Adaptive Neuro-Fuzzy Control for Grid-Connected Renewable Energy Systems

In power electronics, controlling and managing grid integration of renewable energy sources is critically important. This paper proposes a novel adaptive neuro-fuzzy control method for regulating renewable energy interface inverters. The method aims to achieve smooth bidirectional power flow and nonlinear unbalanced load compensation, where traditional proportional-integral controllers often fail due to rapid changes in highly nonlinear system dynamics. The neuro-fuzzy controller's combined capability of handling uncertainties and learning from processes demonstrates advantages in controlling inverters under fluctuating operating conditions. Implementation-wise, the controller utilizes a hybrid architecture where neural networks automatically tune fuzzy membership functions and rule bases through backpropagation learning algorithms. The inverter is actively controlled to simultaneously compensate for harmonics, reactive power, and current imbalances in three-phase four-wire (3P4W) nonlinear loads while injecting renewable energy into the grid. The control algorithm incorporates real-time adaptation mechanisms that continuously optimize performance parameters based on system feedback. Key functions include harmonic detection algorithms using Fast Fourier Transform (FFT) analysis, reactive power calculation modules, and current balancing controllers that employ phase compensation techniques. This integrated approach ensures stable operation despite grid disturbances and load variations, featuring robust disturbance rejection capabilities through adaptive gain scheduling and fuzzy rule-based decision making.