Title: Integrating a superconducting magnetic energy storage system for intelligent control of LFC characteristic in multi-area power system

Authors: S. Zahid Nabi Dar; Mairaj-ud-din Mufti

Addresses: Department of Electrical Engineering, NIT, Srinagar, India ' Department of Electrical Engineering, NIT, Srinagar, India

Abstract: This work exhibits use of adaptive neural control configured genetically tuned PI controller based superconducting magnetic energy storage (SMES) system for enhanced load frequency control (LFC) operation for a three area power system. The suggested scheme for the SMES control is a distinct one, as it overcomes the shortcomings in the contemporary SMES control schemes with automatic adjustment of converter duty ratio. Control in each area is accomplished via a neural estimator and a neural controller online simultaneously. The neural estimator selects the control area dynamics over a functioning zone, this feature leads to pure adaptive control, with modified gain settings obtained by integral square error criterion (ISE). Simulation studies carried out on MATLAB are presented for the power system with steam reheat constraint and governor dead band nonlinearity, after carrying out necessary modelling exercise, which portrays a reduction of 65% and 50% overshoots in frequency and tie-power oscillations respectively.

Keywords: superconducting magnetic energy storage; SMES; load frequency control; LFC; area control error; ACE; new-area control error; NACE; power conditioning system PCS; genetic algorithm; GA.

DOI: 10.1504/IJIED.2018.091803

International Journal of Industrial Electronics and Drives, 2018 Vol.4 No.2, pp.96 - 105

Received: 17 Aug 2017
Accepted: 18 Nov 2017
Published online: 16 May 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article