Research on Power Communication Network Planning Based on Information Transmission Reachability Against Cyber-Attacks

Wu, Yingjun; Chen, Jinfan; Ru, Yingtao; Xu, Huiling; Roger, Mbonyineza; Ni, Ming

doi:10.1109/jsyst.2020.3026997

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…A villamosenergia-rendszer sérülékenységét jellemzően fizikai vagy kiberfizikai támadások esetén mutatott viselkedése alapján értékelhetjük. Utóbbi csoportba tartoznak az általános sérülékenység-vizsgálatok (Singh-Mahajan 2020;Tu-Shen-Xia 2020), az egyes alkalmazásokra és technológiákra gyakorolt hatásokra fókuszáló cikkek (Saraswat et al 2020;Rajkumar et al 2020;Wu et al 2020) és az egészen specifikus tanulmányok (Zhou et al 2020;Shen-Gao-Peng 2020;Pace et al 2020).…”

Section: Bevezetésunclassified

Hogyan befolyásolja a villamosenergia-hálózatról rendelkezésre álló információ a fizikai támadások által okozott sérülékenységről alkotott képet?

Hartmann

2021

ScientSec

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Összefoglaló. A villamosenergia-rendszerek fizikai támadásokkal szembeni ellenálló képessége a közelmúltban világszerte történt események ismeretében egyre nagyobb hangsúlyt kap a tématerület kutatásaiban. Az ilyen eseményekre való megfelelő felkészüléshez elengedhetetlen az üzemeltetett infrastruktúrának, elsősorban annak gyengeségeinek pontos ismerete. A cikkben Magyarország villamosenergia-hálózatának adatai alapján készített súlyozatlan és súlyozott gráfokon végzünk vizsgálatokat, hogy megértsük a különböző stratégia mentén kiválasztott célpontok elleni támadások milyen mértékben csökkentik a topológiai hatékonyságot. A cikk célja egyben a magyar hálózat sérülékenységének általános bemutatása is, mely hasznos bemeneti információ lehet a kockázati tervek elkészítésekor. Summary. Tolerance of the power grid against physical intrusions has gained importance in the light of various attacks that have taken place around the world. To adequately prepare for such events, grid operators have to possess a deep understanding of their infrastructure, more specifically, of its weaknesses. A graph representation of the Hungarian power grid was created in a way that the vertices are generators, transformers, and substations and the edges are high-voltage transmission lines. All transmission and sub-transmission elements were considered, including the 132 kV network as well. The network is subjected to various types of single and double element attacks, objects of which are selected according to different aspects. The vulnerability of the network is measured as a relative drop in efficiency when a vertex or an edge is removed from the network. Efficiency is a measure of the network’s performance, assuming that the efficiency for transmitting electricity between vertices i and j is proportional to the reciprocal of their distance. In this paper, simultaneous removals were considered, arranged into two scenarios (single or double element removal) and a total of 5 cases were carried out (single vertex removal, single edge removal, double vertex removal, double edge removal, single vertex and single edge removal). During the examinations, all possible removal combinations were simulated, thus the 5 cases represent 385, 504, 73920, 128271 and 193797 runs, respectively. After all runs were performed, damage values were determined for random and targeted attacks, and attacks causing maximal damage were also identified. In all cases, damage was calculated for unweighted and weighted networks as well, to enable the comparison of those two models. The aims of this paper are threefold: to perform a general assessment on the vulnerability of the Hungarian power grid against random and targeted attacks; to compare the damage caused by different attack strategies; and to highlight the differences between using unweighted and weighted graphs representations. Random removal of a single vertex or a single edge caused 0.3–0.4% drop in efficiency, respectively, which indicates a high tolerance against such attacks. Damage for random double attacks was still only in the range of 0.6–0.8%, which is acceptable. It was shown that if targets are selected by the attacker based on the betweenness rank of the element, damage would be below the maximal possible values. Comparison of the damage measured in the unweighted and the weighted network representations has shown that damage to the weighted network tends to be bigger for vertex attacks, but the contrary is observed for edge attacks. Numerical differences between the two representations do not show any trend that could be generalised, but in the case of the most vulnerable elements significant differences were found in damage measures, which underlines the importance of using weighted models.

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Section: Bevezetésunclassified

Hogyan befolyásolja a villamosenergia-hálózatról rendelkezésre álló információ a fizikai támadások által okozott sérülékenységről alkotott képet?

Hartmann

2021

ScientSec

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“…The Electric Power Communication Network (EPCN) is the infrastructure for transmitting and processing power service data in the power grid [1][2][3] . It provides communication services for relay protection, automation scheduling, operation control, and source-grid-load interaction.…”

Section: Introductionmentioning

confidence: 99%

Traffic collaboration optimization method for power communication network based on segmentation learning

Kang,

Wu,

Zhang

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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The rapid development of the energy internet, the deep integration of the power system and information system, and the emergence of various new services in the distribution network have led to an explosion of service data. Traditional routing optimization methods are inadequate in satisfying the efficiency and reliability requirements of the power communication network for data transmission. This paper presents a collaborative optimization method for enhancing the traffic in the power communication network based on segmentation learning. Firstly, we propose a power service data transmission routing architecture in EPCN, where multiple routes between the source and destination nodes are available. Secondly, a segmentation learning-based traffic collaboration optimization algorithm for EPCN is proposed, which divides the traffic to explore the transmission performance of multiple routes within one optimization, thereby reducing data congestion at critical nodes. Finally, simulation results demonstrate that the proposed algorithm outperforms in terms of data transmission delay and routing optimization speed.

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“…Smart grid is based on the traditional grid and uses the computational intelligence and networking abilities of various communication networks to ensure a sustainable, robust, efficient and resilient power grid [1]. Communication networks are the backbone of smart grid, in order to achieve efficient, economical and secure communication in smart grid, the power system uses different communication networks from personal local area network to wide area network placed in consumer sites, and a power grid communication network management system is designed to exchange data from electric sites for analysis, calculation and monitoring, control of load profiles and other power facilities [2].…”

Section: Introductionmentioning

confidence: 99%

CBGA: A deep learning method for power grid communication networks service activity prediction

Liu,

Zhou,

Shao

et al. 2024

J Supercomput

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Power grid communication network service activity (PCNSA) refers to the active degree of access devices in the power grid communication networks that are actively providing services over a specific time period as a gauge of the operation status of power devices and load demand. Accurate monitoring of PCNSA helps optimize power dispatch, ensure supply and demand balance, and guide network planning and management. However, due to the complex nonlinear, multi-scale and multivariate characteristics of the PCNSA data, it is often difficult to capture its intrinsic patterns and dynamic changes through a simple model. To address this problem, this paper proposes a convolutional neural network(CNN), bi-directional gated recurrent unit(BiGRU), and attention mechanism(AM)-based service activity prediction method for power grid CBGA communication network, referred to as CBGA. CNN is used to extract the features of time-series data, BiGRU is used to capture the dependencies before and after the extracted data in both forward and backward directions and predict the service activity data of the next hour, and AM is used to capture the influence of feature states and enhance the extraction of important information. In this paper, experiments and comparisons are conducted on three real power grid communication network service datasets, and the experimental results demonstrate that the performance of this method is the best, with the smallest MAE and RMSE and the largest R2 score. In comparison to other models, CBGA model is better suited for the prediction of PCNSA and providing reliable data support for power communication network managers to make decisions .

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Research on Power Communication Network Planning Based on Information Transmission Reachability Against Cyber-Attacks

Cited by 12 publications

References 26 publications

Hogyan befolyásolja a villamosenergia-hálózatról rendelkezésre álló információ a fizikai támadások által okozott sérülékenységről alkotott képet?

Hogyan befolyásolja a villamosenergia-hálózatról rendelkezésre álló információ a fizikai támadások által okozott sérülékenységről alkotott képet?

Traffic collaboration optimization method for power communication network based on segmentation learning

CBGA: A deep learning method for power grid communication networks service activity prediction

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