Self healing in smart-grids

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This document discusses self-healing in smart grids. It defines self-healing as a smart grid's ability to quickly detect and isolate faults and reconfigure itself to restore normal operations. The document outlines the components of a smart grid that enable self-healing, including sensors, communication infrastructure, control algorithms, and actuators. It also describes the goals of a self-healing system as being reliable, fault-tolerant, and resilient. Tools that can help achieve self-healing are discussed, such as software agents, synchronized phasor measurement units, and information communication technology. Self-healing is explored at the levels of transmission grids, distribution grids, and microgrids.

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SMART GRID
COMPONENTS
Sensor System
Communication
Infrastructure
Control Algorithms
Actuator system
CT, PT, PMU, SM,
Temperature, pressure and so
on
PLC, wireless radio, Advance
metering Infrastructure(AMI),
HAN, Fiber-optics networks
and so on
Wide-area monitoring and control, micro
grid management, distribution load
balancing and reconfiguration, demand
response, OPF , VVO, FDIR
HVDC, FACTS, Energy Storage
System, DG, Breakers, Shunts,
tap changers and so on

SELF-
HEALING
SYSTEM
GOALS
Reliable systems
•Quick and accurate
analysis of faults
•protect the system
from harmful
effects
Fault-tolerant
systems:
•When a fault occur
the system
performance is not
affected.
•Assuring the
continuity of
service under any
conditions.
Resilient system
•Minimization of the
service's self-
renewal period
•Reconfigure to cope
with disturbances.

PROCESS
Transmission
grid
Microgrids
Self Healing
in SG
Distribution
grid

TRANSMISSION
GRID:
• PMU along with GPS
• FCC when DGs integration
• In HVDC Transmission use of CBDISTRIBUTION
GRID:
• Overcurrent relay
• wireless token ring protocol
(WTRP)
• islanding when the power system
faces a crisis situation.
MICROGRIDS:
• group of localized generators,
storage devices, and loads
• ability of a SG to switch the
microgrid from/to the islanding
mode

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atmega32avrrtc

TOOLS FOR
SELF-
HEALING
GRIDS
Software agents
•Grid Component Agents
•Distributed Resource
Agents
•End-User Agents
•Failure Control Agents
•Data Analyse Agents
•Graphical Visualizations
Agents
Synchronized phase
measurement unit
•Synchronized PMU is a
digital device that
measures the phasor
value at a time tag, the
time tag is referenced at
multiples of the power
system rated frequency
Information
communication technology
(ICT)
•collecting and exchanging
information, transferring
results, executing
decisions, and monitoring
real-time device states in
all power grid sectors are
the responsibility of the
smart communications
subsystem

REFERENCES:
• [1] M. Amin, “A smart self-healing grid: in pursuit of a more reliable and resilient system [in
my view],” IEEE Power Energy Mag., vol. 12, no. 1, pp. 110-112, Jan./Feb. 2014.
• [2] J. Popovi´c-Gerber, J. Oliver, N. Cordero, T. Harder, J. Cobos, M. Hayes, S. O’Mathuna, and
E. Prem, “Power electronics enabling efficient energy usage: energy savings potential and
technological challenges,” IEEE Trans. Power Electron., vol. 27, no. 5, pp. 2338– 2353, May
2012.
• [3] L. Hernandez, C.Baladr´on, J. Aguiar, B. Carro, A. Esguevillas, J. Lloret, and J. Massana, “A
survey on electric power demand forecasting: future trends in smart grids, microgrids and
smart buildings, ” IEEE Commun. Surveys & Tutorials, vol. 16, no. 3, pp. 1460–1495, 3rd
Quarter 2014.
• [4] E. Santacana, G. Rackliffe, L. Tang, and X. Feng, “Getting smart,” IEEE Power Energy Mag.,
vol. 8, no. 2, pp. 41-48, Mar/Apr. 2010.
• [5] ERGEG. Position paper on smart grids. An ERGEG public consultation paper. Ref: E10-EQS-
38-05. 2009.
• [6] DOE. The smart grid: An introduction; 2008.
• [7] U.S. EPRI. Methodological approach for estimating the benefits and costs of smart grid
demonstration projects. 2010.
• [8] Q. Sun, X. Ge, L. Liu, X. Xu, Y. Zhang, R. Niu, and Y. Zeng, “Review of smart grid

THANKS YOU
HAMZA BASHIR
hamza.digital01@gmail.com

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Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.

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Self healing in smart-grids

1. SELF-HEALING IN SMART GRID HAMZA BASHIR CIIT/FA19-REE-016/LHR

2. TABLE OF CONTENTS: 1. How the Smart grid works? 2. What is self healing in SG? 3. Smart Grid Components 4. Self-Healing System Goals 5. Three Gs in Smart Grid 6. TOOLS FOR SELF-HEALING GRIDS 7. Conclusion 8. References

3. HOW THE SMART GRID WORKS?

4. WHAT IS SELF-HEALING IN SG?

5. SMART GRID COMPONENTS Sensor System Communication Infrastructure Control Algorithms Actuator system CT, PT, PMU, SM, Temperature, pressure and so on PLC, wireless radio, Advance metering Infrastructure(AMI), HAN, Fiber-optics networks and so on Wide-area monitoring and control, micro grid management, distribution load balancing and reconfiguration, demand response, OPF , VVO, FDIR HVDC, FACTS, Energy Storage System, DG, Breakers, Shunts, tap changers and so on

6. SELF- HEALING SYSTEM GOALS Reliable systems •Quick and accurate analysis of faults •protect the system from harmful effects Fault-tolerant systems: •When a fault occur the system performance is not affected. •Assuring the continuity of service under any conditions. Resilient system •Minimization of the service's self- renewal period •Reconfigure to cope with disturbances.

7. PROCESS Transmission grid Microgrids Self Healing in SG Distribution grid

8. TRANSMISSION GRID: • PMU along with GPS • FCC when DGs integration • In HVDC Transmission use of CBDISTRIBUTION GRID: • Overcurrent relay • wireless token ring protocol (WTRP) • islanding when the power system faces a crisis situation. MICROGRIDS: • group of localized generators, storage devices, and loads • ability of a SG to switch the microgrid from/to the islanding mode

9. TOOLS FOR SELF- HEALING GRIDS Software agents •Grid Component Agents •Distributed Resource Agents •End-User Agents •Failure Control Agents •Data Analyse Agents •Graphical Visualizations Agents Synchronized phase measurement unit •Synchronized PMU is a digital device that measures the phasor value at a time tag, the time tag is referenced at multiples of the power system rated frequency Information communication technology (ICT) •collecting and exchanging information, transferring results, executing decisions, and monitoring real-time device states in all power grid sectors are the responsibility of the smart communications subsystem

10. REFERENCES: • [1] M. Amin, “A smart self-healing grid: in pursuit of a more reliable and resilient system [in my view],” IEEE Power Energy Mag., vol. 12, no. 1, pp. 110-112, Jan./Feb. 2014. • [2] J. Popovi´c-Gerber, J. Oliver, N. Cordero, T. Harder, J. Cobos, M. Hayes, S. O’Mathuna, and E. Prem, “Power electronics enabling efficient energy usage: energy savings potential and technological challenges,” IEEE Trans. Power Electron., vol. 27, no. 5, pp. 2338– 2353, May 2012. • [3] L. Hernandez, C.Baladr´on, J. Aguiar, B. Carro, A. Esguevillas, J. Lloret, and J. Massana, “A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings, ” IEEE Commun. Surveys & Tutorials, vol. 16, no. 3, pp. 1460–1495, 3rd Quarter 2014. • [4] E. Santacana, G. Rackliffe, L. Tang, and X. Feng, “Getting smart,” IEEE Power Energy Mag., vol. 8, no. 2, pp. 41-48, Mar/Apr. 2010. • [5] ERGEG. Position paper on smart grids. An ERGEG public consultation paper. Ref: E10-EQS- 38-05. 2009. • [6] DOE. The smart grid: An introduction; 2008. • [7] U.S. EPRI. Methodological approach for estimating the benefits and costs of smart grid demonstration projects. 2010. • [8] Q. Sun, X. Ge, L. Liu, X. Xu, Y. Zhang, R. Niu, and Y. Zeng, “Review of smart grid

11. THANKS YOU HAMZA BASHIR hamza.digital01@gmail.com

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