Parallel Computing Techniques for High Speed Power System Solutions
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. Reduction in the solution time for the power flow algorithm would further boost other applications like contingency analysis, optimal power flow, dynamic studies, etc. This thesis proposes a parallel power flow algorithm based on Newton-Raphson method. Inclusion of reactive power limit constraints at generator buses in the problem formulation stage itself eradicates the need to use heuristic techniques. In this work, the given power system network for which the power flow solution is desired, is decomposed into smaller sub-networks and processed in an independent as well as in a concurrent manner. Partial results from the sub-networks are consolidated to arrive at the solution of original network. The proposed algorithm is implemented on a computer architecture comprising of multiple cores. Results obtained indicate preservation of the superior convergence property of Newton-Raphson method and a significant reduction in the solution time required for the parallel version of the power flow when compared with the sequential version. Transient stability assessment is an important module within the Dynamic Security Assessment application. The objective of transient stability assessment is to obtain the dynamic, low frequency electromechanical phenomenon and determine whether the power system would be able to maintain synchronism after an electrical disturbance. Time domain simulation for the stability assessment by solving thousands of Differential Algebraic Equations (DAEs), even though is the preferred method, is computationally intensive and becomes a major computing challenge as system size increases. The thesis proposes a parallel algorithm based on spatial domain decomposition employing relaxation conditions to speedup the transient stability simulation to handle the aforementioned challenge. A convergence enhancing mechanism through selection of appropriate admittance parameters for the network emulating fictitious buses which mimic the remainder of the system for each sub-network is derived. Also, a technique of port dependency reduction, which guarantees convergence for any general network is presented. Results obtained from implementation on a multicore parallel architecture corroborate the scalability and improved speedup features of the methodology which achieves a significant reduction in the simulation execution time which would greatly aid in reliably operating the power system.