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The progression towards Internet of Things (IoT) has opened new avenues for optimizing energy management in industrial applications. IoT systems, with their ability to collect, analyse, and transmit real-time data, offer unprecedented opportunities to improve efficiency and reliability of energy systems. This paper presents an IoT-based smart grid system specifically designed for industrial applications, incorporating solar Photovoltaic (PV) and wind energy sources into a consistent microgrid framework. The system employs High Gain Improved Luo converter to improve the systems voltage output. Additionally, the system uses Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT controller, to continuously optimize the energy harvested from solar module. The wind energy component, represented by a Doubly Fed Induction Generator (DFIG), complementing solar power, ensuring microgrid remains functional even when solar energy is insufficient. Moreover, to enable practical control and monitoring, the system is equipped with sensors that collect and transmit critical operational data to a centralized IoT platform. Data collected from grid is visualized in Adafruit IoT platform, where it is analysed to provide insights into grid performance, energy efficiency, and potential maintenance needs. Moreover, the proposed system’s performance is confirmed using MATLAB simulations, revealing converter efficiency of 96.21%, demonstrating its effectiveness in optimizing energy management and enhancing grid reliability.

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Smart IoT-Enabled Monitoring and Machine Learning MPPT for Solar-Wind Hybrid Microgrid Systems in Industrial Applications

  • 会议论文

  • 发表日期：

    2026年

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  • R. Sankar,
  • K. S. Kavin,
  • P. Subha Karuvelam,
  • D. Karthikeyan,
  • V. Pujari

摘要

The progression towards Internet of Things (IoT) has opened new avenues for optimizing energy management in industrial applications. IoT systems, with their ability to collect, analyse, and transmit real-time data, offer unprecedented opportunities to improve efficiency and reliability of energy systems. This paper presents an IoT-based smart grid system specifically designed for industrial applications, incorporating solar Photovoltaic (PV) and wind energy sources into a consistent microgrid framework. The system employs High Gain Improved Luo converter to improve the systems voltage output. Additionally, the system uses Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT controller, to continuously optimize the energy harvested from solar module. The wind energy component, represented by a Doubly Fed Induction Generator (DFIG), complementing solar power, ensuring microgrid remains functional even when solar energy is insufficient. Moreover, to enable practical control and monitoring, the system is equipped with sensors that collect and transmit critical operational data to a centralized IoT platform. Data collected from grid is visualized in Adafruit IoT platform, where it is analysed to provide insights into grid performance, energy efficiency, and potential maintenance needs. Moreover, the proposed system’s performance is confirmed using MATLAB simulations, revealing converter efficiency of 96.21%, demonstrating its effectiveness in optimizing energy management and enhancing grid reliability.