Statistical Modeling And Prediction Of GIC And Geomagnetic Index

Magnetic Storms are the earth’s magnetic field changes caused by solar activities, and they maybe have effects on the safe operation of technical system, such as power grids, oil and gas pipelines and so on. Along with the development of long-distance of high-voltage and ultra-high voltage transmission lines in China, the effect of GIC on power should have aroused more and more concern, so that GIC assessment has become more and more important. Currently, there are mainly two kinds of GIC assessment models:physical models and statistical models. When building Physical models, many factors should be considered. But we are often difficult to get their data.Statistical models do not take complex physical mechanism into consideration. Through analyzing the association feature between GIC and the data, we can establish assessment models.In this paper, we mainly study statistical models of GIC. Firstly we build classical regression model between the response variable GIC and predictor variable ap. Secondly, we build GIC quantile regression model by using ap. The former can only describe the effect of ap on the mean of GIC, but it neglects information of GIC distribution. Not only can the latter overcome shortcomings of classical regression, such as the error terms are assumed to be independent random variables having a normal distribution with mean zero and constant variance, but also can overall portray the relation between GIC and ap, especially the high-tail information of GIC. At last, based on the characteristics of the data we present a new weighted quantile regression method, i.e. power weighted quantile regression. Building statistical models, we also introduce the achievement function in R, SAS and Eviews, as well as goodness of fit and quasi-likelihood ratio of self-made R codes.According to the problems of power system security operation, we make GIC risk value table and GIC distribution table for fixed the value of high-frequency ap. These tables are simple and intuitive, have stronger practical operation ability, can easily inquire for the risk values of GIC, and can provide power system security operation with important inferences.