k
based transient stability assessment scheme for online application. The synchronized! f( g. p, o# i' \. J8 J6 X7 d0 g
signals and high sampling frequency give PMUs the capability of observing different4 S! ^1 n& v2 N& T
states across the whole system in a common time frame with great accuracy. System9 ? p; ?6 T) y( U
transient behaviors can also be accurately captured compared to the traditional SCADA- X6 i% M) Y5 G; x/ \
system. The decision tree technique is an effective data mining tool to solve the
$ _0 B- `8 S# H* a( @" Vclassification problems in high data dimensions. It can be used to uncover critical system
/ J( K. S) s6 D9 jattributes that contribute to an objective such as transient stability or voltage security. The( ?+ Q/ k' e9 M4 R
splitting rules and the corresponding thresholds in the decision trees help to build a
& |; [$ R# s" Q9 ^8 Y6 qnomogram in terms of these critical system parameters and guide system operators
: \; d M/ B9 e( {( }8 \/ l# qeffectively.% Z0 e' p6 a- e, M4 M
The project results indicate that out of step swings can be accurately predicted only a few1 Z3 x& x/ z5 x$ i
cycles after the initial disturbance using properly trained decision trees. This is much$ ~; v3 _# P# _3 v+ p
faster than the traditional analysis method. The decision trees also identify transient
3 n" b5 _0 s2 |" _+ j# ]- m" }, \stability indicators that are good candidates for new PMU locations. With an effective
4 f9 g/ |, M% Y3 Rcontrolled islanding strategy, the system can be stabilized faster with less amount of load3 A' X z# S1 q; {8 z! r9 P
to be shed than the uncontrolled separation case.
* d) D) |5 Y) {We developed a software platform that implements the entire approach based on data
# G/ _; N2 |8 C5 wprovides the DSATools suite of software from Powertech Labs. The approach also uses- T. _& n, c( @: H$ z
commercially-available software called CART developed by Salford Systems for4 h4 w/ Z5 e! x. Y. `: ^
decision tree training and testing.
! A4 y8 z0 I- JNext Steps to Advancing this Research! C* x7 _) {( [9 V, @( Q
At Texas A&M University, future steps to implementing algorithms for the detecting,
8 O! c! m# d7 b# Vclassifying, and mitigating cascading events require selection of a utility test set-up.
/ K! ?. F) |9 x7 H9 D8 ] y* g% QCurrently discussions with several utilities under auspices of DOE-CERTS, EPRI, and
! j6 \) P) E1 w! |ERCOT are under way to determine whether such arrangements for the final testing work' K I6 T' T( g: G9 |! m$ C
can be made.: r# @0 }5 U4 L5 C: B- A' N
The Washington State University team will continue to work with PSERC utilities and
; W3 p& T! l, l: V+ vsoftware vendors for field implementations as well as testing, tuning and enhancement of+ C- ?# R& \$ d2 F
OMS capabilities. Future research needs to focus on determining correct operator actions9 ?9 H. B. j% D% f: V2 u9 Q
as well as automatic control actions to improve the damping of problematic oscillatory
- ~/ s2 D X! J, lmodes when such problems are detected by OMS.
& `& a& q2 N ^2 eAt Arizona State University, future steps to implementing the controlled islanding work O8 A1 f' u4 X: b( d# [
would require testing and implementation at an electric utility. Some of this work is being9 v! R' t9 G( T p+ u. w4 _) A& ]
done in a CERTS project and discussions are under way to consider implementation at a! }! e0 E- U9 ^# h- n
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