Neural Network MPPT Control for PV–Wind–Battery System
- lms editor
- 2 days ago
- 4 min read
👋 Introduction
Welcome to LMS Solution. In this blog, we explain the neural network–based MPPT control of a PV–wind–battery system, which operates as an islanded hybrid AC/DC microgrid. In islanded mode, the system is completely independent of the utility grid, and all power generation, storage, and load balancing are managed internally using intelligent control strategies.
The key feature of this model is the use of artificial neural networks (ANNs) to extract maximum power from both solar PV and wind energy conversion systems, while maintaining DC bus voltage stability with battery energy storage.
🧩 Overall System Architecture
The proposed hybrid microgrid consists of the following main subsystems:
🌬️ Wind Energy Conversion System (WECS)
☀️ Solar PV System
🔋 Battery Energy Storage System (BESS)
🔁 DC Bus (400 V)
🔌 AC Load via Inverter and LCL Filter
🧠 Neural Network–based MPPT Controllers
All sources are interconnected through a common DC bus, enabling flexible power sharing and reliable islanded operation.
🌬️ Wind Energy Conversion System (WECS)
The wind energy subsystem includes:
🌪️ Wind turbine rated at 2.9 kW
⚙️ Permanent Magnet Synchronous Generator (PMSG)
🔄 Three-phase diode rectifier (AC–DC conversion)
⬆️ DC–DC boost converter
The rectifier converts the PMSG AC output into DC. This DC power is then processed through a boost converter, which is controlled using a neural network MPPT controller.
📥 Neural Network Inputs for Wind MPPT:
Rectifier voltage (VRC)
Rectifier current (IRC)
📤 Neural Network Output:
Duty cycle for boost converter
The duty cycle is fed to a PWM generator, which produces switching pulses for the MOSFET in the boost converter, ensuring maximum power extraction from the wind turbine under varying wind speeds.
☀️ Solar PV System
The solar subsystem is designed as follows:
🔋 PV array rated at 2 kW
🔢 8 series-connected modules per string
🔗 1 parallel string
📐 Each PV module rated at 250 W
The PV array is connected to a DC–DC boost converter, whose duty cycle is determined by a separate neural network MPPT controller.
📥 Neural Network Inputs for PV MPPT:
PV voltage (Vpv)
PV current (Ipv)
📤 Neural Network Output:
Duty cycle for PV boost converter
This intelligent control ensures that the PV system continuously operates at its maximum power point, even under rapid irradiance changes.
🔋 Battery Energy Storage System (BESS)
To maintain system stability and DC bus voltage regulation, a battery energy storage system is integrated:
🔋 Battery bank consisting of 22 batteries
🔁 Bidirectional DC–DC converter
⚙️ Control Strategy:
DC bus voltage is continuously measured
Compared with reference value (400 V)
Error processed through a PI controller
PI output generates duty cycle for bidirectional converter
🔄 Depending on system conditions:
Battery charges when surplus power is available
Battery discharges when generation is insufficient
This ensures uninterrupted power supply and stable DC bus operation.
⚡ DC Bus and Voltage Regulation
🔌 Common DC bus voltage maintained at 400 V
Acts as the central power exchange node
Receives power from PV, wind, and battery
Supplies power to DC and AC loads
The DC bus stability confirms effective coordination between renewable sources and energy storage.
🔌 AC Load and Inverter Control
The AC load is supplied through:
🔄 Full-bridge inverter
🎚️ LCL filter for harmonic mitigation
📊 Load Configuration:
Initial load: 1000 W
Additional load: 1400 W (connected after 2 seconds)
The inverter ensures:
Stable AC voltage
Smooth current waveform
Proper power sharing under load variation
🌦️ Operating Conditions (Test Scenarios)
☀️ Irradiance Variation
Irradiance is varied every 0.3 seconds
Sequence: 1000 → 500 → 10 → 500 → 1000 W/m²
🌬️ Wind Speed Variation
Initial wind speed: 12 m/s
Reduced to 1.2 m/s after 2 seconds
These variations are applied to test the robustness of the neural network MPPT controllers.
📊 Simulation Results and Observations
☀️ PV System Performance
PV voltage maintained around 250 V
PV current varies with irradiance
Power extracted:
~1050 W at 1000 W/m²
~990 W at 500 W/m²
0 W at 10 W/m²
~2000 W when irradiance returns to 1000 W/m²
✔️ Confirms effective neural network MPPT operation.
🌬️ Wind System Performance
Wind power initially ≈ 2.9 kW at 12 m/s
Drops to ≈ 2 kW when wind speed reduces to 1.2 m/s
✔️ Neural network MPPT ensures optimal energy extraction across wind speed changes.
🔋 Battery Behavior
Battery charging current varies dynamically
High charging current during surplus generation
Reduced current when wind speed drops
✔️ Demonstrates proper energy balancing and storage management.
⚡ DC Bus and Load Performance
DC bus voltage maintained close to 400 V throughout
DC and AC load power accurately tracked
Total AC load increases from 1000 W to 2400 W after 2 seconds
Source–load power balance maintained at all times
✔️ Confirms stable islanded microgrid operation.
🧠 Role of Neural Network MPPT
Handles nonlinear characteristics of PV and wind systems
Responds faster than conventional MPPT methods
Reduces oscillations around the maximum power point
Improves dynamic performance under rapid environmental changes
🏁 Conclusion
This blog presented a detailed explanation of a neural network MPPT–controlled PV–wind–battery hybrid AC/DC microgrid operating in islanded mode. Simulation results clearly demonstrate:
Reliable maximum power extraction from PV and wind sources
Effective DC bus voltage regulation using battery storage
Stable AC and DC load supply under varying irradiance, wind speed, and load conditions
The proposed system is highly suitable for remote microgrids, standalone renewable systems, and advanced research in intelligent energy management.
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