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Battery management in IoT hybrid grid system using deep learning algorithms based on crowd sensing and micro climatic

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  • 1Department of Electrical and Electronics Engineering, Anna University, Chennai, Tamilnadu, India. pavisshsrini@gmail.com.

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Summary

This study introduces an IoT-enabled Hybrid Grid System (HGS) using deep learning for residential power management. The system optimizes energy, reduces grid demand, and enhances stability for domestic loads.

Keywords:
Anti-windup proportional integral (AWPI) controlEnergy storage systemIPWSSuper capacitorZero export inverter

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Artificial Intelligence in Energy

Background:

  • Residential Hybrid Grid Systems (HGS) face challenges managing diverse domestic loads with varying duty cycles.
  • Integrating renewable sources like photovoltaic (PV) and wind power systems (PWS) requires intelligent control for stability and efficiency.
  • Existing systems struggle with grid interaction, energy export, and optimal battery/super-capacitor management.

Purpose of the Study:

  • To propose an Internet of Things (IoT) enabled PWS (IPWS) for residential HGS.
  • To implement advanced deep learning algorithms for optimizing energy flow, grid interaction, and stability.
  • To evaluate the performance of different deep learning controllers and energy storage configurations.

Main Methods:

  • Developed an IPWS integrating PV, wind, Lithium-Phosphate battery, and super-capacitor with IoT capabilities.
  • Utilized crowd-sensing for microclimatic data acquisition to tune zero-export converters and Battery Management System (BMS).
  • Implemented and compared hybrid deep learning algorithms (SCO-LSTM, JO-LSTM, HBO-LSTM) for the controller and BMS.

Main Results:

  • The IPWS with zero-export converters reduces electricity demand on the grid and stores excess energy in super-capacitors.
  • IPWS with JO-LSTM/HBO-LSTM based BMS effectively eliminates output power fluctuations, enhancing transient stability (TS) and damping ratio (DR).
  • IPWS using JO-LSTM controller and super-capacitor improved power factor by 29%, reduced harmonics by 14%, increased DR by 6%, and achieved low TS for residential loads.

Conclusions:

  • The proposed IPWS offers a robust solution for managing domestic loads in residential HGS.
  • Deep learning controllers significantly improve system stability, power quality, and energy efficiency.
  • The integration of IoT and advanced algorithms provides a scalable and intelligent approach to renewable energy management in homes.