Research On Data Analysis And Mining Method For Big Data In Power Dispatching And Control System

With the development of power grid and dispatching and control center,the relationship between the various business of dispatching and control system has become more and more closely.The power grid dispatching and control system has involved large amount of multi-source heterogeneous data with high complexity,then the data has typical features of big data.The big data analysis and data mining technologies are applied in the power grid dispatching and control system to focus on information and knowledge hidden in big data of dispatching and control system.The method is proposed to provide useful knowledge and decision support for the business demands of new intelligent dispatching.In Era of electric power big data,big data analysis method will provide new thinking pattern for the research of key problems in power system.The thing that matters is that the rapid accumulation of big data in the power grid dispatching and control system provides a sufficient condition for the risk analysis of power grid equipment.Based on the analysis of characteristics of big data in the dispatching and control system,a universal analysis framework of big data in power dispatching and control system is built,and the application of risk management and control in power grid,a data mining method for high risk equipment based on HR-Tree is proposed.Equipment risk influence degree is the foundation of high risk equipment set mining.From the perspective of multiple factor analysis of equipment risk,considering impact of equipment importance and equipment hidden danger on equipment risk,equipment risk influence degree is defined,equipment importance indicators and equipment hidden danger indicators are proposed.The index system of calculating equipment risk influence degree is established.On the basis of this system,the use of equipment relative importance matrix and equipment relative hidden danger is for the calculation model of equipment risk influence degree.The risk influence degree of each equipment is obtained to identify high risk equipment that has an important impact on reliable,stable and safe operation of power grid.The equipment risk value as the target of mining high risk equipment,the construction of HR-Tree retaining the original database of equipment risk value and equipment risk prior knowledge information is for mining high risk equipment set,specifically the high risk equipment risk set meeting the threshold will be obtained according to the HR-Tree path information.Finally,the method in this paper is simulated based on massive historical alarm data in dispatching and control system.The simulation results show that HR-Tree can quickly deal with the alarm data to get high risk equipment set meeting the conditions,and it can reflect the potential association between high risk equipment,so it will provide reference for the follow-up of power grid risk management and control.