Monitoring of the Power Grid State of the Art
description
Transcript of Monitoring of the Power Grid State of the Art
1
Monitoring of the Power Grid State of the Art
Speaker: Yee Wei LawCollaborators: Umith Dharmaratna, Jiong Jin, Slaven Marusic, Marimuthu Palaniswami
2
Introduction to the grid Introduction to the grid sensors Motivation for the Smart Grid Smart Grid components
◦ Wide-area Monitoring System (WAMS)◦ Distribution Automation (DA)
Conclusion
Organization
3
Introduction to the Grid
AS 60038-2000 “Standard voltages”
> 110kV
66kV, 33kV
< 33kV
4
For conductor◦ Temperature
For insulator, transmission line surge arrester◦ Leakage current
Sample sensors for overhead lines
Ice build-up
RF temperature sensor
RF leakage current sensor
5
For transformers◦ Detection of hydrogen in oil
For on-load tap changers◦ Detection of gas in oil
(symptom of overheating)
For bushings◦ Leakage current
Sample sensors for substations
Internally mounted tap changer
15 kV 242 kV69 kV
Metal insulated semiconducting (MIS) sensor for detecting hydrogen
6
Sensor technologies for underground cables
Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.
MIS sensor
7
Rating: maximum value of parameter (e.g. power, current) Dynamic rating vs nominal rating
◦ increases capacity by 5-15%
The primary limitation on power flow is thermal
Dynamic rating
Example:Thermal model of overhead lines [Black ‘83]:
: mass of the line: specific heat of the line: temperature: Ohmic loses per unit length: solar heat input per unit length: radiated heat loss per unit length: convected heat loss per unit length
8
Transmission-line robots◦ Developed by Tokyo-based HiBot
◦ Able to navigate around obstacle
◦ Laser-based sensors for detecting scratches, corrosion, changes in cable diameter
◦ HD camera for recording images of bolts and spacers up close
◦ Energy is a constraint
Non-static sensors (1)
9
Unmanned airborne vehicles aerial snapshot◦ E.g. SP AusNet to automate conductor
localization and spacer detection [Li ‘10]
◦ Line detection: template matching
◦ Spacer detection: Gabor filtering
Non-static sensors (2)
10
Why so much attention on the Grid? Ageing hardware + population growth = equipments at
limits Market deregulation
◦ Advances in communications infrastructure
Climate change◦ Government initiatives (USA, Europe, China, Japan, Australia..)◦ Renewable energy and distributed generation ($652m fund)
Cost of outages in USA in 2002: $79B
11
Introducing “Smart Grid” Smart grid = envisioned next-gen power grid that is [DOE,
USA]:
Intelligent(senses
overload, rerouting) Efficient
(meets demand
without more cost)
Accommo-dating
(renewable energy)
Motivating(demand response)
Quality-focused(minimal
disturbances, interruptions)
Resilient(to attacks, disasters)
“Green”(minimal
environment impact)
12
Generation◦ Distributed generation◦ Microgrid
Transmission◦ Wide-area monitoring system
Distribution◦ Distribution automation
Consumption◦ Demand response
Smart Grid components
13
Remotely and efficiently identify and resolve system problems
Alleviates overload conditions, and enables computer-optimized load shifting
Reconfigures the system after disturbances or interruptions Facilitates coordination with customer services such as
time-of-use pricing, load management and DERs
Distribution Automation (DA)
Control center
Substation Distribution network
14
Auto-recloser: circuit breaker that re-closes after interrupting short-circuit current
Voltage regulator: usually at the supply end, but also near customers with heavy load
Switched capacitor bank: switched in when load is heavy, switched out when otherwise
Examples of equipment to be connected
RecloserVoltage regulator
Switched capacitor bank
15
EPRI proposed advanced DA – complete automation of controllable equipment
Two critical technologies identified:◦ Open communication architecture◦ Redeveloped power system for component interoperability
Urban networks: fiber optics Rural networks: wireless
DA and communication
16
Standard architectureLow voltage
Transmission gridDistribution gridUrban area
City power plant
Industrial customers
Distributed Energy Resources
Rural area
BAN
HAN
IAN
NAN
Collector
Collector
NAN
)))(((
)))
)))
)))
)))
FAN
Collector
Substations as gateways
)))(((
)))(((
)))(((
)))(((
)))(((
Pole with wireless communication capability
NAN = Neighborhood Area Network; FAN = Field Area Network HAN/BAN/IAN = Home/Building/Industry Area Network WAN standard is TCP/IP
17
Standard architecture – alternate perspective
SecureMesh
18
Wireless comm technologies for DA CDMA2000 GE-MDS 900MHz Silver Spring
NetworksWi-Fi/IEEE 802.11 WiMAX/IEEE
802.16Interoper-ability
Open standard Proprietary Proprietary Open standard Open standard
Capacity 76.8 kbps (80-ms frame)153.6 kbps (40-ms frame)307.2 kbps (20-ms frame)
19.2 kbps (80 km)115 kbps (48 km)1 Mbps (32 km)
100 kbps 54 Mbps (802.11a)11 Mbps (802.11b)54 Mbps (802.11g)72 Mbps (802.11n)
9 Mbps
Latency Hundreds of milliseconds Tens of milliseconds Tens of milliseconds
Milliseconds Milliseconds
Interference rejection
DSSS, 2 GHz frequency band allows frequency band re-use
FHSS, 902-928 MHz FHSS, 902-928 MHz
802.11a: ODFM, 5 GHz802.11b: DSSS, 2.4 GHz802.11g: OFDM/DSSS, 2.4 GHz802.11n: OFDM, 2.4/5 GHz*2.4 GHz band is crowded; 5 GHz less so
OFDM, 3.65-3.70 GHz
Transmission range
Nation-wide service coverage
80 km Unknown 802.11a: 120 m802.11b/g: 140 m802.11n: 250 m
20 km
Configuration Point-to-multipoint Point-to-point, point-to-multipoint
Point-to-point Point-to-point, point-to-multipoint
Point-to-multipoint
Jemena, United Energy, Citipower and Powercor SP AusNet and Energy Australia
* Note: ZigBee is not in here
19
WiMAX supports mesh?
2002
2004
2009
First publishedBeyer et al. “Tutorial: 802.16 MAC Layer Mesh Extensions Overview”:• Centralized scheduling• Coordinated distributed scheduling• Uncoordinated distributed scheduling
802.16.2-2004 describes recommended practice for coexistence of point-to-multipoint and mesh systems
802.16j-2009 adds relay (tree) support
Year
4G status not until 802.16m
20
Proprietary mesh networks (1)Silver Spring Networks UtilityIQ:
21
Proprietary mesh networks (2)Itron OpenWay:
22
Standard by HART foundation Physical layer: IEEE 802.15.4 (since version 7); DSSS+FHSS Data link layer: TDMA Network layer: Graph routing or source routing Notable player: Dust Networks (founded by the Smart Dust
people)
Open standard mesh - WirelessHART
Source: Lennvall et al. “A Comparison of WirelessHART and ZigBee for Industrial Applications,” IEEE WFCS 2008
23
IPv6 for low-power wireless personal area networks Motivation: interoperability with existing IP-based devices Standardized by IETF in RFC4919, RFC4944 etc. Physical and data link layer: IEEE 802.15.4 Network layer: still being standardized by the ROLL working
group (Routing Over Low power and Lossy networks) Notable player: Sensinode
Open standard mesh – 6LoWPAN
24
DA makes dynamic reconfiguration possible
Multi-objective optimization problem◦ Objectives: minimize real losses, regulate voltage profile, load-
balancing
◦ Optimal topology: quadratic minimum spanning tree (q-MST) is NP-hard
◦ Bio-inspired heuristics, e.g. Artificial Immune System and Ant Colony Optimization
Distribution network reconfiguration
25
Grid Sensors
Smart Grid
Distribution Automation
Wide-Area Monitoring System
26
8-10% energy lost in transmission and distribution networks
Energy Management System (EMS): control generation, aggregation, power dispatch
EMS performs optimal power flow
However, SCADA-based EMS gives incomplete view of system steady state
Wide-Area Monitoring System (WAMS)
Hence WAMS
27
Generic architecture of the WAMS
PMU PMU PMU PMU...
PDC
Application Data Buffer
Real-Time Monitoring
Real-Time Control
Real-Time Protection
Layer 1: Data acquisition
Layer 2: Data management
Layer 3: Data services
Layer 4: Applications
WAN
28
Synchronized phasor measurement units or synchrophasors for measuring voltage and current (phasor: )
Typically 30 time-stamped samples per sec Invented by Phadke and Thorp of Virginia Tech in 1988 IEEE 1344 completed in 1995, replaced by C37.118 in 2005
Phasor measurement units (PMUs)
For frequency, use Frequency Disturbance Recorder
29
Examples of PMUs
ABB’s RES521
Macrodyne’s model 1690
MiCOM P847
30Source: North American SynchroPhasor Initiative (NASPI)
31
Applications of synchrophasors
Oscillation control Voltage control The goal is to
calculate maximum loadability using optimal power flow
Frequency control The goal is to select
which loads to shed, to minimize overvoltages or steady-state angle differences
References: • M. Zima et al., “Design aspects for wide-area monitoring and
control Systems,” Proc. IEEE, 93(5):980–996, 2005.• M. Larsson et al., “Predictive Frequency Stability Control based on
Wide-area Phasor Measurements,” IEEE Power Engineering Soc. Summer Meeting, 2002.
32
System equation:
Weighted least square◦ ]
State estimation
Measurements Errors
Measurement Jacobian
PMU measurement s.d.
33
Observability: whether the system state can be uniquely estimated◦ unobservable when cannot be inverted
Critical measurement: absence of which destroys observability ◦ Residual sensitivity matrix ◦ If row and column are zeroes, then th measurement is critical
Redundant measurement: non-critical measurement
Some definitions
34
For an -bus system, the PMU placement problem can be formulated as an integer programming problem:
is a vector function, whose entries are non-zero if the corresponding bus voltage is solvable given the measurement – the problem becomes defining
Identify critical measurements; so that their removal doesn’t cause unobervability [Chen ‘05]
Recent study [Emami ‘10]:◦ To improve robustness against contingencies and failures ◦ To detect bad data among critical measurements
Optimal placement of PMUs
• is cost of installing a PMU at bus
• if a PMU is installed at bus
min∑𝑖
𝑛
𝑐 𝑖𝑥 𝑖
s . t . 𝑓 ( 𝑋 )≥𝟏 , 𝑋= [𝑥1 … 𝑥𝑛 ]𝑇
35
Linearized model: Common bad data detection mechanism Q: Suppose true state is , error in measurement is , how much
error in measurements will result in estimated state ? A: By def. , maximizes probability that
Bad data identification#1
#3
#2
#4
#6
#5
Classification
SingleMultiple
Non-interacting Interacting
e.g. #1 and #6 not correlated
e.g. #2 and #5 not correlated
Non-conforming Conforming
e.g. #2 and #5 correlated
Opportunity for attack
Bus
36
False data injection attack (1)
Symbols: = number of hacked PMUs = number of measurements = number of system states = deviation from true states = induced measurement errors
Attacker controls PMUs [Liu ‘09]
Don’t care about Want specific
?
always exists exists depending on structure of
Suppose, for example , exists depending on structure of yes no
37
Privatization of electricity market recent (‘80s) Locational marginal pricing (LMP) aka nodal pricing
◦ Case no constraint on Tx line: uniform market clearing price is the highest marginal generator cost
◦ Case congestion on Tx line: price varies with location
False data injection attack (2)
Attack [Xie ‘10]:1. In the day-ahead forward market,
buy and sell virtual power at two different locations and
2. Inject false data to manipulate the nodal price of the Ex Post market
3. In the Ex Post market, sell and buy virtual power at and respectively
4. Profit
38
Grid modernization stimulates multi-disciplinary research National priority vs. business priority In progress:
◦ $100m Smart Grid, Smart City demo project in Newscastle◦ Intelligent Grid: CSIRO and five universities
What’s next?
ConclusionNotable omission in this presentation:• Distributed generation, microgrid• Demand response
39
B.K. Panigrahi et al., “Computational Intelligence in Power Engineering”, Springer-Verlag Berlin Heidelberg, 2010.
A. Monticelli and F.F. Wu, “Network Observability: Theory,” IEEE Trans. Power Apparatus and Systems, PAS-104(5):1042-1048, 1985.
A. Monticelli, “Electric Power System State Estimation,” Proc. IEEE, pp. 262-282, 2000.
A. Abur and A.G. Exposito, “Power System State Estimation: Theory and Implementation,” Marcel Dekker Inc., 2004.
J. Chen and A. Abur, “Improved Bad Data Processing via Strategic Placement of PMUs,” IEEE Power Engineering Society General Meeting, 2005.
R. Emami and A. Abur, “Robust Measurement Design by Placing Synchronized Phasor Measurements on Network Branches,” IEEE Trans. Power Systems, 25(1):38-43, 2010.
Y. Liu et al., “False data injection attacks against state estimation in electric power grids,” Proc. 16th ACM Computer and Communications Security, 2009.
O. Kosut et al., “Limiting false data attacks on power system state estimation,” Proc. 44th Conf. Information Sciences and Systems, 2010.
L. Xie et al., “False data injection attacks in electricity markets,” Proc. 1st International Conference on Smart Grid Communications, 2010.
J. Momoh and L. Mili, “Economic Market Design and Planning for Electric Power Systems,” IEEE-Wiley Press, 2010.
Select references
40
Sensor technologies for overhead lines
Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.
RF leakage current sensor
*TLSA=Transmission Line Surge Arrester
(corrosion, vandalism, animals)
RF temperature sensor Ice build-up
41
Sensor technologies for the substation
Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.
42
is to make sure every pair of observable islands upon removal of each critical bus will have at least one PMU
Optimal placement of PMUs (2)
J. Chen et al. “Improved Bad Data Processing via Strategic Placement of PMUs,” IEEE Power Engineering Society General Meeting, 2005
𝐴=[11001
11111
01110
01110
11001] bus
bus
Bus-to-bus connectivity matrix
bus
island
Branch 1-2
Bus 2
43
WiMAX in mesh mode
Centralized scheduling Coordinated distributed scheduling
Uncoordinated distributed scheduling
schedule
44
Where the measurements are used:
Study network analysis
Real-time network analysis
Real-time contingency analysis
45
Proprietary mesh networks (3)Tropos GridCom: