Analysis of the external network parameters influence on the operating characteristics of self-excited induction generators
DOI:
https://doi.org/10.20998/2074-272X.2026.3.02Keywords:
self-excited induction generator, voltage stability, current distortion, reliability enhancement, rotor, capacitorAbstract
Introduction. Self-excited induction generators (SEIGs) play a vital role in renewable energy systems, particularly in remote regions. However, their performance is highly sensitive to excitation capacitance, rotor speed and load variations, making stability and reliability key challenges. Problem. Simplified analytical models fail to capture the complex internal interactions within SEIGs, limiting the analysis of how external network variations influence their dynamics. Moreover, the gradual degradation of excitation capacitors, a common fault in practice, significantly reduces generator efficiency. The goal of this work is to analyze the influence of excitation capacitance, rotor speed and load variations on SEIG performance, focusing on gradual capacitor degradation and open-phase faults to provide guidelines for reliable and efficient design. Methodology. Finite element modeling (FEM) with ANSYS Maxwell is used to accurately simulate electromagnetic and mechanical dynamics under realistic operating conditions. Results. Simulations show how changes in capacitance, rotor speed and load greatly affect voltage and current stability. Capacitor faults and open-phase conditions cause current distortion, voltage unbalance and reduced efficiency. Scientific novelty of this work lies in the FEM-based analysis of gradual excitation capacitor degradation in SEIGs. It was determined that this degradation directly impacts voltage balance, current waveform distortion and overall efficiency. Practical value. The findings provide clear guidelines for selecting optimal excitation capacitance and load ranges, reducing costs while enhancing the reliability and efficiency of SEIGs, particularly in isolated regions. Also this study offers new physical insight and a reliable framework for generator condition monitoring and design optimization. References 24, tables 1, figures 12.
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