Extreme Disaster Risk Assessment of Transmission Line Based on Improved Cloud Model and Eclat Algorithm

Abstract

At present, the cloud model was applied in the transmission line risk assessment. However, there was an insufficiency that the quantified level in the cloud model depended upon factitious and subjective grading. Thus, an extreme disaster risk assessment method of transmission line based on improved cloud model and Eclat algorithm was proposed. Firstly, based on disaster feature information, disaster characteristic factors and technical elements of typical extreme disasters were selected. Secondly, by means of FCM algorithm the one-dimensional data clustering center was obtained, and by use of combining clustering center with digital characteristics of traditional subjective cloud model the improved combined standard cloud was acquired. After the dynamic modification of the data, in which the outage time caused by disaster, the anti-disaster ability of transmission line and the accumulative effect of disaster risk were considered, the grading of the quantified level were performed in the standard cloud. Finally, the Eclat algorithm was applied to mine the association rule between quantified disaster characteristic factors and risk technical elements to obtain the risk assessment forecasting model. Result of instance shows that using the improved cloud model the accuracy of disaster assessment can be improved, and the obtained association rule can be utilized in the forecast assessment on disaster risk of transmission line.