1 / 45

Monitoring of the Power Grid State of the Art

Monitoring of the Power Grid State of the Art. Speaker: Yee Wei Law Collaborators: Umith Dharmaratna , Jiong Jin, Slaven Marusic , Marimuthu Palaniswami. Organization. Introduction to the grid Introduction to the grid sensors Motivation for the Smart Grid Smart Grid components

jace
Download Presentation

Monitoring of the Power Grid State of the Art

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Monitoring of the Power Grid State of the Art Speaker: Yee Wei Law Collaborators: UmithDharmaratna, Jiong Jin, SlavenMarusic, MarimuthuPalaniswami

  2. Organization • 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

  3. Introduction to the Grid > 110kV AS 60038-2000 “Standard voltages” 66kV, 33kV < 33kV

  4. Sample sensors for overhead lines • For conductor • Temperature • For insulator, transmission line surge arrester • Leakage current RF temperature sensor RF leakage current sensor Ice build-up

  5. Sample sensors for substations • For transformers • Detection of hydrogen in oil • For on-load tap changers • Detection of gas in oil (symptom of overheating) • For bushings • Leakage current Metal insulated semiconducting (MIS) sensor for detecting hydrogen Internally mounted tap changer 15 kV 69 kV 242 kV

  6. Sensor technologies for underground cables MIS sensor Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.

  7. Dynamic rating • 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 • 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. Non-static sensors (1) • 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

  9. Non-static sensors (2) • 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

  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]: Motivating (demand response) Accommo-dating (renewable energy) Intelligent (senses overload, rerouting) Quality-focused (minimal disturbances, interruptions) “Green” (minimal environment impact) Efficient (meets demand without more cost) Resilient (to attacks, disasters)

  12. Smart Grid components • Generation • Distributed generation • Microgrid • Transmission • Wide-area monitoring system • Distribution • Distribution automation • Consumption • Demand response

  13. Distribution Automation (DA) • 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 Control center Substation Distribution network

  14. Examples of equipment to be connected • 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 Switched capacitor bank Voltage regulator Recloser

  15. DA and communication • 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

  16. Standard architecture • 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 Jemena, United Energy, Citipower and Powercor SP AusNet and Energy Australia * Note: ZigBee is not in here

  19. WiMAX supports mesh? Year • First published • Beyer et al. “Tutorial: 802.16 MAC Layer Mesh Extensions Overview”: • Centralized scheduling • Coordinated distributed scheduling • Uncoordinated distributed scheduling 2002 2004 802.16.2-2004 describes recommended practice for coexistence of point-to-multipoint and mesh systems 802.16j-2009 adds relay (tree) support 2009 4G status not until 802.16m

  20. Proprietary mesh networks (1) Silver Spring Networks UtilityIQ:

  21. Proprietary mesh networks (2) ItronOpenWay:

  22. Open standard mesh - WirelessHART • 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) Source: Lennvall et al. “A Comparison of WirelessHART and ZigBee for Industrial Applications,” IEEE WFCS 2008

  23. Open standard mesh – 6LoWPAN • 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

  24. Distribution network reconfiguration • 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

  25. Grid Sensors Smart Grid Distribution Automation Wide-Area Monitoring System

  26. Wide-Area Monitoring System (WAMS) • 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 Hence WAMS

  27. Generic architecture of the WAMS

  28. Phasor measurement units (PMUs) • Synchronized phasor measurement units or synchrophasorsfor 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 For frequency, use Frequency Disturbance Recorder

  29. Examples of PMUs Macrodyne’s model 1690 ABB’s RES521 MiCOM P847

  30. Source: North American SynchroPhasor Initiative (NASPI)

  31. Applications of synchrophasors • Oscillation control • Frequency control • The goal is to select which loads to shed, to minimize overvoltagesor steady-state angle differences • Voltage control • The goal is to calculate maximum loadability using optimal power flow • 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 PhasorMeasurements,” IEEE Power Engineering Soc. Summer Meeting, 2002.

  32. State estimation • System equation: • Weighted least square • ] Measurements Errors Measurement Jacobian PMU measurement s.d.

  33. Some definitions • 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

  34. Optimal placement of PMUs • 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 • is cost of installing a PMU at bus • if a PMU is installed at bus

  35. Bad data identification Classification • 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 Multiple Single #1 #2 Bus #5 Non-interacting Interacting #3 #4 e.g. #1 and #6 not correlated Non-conforming Conforming #6 e.g. #2 and #5 not correlated e.g. #2 and #5 correlated Opportunity for attack

  36. False data injection attack (1) Attacker controls PMUs [Liu ‘09] Don’t care about Want specific ? Suppose, for example , exists depending on structure of yes no exists depending on structure of always exists Symbols: = number of hacked PMUs = number of measurements = number of system states = deviation from true states = induced measurement errors

  37. False data injection attack (2) • 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 Attack [Xie ‘10]: In the day-ahead forward market, buy and sell virtual power at two different locations and Inject false data to manipulate the nodal price of the Ex Post market In the Ex Post market, sell and buy virtual power at and respectively Profit

  38. Conclusion • 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? • Notable omission in this presentation: • Distributed generation, microgrid • Demand response

  39. Select references • 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.

  40. Sensor technologies for overhead lines (corrosion, vandalism, animals) *TLSA=Transmission Line Surge Arrester RF temperature sensor RF leakage current sensor Ice build-up Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009.

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

  42. Optimal placement of PMUs (2) • is to make sure every pair of observable islands upon removal of each critical bus will have at least one PMU Bus-to-bus connectivity matrix bus bus bus Branch 1-2 island Bus 2 J. Chen et al. “Improved Bad Data Processing via Strategic Placement of PMUs,” IEEE Power Engineering Society General Meeting, 2005

  43. WiMAX in mesh mode Centralized scheduling Coordinated distributed scheduling Uncoordinated distributed scheduling schedule

  44. Where the measurements are used: Real-time contingency analysis Real-time network analysis Study network analysis

  45. Proprietary mesh networks (3) TroposGridCom:

More Related