Risk evaluation of communication network of electric power using logarithm least squares and restriction coefficient

Gao, Haichun; Ran, Jing-xue; Xie, Haiyang; Li, Cong-Cong

doi:10.1109/wcica.2008.4592815

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…In addition to the use of fuzzy logic and multi-criteria decision methods in risk assessment, the logarithm-based calculation method is often used for weighting. The most important factors are emphasized [11,12].…”

Section: Purchasing Riskmentioning

confidence: 99%

Supplier risk assessment strategy

Sipos

Szűcs

2022

IRASE

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The aim of the study is to create an effective and standard risk assessment tool that provides the company with support and security in purchasing of new products. The goal was to create a tool that complements and standardizes risk assessment forms and shows rapid results. Using the procurement risk management system, the risk associated with a given product can be determined easily and in a short time. In the process, critical areas where hazards may occur can be clearly identified and the risk can be minimized if properly managed.

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Section: Purchasing Riskmentioning

confidence: 99%

Supplier risk assessment strategy

Sipos

Szűcs

2022

IRASE

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Risk Assessment of Grid Communication System Based on MIC-FNN under SG and UPIOT

Yue

Xuefeng

2020

IOP Conf. Ser.: Earth Environ. Sci.

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The communication system is the carrier of grid and information network coupling. The construction of reliable Smart Grid and Ubiquitous Power Internet of Things depends on the high coupling between the power grid and the information network. With the development of” the Grid and Internet”, the risk assessment of the power grid communication system has the characteristics of various levels, complex indexes and strong correlation. To deal with the risk effectively, this paper proposes an index system for evaluating the operation risk of grid communication system, which includes the cable loss level, the communication network power supply risk, the equipment operation risk, the external environment risk, the business interruption risk and the management risk. At the same time, a risk assessment model is built based on a fuzzy neural network, which overcomes the problem that traditional risk assessment methods rely too much on expert experience and makes prior knowledge embedded in the network. Finally, the model is proved to be scientific and practical by an example analysis.

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