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Performance analysis of DFIG support microgrid using GA optimized restricted Boltzmann Machine algorithm.

Rajeswari Bhol1, Sarat Chandra Swain1, Ritesh Dash2

  • 1School of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, India.

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|May 21, 2024
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Summary

This study demonstrates an efficient control system for Static Synchronous Compensator (STATCOM) coordination in a microgrid with Doubly-Fed Induction Generators (DFIGs). The proposed method enhances grid stability and power quality by optimizing STATCOM performance.

Keywords:
AlgorithmBoltzmann Machine AlgorithmGAPSOPSO-LSTMSearch space

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Control Systems

Background:

  • Microgrids require robust voltage and reactive power regulation, especially with intermittent renewable sources like wind energy.
  • Static Synchronous Compensators (STATCOMs) are vital for mitigating power quality issues such as voltage fluctuations and reactive power imbalances.
  • Effective integration of STATCOMs necessitates coordination with existing controllers, particularly the Doubly-Fed Induction Generator (DFIG) controller in microgrids.

Purpose of the Study:

  • To develop and validate an efficient control algorithm for coordinating STATCOM with DFIG controllers in a deregulated microgrid.
  • To ensure high-end stability and independent control within the microgrid system.
  • To demonstrate the effectiveness of a proposed control system using Restricted Boltzmann Machines (RBM) for STATCOM management.

Main Methods:

  • Development of a Simulink model for a DFIG-based microgrid integrated with STATCOM.
  • Utilization of a Restricted Boltzmann Machine (RBM) for the STATCOM control system.
  • Employing a Genetic Algorithm (GA) to calibrate the RBM for optimal hyperparameter determination.
  • Simulating and analyzing the coordinated control strategy under various microgrid operating conditions.

Main Results:

  • The proposed RBM-based control system effectively manages STATCOM operations within the DFIG-based microgrid.
  • The coordinated control strategy successfully maintained system stability and improved power quality.
  • GA-based RBM calibration proved efficient in determining optimal hyperparameters, reducing computational load.

Conclusions:

  • The developed control system offers an efficient solution for STATCOM coordination in microgrids, enhancing stability and power quality.
  • The integration of RBM, optimized by GA, provides a streamlined approach to STATCOM control, particularly beneficial for large datasets and time constraints.
  • The research validates the proposed method's effectiveness in managing microgrid operations with renewable energy sources.