Design of instrumentation for power analysis and measurement in mathematical models
DOI:
https://doi.org/10.34185/1562-9945-2-163-2026-22Keywords:
mathematical modeling, active power, reactive power, apparent power, non-sinusoidal regimes, electric drive, Simulink, energy efficiencyAbstract
The purpose of this study is to select a method for calculating the active and reactive components of apparent power, to develop and implement software-based instrumentation tools (measurement blocks) for their determination in mathematical models, and to verify their adequacy under non-sinusoidal operating conditions.
The study employs methods of mathematical modeling of electromechanical systems, analysis of existing approaches to determining power components, as well as integral methods for calculating active and reactive power. The implementation is carried out in the Simulink environment using user-defined S-functions written in the C programming language. Computational experiments are performed on models of linear and nonlinear electrical circuits.
An algorithm and a software block for measuring active and reactive power, as well as energy, in mathematical models have been developed. Testing under various operating conditions (DC circuits, sinusoidal and non-sinusoidal regimes, and induction motor starting) has confirmed the operability and sufficient accuracy of the proposed approach. It is shown that standard tools may produce overestimated results, whereas the developed block provides more accurate evaluations.
The novelty lies in improving the approach to determining the components of apparent power in non-sinusoidal electrical circuits based on integral relationships without the need for Fourier series decomposition, as well as in creating a universal measurement block suitable for use in mathematical models of electromechanical systems.
The developed instrumentation can be used for analyzing energy consumption processes, improving the energy efficiency of electric drives, and studying the operating modes of power systems in simulation environments. The obtained results can be applied in scientific research and engineering practice for the design of energy management systems.
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