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Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC–SMES

  • Preeti Dahiya
  • Veena Sharma
  • R. NareshEmail author
Original Article

Abstract

In this article, disrupted oppositional learned gravitational search algorithm (DOGSA)-optimized sliding mode control (SMC) is proposed for the load frequency regulation problem in deregulated power system including flexible alternating current transmission system devices, namely thyristor-controlled series capacitor (TCSC) and superconducting magnetic energy storage (SMES) without and with doubly fed induction generator (DFIG) wind turbine. Initially, the performance of the proposed control scheme is compared with genetic algorithm-tuned integral controller reported in the literature for two-area interconnected power system with TCSC in tie-line under deregulated environment. Further, the SMES unit is also considered in one of the areas and the system performance is analyzed and found to be better with the combination of optimized SMC and TCSC–SMES for unilateral, bilateral as well as contract violation cases in comparison with system with optimal SMC and TCSC and also with the controller reported in the literature. The potential of the proposed schemes is further analyzed and studied in the presence of nonlinear constraints, namely generation rate constraint, governor deadband and time delay which are present in the real-time power system. From the simulation results, it is deduced that the optimal SMC-generated control signal eliminates the chattering problem in the controller output. Also, the combination of TCSC and SMES improves the transient performance of the system. Thus, the frequency regulation in deregulated power system has been performed using the DOGSA-tuned SMC and also investigated the coordinated action of TCSC–SMES with inclusion of DFIG wind turbine in both the control areas of the system.

Keywords

DFIG wind turbine FACTS devices Gravitational search algorithm Sliding mode control 

Notes

Acknowledgements

This work is supported by Council of Scientific & Industrial Research, New Delhi, India, under Research and Development Project Grant 22(0692)/15/EMR-II.

Compliance with ethical standards

Conflict of interest

Dr. Veena Sharma has received research grants from Council of Scientific & Industrial Research, New Delhi, India.

Research involving human participants and/or animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This paper does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentNational Institute of TechnologyHamirpurIndia

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