Artificial neural network tuned PID controller for LFC investigation including distributed generation. (9th March 2020)

• Record Type: Journal Article
• Authors: Debnath, Manoj K.; Agrawal, Ramachandra; Tripathy, Smruti Rekha; Choudhury, Shreeram
• Other Names: De Gersem Herbert guestEditor.; Kurz Stefan guestEditor.; Schöps Sebastian guestEditor.; Geuzaine Christophe guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 33:Number 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-09
• Subjects: artificial neural network -- distributed generation -- grasshopper optimization algorithm -- load frequency control -- PID controller -- renewable sources; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2740
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: 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.