Grid Connected Solar PV with Battery Energy Storage and ANN MPPT in MATLAB

This project demonstrates the modeling and simulation of a grid-connected photovoltaic energy system integrated with battery energy storage and an Artificial Neural Network (ANN)-based Maximum Power Point Tracking controller. The ANN MPPT receives normalized PV voltage and PV current as inputs and generates the duty cycle command for the PV boost converter.

The PV source supplies a common DC bus through a boost converter. A battery energy storage system is connected through a bidirectional DC-DC converter to support DC-bus voltage regulation and handle charging/discharging according to the power balance. A single-phase full-bridge inverter connects the DC system to the utility grid. Grid synchronization, current-reference generation, current regulation, and PWM generation are included in the inverter-control section.

Main System Features

• Solar PV array modeling

• ANN-based MPPT

• ANN inputs: PV voltage Vpv​ and PV current Ipv​, in per-unit

• ANN output: boost-converter duty cycle

• PV boost DC-DC converter

• Approximately 400 V DC bus

• DC load connected to the common DC link

• Battery Energy Storage System

• Battery voltage approximately 260 V

• Bidirectional buck-boost DC-DC converter

• DC-bus voltage controller

• Single-phase full-bridge grid-connected inverter

• Grid synchronization/PLL

• Current-reference generation

• Current PI controller

• PWM switching control

• PV, battery, DC-load, DC-bus, inverter and grid measurements

ANN MPPT Dataset

The included ANN MPPT dataset contains 6,000 operating samples.

The dataset also contains normalized Vpv-pu and Ipv-pu variables for ANN training.

ANN Training Results

The package includes ANN training and validation plots. The recorded training results show:

• Best validation MSE: 0.0015071

• Best validation epoch: 8

• Training regression: R = 0.89068

• Validation regression: R = 0.88636

• Testing regression: R = 0.89337

• Overall regression: R = 0.89045

Simulation Operating Results

The simulation results demonstrate variable PV generation and corresponding battery power balancing.

The PV power changes approximately through:

2 kW → 1 kW → near 0 W → 1 kW → 2 kW

while the DC load remains approximately 1 kW. The bidirectional battery converter responds by alternating between charging and discharging. The DC-bus controller maintains the common bus close to 400 V despite changes in PV generation.

The battery voltage remains approximately 255-260 V, while battery current changes polarity according to power availability. The SOC plot confirms both charging and discharging operation.

The package also demonstrates grid-connected inverter voltage/current waveforms and DC-bus current balance.

Included Results

The uploaded package currently contains:

• Complete Simulink system 

• 10-page PDF showing the Simulink architecture and controller subsystems

• ANN MPPT dataset - 6,000 samples

• ANN MPPT prediction/result dataset

• ANN dataset 3-D map

• Dataset sample plots

• ANN training-performance plot

• Training/validation/testing regression plots

• Error histogram

• ANN target-versus-prediction plots

• Prediction-error plot

• Test-data comparison

• ANN architecture figure

• PV voltage/current/power results

• Battery voltage/current results

• Battery SOC result

• DC-bus voltage/load-current/load-power results

• DC-bus current-balancing results

• Inverter and grid voltage/current results

Applications

This model is suitable for:

• B.Tech/B.E. projects

• M.Tech/M.E. projects

• Ph.D. research demonstrations

• Renewable-energy laboratory studies

• ANN-based MPPT research

• Grid-connected PV studies

• Battery energy-management studies

• Intelligent power-electronics control research

• Research-paper simulation validation