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Artificial neural network tuned PID controller for LFC investigation including distributed generation
Author(s) -
Debnath Manoj K.,
Agrawal Ramachandra,
Tripathy Smruti Rekha,
Choudhury Shreeram
Publication year - 2020
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2740
Subject(s) - pid controller , control theory (sociology) , settling time , electric power system , controller (irrigation) , control engineering , computer science , engineering , power (physics) , temperature control , control (management) , step response , artificial intelligence , agronomy , physics , quantum mechanics , biology
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.