Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems

Ahmed, Arman; Krishnan, V. V. G.; Foroutan, S. A.; Touhiduzzaman, Md; Srivastava, A.; Wu, Yinghui; Hahn, Adam; Sindhu, S.

doi:10.1109/ias.2018.8544672

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…For increased security against cyberattacks, extra measurements must be taken (see ref. [52]). COVID-19 impacted power demand, mostly when lockdown periods went into effect.…”

Section: Impact Of Covid-19 On the Power Grid Operationmentioning

confidence: 99%

“…Physical, manmade Riots, vandalism, war [42][43][44][45] Physical, natural Hurricane, earthquake, storms [46][47][48] Cyber Phishing, data injection [49][50][51][52] Non-physical, natural COVID-19, Spanish flu natural events. Resiliency usually is defined under high-impact low-probability weather events that impact physical assets of power grids.…”

Section: Classification Of Events Examplesmentioning

confidence: 99%

“…Ahmed et al. [52] develop a ML technique to monitor transmission protection systems and detect malicious activity using long short‐term memory (LSTM). These data are also utilised for failure diagnosis.…”

Section: Solutions For Minimising Impact Of Covid‐19mentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

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“…For increased security against cyberattacks, extra measurements must be taken (see ref. [52]). COVID-19 impacted power demand, mostly when lockdown periods went into effect.…”

Section: Impact Of Covid-19 On the Power Grid Operationmentioning

confidence: 99%

Section: Classification Of Events Examplesmentioning

confidence: 99%

See 1 more Smart Citation

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

Self Cite

View full text Add to dashboard Cite

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“…Such structured attacks can lead to cascading failures in the system. Therefore, the tight cyber-physical coupling is necessary to extend power system security into both cyber and physical attacks [175][176][177][178]. Integration of big data analytics provides an excellent opportunity to timely identify such malicious attacks and prevent the system from huge damages.…”

Section: Applications To Cyber-physical Systemsmentioning

confidence: 99%

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

et al. 2019

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Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive stateof-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

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“…A collaborative intrusion detection system (IDS) has been presented in [22] for generic object oriented substation event (GOOSE) and sampled value (SV) packets in IEC 61850 protocol. A machine learning based method has been used in [24] to identify cyberattacks against phasor measurement units (PMUs).…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

Jahromi

Sanner

et al. 2020

2020 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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The growing use of information and communication technologies (ICT) in power grid operational environments has been essential for operators to improve the monitoring, maintenance and control of power generation, transmission and distribution, however, at the expense of an increased grid exposure to cyber threats. This paper considers cyberattack scenarios targeting substation protective relays that can form the most critical ingredient for the protection of power systems against abnormal conditions. Disrupting the relays operations may yield major consequences on the overall power grid performance possibly leading to widespread blackouts. We investigate methods for the enhancement of substation cybersecurity by leveraging the potential of machine learning for the detection of transformer differential protective relays anomalous behavior. The proposed method analyses operational technology (OT) data obtained from the substation current transformers (CTs) in order to detect cyberattacks. Power systems simulation using OPAL-RT HYPERSIM is used to generate training data sets, to simulate the cyberattacks and to assess the cybersecurity enhancement capability of the proposed machine learning algorithms.

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Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems

Cited by 17 publications

References 21 publications

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

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