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<p>The rapid evolution of Smart Grid (SG) technologies necessitates robust control and monitoring systems. This study introduces an effective SG energy monitoring system utilizing Type 2 Fuzzy Logic Controller (T2FLC), enhanced with the Mayfly Optimization Algorithm (MOA). The MOA plays a crucial role, drawing inspiration from the natural mating behavior of mayflies to perform an efficient search space exploration, thus optimizing the T2FLC parameters including membership functions and rule weights. The system utilizes a Wireless Sensor Network (WSN) at its core for real-time data acquisition of key electrical parameters. The system comprehensively manages both Photovoltaic (PV) systems and Wind Energy Conversion Systems (WECS), ensuring stable power output using the innovative Mayfly Algorithm optimized Type 2 Fuzzy Logic Controller (MF-T2FLC). Its primary function is to compute and generate reference power for the associated DC-DC and AC-DC converters connected to the PV system and WECS, respectively. The efficacy of the MF-T2FLC is thoroughly verified through both laboratory prototype implementations and MATLAB simulations, particularly under variable environmental conditions. The MF-T2FLC showcases remarkable performance through improvements in settling times and steady-state errors, with zero overshoot, outperforming conventional controllers. Moreover, the real-time energy monitoring system is instrumental in enhancing SG performance, contributing to power delivery optimization and resource utilization.</p>

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Enhanced energy management in smart grids through type-2 fuzzy logic, and the Mayfly algorithm

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • P. Saranya,
  • R. Rajesh,
  • H. Vennila

摘要

The rapid evolution of Smart Grid (SG) technologies necessitates robust control and monitoring systems. This study introduces an effective SG energy monitoring system utilizing Type 2 Fuzzy Logic Controller (T2FLC), enhanced with the Mayfly Optimization Algorithm (MOA). The MOA plays a crucial role, drawing inspiration from the natural mating behavior of mayflies to perform an efficient search space exploration, thus optimizing the T2FLC parameters including membership functions and rule weights. The system utilizes a Wireless Sensor Network (WSN) at its core for real-time data acquisition of key electrical parameters. The system comprehensively manages both Photovoltaic (PV) systems and Wind Energy Conversion Systems (WECS), ensuring stable power output using the innovative Mayfly Algorithm optimized Type 2 Fuzzy Logic Controller (MF-T2FLC). Its primary function is to compute and generate reference power for the associated DC-DC and AC-DC converters connected to the PV system and WECS, respectively. The efficacy of the MF-T2FLC is thoroughly verified through both laboratory prototype implementations and MATLAB simulations, particularly under variable environmental conditions. The MF-T2FLC showcases remarkable performance through improvements in settling times and steady-state errors, with zero overshoot, outperforming conventional controllers. Moreover, the real-time energy monitoring system is instrumental in enhancing SG performance, contributing to power delivery optimization and resource utilization.