Disaster Recovery Power and Communications for Smart Critical Infrastructures

Alqahtani, Abdulaziz; Abhishek, Rohit; Tipper, David; Medhi, Deep

doi:10.1109/icc.2018.8422982

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…The model was formulated as a nonconvex mixed-integer nonlinear program and then transformed into a mixed-integer linear program to improve its computational efficiency. [145] presented a framework to support post-disaster communications by integrating microgrids and cellular networks. In the proposed model, microgrids power the critical infrastructure and cellular network base stations in select disaster areas to facilitate wireless mesh networks and local edge computing for postdisaster emergency communications.…”

Section: A Recovery Logisticsmentioning

confidence: 99%

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

et al. 2021

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Wildfires pose a significant challenge to the natural and the built environments, as well as the safety and economic wellbeing of the communities residing in wildfire-prone areas. The electric power grid is specifically among the built environments most affected by, and contributing to, wildfires. In this paper, we propose a three lines of defense (3LD) framework for wildfire risk management in electric power infrastructure and review the literature from this lens. An overview of the physics and phenomenology of the wildfires as it relates to power grids is presented, and the logic for the proposed 3LD framework is discussed. The reviewed literature based on the 3LD theme includes the most relevant and emerging research work on wildfire prevention as the first line of defense, wildfire mitigation and proactive response as the second line of defense, and wildfire recovery preparedness as the third line of defense. This study reveals that while the state of the art, to a large extent, stands comprehensive in various aspects of power system resilience and wildfire risk management, there is a gap in the literature in addressing this emerging risk in a holistic, interdisciplinary approach.

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Section: A Recovery Logisticsmentioning

confidence: 99%

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

et al. 2021

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“…Specifically, in large companies, power outage loss costs over a million dollars an hour and around 150 USD annually [4]. Several works have already been conducted with the aim of improving the resilience of critical infrastructures by applying machine learning (ML) [5], deep learning (DL) [6], distributed edge computing [7,8], and transfer learning [9]. All these works constructed their prediction model either by collecting data from the agents (e.g., ML, DL) or receiving an update for some data from the distributed agents (e.g., distributed ML).…”

Section: Motivationmentioning

confidence: 99%

FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

et al. 2021

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Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.

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“…From a cyber perspective, if the local controller loses the connection to the centralised controller for a period of time (a threshold value), an emergency wireless network formation is triggered. The controllers that enable direct D2D communication can move to the emergency mode to form an ad-hoc and self-organised emergency wireless network serving for a regional islanded MG. A reconfiguration protocol of the cyber layer can support the discovery of neighbour nodes [17]. To support such an application, various neighbour discovery protocols [28,29] can be adopted or modified.…”

Section: Overview Of Centralised-to-decentralised Frameworkmentioning

confidence: 99%

“…Although advanced communication technologies enable the real-time efficient centralised control framework, which is superior to the decentralised one in terms of control performance and implementation efficiency, such centralised framework suffers from a single-point failure. In addition, base stations that support the management of wireless resources are vulnerable to natural disasters or cyber-attacks, and they may fail to function if losing the backhaul connection to the core network or being physically damaged [17,18]. Hence, for instance, ad-hoc communication technology has been utilised to realise the self-organised MGs in response to disasters [17,19].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, base stations that support the management of wireless resources are vulnerable to natural disasters or cyber-attacks, and they may fail to function if losing the backhaul connection to the core network or being physically damaged [17,18]. Hence, for instance, ad-hoc communication technology has been utilised to realise the self-organised MGs in response to disasters [17,19]. However, further detailed cyber-layer scheduling and implementation in response to contingencies have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Resilience-Oriented Centralised-to-Decentralised Framework for Networked Microgrids Management

Ge,

Teng,

Konstantinou

et al. 2021

Preprint

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This paper proposes a cyber-physical cooperative mitigation framework to enhance power systems resilience under extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physicallayer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physicallayer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centers, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber-physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink.

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Disaster Recovery Power and Communications for Smart Critical Infrastructures

Cited by 16 publications

References 12 publications

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

A Resilience-Oriented Centralised-to-Decentralised Framework for Networked Microgrids Management

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