Research on Dynamic Assessment System of Composite Fault Risk of Transmission Line Based on Blockchain Energy. (5th May 2022)

• Record Type: Journal Article
• Authors: Liu, Jie; Jia, Boyan; Ding, Likun; Zhang, Zhimeng; Sun, Cuiying
• Other Names: K K Aruna Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-05-05
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/7426559
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : In order to study the dynamic assessment system of composite fault risk of transmission line based on blockchain energy and in order to study the transmission line compound fault risk dynamic assessment system based on blockchain, firstly, according to the coupling relationship between power grid and natural disasters, the information resources such as data collected by power grid intelligent devices and natural meteorology are excavated, and the overall architecture of power grid disaster early warning and decision-making system supported by blockchain is built. Then, from the perspective of risk, combined with analytic hierarchy process, an index system for reasonable evaluation of distribution network fault benchmark risk is established. Quantitative assessment and risk classification shall be carried out for the failure probability, failure impact consequence, and comprehensive failure risk, so as to facilitate the adoption of risk response measures. Finally, taking several 220 kV lines in the northwest and central part of a city as examples, the icing prediction analysis verifies the feasibility and effectiveness of the proposed power grid disaster early warning decision system based on blockchain to predict the icing thickness. The experimental results show that taking the icing disaster as an example, the MPC method is used to modify the icing thickness prediction model, improve the accuracy of the icing prediction model, and verify the feasibility and effectiveness of the prediction and early warning system based on blockchain.