MATLAB Implementation of PID Tuning in Solar PV System with MPPT

Here, we delve into the practical implementation of PID controller tuning in a solar photovoltaic (PV) system using MATLAB. The system integrates a boost converter and an MPPT (Maximum Power Point Tracking) algorithm, specifically the Incremental Conductance (InC) method. Let’s explore how the components are designed, modeled, and tuned to ensure optimal energy extraction from the solar panel.

Designing the Boost Converter for PV System

The PV system is designed with the following specifications:

Open Circuit Voltage: 70V
Voltage at Maximum Power Point (Vmp): 60V
Short Circuit Current: 5.1 A
Current at Maximum Power Point: 4.2 A

To step up the voltage from 60V (input) to 120V (output), a boost converter is used. The converter’s design relies on essential parameters such as switching frequency (set to 10 kHz), ripple values, and inductor-capacitor sizing.

The inductance and capacitance are calculated using ripple equations:

Ripple inductor current and capacitor voltage
Output load resistance is determined afterward
An input capacitor of around 100 µF is used to stabilize the input

Implementing MPPT using Incremental Conductance

The Incremental Conductance (InC) MPPT algorithm is chosen for its efficiency in tracking the maximum power point under changing conditions. The algorithm uses:

Measured PV voltage and current
Calculations for changes in voltage (ΔV) and current (ΔI)

Based on the signs of ΔP/ΔV (where P = VI), the algorithm adjusts the reference voltage (Vref). Conditions handled include:

If ΔV = 0 and ΔI = 0, system is at MPP
If ΔP/ΔV > 0, increase voltage
If ΔP/ΔV < 0, decrease voltage

The Vref is kept within defined limits (e.g., 55V to 62V), and system variables (previous and current voltage/power/current) are updated iteratively.

Integrating the PID Controller

The reference voltage generated from the MPPT algorithm is compared with the actual output voltage. The PID controller processes the error to adjust the duty cycle, thereby controlling the output voltage of the boost converter.

A default PID setup is used initially, which then undergoes tuning based on system response and performance.

Tuning the PID Controller in MATLAB

The PID tuning process includes:

Fixing load and irradiance conditions
Using MATLAB’s Plant Identification tool to simulate and collect I/O data
Setting parameters such as input offset and step values
Estimating the plant model based on the data

The tool identifies the system’s transfer function and generates optimal PID parameters (Kp, Ki, Kd). This process ensures accurate dynamic response and minimal delay.

After simulation, results are compared using actual vs. tuned response curves. The PID values are fine-tuned for better transient and steady-state performance.

Exploring Alternative Plant Models

To validate flexibility, a second model using a one-pole transfer function is tested. Using the same estimation approach:

A new plant is identified
Parameters are auto-estimated
PID values are updated accordingly

This method allows multiple model configurations and reinforces the robustness of the tuning methodology.

Testing with Variable Conditions

To further validate the controller’s adaptability:

Irradiance levels are varied every 2 seconds
Load resistance is changed every 3 seconds

Despite these variations, the system consistently maintains maximum power point tracking. Metrics such as PV voltage/current, boost converter output, and overall system power are monitored to confirm performance.

Final Observations and Results

After extensive tuning and testing:

Optimal PID values such as Kp ≈ 2389 and R ≈ 4.78 are achieved
System effectively tracks MPP under both steady and dynamic conditions
MATLAB tools provide a powerful framework for control system design and tuning in renewable energy applications

Conclusion

This implementation demonstrates the practical application of PID tuning in a solar PV system with MPPT using MATLAB. From hardware modeling to real-time tuning and simulation, the workflow highlights how engineers can optimize power extraction and system response. Whether working with fixed or varying environmental conditions, this method provides reliable performance and flexibility.