Risk assessment model of power network based on equipment condition and entropy combined weight method

Du, Yue; Shi, Chengbin; Guo, Ma; Chang, Fangyuan; Xu, Baoping; Fan, Yonghua; Sun, Zhen

doi:10.1109/eic.2017.8004713

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…To weigh the influence of different attributes on the device trust value, it is necessary to determine the weight of each attribute, and different devices have different weight vectors T . In multiattribute decision-making problems, weight determination methods include the entropy method [29], fuzzy clustering method [30], principal component analysis method [31], and analytic hierarchy process (AHP) [32]. In this paper, AHP is used to confirm the weight, and the specific operation process is as follows:…”

Section: Multiattribute Trust Evaluation Modelmentioning

confidence: 99%

Multiattribute Evaluation Model Based on the KSP Algorithm for Edge Computing

Liang

2020

IEEE Access

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To solve the problems of single evaluation attributes and highly overlapping trust paths in the current trust model, a multiattribute trust evaluation model based on the K shortest paths (KSP) algorithm is proposed. The model refines the evaluation attributes among nodes and uses the analytic hierarchy process (AHP) to allocate the weights based on users' preferences to meet the special needs of individual users. Also, the model introduces the penalty factor algorithm idea of KSP and proposes a trust path optimization algorithm RKSP based on the A * algorithm. It can filter highly overlapping trust paths during the formation of recommended trust paths so that the searched trust paths have certain differences.Through comparative experiments, it is proven that the model can reduce the resource overhead of edge devices, improve the accuracy of evaluation, ensure load balancing within the domain, and better align the results of the model recommendation with user needs.

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Section: Multiattribute Trust Evaluation Modelmentioning

confidence: 99%

Multiattribute Evaluation Model Based on the KSP Algorithm for Edge Computing

Liang

2020

IEEE Access

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“…Power communication equipment exhibits extensive deployment, extended replacement cycles, stringent stability requisites, and widespread operational implications. In order to enhance the stable operation of power communication systems, the evaluation of the health status of power communication equipment is imperative [1] . This proactive assessment allows for the implementation of preventive measures, mitigating unnecessary interruptions and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation method for communication equipment health status based on dynamic composite weights

Yang,

Huang,

Liang

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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To address the health status assessment of communication equipment under various uncertainty factors, a novel evaluation method for communication equipment health status has been developed, considering the significance of equipment metrics comprehensively. This framework integrates the subjective Analytic Hierarchy Process (AHP) and the objective Criteria Importance Through Intercriteria Correlation (CRITIC) method to determine the weighting of assessment metrics. Furthermore, a method for dynamically adjusting metric weights based on data volatility is proposed, thereby circumventing the issue of inaccurate assessments stemming from metric data fluctuations when encountering equipment instability. Empirical testing demonstrates the practical utility of this approach in accurately assessing the health status of communication equipment.

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“…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]. In reference [1], the risk assessment model of power communication network based on traditional matter-element fails to fully consider the fuzziness and randomness associated with determining the risk for the boundary value or measured value of each index.…”

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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Risk assessment model of power network based on equipment condition and entropy combined weight method

Cited by 7 publications

References 7 publications

Multiattribute Evaluation Model Based on the KSP Algorithm for Edge Computing

Multiattribute Evaluation Model Based on the KSP Algorithm for Edge Computing

Evaluation method for communication equipment health status based on dynamic composite weights

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

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