⚡ Neural Network MPPT Controlled PV–Wind–Battery System 🌞💨🔋

 MATLAB/Simulink implementation of a Neural Network (NN) based MPPT controlled PV–Wind–Battery system, also referred to as an islanded hybrid AC and DC microgrid. The complete system operates in islanded mode, where renewable sources and energy storage collectively supply both DC and AC loads without grid support.

Neural network mppt controlled pv wind battery system

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🌐 System Concept: Islanded Hybrid Microgrid

This hybrid microgrid integrates:

• ☀️ Solar PV System

• 💨 Wind Energy Conversion System (WECS)

• 🔋 Battery Energy Storage System (BESS)

• 🔌 DC and AC Loads

• 🧠 Neural Network based MPPT controllers

Both the PV system and wind system are controlled using neural network MPPT, ensuring maximum power extraction under rapidly changing irradiance and wind speed conditions.

🧠 Neural Network MPPT – Key Idea

The neural network MPPT controller receives four inputs:

• ⚡ Vpv, Ipv → PV voltage and current

• ⚡ Vrc, Irc → Rectifier voltage and current from WECS

Based on these inputs, the neural network predicts the optimal duty cycle required for maximum power extraction. The generated duty cycle is then applied to the corresponding boost converters through PWM control.

💨 Wind Energy Conversion System (WECS)

The wind subsystem consists of:

• 🌬️ Wind Turbine rated at 2.9 kW

• ⚙️ Permanent Magnet Synchronous Generator (PMSG)

• 🔄 AC–DC Rectifier

• 🔼 Boost Converter with NN-based MPPT

Operation

• The wind turbine drives the PMSG

• The generated AC power is rectified to DC

• The DC output is boosted using a neural-network-controlled boost converter

• The controller uses Vrc and Irc to generate PWM pulses

• Maximum wind power is extracted and fed to the 400 V DC bus

☀️ Solar PV System

The solar subsystem is designed as follows:

• 🔆 PV Rating: 2 kW

• 🔋 Panel Rating: 250 W each

• 🔗 Series Modules per String: 8

• 🔁 Parallel Strings: 1

The PV array is connected to a boost converter, which is also controlled using a neural network MPPT algorithm.

Operation

• PV voltage and current (Vpv, Ipv) are fed to the NN

• The NN determines the optimal duty cycle

• PWM pulses drive the IGBT of the boost converter

• Maximum PV power is extracted and delivered to the DC bus

🔋 Battery Energy Storage System (BESS)

To maintain a stable DC bus voltage, a battery system is integrated:

• 🔋 Battery bank: 22 batteries

• 🔄 Connected via a bidirectional DC–DC converter

Control Strategy

• DC bus voltage is measured and compared with the reference (400 V)

• The error is processed through a PI controller

• The PI controller generates duty cycle commands

• PWM pulses control the bidirectional converter

• The battery operates in charging or discharging mode to stabilize the DC bus

🔌 AC Load Integration

• DC bus feeds an inverter with LCL filter

• Two AC loads are connected:

  • 🔹 Load 1: 1000 W (connected initially)

  • 🔹 Load 2: 1400 W (connected after 2 seconds)

The inverter ensures:

• Balanced AC voltage and current

• Stable power delivery under load variations

🌦️ Dynamic Operating Conditions

To test system robustness, environmental conditions are varied:

🌬️ Wind Speed

• Initially: 12 m/s

• After 2 seconds: 1.2 m/s

☀️ Solar Irradiance (every 0.3 s)

• 1000 W/m² → 500 W/m² → 10 W/m² → 500 W/m² → 1000 W/m²

📊 Simulation Results & Observations

☀️ PV Performance

• At 1000 W/m² → PV power ≈ 2000 W

• At 500 W/m² → PV power ≈ 990 W

• At 10 W/m² → PV power ≈ 0 W

• PV voltage and current adapt dynamically using NN MPPT

💨 Wind Performance

• At 12 m/s → Wind power ≈ 2.9 kW

• At 1.2 m/s → Wind power drops significantly

🔋 Battery Behavior

• Battery initially charges with higher current

• Charging current varies based on renewable power availability

• After wind speed reduction, battery current reduces close to zero

⚡ DC Bus Stability

• DC bus voltage is consistently maintained around 400 V

• Confirms effective battery voltage regulation

🔌 AC Load Performance

• 0–2 s → Load power ≈ 1000 W

• After 2 s → Total load ≈ 2400 W

• Inverter voltage, current, and power remain stable

⚖️ Power Balance Validation

The results clearly demonstrate that:

• Power balance is maintained between sources and loads

• Neural network MPPT ensures maximum power extraction

• Battery compensates power mismatch

• System remains stable despite irradiance, wind speed, and load changes