Research On Power System Situation Awareness Method Based On Random Matrix Theory

The development of smart grid has brought many difficulties for grid operation control.To ensure the safety and economy of grid operation,it has become an important trend of power grid development to introduce the concept of situational awareness into the operation control of power grid.On the other hand,with the maturity of smart grid hardware construction,the explosive growth of data sets challenges for data processing and knowledge extraction,and provides better conditions for situational awareness technology.Random matrix theory(RMT),as a universal big data technology,uses data instead of model as the main driving force of system analysis,and uses the correlation between data to describe the status of the whole system formed new view of the power system which provides a new means for system understanding.Therefore,the research on power system situational awareness based on RMT has important theoretical significance and considerable application potential.Firstly,this paper introduces the research background of the subject,summarizes the research status at home and abroad,and then explains the basic theory of RMT.It focuses on the analysis of the change of system statistical characteristics in the case of short-circuit fault,and gives the data preprocessing analysis method that is suitable for RMT.Secondly,this paper proposes a novel method for abnormal state detection in low SNR environment by employing Maximum Eigenvalue of Sample Covariance Matrix(MESCM).In this way,the situation awareness and early warning for interconnected power systems could be achieved by MESCM calculation and its violation check.The case studies have been carried on an IEEE 39-bus system and a planning system of China Southern Power Grid.The results show that the proposed methodology has the advantage of higher noise resistance and less computing time in comparison with the traditional mean spectral radius(MSR)based method and preliminarily verifies that it would be robust under incomplete information.Thirdly,based on the MESCM detection,combined with the Spiked population model,a dynamic identification method based on the Spiked population model for abnormal state of the grid is proposed,which realizes the dynamic identification of the abnormal state.The method uses the classical spectral estimation method corrected by Kaiser window function to estimate the global signal-to-noise ratio,and then obtains the corresponding dynamic threshold,which is compared with MESCM to identify abnormal states.The IEEE 50 machine standard system case shows that the threshold setting method based on SNR estimation proposed in this chapter is more objective and reasonable than the traditional threshold.Finally,combined with the entropy theory,a method for identifying the key nodes of the power grid is proposed.Based on the RMT correlation analysis method,the augmented matrix is constructed,and the MSR product is calculated.Then the entropy theory is used to obtain the evaluation values of each node for sorting to realize the identification of the key nodes of the power grid.The validity and accuracy of the proposed method are verified by the analysis in IEEE39 node system.