Smart grid security evaluation with a big data use case

Terzi, Duygu Sinanç; Arslan, Bilgehan; Sağıroğlu, Şeref

doi:10.1109/cpe.2018.8372536

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…These include, consumer energy utilization pattern data, smart meter data, data for managing, control and maintenance of devices (such as PMUs, IEDs , relays, etc.) data from generation, distribution and transmission networks, and operational data [101]. These volume of data as a result of increasing PV penetration conforms with the seven big data characteristics, which are [101-105]…”

Section: Big Datamentioning

confidence: 86%

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

et al. 2018

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Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid's security, reliability and resiliency.

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Section: Big Datamentioning

confidence: 86%

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

et al. 2018

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“…Similarly, smart grids are the hot points for security and privacy attacks during collecting, receiving and sharing data. If an attack is successful, it may result in the deterioration of government services such as telecommunication companies, energy distribution and other associated services [43]. The loss may be in the form of data or service impairment.…”

Section: Fog-enabled Iot Applications Security Requirementsmentioning

confidence: 99%

The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey

Tariq

Asim

Al-Obeidat

et al. 2019

Sensors

218

171

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The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

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“…The suggested technique is based on a decision-tree algorithm for taking a precise decision to control thermostatically controllable loads (TCL). Terzi et al in (178) 184) built a model for probabilistic time series forecasting that allow the inclusion of a possibly large set of exogenous variables in Smart Grids. The approach was based on boosted additive models.…”

Section: Decision Treementioning

confidence: 99%

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos

Sarigiannidis

Ioannidis

et al. 2021

Computer Science Review

193

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Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

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Smart grid security evaluation with a big data use case

Cited by 11 publications

References 22 publications

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

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