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发表于 2009-1-28 11:04:14
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Executive Summary
3 n& J4 X5 [# `* |% I2 [$ V9 P6 T4 `4 VThe risk of cascading outages in power systems manifests itself in a number of ways.7 b& W0 ?/ Z; N1 `
With the advent of structured competitive power markets, and with the lack of needed# ]( {% w' z9 N/ J( D
investment in the transmission grid, electric power systems are increasingly being
, s% V' E, i) I/ }2 T: j4 |operated close to their limits. Potential terrorist threats raise concerns about power system
) @& ]: h" B( r. _" u. n9 Ebeing placed in unforeseen operating conditions. Recent blackouts in North America and) U; N% R- Q7 C! X+ K' B5 S {2 B
in Europe show that the risk of cascading events is real with the cost of the 2003 North
: Y6 a# C3 ^9 D% l, i1 EAmerican blackout estimated to be around $10 Billion. As the power system becomes# S* b& ~) m; W7 B7 e" \7 _3 Q
more stressed there are a host of reasons (such as weak connections, unexpected events,
6 C" j% E* I- M- W* ~" ^hidden failures in protection system, and human errors) for the loss of stability eventually" S! s& t$ y9 E' e5 a0 ~) R/ z' F
leading to catastrophic failures.
! g8 r7 [ U( E2 v' L* o' }: S6 u3 J9 TWhen a power system is subjected to large disturbances control actions need to be taken
& j; I4 Z% M# P: k% @$ s+ Qto steer the system away from severe consequences and to limit the extent of the3 k* p" w+ i; W: r, K
disturbance. This is particular true if system is in an operating condition that makes it
% h, x6 t5 Z; G, v! H! t! Nunusually vulnerable to catastrophic failure. In a previous PSERC project (S-19 that5 i9 K% q- B1 G
ended in 2005), we developed novel algorithms for each of the following steps:
1 ] T9 P2 Y: t- ~, P& K6 Z1. Detection of major disturbances and protective relay operations leading to! z( ?& [% S! o
cascading events. The detection algorithms improved capabilities of real-time! H T4 z& o5 d0 y
fault detection and analysis to classify the impact of a fault towards initiating
# K8 q' s0 n; H! @4 zcascading outages.
3 v4 `& y$ [5 t: J2. Wide-area measurement based detection and remedial control actions. The9 ]' U% H0 T. J8 C0 A6 a. r7 J' p
wide-area mitigation algorithms include methods for reliably extracting modal o+ O m$ z8 K+ l& n5 p
information on critical wide-area modes through real-time wide-area Phasor8 b4 F- ~5 v1 b7 S# a3 O
Measurement Unit (PMU) measurements. They also provide specific control/ z9 u7 L& s5 q/ c. f* ]
actions to damp out the oscillations when problems are detected.
0 h1 @7 r; l' `3 n! M. O9 m. P9 x3. Adaptive islanding with selective under-frequency load shedding. The
0 h! c7 u) F% V" z" uadaptive islanding algorithms suggest methods for controlled islanding of the
7 B' M9 b. t6 |) w- s: T$ N! Lsystem should mitigation strategies fail.1 ~; ~0 }" P: ?- k
The algorithms were shown to be effective using realistic computer models of test power: }; L3 b8 K( A1 k% e% l/ A
systems. In this project, we focused on prototype implementations of those algorithms at
2 K+ }& N% A1 ~7 {+ T' p3 Ycollaborating PSERC member utilities.
3 z! k( K, Z) f f: QVolume I: Detection of major disturbances and protective relay operations leading j7 j4 h$ A( U- n( t% I5 R/ ^
to cascading events (Lead: Mladen Kezunovic, Texas A&M University).# T+ S2 {) r4 ?5 J0 y; q0 F
This research volume proposes a system-wide monitoring and control solution intended5 K# J5 l8 j- a
for use at control centers. The solution includes steady state and dynamic analysis tools.
/ D: ~' w( o- x! D6 g7 ?The tools may be used to check system stability, find the vulnerable elements, and send
' L. h: @0 A6 ycommands to the local tools for initiating detailed monitoring.5 Q8 `& I, @* h; i' p" J6 Y5 l
The local monitoring and control tools are intended for installation at substations. They
, w. C! A A$ @8 ]7 ]! u$ N2 Y, whave the capability for advanced fault analysis and relay monitoring using neural network6 G6 ]* D' N% c7 c8 v/ Y' l
ii
( r4 G% A% c3 t; ~* [for fault detection and classification, synchronized sampling for fault location, and event
7 L5 g6 U# m Y1 h+ a3 t) G) Ktree analysis for relay operation verification. The local monitoring and control tools can* M# A) a5 S' i; v$ @' M
characterize a disturbance and make a correction if there is an unintended relay operation.
4 ^$ k0 F% M9 ~# x; t( I. VThis information can be sent to a system monitoring and control tool for better security% l: y" g* t8 y) H/ d
control.
% i3 \* X/ [# V- {Implementation of the proposed algorithms requires assessment of data handling
6 U) E% g g+ F: x, V0 H# Hrequirements. Data handling includes obtaining, converting and storing data.
% v. ~ B/ X& X1 G0 t Q- @Consideration must also be given to integrating data from various sources: Supervisory
, l3 X* x" j, r) J( F s% j9 FControl and Data Acquisition (SCADA) systems, Intelligent Electronic Devices, and
9 I$ K1 m& Z' e0 s7 i0 V* lother add-on high speed data acquisition systems. Data from system simulation packages( t) N% ~0 p' j* o/ H. a4 v3 o+ e
is also needed to perform steady state analysis.
2 D" Q- t) W5 i. @- y/ o0 F/ R# fSteady state and dynamic analysis cases were studied using a model of the entire Entergy
& I6 I- \# W( u$ w9 }system. Vulnerability Index and Margin Index for each bus, generator, and transmission' V1 k- q. V* \+ v
line were calculated to identify the vulnerability and security margin information for each- ^" N9 P: ~9 U9 W
individual power system element. A 500kV Extra High Voltage transmission line was- `1 u5 A( \( x; \& d+ e( r" p( x
modeled for evaluating local monitoring and analysis tools. The results indicate that& q: R7 S) {$ {4 G% c
when using adequate training sets and time synchronized data, the new algorithms are( x v/ J' c3 E! ~5 \4 o- W! C
quite accurate and provide assessment of system conditions during cascades that are not
; H* p, k7 d( E5 p! ofeasible with any of the existing techniques.# O$ p' [% o9 v+ f( B9 F
Volume II: Wide-area measurement based detection and remedial control actions
" H- ^1 Y3 d1 y* `+ x(Lead: Mani V. Venkatasubramanian, Washington State University)% K. [. N7 a4 g4 K% C) {
If persistent over an extended period of time (e.g., 30 minutes), poorly damped
5 j4 v8 R7 o/ I- P7 }oscillations in a power system can lead to permanent damage of expensive power system
* q& }* Q5 d) D; n4 wequipment and pose power quality issues. Negatively-damped oscillations can be even
! H. f* j) _. H2 \, D# {% }more problematic by resulting in sudden tripping of generators and/or widespread system* k7 p! u, R0 i7 Y; ?
blackout such as occurred in the August 10, 1996 western power system blackout.- ~4 K/ \6 G8 }% E |$ i8 [7 I$ ~
In this research project, we designed, developed and implemented an Oscillation
7 @. x& U0 \- V) HMonitoring System (OMS) that uses wide-area PMU measurements for automatically
: Z: {. H- X# | k. h" {2 ^monitoring for poorly damped and/or negatively-damped oscillatory modes. OMS
, z- K% j- W$ s! S1 v; dincludes two complementary engines that provide real-time modal analysis: 1) an' u; c5 }3 w e2 ]
automatic Prony-type analysis of power system responses following routine events such5 F N7 d0 o4 T9 b
as line tripping and generator outages; and 2) an engine for continuous estimation of. i" f6 I/ d$ \. s4 b# U: v
poorly damped mode frequencies and their damping ratios from routine ambient noise( p* }- I$ T+ Q" y" T! |5 p! r4 {/ @
PMU measurements. The algorithms were structured as a rule-based expert system for* d' Y& r+ N, [2 c
simplifying the Prony analysis of nonlinear PMU responses in real-time while avoiding8 Y, G- T' @: a" P1 P. k& D
false alarms and incorrect estimates. The OMS can issue operator alerts as well as initiate
+ t7 {% S' k+ striggers to start appropriate control actions to improve damping of problematic oscillatory
; u* F6 \; \1 u: u# Q7 l6 Smodes. A wide-area damping control strategy that uses Static VAR Compensators was
2 Z5 B" {0 [% b* v; H. q% jdeveloped in the previous PSERC project. Another damping control strategy was
# o) F2 a v+ j% ?4 {4 Gdeveloped in this project. It uses HVDC modulation to improve the damping of inter-area
5 l& E" p# S; n" q& }9 |5 aoscillatory modes./ \3 S- [$ @6 J9 r l& F
iii5 N( Q4 F$ l' K* {. U% |
A prototype version of OMS has been integrated into real-time monitoring capability of
3 m( g" d# r+ l$ n! jthe Phasor Data Concentrator at TVA. At present, it monitors PMU data from within/ }! N3 l4 `$ N8 l, r5 Z, W
TVA for estimating the frequency, damping, and mode shape of oscillatory modes. We. ]9 N4 F( w% W* R' x3 {
are also collaborating with the Electric Power Group and Bonneville Power& E6 g$ m2 |9 d; g! R# U
Administration (BPA) to implement a version of the Prony-type first engine of OMS at
7 b% S. t, _/ @' M- @0 _) Ytwo PSERC companies: BPA and the California Independent System Operator. An offline
6 h- m9 ~, i3 lversion of three Prony type algorithms from this project has been integrated into an7 ^, i. A# J8 |* V+ [
event analysis software package being developed by Electric Power Group Inc., and the- j& b. p- Y4 L: T, M7 s9 D
package aimed at off-line modal analysis of PMU measurements is available to PSERC& T; w5 Q, G+ o8 _
members.
/ B7 Y1 H7 V, |4 w3 ^* d9 GVolume III: Adaptive islanding with selective under-frequency load shedding (Lead:
% ^8 Y, f$ ]; n8 E" r [- AVijay Vittal, Arizona State University)# M1 w: {1 g* o
The main objective of this portion of the research project was to develop a fast and0 Z: Y4 l6 h# s0 z( F6 \
accurate assessment tool to determine the timing of conducting controlled islanding3 c( d% ?7 c# _5 J
scheme in real time for preventing cascading events that could lead to a large scale
# ~. l6 n7 P0 l. `$ w$ b: nblackout. This work demonstrated an event-initiated controlled islanding scheme using* q' G# t) ?) E0 _
phasor measurement units and decision trees to stabilize power systems following severe3 h- m" X: I' Z3 ]# U, d
contingencies. The demonstration was performed on a test system provided by Entergy.# t' ~' X X U0 J
A control scheme for must be designed very carefully since the costs can be quite high" @" j! a3 ` ?9 [" L& e3 ^
for unneeded controlled separation operation and for failure to operate when needed.
+ z8 C) g e5 u( YNormally, there are two major issues associated with the design of a controlled separation' Q, U9 M2 N4 N- I) J5 T% p Z" M
scheme:
1 v. A6 J+ |. P6 A: m( x% ]: H9 mi. Where to island? This issue mainly focuses on searching for the optimal cut set/ {1 o: q% V6 V0 t2 T( a9 u E) ~
in the system to satisfy certain constraints, such as (a) coherent generators should) |+ O c: ?+ y6 X2 d
stay in one island and (b) the load/generation imbalance of each island should be
' Y% c* t9 X' f5 L% W/ g, O* g. Jminimized. Much of the research effort of a previous PSERC project (S-19)3 s: j" y' j8 |7 `% g
focused on addressing this issue. Due to the large computational burden
, G" n( m1 k$ q5 T9 Rassociated with this aspect, the controlled separation cut set is usually obtained F4 z+ k* W; I" ?* y
offline.
! o" {+ J$ ]9 h; Y* U1 ]+ i1 z+ Q4 Dii. When to island? The second issue is to accurately determine the timing of
f6 f0 Q P% ?7 ~controlled separation. This is the main focus of this research. The objective was to" y5 O K1 q6 I, N% \4 e$ u3 p7 W
develop an online transient stability prediction scheme to determine whether
- _, W* }6 h* {, S, U9 h* icertain contingencies can initiate severely unstable swings and cause cascading
3 \! ~: |( S) ievents. If so, controlled separation should be initiated to prevent a large scale. Z8 _( d; t7 Y: D6 a8 W* G0 B& f
blackout.
( ^* j. d6 L7 b) n/ _& X3 e% bTaking advantage of synchronized phasor measurements, we developed a decision tree2 @8 v8 L' G' u: p9 ~
based transient stability assessment scheme for online application. The synchronized. \ k m* ~8 {" u& |1 g+ H
signals and high sampling frequency give PMUs the capability of observing different
/ ?1 V9 i* T2 ~4 G8 ]; [states across the whole system in a common time frame with great accuracy. System
" C9 V7 U8 v$ I. e. \transient behaviors can also be accurately captured compared to the traditional SCADA
# H: l9 y9 G, xsystem. The decision tree technique is an effective data mining tool to solve the
. [1 s6 d6 W' m, j$ Q7 bclassification problems in high data dimensions. It can be used to uncover critical system& l) I, N& ?* Y0 ^, H% N+ ^
attributes that contribute to an objective such as transient stability or voltage security. The
1 W9 s( Q$ s- P9 g) dsplitting rules and the corresponding thresholds in the decision trees help to build a: O% R7 G# l3 h" I+ f2 D5 Q
nomogram in terms of these critical system parameters and guide system operators5 E' G4 `) {- e i! _! @2 {; n
effectively.+ A: N& J8 B0 ?7 Z
The project results indicate that out of step swings can be accurately predicted only a few# w4 n& X. O. J- {9 \3 w
cycles after the initial disturbance using properly trained decision trees. This is much3 q( Y' z$ o8 Q3 `* S8 A. @) r {; h
faster than the traditional analysis method. The decision trees also identify transient
1 t; T" |0 M* p9 _5 k3 s# L7 ?4 ostability indicators that are good candidates for new PMU locations. With an effective8 M* S4 [9 X' U0 x
controlled islanding strategy, the system can be stabilized faster with less amount of load. N+ R' s; L+ Q J: F! f
to be shed than the uncontrolled separation case.
2 n7 F) U- d) JWe developed a software platform that implements the entire approach based on data1 H, m5 x Z; W# x! f! G- D" K. Z `
provides the DSATools suite of software from Powertech Labs. The approach also uses
7 j' s2 H. Q8 T/ A! {commercially-available software called CART developed by Salford Systems for) t8 |9 m: d% S" l" j* ]( W
decision tree training and testing.
3 T @0 C$ E$ i* X9 s9 q" FNext Steps to Advancing this Research
& q( j0 n4 u5 G7 F: OAt Texas A&M University, future steps to implementing algorithms for the detecting,
+ j8 K h6 F8 I; Tclassifying, and mitigating cascading events require selection of a utility test set-up.; D# l& R" M8 T6 h+ m; _/ S7 V" M+ F
Currently discussions with several utilities under auspices of DOE-CERTS, EPRI, and8 K2 O: R# c# l4 _2 v9 e7 u
ERCOT are under way to determine whether such arrangements for the final testing work
/ l" ]2 B. k7 W; Zcan be made.& C9 m1 _. K5 V3 _5 j
The Washington State University team will continue to work with PSERC utilities and
4 X* [1 `2 l d; k. Wsoftware vendors for field implementations as well as testing, tuning and enhancement of
) C- W% R: j! N/ a0 d. nOMS capabilities. Future research needs to focus on determining correct operator actions
' b e* Q( O: G8 v4 M0 Was well as automatic control actions to improve the damping of problematic oscillatory
* q$ z6 k& W/ V& r/ \modes when such problems are detected by OMS.
0 l6 Q5 W- V" n' d* eAt Arizona State University, future steps to implementing the controlled islanding work& S$ E. D" B- X# P
would require testing and implementation at an electric utility. Some of this work is being
- u) H4 B9 z; x" v! d6 `0 \! adone in a CERTS project and discussions are under way to consider implementation at a* S( X. `+ V7 W
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