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PV Wind Battery Based DC Microgrid with Neural Network MPPT

PV Wind Battery Based DC Microgrid with Neural Network MPPT

Welcome to LMS Solution! Today, we embark on an exciting journey into the world of microgrids, specifically focusing on a cutting-edge PV (Photovoltaic) and Wind-based DC (Direct Current) microgrid. This remarkable system is controlled by a sophisticated neural network Maximum Power Point Tracking (MPPT) algorithm, ensuring optimal energy utilization. Join us as we explore this simulation model, developed to enhance the efficiency of microgrids by seamlessly integrating wind and solar energy sources.

The Microgrid Components: Our microgrid system comprises three primary components: a Wind Energy Conversion System (WECS), a Photovoltaic (PV) array, and an energy-storing Battery System. Let's break down each element:

Wind Energy Conversion System (WECS):

  • Wind Turbine: The system features a powerful wind turbine capable of generating up to 6.5 kilowatts of power under optimal wind conditions.

  • PMSG (Permanent Magnet Synchronous Generator): This component converts wind energy into electrical energy.

  • Diode Rectifier: Ensures the rectification of the AC power generated by the PMSG.

  • Boost Converter: This vital component, controlled by a neural network MPPT algorithm, optimizes power extraction from the wind turbine.

Photovoltaic (PV) Array:

  • The PV array consists of multiple solar panels, each rated at 250 watts.

  • Maximum Power Point Tracking: We employ the neural network MPPT algorithm to maximize power output, utilizing the PV array's potential efficiently.

Battery Energy System:

  • Battery Rating: The battery system boasts a 240-volt rating with a rated capacity of 48 Ah.

  • Bidirectional Converter: To facilitate bidirectional power flow, we employ a bidirectional converter, ensuring seamless energy transfer between the battery and the microgrid.

Microgrid Operation: Our microgrid is designed to balance power generation and consumption seamlessly. Here's how it works:

Wind and Solar Energy Harvesting:

  • The wind turbine and PV array generate electrical power.

  • The neural network MPPT algorithms optimize power extraction from both sources.

Power Management:

  • The microgrid maintains a DC bus voltage at around 400 volts.

  • The DC load receives a constant 2000-watt power supply, ensuring uninterrupted operation.

Battery Energy Storage:

  • Excess power from wind and PV sources is used to charge the battery.

  • When power generation falls short of demand, the battery supplements the load, ensuring a continuous power supply.

Simulation Results: We conducted simulations to evaluate the microgrid's performance under varying conditions:

Optimal Conditions (1000 W/m² irradiance, 12 m/s wind speed):

  • PV power generation: 6 kW

  • Wind power generation: 5.3 kW

  • Battery in charging mode

  • Constant 2000 W load supplied

Reduced Irradiance (400 W/m² irradiance):

  • PV power generation: 2 kW

  • Wind power generation: 2.2 kW

  • Battery in charging mode initially, then discharging

  • Constant 2000 W load supplied

Low Wind Speed (10 m/s wind speed):

  • Wind power generation: 2.2 kW

  • PV power generation: 0 W

  • Battery in discharging mode

  • Constant 2000 W load supplied

Conclusion: Our PV, Wind, and Battery-based DC microgrid, controlled by a neural network MPPT algorithm, showcases the potential of renewable energy integration. This innovative system seamlessly balances energy generation and consumption, ensuring uninterrupted power supply under various conditions. It's a testament to the future of sustainable and efficient microgrid technology.

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