Artificial neural network tuned PID controller for LFC investigation including distributed generation

To facilitate the frequency regulation, here an adaptive artificial neural network (ANN) tuned proportional-integral-derivative (PID) controller is suggested for load frequency control (LFC) investigation in a system with distributed generation (DG) resources. The various DG resources include wind turbine generators (WTG), battery energy storage system (BESS), aqua electrolyzer (AE), diesel engine generators (DEG), and fuel cell (FC). Initially, an isolated thermal generating system is considered with DG. Then an interconnected two-area thermal power system with DG is considered for LFC analysis. The implemented PID controller parameters are achieved using two methodologies. In the first case, the PID controller parameters are tuned by a recent optimization technique known as grasshopper optimization algorithm (GOA). In the second case, the PID controller parameters are tuned by an ANN. The dynamic behavior of the two categories of the system is inspected with GOA tuned PID controller and ANN tuned PID controller and it is established that ANN tuned PID controller exhibits superior performance as compared to GOA tuned PID controller in terms of time-based performance evaluative factors such as minimum undershoots, settling time and maximum overshoots. Also, the robustness of the recommended ANN tuned PID controller is verified by applying random loading in the system.