Improved control strategy for Cuk converter assisted wind‐driven SEIG for DC nanogrid. Issue 10 (10th July 2020)

• Record Type: Journal Article
• Authors: Kumar, Saurabh; Mukkapati, Ashok Bhupathi Kumar; Krishnasamy, Vijayakumar; Kaur, Rajvir; Chitti Babu, B.
• Is Part Of: IET electric power applications. Volume 14:Issue 10(2020)
• Page Start: 1906
• Page End: 1917
• Publication Date: 2020-07-10
• Subjects: wind power plants -- voltage control -- electric current control -- nonlinear control systems -- genetic algorithms -- voltage regulators -- PI control -- power convertors -- DC‐DC power convertors -- dynamic response -- variable structure systems -- power generation control; voltage loop -- ROSMC -- PI controller -- ripple‐free reference current -- AIROSMC -- extensive simulation study -- fixed frequency SMC -- variable SMC -- diode bridge rectifier‐based SEIG topology -- WECS -- controlled rectifier -- Cuk converter assisted wind‐driven SEIG -- power management -- voltage regulation -- power balance -- interfacing converter -- multiobjective control -- time‐variant control -- current mode control strategy -- Cuk converter assisted self‐excited induction generator -- sliding mode controller -- nonlinear controller -- DC nanogrid application -- static response -- input–output disturbance rejections -- state variables -- control complexity -- reduced‐order SMC -- dynamic response -- DC nanogrid -- MATLAB -- Simulink -- harmonic spectrum analysis -- diode bridge rectifier -- SEIG topology -- wind energy conversion system; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2020.0412
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : In this study, the current mode control strategy for a Cuk converter assisted self‐excited induction generator‐based wind energy conversion system (WECS) for DC nanogrid is proposed. The sliding mode controller (SMC) is a non‐linear controller and, is preferred because of good static and dynamic response for input ‐output disturbance rejections. However, the knowledge of all the state variables is required, which increases the number of sensors and control complexity. To reduce the number of sensors, reduced‐order SMC (ROSMC) can be implemented. Conventionally design of the voltage loop in ROSMC does not consider the uncertainty of the control parameter. However, the accuracy of the control signal generated by the ROSMC also depends on how accurately the reference current is generated. Therefore, a genetic algorithm tuned PI controller is proposed to generate the ripple‐free reference current. Artificial intelligence assisted ROSMC (AIROSMC) is proposed because of its advantages over SMC, which are a reduced number of sensors, improved dynamic response and easy implementation. The extensive simulation study is carried in MATLAB\Simulink for comparative analysis of fixed frequency SMC, two variable SMC, ROSMC, and AIROSMC. The harmonic spectrum analysis is carried out to justify the selection of DBR instead of the controlled rectifier.