Open AccessModelling and robust control design of a standalone wind‐based energy storage generation unit powering an induction motor‐variable‐displacement pressure‐compensated pump
Author(s) -
Kassem Ahmed M.
Publication year - 2016
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.2014.0376
Subject(s) - control theory (sociology) , rectifier (neural networks) , turbine , energy storage , engineering , wind power , pid controller , controller (irrigation) , electronic speed control , power (physics) , automotive engineering , control engineering , computer science , artificial neural network , temperature control , electrical engineering , mechanical engineering , agronomy , physics , stochastic neural network , control (management) , quantum mechanics , artificial intelligence , machine learning , recurrent neural network , biology
This study investigates the application of the sliding mode controller (SMC) for induction motor drive variable‐displacement pressure‐compensated pump (VDPC) system powered by an isolated wind/storage unit. The variable‐speed wind turbine (WT) is proposed to drive a permanent magnet synchronous generator (PMSG) which, feeds a storing energy unit and stand‐alone dynamic load. Energy storage systems are required for power balance quality in isolated wind power systems. Initially, the holistic model of the entire system is achieved, including the PMSG, the uncontrolled rectifier, the buck converter, the storage system, induction machine and the VDPC pump. The power absorbed by the connected loads can be effectively delivered and supplied by the proposed WT and energy storage systems, subject to sliding mode control. The main purposes are to supply 220 V/50 Hz through a three‐phase inverter and adjust the IM speed and VDPC pump flow rate. The performance of the proposed system is compared with the neural network control and the conventional PID control. The simulation results show that the proposed system with the SMC and neural network controllers has good performance and good prediction of the electrical parameter waveforms compared with the case of the conventional PID controller.