Research On Identification Of Disturbance Influence Domain Of Power Grid Based On Spark

With the increasing scale and coupling strength of power grid interconnection,it's operating environment becomes increasingly complex.Thus,it is an emergency to enhance the data mining depth and utilization capability of multi-source data of power grid through big data technology.Meanwhile,local disturbances are more likely to spread to a larger area,it has an engineering value to effectively identifying disturbance influence domain for disturbance rejection.In the view of the massive storage and efficient processing of the wide-area sequences data of large power grid,this thesis proposed the design of the bid data architecture of power grid based on Spark.We took the advantages of Spark in distributed computing to design a big data platform of power grid.We discuss the detailed design of the platform and describes the Spark's advantage in processing the spatiotemporal sequences data.Moreover,to evaluate the superior performance of Spark in processing big data,we compare the performance of Spark and Hadoop in the experiments.Experimental results reveals that Spark performs well in processing data compared with Hadoop Map Reduce.Considered the vulnerability of power grid structure and operation state,this thesis proposed the potential energy intensity index to measure the degree of the influence degree of each node with the disturbance of power grid.Discussing the straitjacket of single variable in identifying disturbance influence domain through variable correlation.And introducing the common index of structural vulnerability electric quantity.Then,based on energy function structure method,we constructed node potential energy equation,which use electric quantity as the node potential energy(the operation state of vulnerability)weight for potential energy intensity index.Comparing simulation analysis between different malfunction of IEEE 39-bus system,we concluded that the node with high value of electric quantity is more likely to be the path of potential energy propagation,and verified the correctness of the proposed potential energy intensity index.We use IEEE 39-bus system simulation data as data source to simulate the streaming data,and analyze the influence of online disturbance based on Spark Streaming component.According to the Gini coefficient theory,we proposed the method of potential energy intensity Gini coefficient to evaluate the disturbance of the power grid after malfunctions.Online computing the value of potential energy intensity and potential energy intensity Gini coefficient,the value of potential energy intensity,which is used as clustering object.K-Means clustering algorithm is used to identify the disturbance influence domain.Without any measures to restrain the disturbance propagation,we dynamically analyzed disturbance influence domain after malfunctions and evolution of the potential energy intensity Gini coefficient.After the comprehensive analysis,we find that the spread of disturbance always leads to decreasing stability of local area.