ANN-Enhanced Adaptive Sliding-Mode Control for STATCOM- Assisted Self-Excited Induction Generator in Wind Energy Conversion Systems

Abstract

The study introduces a new Adaptive Sliding-Mode Control approach that involves the application of artificial neural networks in the control of Static Synchronous Compensator systems coupled with Self-Excited Induction Generators of Wind Energy Conversion Systems. The proposed ASMC is guided towards enhancing the voltage regulation, reactive power support and stability at varying wind and fault conditions. The ASMC, in contrast to traditional Sliding-Mode Controllers (SMC) is an adaptive control system applying control gains dependent on system conditions reducing disturbances and improving resilience to parameter uncertainties and disturbances. The overall dq-axis plant model of SEIGSTATCOM system is constructed and simulated in MATLAB/Simulink to test the transient response, power quality and low-voltage ride-through (LVRT) capability. The results of simulation indicate that the ANN-enhanced ASMC has superior voltage stability and faster recovery in 40% voltage sags as a result of three-phase faults, and decreases Total Harmonic Distortion (THD) and reactive oscillations of power. The ANN-ASMC provides a smoother control action, better fault-ride-through, and smoother reactive power compensation in comparison with the SMC and PI controllers, which validates its use in the modern wind power systems that require high reliability and power quality.

Keywords: Adaptive Sliding-Mode Control, Static Synchronous Compensator, Self-Excited Induction Generator Wind Energy Conversion System