Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

S. M. Muyeen, Hany M. Hasanien, Ahmed Al-Durra

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC-DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system.

Original languageBritish English
Pages (from-to)412-420
Number of pages9
JournalEnergy Conversion and Management
Volume78
DOIs
StatePublished - 2014

Keywords

  • DC-DC converter
  • Superconducting magnetic energy storage (SMES) system
  • Transient stability
  • Voltage source converter (VSC)
  • Wind energy

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