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Monitoring of the Power Grid State of the Art

Speaker: Yee Wei LawCollaborators: Umith Dharmaratna, Jiong Jin, Slaven Marusic, Marimuthu Palaniswami

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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

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Introduction to the Grid

AS 60038-2000 “Standard voltages”

> 110kV

66kV, 33kV

< 33kV

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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

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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

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Sensor technologies for underground cables

Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.

MIS sensor

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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

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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)

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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)

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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

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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)

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Generation◦ Distributed generation◦ Microgrid

Transmission◦ Wide-area monitoring system

Distribution◦ Distribution automation

Consumption◦ Demand response

Smart Grid components

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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

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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

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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

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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

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Standard architecture – alternate perspective

SecureMesh

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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

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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

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Proprietary mesh networks (1)Silver Spring Networks UtilityIQ:

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Proprietary mesh networks (2)Itron OpenWay:

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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

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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

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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

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Grid Sensors

Smart Grid

Distribution Automation

Wide-Area Monitoring System

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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

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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

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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

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Examples of PMUs

ABB’s RES521

Macrodyne’s model 1690

MiCOM P847

30Source: North American SynchroPhasor Initiative (NASPI)

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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.

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System equation:

Weighted least square◦ ]

State estimation

Measurements Errors

Measurement Jacobian

PMU measurement s.d.

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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

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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

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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

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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

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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

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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

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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

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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

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Sensor technologies for the substation

Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.

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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

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WiMAX in mesh mode

Centralized scheduling Coordinated distributed scheduling

Uncoordinated distributed scheduling

schedule

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Where the measurements are used:

Study network analysis

Real-time network analysis

Real-time contingency analysis

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Proprietary mesh networks (3)Tropos GridCom: