Massive Streaming PMU Data Modelling and Analytics in Smart Grid State Evaluation based on Multiple High-Dimensional Covariance Test - 2018


Analogous deployment of part measurement units (PMUs), the increase of data quantum and deregulation of energy market, all decision for strong state analysis in large scale power systems. Implementing model primarily based estimators is impracticable because the complexity scale of solving the high dimension power flow equations. During this Project, we have a tendency to 1st represent massive streaming PMU knowledge as big random matrix flow. Motivated by exploiting the variations within the covariance matrix of the large streaming PMU information, a completely unique power state evaluation algorithm is then developed based on the multiple high dimensional covariance matrix take a look at. The proposed test statistic is nonparametric without assuming a selected parameter distribution for the PMU information and of a big selection of data dimensions and sample size. Besides, it can jointly reveal the relative magnitude, length and site of an system event. For the sake of practical application, we tend to cut back the computation of the proposed check statistic from O(en g 4 ) to O(?n g two ) by principal component calculation and redundant computation elimination. The novel algorithm is numerically evaluated utilizing the IEEE thirty-, 118-bus system and a Polish 2383-bus system and a true 34-PMU system. The case studies illustrate and verify the superiority of proposed state evaluation indicator.

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