Abstract
The typical layout of power systems is experiencing significant change, due to the high penetration of renewable energy sources (RESs). The ongoing evaluation of power systems is expecting more detailed and accurate mathematical modeling approaches for RESs which are dominated by power electronics. Although modeling techniques based on state–space averaging (SSA) have traditionally been used to mathematically represent the dynamics of power systems, the performance of such a model-based system degrades under high switching frequency. The multi-frequency averaging (MFA)-based higher-index dynamic phasor modeling tool is proposed in this paper, which is entirely new and can provide better estimations of dynamics. Dynamic stability analysis is presented in this paper for the MFA-based higher-index dynamical model of single-stage single-phase (SSSP) grid-connected photovoltaic (PV) systems under different switching frequencies.
Highlights
In recent years, renewable energy sources (RESs) have been gaining more attention worldwide due to their environmentally friendly operations and the variable prices of fossil fuels
This paper aims to represent a detailed dynamic phasor model of the single-stage single-phase (SSSP) grid-connected PV
The dynamic phasor averaging approach proposed in this paper has some advantages over conventional state–space averaging (SSA) methods for modeling a SSSP grid-connected PV system
Summary
Renewable energy sources (RESs) have been gaining more attention worldwide due to their environmentally friendly operations and the variable prices of fossil fuels. The quasi-steady-state approximation models are generally useful for systems with electro-mechanical transients These types of tools do not work for systems with fast electromagnetic transients e.g., power electronic converters with high-frequency switches [10]. The switching frequency-sensitive MFA-based dynamic phasor approach provides a detailed mathematical representation of the dynamical system, which is able to address DC, fundamental, and other harmonic components of the state variable in the form of Fourier series [16]. This modeling technique provides negligible flexibilities for harmonic components which have no considerable impact on state variables.
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