Efficient computation of the periodic steady state solution of nonlinear electric systems applying parallel processing techniques

Purpose - To introduce an efficient methodology for the computation of the periodic steady state solution of power systems with nonlinear and time-varying components which combines a Newton method based on a numerical differentiation procedure to obtain a fast steady state solution in the time domain and parallel process techniques. Design/methodology/approach - Nonlinear electric systems are represented by a set of differential equations, the conventional solution in the time domain is accelerated by a Newton method based on a numerical differentiation procedure for the convergence of state variables to the limit cycle and thus to the network periodic steady state solution. The efficiency of the solution is further enhanced with the application of parallel processing technology based on parallel virtual machine (PVM) and multi-threading (MT). Findings - The periodic steady state solution of nonlinear electric systems, even of large-scale, can be efficiently obtained in the time domain with the application of Newton methods for the fast converge of state variables to the limit cycle. The efficiency of the computer solution can be dramatically enhanced with the application of parallel processing technology. The potential of the PVM and MT platforms is shown in the investigation. A comparison of advantages and disadvantages associated with each parallel processing platforms is given; a quantitative comparison between PVM and MT is provided. Practical implications - The steady state solution of nonlinear electric systems can be efficiently obtained with a combination of Newton methods for the convergence acceleration to the limit cycle and parallel processing techniques. Originality/value - The steady state solution of nonlinear electric systems using a Newton method based on a numerical differentiation procedure for the convergence acceleration to the limit cycle and parallel processing based on the PVM and NIT platforms has not, to the authors' knowledge, reported before.