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发表于 2009-1-28 11:04:14
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Executive Summary
! r( s, k& D, QThe risk of cascading outages in power systems manifests itself in a number of ways.
' S) r: y: d& D J( UWith the advent of structured competitive power markets, and with the lack of needed! e5 s1 t, _" R
investment in the transmission grid, electric power systems are increasingly being* }% ^- G+ g( v/ a! l6 t0 m0 O- ]
operated close to their limits. Potential terrorist threats raise concerns about power system
% ? k" N0 a& k- Abeing placed in unforeseen operating conditions. Recent blackouts in North America and: ?5 G2 R( I" z) ~
in Europe show that the risk of cascading events is real with the cost of the 2003 North
" b# f9 w; C+ u- c1 O) y9 XAmerican blackout estimated to be around $10 Billion. As the power system becomes& {$ \* \& [3 E2 `* e
more stressed there are a host of reasons (such as weak connections, unexpected events,
) E1 R: l1 Z' x. A2 y& H/ phidden failures in protection system, and human errors) for the loss of stability eventually, X) F4 s/ S( l) E% M+ u( W. R: r7 o
leading to catastrophic failures.( ?1 J. N* b' @
When a power system is subjected to large disturbances control actions need to be taken
5 a9 |! I1 ~! [" @to steer the system away from severe consequences and to limit the extent of the/ `( @$ s5 q0 E
disturbance. This is particular true if system is in an operating condition that makes it
; ^9 A R# {# A2 u$ Dunusually vulnerable to catastrophic failure. In a previous PSERC project (S-19 that2 F5 a# S# M e
ended in 2005), we developed novel algorithms for each of the following steps:
4 z _" E% o7 Q1. Detection of major disturbances and protective relay operations leading to
* Q; S6 X7 R$ k$ ~* J0 T3 N6 u4 Tcascading events. The detection algorithms improved capabilities of real-time. I$ N- c* L! ]4 X5 y$ X9 y- V
fault detection and analysis to classify the impact of a fault towards initiating' l+ t2 t/ o! ?, o
cascading outages.) p* A" \! s$ k7 M1 L
2. Wide-area measurement based detection and remedial control actions. The
3 T1 M, x% N1 s% r# ~wide-area mitigation algorithms include methods for reliably extracting modal
- S* I8 f1 q# }: [* Z& J2 k! C" Binformation on critical wide-area modes through real-time wide-area Phasor
& E/ \- X! ~+ h: PMeasurement Unit (PMU) measurements. They also provide specific control
- Y5 M" Y( v0 \: K8 g, Z5 R* Uactions to damp out the oscillations when problems are detected.
/ w# }' b6 [! Z1 n) V' m3. Adaptive islanding with selective under-frequency load shedding. The
; U' w. E5 a+ ladaptive islanding algorithms suggest methods for controlled islanding of the+ h. [/ q' w0 z6 V- e1 D( Z8 u
system should mitigation strategies fail.
$ ?8 J1 B3 _0 F7 i, X) S. B" j" {The algorithms were shown to be effective using realistic computer models of test power
8 O2 N& v6 L; t; a; J2 a* _/ asystems. In this project, we focused on prototype implementations of those algorithms at
! S" P! f: Z4 m( Ocollaborating PSERC member utilities.
4 B( j$ Q5 f: T; @( yVolume I: Detection of major disturbances and protective relay operations leading( v7 V3 u& `+ P- R! O
to cascading events (Lead: Mladen Kezunovic, Texas A&M University).( `" e& j, N, e O/ L H" b
This research volume proposes a system-wide monitoring and control solution intended# P) t S/ ]+ i
for use at control centers. The solution includes steady state and dynamic analysis tools.
. ^9 j$ S& J! a' q* {; o$ jThe tools may be used to check system stability, find the vulnerable elements, and send+ F0 R z* e( L4 d* Z& |+ e5 `
commands to the local tools for initiating detailed monitoring.; a$ v% O( J& x7 M+ `) \' T
The local monitoring and control tools are intended for installation at substations. They% g$ v5 c6 |& d; ~' k
have the capability for advanced fault analysis and relay monitoring using neural network
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5 D0 L& @$ [) y0 h1 j9 U% Vfor fault detection and classification, synchronized sampling for fault location, and event) C* o7 f$ h) y3 N6 r
tree analysis for relay operation verification. The local monitoring and control tools can
9 ?/ }8 {8 A r+ n, {7 i, b- |characterize a disturbance and make a correction if there is an unintended relay operation.. m" I2 H+ z9 _3 J, E/ W& U' w
This information can be sent to a system monitoring and control tool for better security, A' R/ C/ W9 B) Y3 E
control.
5 L, R4 b/ G- ?, }9 }Implementation of the proposed algorithms requires assessment of data handling! `- q$ D" m! w8 B: E
requirements. Data handling includes obtaining, converting and storing data.
1 u4 m) U a; p3 ~5 y4 jConsideration must also be given to integrating data from various sources: Supervisory
/ ^3 E9 ]% A0 V( C d" T" ~Control and Data Acquisition (SCADA) systems, Intelligent Electronic Devices, and
! a1 {% i4 K( ?0 u, @* |( ?other add-on high speed data acquisition systems. Data from system simulation packages5 p% d0 \) [, t# @; m
is also needed to perform steady state analysis.
% y, D9 @6 i( w) ?' K$ JSteady state and dynamic analysis cases were studied using a model of the entire Entergy
0 n/ ?/ A; w2 j( ?6 E! [system. Vulnerability Index and Margin Index for each bus, generator, and transmission5 h7 v+ s0 E+ `6 P1 K
line were calculated to identify the vulnerability and security margin information for each
- t- a& @! {" e7 s+ M+ w- xindividual power system element. A 500kV Extra High Voltage transmission line was
1 O* T' w0 \- j# K( f4 }% [ Lmodeled for evaluating local monitoring and analysis tools. The results indicate that
" g# Z2 D I; [+ b% P0 k6 A) \when using adequate training sets and time synchronized data, the new algorithms are" i1 f6 y7 ^6 j o, g: r8 U
quite accurate and provide assessment of system conditions during cascades that are not6 D; h4 A! a+ D" R$ O1 i
feasible with any of the existing techniques.# ~3 @. | ]! J
Volume II: Wide-area measurement based detection and remedial control actions$ b' P3 @$ S& Z& v
(Lead: Mani V. Venkatasubramanian, Washington State University)$ T8 ^4 p' l$ W6 c
If persistent over an extended period of time (e.g., 30 minutes), poorly damped
3 ]' V9 g9 y( Z! {( doscillations in a power system can lead to permanent damage of expensive power system
- @* |3 s; d5 `' E5 H7 c; kequipment and pose power quality issues. Negatively-damped oscillations can be even6 \4 f0 @$ v2 B6 a D
more problematic by resulting in sudden tripping of generators and/or widespread system( k8 E: c2 f4 r' S S/ V, p$ K
blackout such as occurred in the August 10, 1996 western power system blackout.# E9 n) L% g7 `4 `
In this research project, we designed, developed and implemented an Oscillation
7 o) b) Z9 J" ]) u9 j7 x g. qMonitoring System (OMS) that uses wide-area PMU measurements for automatically
; H/ ~' c+ L8 b! |3 {& ^6 z8 zmonitoring for poorly damped and/or negatively-damped oscillatory modes. OMS
0 {9 M: R# F: Dincludes two complementary engines that provide real-time modal analysis: 1) an
( ~( x1 p, J; q& P+ x2 N2 dautomatic Prony-type analysis of power system responses following routine events such
5 n' ~# `9 a8 G7 q1 `as line tripping and generator outages; and 2) an engine for continuous estimation of: q- T [1 Z% T- i* m9 G4 u" a
poorly damped mode frequencies and their damping ratios from routine ambient noise
- N; y# Z8 z3 R& kPMU measurements. The algorithms were structured as a rule-based expert system for
3 R' o" T; H7 Q; `( d; Osimplifying the Prony analysis of nonlinear PMU responses in real-time while avoiding5 r& p; q' N& R( R A/ i
false alarms and incorrect estimates. The OMS can issue operator alerts as well as initiate5 u7 l& |4 p2 _9 _: h' C
triggers to start appropriate control actions to improve damping of problematic oscillatory
0 I4 e/ e" s4 y/ V" S# X4 U: Zmodes. A wide-area damping control strategy that uses Static VAR Compensators was* o3 P# G# H7 |; P" l2 J$ [7 h0 }
developed in the previous PSERC project. Another damping control strategy was+ ~& O V q' F* f6 V" A. y/ R" H( s
developed in this project. It uses HVDC modulation to improve the damping of inter-area
4 D7 v( N6 G9 Z7 Z3 \. K6 B) `oscillatory modes.
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* ^- P, `! x+ g5 n7 vA prototype version of OMS has been integrated into real-time monitoring capability of
4 w$ b9 n3 M# ^- K4 j4 J/ p6 L; zthe Phasor Data Concentrator at TVA. At present, it monitors PMU data from within) ~6 X, s: b( _( z' ~# r* Y
TVA for estimating the frequency, damping, and mode shape of oscillatory modes. We- Q3 F2 {5 C9 U6 d3 A- m" \, x+ @
are also collaborating with the Electric Power Group and Bonneville Power2 M+ l# C% n' C/ j
Administration (BPA) to implement a version of the Prony-type first engine of OMS at; R8 R1 c0 @. o9 }% B
two PSERC companies: BPA and the California Independent System Operator. An offline# r3 `+ R' f- c5 v4 \% U$ L
version of three Prony type algorithms from this project has been integrated into an9 P- s! ~& Y/ w
event analysis software package being developed by Electric Power Group Inc., and the$ D# V1 c5 O9 f; U; ]
package aimed at off-line modal analysis of PMU measurements is available to PSERC# A: F5 G# ]7 i7 s9 ]5 |$ L0 `
members.
4 f) Y$ r! G& T4 G6 I/ T6 JVolume III: Adaptive islanding with selective under-frequency load shedding (Lead: I. p7 c6 q5 x K5 Q" Q% ~
Vijay Vittal, Arizona State University)
1 P! t8 l7 e8 `3 @4 F3 JThe main objective of this portion of the research project was to develop a fast and8 h. M- R1 ?7 h* A1 q7 r
accurate assessment tool to determine the timing of conducting controlled islanding
' i9 d2 T: } }3 }+ w: U; zscheme in real time for preventing cascading events that could lead to a large scale
5 U5 v8 Q3 b1 z0 I$ Nblackout. This work demonstrated an event-initiated controlled islanding scheme using4 S4 t, T* ? w1 E5 t( Q* k6 H/ c
phasor measurement units and decision trees to stabilize power systems following severe0 y) ?# ]& b4 h4 p% u# x
contingencies. The demonstration was performed on a test system provided by Entergy.
& w: p' U1 Y* A6 v1 kA control scheme for must be designed very carefully since the costs can be quite high: J2 g9 ], U1 e4 p+ a5 h) U y6 J
for unneeded controlled separation operation and for failure to operate when needed.5 i0 A0 s; J9 r
Normally, there are two major issues associated with the design of a controlled separation7 P( b- I4 e3 v* y' _
scheme:! P* Y( H5 ~- p
i. Where to island? This issue mainly focuses on searching for the optimal cut set. s/ m4 F' }3 h- g' f
in the system to satisfy certain constraints, such as (a) coherent generators should
* B$ V5 H- p* v; P( q pstay in one island and (b) the load/generation imbalance of each island should be3 P2 g: I0 d8 R8 ]
minimized. Much of the research effort of a previous PSERC project (S-19)) T6 E# q+ T" }9 U! w$ C) L$ r' G
focused on addressing this issue. Due to the large computational burden9 R @$ U' T# ?
associated with this aspect, the controlled separation cut set is usually obtained
2 \" z: q4 f- e: J" T Xoffline.0 c: w+ P' q* n, d% n; r3 d
ii. When to island? The second issue is to accurately determine the timing of! D, R/ I2 j; [( r3 q e2 w
controlled separation. This is the main focus of this research. The objective was to
+ E. V2 R9 {! f* l- Qdevelop an online transient stability prediction scheme to determine whether
; q& w, P- j2 l* dcertain contingencies can initiate severely unstable swings and cause cascading
( W5 G9 j# Q; r# K& {" qevents. If so, controlled separation should be initiated to prevent a large scale
, U5 B: P; U1 M! sblackout.
# [* S0 R! g9 J; GTaking advantage of synchronized phasor measurements, we developed a decision tree
3 b1 ]3 A! {& g' p, ^' {1 H. Y M! ebased transient stability assessment scheme for online application. The synchronized
9 ?$ f# y; T1 _- G- Csignals and high sampling frequency give PMUs the capability of observing different
( z/ b- j( K, Nstates across the whole system in a common time frame with great accuracy. System
8 _, C, p6 L. Ztransient behaviors can also be accurately captured compared to the traditional SCADA
% c7 w. X7 f+ Y; Q- Y3 m( g' tsystem. The decision tree technique is an effective data mining tool to solve the( g4 ^* `' t* Q1 n3 p/ R/ {6 E
classification problems in high data dimensions. It can be used to uncover critical system- K5 S3 e& p7 c* l, G% ]$ w
attributes that contribute to an objective such as transient stability or voltage security. The
7 }1 s% w: B$ Jsplitting rules and the corresponding thresholds in the decision trees help to build a
8 E1 i! X) M; ~1 q3 M6 anomogram in terms of these critical system parameters and guide system operators
' X! r$ l- b' h S }/ A' m+ veffectively.( g/ q$ U" ?/ z Q
The project results indicate that out of step swings can be accurately predicted only a few
2 y1 H4 {' f. P1 l) F" g c6 S( Tcycles after the initial disturbance using properly trained decision trees. This is much0 e# ? L" m0 E5 s9 ^: a
faster than the traditional analysis method. The decision trees also identify transient
3 {2 _' ~8 ?3 Y5 K( e9 }stability indicators that are good candidates for new PMU locations. With an effective; Q8 u6 P1 U. l/ \6 K/ ?
controlled islanding strategy, the system can be stabilized faster with less amount of load
% C; M7 F6 b, l1 [1 n, @/ D0 `5 pto be shed than the uncontrolled separation case.
& Z: b0 \; m3 z( J/ vWe developed a software platform that implements the entire approach based on data2 e9 `) l& O7 `7 U) O! E# w+ N# p1 D% E
provides the DSATools suite of software from Powertech Labs. The approach also uses
$ b! ^ V; N. K' ?commercially-available software called CART developed by Salford Systems for- ?8 m- K& J+ Z. o
decision tree training and testing.
R' O/ R1 i8 `& W/ Q" X( pNext Steps to Advancing this Research7 i. J6 r8 R( M0 g% [% v- x; x H
At Texas A&M University, future steps to implementing algorithms for the detecting,( s" i8 x6 S5 S! i7 m) q( i
classifying, and mitigating cascading events require selection of a utility test set-up.3 Y; }/ t6 x! L
Currently discussions with several utilities under auspices of DOE-CERTS, EPRI, and
3 Y8 [) k6 n; B7 `( C. b+ |ERCOT are under way to determine whether such arrangements for the final testing work/ H8 e; r& d- g, _' l; v8 e: T
can be made.- C: j4 A8 K+ l1 a
The Washington State University team will continue to work with PSERC utilities and! U6 [9 O: b9 G% M
software vendors for field implementations as well as testing, tuning and enhancement of3 B# Z- {7 y, e& J$ g2 W7 E/ y
OMS capabilities. Future research needs to focus on determining correct operator actions R; I; t/ Q& K3 ^- W& U
as well as automatic control actions to improve the damping of problematic oscillatory! P9 t3 P7 T' q1 i
modes when such problems are detected by OMS.; `" x w/ G# C8 ]
At Arizona State University, future steps to implementing the controlled islanding work% R, X0 h T- i* G! E
would require testing and implementation at an electric utility. Some of this work is being4 O& S( d: F$ ^1 Q
done in a CERTS project and discussions are under way to consider implementation at a
5 k& U p% {/ [/ g% P$ Hspecific company. |
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