Parallel Computing Techniques for High Speed Power System Solutions

Abstract

Modern power systems are enormously large and complex entities. Planning, maintaining and operating such a system would be cumbersome if it were not for the wide assortment of analytical methods available to assist the power engineer. With the advent of interconnected systems came the necessity of developing techniques for enabling the power system operator to determine the electrical state of the network and to predict how it would respond to different disturbances such that reliability and other economic criteria are always met. Increase in system size, introduction of complex controls, uncertainties in forecasting, etc. necessitate faster software tools to handle power system planning, operation and operator training. This thesis aims to improve the performance of power system software tools by proposing parallel algorithms with the objective of reducing their execution time. Solution of a sparse set of linear algebraic equations is one of the most essential modules used in almost all power system software tools. The thesis addresses the issue of reducing the execution time of sparse linear algebraic solver by parallelizing sparse matrix factorization. A LU factorization algorithm which is more amenable for parallelization is identified and chosen. In this work, the structural symmetry property of power system sparse matrices is exploited to maximize the column or node level parallelism. Results obtained from the implementation of the proposed algorithm on Graphical Processing Units (GPUs) corroborate its efficacy by achieving significant reduction in the solution time when compared with state of the art CPU based sequential sparse linear solvers. Power flow algorithm is one of the most frequently executed algorithms with respect to the steady state realm of the power system. The output of the power flow algorithm is the phasor bus voltages and line flows for the given load-generation pattern...