ANN MPPT for grid Connected PV wind battery system
Introduction:
Welcome, viewers, to LMS Solutions! In today's discussion, we will explore the intricacies of a neural network MPPT-controlled grid-connected PV wind battery system, also known as a hybrid DC and AC microgrid. This advanced system is designed to efficiently manage power flow and optimize energy generation from renewable sources.
System Components:
Wind Energy Conversion System:
PMSG (Permanent Magnet Synchronous Generator)
Rectifier to convert AC to DC
Boost converter for maximum power point tracking (MPPT)
Solar PV System:
PV panels arranged in series
Boost converter for MPPT using neural network control
Battery Energy Storage System:
Bi-directional converter for maintaining the DC bus voltage at 400 volts
Voltage control method to regulate the battery charging and discharging
DC Load:
Draws power from both PV and battery systems
Inverter:
Connected to the grid via a low-pass filter
Current control concept for controlling power flow
Neural network-controlled MPPT for solar PV and wind energy systems
Control Strategies:
Neural Network MPPT (Maximum Power Point Tracking):
Wind Energy System: Receives rectifier voltage and current as inputs to generate duty cycle for the boost converter.
Solar PV System: Takes PV voltage and current as inputs to optimize power extraction.
Voltage Control Method:
Regulates the DC bus voltage at 400 volts for stable operation.
Current Control Concept:
Determines reference current based on battery state of charge (SOC) and PV power.
Controls the inverter to manage power flow between the microgrid and the grid.
Simulation and Analysis: The simulation involves varying wind speed, irradiation conditions, and SOC to observe the system's response. The key parameters monitored include PV and wind power, battery SOC, DC bus voltage, inverter current, and power flow to the grid.
Simulation Results:
PV and Wind Power Variation:
Efficient MPPT control ensures maximum power extraction from both solar and wind sources.
Battery Charging and Discharging:
Bi-directional converter maintains a stable SOC based on the power balance in the system.
DC Bus Voltage Regulation:
Voltage control method keeps the DC bus voltage constant at 400 volts.
Inverter Operation:
Current control concept ensures smooth power flow, and neural network MPPT adapts to varying conditions.
Conclusion: The neural network MPPT-controlled hybrid DC and AC microgrid effectively integrates renewable energy sources, energy storage, and grid connectivity. The system demonstrates robust performance under changing environmental conditions, showcasing the potential for reliable and sustainable power generation.