Real-time simulation of large-scale wind farms with detailed modeling can provide accurate insights into system transient behaviors,but entails challenges in computing resources.This paper develops a compact real-time...Real-time simulation of large-scale wind farms with detailed modeling can provide accurate insights into system transient behaviors,but entails challenges in computing resources.This paper develops a compact real-time simulator based on the field programmable gate array(FPGA)for large-scale wind farms,in which the spatial-temporal parallel design method is proposed to address the huge computation resource demand associated with detailed modeling.The wind farm is decoupled into several subsystems based on model consistency,and the electrical system and control system of each subsystem are solved in parallel.Both the module-level pipeline technique and superscalar pipeline technique are introduced to the wind farms’simulation to effectively improve the utilization of hardware resources.In case studies,real-time simulations of two modified wind farms are separately carried out on a single FPGA,including one with 13 permanent magnet synchronous generators under a time-step of 11µs,and the other with 30 squirrel-cage induction generators under a time-step of 8µs.Simulation tests,under different scenarios,are implemented to validate the numerical performance of the real-time simulator,and a comparison with the commercial tool PSCAD/EMTDC demonstrates the accuracy and effectiveness of the proposed design.展开更多
Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tre...Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant No.U1866207,No.51807131No.51961135101the Swedish Research Council under Grant No.2018-06007。
文摘Real-time simulation of large-scale wind farms with detailed modeling can provide accurate insights into system transient behaviors,but entails challenges in computing resources.This paper develops a compact real-time simulator based on the field programmable gate array(FPGA)for large-scale wind farms,in which the spatial-temporal parallel design method is proposed to address the huge computation resource demand associated with detailed modeling.The wind farm is decoupled into several subsystems based on model consistency,and the electrical system and control system of each subsystem are solved in parallel.Both the module-level pipeline technique and superscalar pipeline technique are introduced to the wind farms’simulation to effectively improve the utilization of hardware resources.In case studies,real-time simulations of two modified wind farms are separately carried out on a single FPGA,including one with 13 permanent magnet synchronous generators under a time-step of 11µs,and the other with 30 squirrel-cage induction generators under a time-step of 8µs.Simulation tests,under different scenarios,are implemented to validate the numerical performance of the real-time simulator,and a comparison with the commercial tool PSCAD/EMTDC demonstrates the accuracy and effectiveness of the proposed design.
文摘Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.
