ANN-Based SVC for Optimal Performance of Wind-Driven Self-Excited Synchronous Reluctance Generator

Mostafa Rida Mohamed, Adel El Samahy, Essam Rashad, Mohamed El Korfolly, Erhab Youssef

Abstract


Abstract- Self-Excited Synchronous Reluctance Generator (SESRG) is one of the cost-effective ways in wind energy systems. However, in stand-alone systems, SESRG operates over a wide range of load conditions with variable speeds and loads that decrease the terminal voltage of the generator. Thus, the excitation capacitances should be varied to meet the required reactive power. Hence, a Static VAR Compensator (SVC) is presented in this article to control the voltage of wind-driven SESRG under balanced and unbalanced load conditions. Furthermore, an Artificial Neural Network (ANN) is proposed to predict the suitable excitation capacitance and calculate the corresponding firing angle required to deliver the reactive power for each load condition. The validation simulation results are carried out using MATLAB/Simulink platform. The obtained results showed better performance of SVC-based ANN.


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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i4.13582.g8570

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