Open AccessGrasshopper optimisation based robust power/frequency regulator for shipboard micro‐grid
Author(s) -
Choudhary Atul Kumar,
Prakash Surya,
Sharma Mandeep,
Dhundhara Sandeep
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2020.0849
Subject(s) - control theory (sociology) , particle swarm optimization , controller (irrigation) , computer science , renewable energy , electric power system , engineering , grid , adaptive neuro fuzzy inference system , diesel generator , photovoltaic system , control engineering , automatic frequency control , fuzzy control system , automotive engineering , fuzzy logic , power (physics) , diesel fuel , electrical engineering , control (management) , geometry , mathematics , quantum mechanics , artificial intelligence , physics , machine learning , agronomy , biology
Due to the rapid increase in electrical energy requirements in marine power systems (MPSs), and to reduce the consumption of fossil fuel, there is an emergent need to utilise renewable energy sources (RESs) in MPSs, which has been an attractive field of research. This research aims to present a novel load frequency control (LFC) scheme for a shipboard micro‐grid (SMG) system. Therefore, a MPS with photovoltaic, wind turbine, hybrid energy storage system (ESS), and diesel generator (DG) have been simulated to relate an exact mobile islanded SMG. The system is designed using the transfer function models with the above said generating units and storage systems. Grasshopper optimisation algorithm (GOA) tuned fuzzy‐based proportional–integral–derivative with filter control technique has been proposed to investigate the performance of the LFC scheme for the proposed SMG system. GOA and particle swarm optimisation optimised controllers have been designed and performance evaluation has been carried out on the conventional, adaptive neuro‐fuzzy inference system, and fuzzy cascaded with a conventional controller. The responses obtained from the simulations for different cases are analysed to justify the novelty and superiority of the proposed technique for frequency and power regulation.