Power Grid Enterprise Intelligent Risk Identification Model Considering Multi-Attribute and Low Correlation Data

Abstract

Accurate risk calculation is beneficial to improve the accuracy of risk identification, which plays a crucial role in auditing in smart grid enterprises. With the development of smart grid, electric utilities have accumulated vast amount of data, big data which has low correlation with risk can cumulative effect on risk and the indicators that affect risk have multiple attributes. In view of above problem, in this paper, a two-layer neural network model is proposed, which inputs the multi-attribute of indicators and the full-scale indicators of different degrees of influence into the model. The problems that the accumulation of multi-attribute and multiple low-related factors of the indicators have an effect on risk calculation are solved in the model. This paper also builds a big data audit platform to carry out the intelligent data collection and risk calculate The results of the examples show that the model has higher accuracy of risk calculation.

Keywords

• Risk identification
• neural network
• big data
• intelligent audit platform
• power system