Risk Assessment of Power Communication Network Based on LM-BP Neural Network

Wang, Yanan; Wang, Ke; Zhang, Ran; Xue, Qiao; Chen, Xiangzhou; Zhang, Geng

doi:10.1088/1742-6596/1187/2/022063

Cited by 3 publications

(2 citation statements)

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“…For example, Zhang (2021) established a regression model through BP network and used PSO algorithm to optimize connection weights to evaluate the slow convergence of BP network, in order to improve the accuracy of rockburst prediction [ 13 ]. Wang (2019) et al used LM algorithm to improve the operation efficiency and accuracy of traditional BP neural network and provided an effective theoretical basis and modeling method for risk prediction of power communication network [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Shi

et al. 2021

Computational Intelligence and Neuroscience

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Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Shi

et al. 2021

Computational Intelligence and Neuroscience

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“…Since the proposal of the risk assessment theory of the power communication network, extensive research has been conducted by relevant researchers. The evaluation methods primarily consist of approaches based on uncertainty analysis theory [1][2][3][4],reliability theory [5][6][7][8][9], and artificial intelligence algorithms [10][11][12][13]. In recent years, the uncertainty analysis theory-based evaluation method has been widely applied in power quality assessment [14][15][16], power safety risk assessment [17,18], power system equipment condition assessment [19][20][21][22], and power grid operation risk assessment [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Chen,

Xu,

et al. 2024

IEEE Access

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The failure of the power communication network often leads to a cascading failure of the power system. Evaluating the potential risks in the power communication network is crucial to ensure the stable operation of the power system. This paper proposes a comprehensive risk assessment method for qualitative and quantitative measures. Firstly, an index system for risk assessment, considering both static structure and dynamic operation, is established. The optimal weight of each index is determined using the combination game weighting method. Additionally, a risk assessment model for the power communication network is established by coupling gray cloud theory and matter-element extension model. The fuzzy theory's triangular modulus operator is utilized to fuse three types of fault probabilities, quantify the risk value of the power communication network, and assign it a grade. Finally, experimental results demonstrate that this method improves discrimination degree by 1.2%, 92%, and 85% respectively compared to other traditional algorithms.

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Application of Big Data Information Technology in Electric Power Communication Network

Sun¹

2023

Lecture Notes on Data Engineering and Communications Technologies

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Risk Assessment of Power Communication Network Based on LM-BP Neural Network

Cited by 3 publications

References 0 publications

[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Application of Big Data Information Technology in Electric Power Communication Network

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