Secure Communications in Smart Grid: Networking and Protocols

McLaughlin, Kieran; Friedberg, Ivo; Kang, BooJoong; Maynard, Peter; Sezer, Sakir; McWilliams, Gavin

doi:10.1016/b978-0-12-802122-4.00005-5

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…The training and testing outputs for the neural network are shown in Figure 11a,b. The neural network performance is measured in terms of R 2 and MSE [71][72][73][74]. The model has achieved an R 2 value of 0.9739 during training and 0.9738 during testing.…”

Section: Development and Performance Evaluation Of Feedforward Neural...mentioning

confidence: 99%

“…A smart grid is a solution to this. The smart grid system is a digital future electricity system that enables a two-way flow of communication, i.e., between the center and the device to the center [2].…”

Section: Introductionmentioning

confidence: 99%

“…This paper used the DSGC model to define and relate the price to the grid frequency [2,4]. The mathematical model based on DSGC differential equations seeks to find grid instability for a four-node star architecture [5].…”

Section: Introductionmentioning

confidence: 99%

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Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Omar

Ibrahim

Mantri

et al. 2022

Sensors

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A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.

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Section: Development and Performance Evaluation Of Feedforward Neural...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Omar

Ibrahim

Mantri

et al. 2022

Sensors

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“…The transition of conventional grid into a future grid is a time-consuming process, which will involve retrofitting some of the conventional devices to integrate IT services. This retrofitting of supervisory control and data acquisition (SCADA) system has left the remote terminal units (RTUs) vulnerable to cyber attacks [1,2] because of the lower level of cybersecurity features available in them compare to their counterpart, i.e. phasor measurement units (PMUs).…”

Section: Introductionmentioning

confidence: 99%

Strategic PMU placement to alleviate power system vulnerability against cyber attacks

Khare

Mohapatra

Singh

2021

Energy Conversion and Econom

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A strategic phasor measurement unit (PMU) placement scheme is proposed in this work to reduce the cyber vulnerability of the power system against cyber attacks. A multi-stage PMU placement strategy is developed to alleviate the power system vulnerability against the possible false data injection attacks using the forward dynamic programming to distribute the capital cost of PMUs over a time horizon. An index is also proposed to quantify the vulnerability of the nodes of a grid against false data injection attacks. This index is useful in selecting the optimal set of candidate buses for PMUs placement and prioritising the candidate buses for PMUs placement in a specific deployment stage. The proposed scheme will also ensure the critical observability of the grid in its first stage and increment in observability levels in subsequent stages. Simulation results are provided for IEEE 14bus, IEEE 39-bus, and IEEE 118-bus test systems, considering the impact of zero injection bBuses (ZIBs) on optimisation process, to justify the effectiveness of the proposed index and PMUs placement approach.

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“…Secondary substation automation, where many devices are in the hardto-reach areas, will need a reliable communication solution to connect field assets to the control centres. Wireless technologies provide an obvious solution for such use cases, but deploying any wireless technology will require spectrum [1]. While some wireless systems, such as LoRaWAN, rely on unlicensed spectrum, depending on such spectrum for makes quality of service difficult to guarantee.…”

Section: Introductionmentioning

confidence: 99%

Bandwidth and Security Requirements for Smart Grid

Ghanem

Asif

Ugwuanyi

et al. 2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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With the evolution of smart grid and applications, power distribution networks need scalable, flexible, distributed and secure end-to-end communication. This work aims to highlight the necessary bandwidth that is needed to effectively monitor and communicate with all the secondary substations of Distribution Network Operators (DNOs). In order to determine the bandwidth requirements, the current and future applications in each secondary substation should be known. Two levels of security are employed -IPsec and TLSto give flexibility and resilience. Different test scenarios and several setups were employed on a fully secured IP based Remote Terminal Unit (RTU) through an IEC 62870-05-104 protocol, aiming to understand the bandwidth cost of different security techniques. The analysis depicts an average of 2-3 fold increase in bandwidth if both IPsec and TLS are used be to secure the connected asset.

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Secure Communications in Smart Grid: Networking and Protocols

Cited by 11 publications

References 26 publications

Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs

Strategic PMU placement to alleviate power system vulnerability against cyber attacks

Bandwidth and Security Requirements for Smart Grid

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