Improving the Energy Efficiency of Induction-Based Wind Turbine Systems through Adaptive Control

The challenge of improving the efficiency of generators to increase the production capacity of industries that rely heavily on them is addressed through energy efficiency enhancements in doublyfed induction generator (DFIG) machines using an adaptive control approach. This involves characterizing the DFIG, designing an adaptive control rule base to boost efficiency, training an artificial neural network (ANN) within this rule base to improve energy utilization and control, developing a conventional proportional–integral (PI) control system for comparison, creating a Simulink model for adaptive energy efficiency improvements, and validating the outcomes against conventional methods. The results show that for Generator 1, the conventional system achieves a stable efficiency of 59.7% between 4s and 10s. When an adaptive controller is applied, efficiency improves to 60.77% in the same period, representing a 1.07% increase. For Generator 2, the maximum efficiency with the conventional method is 76.5%, while the adaptive control raises it to 77.87%, yielding a 1.3% improvement. These results demonstrate that incorporating adaptive control into DFIG systems enhances overall energy efficiency compared to traditional control methods.