Augmentation of Low Voltage Ride through (LVRT) Capability using ANN based electric spring for a Grid-tied Wind Energy Conversion System. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Udayakumar, M.D.; Saradha Devi, A.; Raja, E.; Nazar Ali, A; Stonier, Albert Alexander
• Is Part Of: IOP conference series. Volume 1055:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1055/1/012156
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Electric Spring (ES) is an innovative approach for distributed voltage control (DVC) whereas at the same time accomplishing nominal demand-side management (DSM) technique for continuously varying renewable energy sources. In order to demonstrate the efficiency of ES when deployed in enormous quantities across the grid-connected power distribution system, it is important to build easy and reliable simulation models of ES that can be integrated into large-scale simulation studies of the power system. The ES study gap identification method defines the ES dynamic simulation methodology that is appropriate for the WECS grid-connected voltage and frequency control studies. The integration of WECS to the electric grid leads to many serious power quality problems. Low voltage ride through (LVRT) capability improvement is one of the important constraints in grid connected WECS. This articleexamines the enhancement of LVRT capability of Doubly Fed Induction Generator (DFIG) based grid connected WECS by usingES. The effectiveness of proposed Artificial Neural Network (ANN) based Electric spring controller is compared with existing PI controller using MATLAB/Simulink software. It is evident that the proposed controller nurtures the trustworthiness of WECS when integrated with existing electric power grid system.