Modified iterative learning controller for efficient power management of hybrid AC/DC microgrid. (5th March 2021)

• Record Type: Journal Article
• Authors: Angalaeswari, S.; Jamuna, K.
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 1(2021)
• Page Start: 24
• Page End: 36
• Publication Date: 2021-03-05
• Subjects: iterative learning controller -- ILC -- hybrid microgrid -- HMG -- power management -- sequential quadratic programming -- SQP -- voltage stability; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In this paper, modified ILC is proposed for maintaining stable voltage, frequency and for efficient power management in a hybrid micro grid (HMG). The system is modelled with solar, battery, DC loads at DC bus, wind turbine, utility grid and AC loads at AC bus. An interlinking converter (IC) is connected between the AC and DC bus to facilitate bidirectional power flow. The control of voltage at AC/DC bus and management of power are obtained in the modelled hybrid micro grid with set point weighting iterative learning controller (SPW-ILC) by controlling the interlinking converter both in autonomous and grid connected mode of operation. To minimise the error signal to the controller, the classical optimisation method of sequential quadratic programming (SQP) has been employed to improve the performance of the controller. The simulation results show that the proposed controller have better performance than other controllers under variable source and load conditions.