Enhancing the Fault Ride-through Capability of a DFIG-WECS Using a High-Temperature Superconducting Coil

Mohamed I. Mosaad,Hani Albalawi,Mohamed M. Ismaiel,Ahmed Abu-Siada,Ahmed Fahmy

doi:10.3390/en14196319

Abstract

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.

Highlights

With the increased penetration of wind-based generation into power networks, major concerns such as poor power quality and a full or partial blackout may arise if a proper control system is not adopted [1,2,3]
The results indicated that the Grey Wolf Optimizer (GWO) and Harmony Search (HS) provided better fitness than particle swarm optimization (PSO) with a superiority of G4W
The results showed that the parameters calculated by the GWO and HS provided better performance than that calculated by the PSO

Summary

Introduction

With the increased penetration of wind-based generation into power networks, major concerns such as poor power quality and a full or partial blackout may arise if a proper control system is not adopted [1,2,3]. Several modern evolutionary computing techniques have been used in various power system applications including optimum design, size and control parameters calculation. Three up-to-date optimization methods: Harmony Search (HS), Modified Flower Pollination Algorithm, and Electromagnetic Field Optimization, are employed for fine-tuning the PI control parameters to improve the power quality of fuel cells for on-grid applications [22]. Another recent method used to optimize the conventional PI control parameters for a DFIG-WECS application is the grouped Grey Wolf Optimizer (GWO) [23].

Results and Discussions

Case Study 1

Case Study 2

Conclusions