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<p>The permanent magnet synchronous generator (PMSG) integrated with flywheel energy storage system (FESS) increases the efficiency level and operational reliability of grid-connected wind generators through its effective power storage and stabilization capabilities. A hybrid method aims to assess economically the PMSG connected to FESS for grid-connected wind generation purposes. This paper presents a novel hybrid approach combining the coati optimization algorithm (COA) with the triple-memristor Hopfield neural network (TMHNN) for the economic performance of a PMSG integrated with a FESS for grid-connected wind generation. The aim is to improve the economic performance of the PMSG integrated with a FESS by lowering the levelized cost of energy (LCOE). The COA is utilized to optimize the operational parameters of the PMSG and the FESS, ensuring efficient energy management (EM) and storage. The TMHNN is utilized to forecast energy generation and demand trends, enabling optimized scheduling and resource allocation for enhanced EM. A MATLAB platform implements the proposed method for testing with the existing approaches including wild horse optimizer (WHO) and their counterparts gases Brownian motion optimization (GBMO) and artificial bee colony optimization (ABCO) and Archimedes optimization algorithm (AOA) and particle swarm optimization (PSO). The research introduces a complete framework which decreases LCOE to $0.28/kWh alongside improving wind energy system operational stability and efficiency. Unlike traditional optimization methods, which often focus on single aspects of EM, the COA-TMHNN technique integrates advanced forecasting capabilities with proactive EM strategies, ensuring better adaptability to fluctuating energy demands and generation patterns.</p>

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Economic analysis of grid-connected wind generators with permanent magnet synchronous generator and flywheel energy storage using coati optimization algorithm and triple-memristor Hopfield neural network

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • Shanmugasundaram Venkatarajan,
  • Panneer Selvam Manickam,
  • Sumana Sreenivasa Rao,
  • Jyoti Prasad Patra

摘要

The permanent magnet synchronous generator (PMSG) integrated with flywheel energy storage system (FESS) increases the efficiency level and operational reliability of grid-connected wind generators through its effective power storage and stabilization capabilities. A hybrid method aims to assess economically the PMSG connected to FESS for grid-connected wind generation purposes. This paper presents a novel hybrid approach combining the coati optimization algorithm (COA) with the triple-memristor Hopfield neural network (TMHNN) for the economic performance of a PMSG integrated with a FESS for grid-connected wind generation. The aim is to improve the economic performance of the PMSG integrated with a FESS by lowering the levelized cost of energy (LCOE). The COA is utilized to optimize the operational parameters of the PMSG and the FESS, ensuring efficient energy management (EM) and storage. The TMHNN is utilized to forecast energy generation and demand trends, enabling optimized scheduling and resource allocation for enhanced EM. A MATLAB platform implements the proposed method for testing with the existing approaches including wild horse optimizer (WHO) and their counterparts gases Brownian motion optimization (GBMO) and artificial bee colony optimization (ABCO) and Archimedes optimization algorithm (AOA) and particle swarm optimization (PSO). The research introduces a complete framework which decreases LCOE to $0.28/kWh alongside improving wind energy system operational stability and efficiency. Unlike traditional optimization methods, which often focus on single aspects of EM, the COA-TMHNN technique integrates advanced forecasting capabilities with proactive EM strategies, ensuring better adaptability to fluctuating energy demands and generation patterns.