Abstract
Load Flow (LF) analysis is a fundamental and significant issue in electric power systems. Because of the nonlinearity of the power mismatch equations, the accuracy of the nonlinear solvers is important. In this study, a novel and efficient nonlinear solver is proposed with active applications to LF problems. The formulation of the Proposed Method (PM) and its workflow and mathematical modeling for its application in LF problems have been discussed. The performance of the PM has been validated on the IEEE 14-bus and 30-bus test systems against several existing methods. The simulation results show that the PM exhibits higher order accuracy, faster convergence characteristics, smaller number of iterations, and lesser computation times in comparison with the other benchmark methods.
Highlights
A New and Efficient Nonlinear Solver for Load Flow ProblemsAbstract—Load Flow (LF) analysis is a fundamental and significant issue in electric power systems
Most mathematical models arising in science and engineering are complex and nonlinear in nature
New methods with robust solutions and fast convergence are still searched and these fast methods for the Load Flow (LF) problems are needed in the field of power systems, for optimal power factor extraction and improvement, maximum power point tracking in solar photovoltaic systems [8], power electronics with stability and control [7, 9], steady-state and transient stability of power systems [10,11,12,13]
Summary
Abstract—Load Flow (LF) analysis is a fundamental and significant issue in electric power systems. Because of the nonlinearity of the power mismatch equations, the accuracy of the nonlinear solvers is important. A novel and efficient nonlinear solver is proposed with active applications to LF problems. The formulation of the Proposed Method (PM) and its workflow and mathematical modeling for its application in LF problems have been discussed. The performance of the PM has been validated on the IEEE 14-bus and 30-bus test systems against several existing methods. The simulation results show that the PM exhibits higher order accuracy, faster convergence characteristics, smaller number of iterations, and lesser computation times in comparison with the other benchmark methods
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