Abstract
With the rapid development of society economy and the rapid improvement of the people’s living standards.Social demand for power system security are getting higher and higher.Power system security and stability analysis is necessary to the security and stability operation of power grids.In today's development trend of smart grid and Internet of things,hundreds of millions of devices are being integrated into the power system.As the power system network increases in size and complexity,traditional power system risk assessment methods have been difficult to meet the real-time analysis application requirements of large-scale complex power grids.The development of big data technology provides an effective solution for the analysis and processing of large-scale data.In this paper,based on the big data parallel computing technology,we improve the topology analysis and power flow calculation module in method of power system risk assessment, and realizes the online risk assessment of power system.The main contents of the study are as follows:
We first introduced the commonly used big data framework,aiming at the characteristics of complex scale and large number of nodes in the power system network. We introduced Hadoop,Spark,Hive,GraphX and other big data analysis components in detail and analyzed the pros and cons.We selected Spark,a memory computing component with high computational efficiency,as the core computing framework,and Hadoop framework as the low-level support framework.We designed a power big data architecture suitable for power system risk assessment.
Then,due to traditional power system risk assessment algorithms have insufficient computing power for complex power systems and they cannot meet real-time requirements.We adopt the method of large-scale data parallel computing to improve the topology analysis calculation module and the power flow calculation module in the risk assessment https://www.doczj.com/doc/9218164489.html,bining flow computing technology,graph computing technology and Spark RDD memory computing technology,we designed topological analysis algorithms and power flow calculation methods based on big data parallel computing.
Finally,we use the CDH framework to build a big data platform,compile topological analysis and power flow calculation programs to submit to big data clusters,and use a series of scale power systems to test the programs,from speedup,efficiency,
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computational input cores and calculation time,etc.The aspects of the algorithm used in this paper and the traditional risk assessment are analyzed.The results show that compared with the traditional power system topology analysis algorithm and power flow calculation algorithm,the performance and efficiency of the topological analysis algorithm and the power flow calculation method based on large data parallel computing are better than the traditional methods,and the fast online calculation and analysis of the complex power system can be realized.And meet the power system online risk assessment of real-time requirements.
Key words:Risk Assessment of Power System;big data;parallel computing;topology analysis;power flow calculation
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