MATLAB Implementation of PV-Fed Flyback Converter with P&O MPPT Algorithm
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📘 Introduction
This blog explains the MATLAB/Simulink implementation of a photovoltaic (PV) fed flyback converter using the conventional Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The main objective of this model is to extract the maximum available power from a solar PV panel under varying irradiance and temperature conditions and deliver it efficiently to the load through a flyback DC–DC converter.
🌞 PV Array Modeling
In this simulation, a single 250 W solar PV module is considered. Since only one module is used, both the series-connected modules and parallel strings are set to one.
Key PV specifications at Standard Test Conditions (STC):
⚡ Voltage at maximum power point (V_MPP): 30.7 V
🔌 Current at maximum power point (I_MPP): 8.11 A
🔋 Rated power: 250 W
The I–V and P–V characteristics clearly indicate that the peak power point shifts with changes in irradiance and temperature, highlighting the necessity of MPPT control.
🎯 Need for MPPT
Due to the nonlinear characteristics of PV panels, directly connecting the PV array to a load does not ensure maximum power extraction. The operating point depends on the load impedance, which varies with operating conditions.
✔️ To overcome this limitation, an MPPT algorithm is employed to dynamically regulate the converter duty cycle so that the PV array continuously operates at its maximum power point.
🧠 Perturb and Observe (P&O) MPPT Algorithm
In this work, a conventional P&O MPPT algorithm is implemented due to its simplicity and effectiveness.
Algorithm operation steps:
📥 Measure PV voltage and current
🧮 Compute instantaneous power (P = V × I)
🔄 Evaluate change in voltage (ΔV) and power (ΔP)
🔀 Increment or decrement duty cycle based on four logical conditions
🔒 Enforce minimum and maximum duty cycle limits
♻️ Update previous voltage, power, and duty cycle values
This continuous process ensures accurate tracking of the maximum power point under dynamic environmental conditions.
🔧 Flyback Converter Design
The flyback converter serves as the power-conditioning interface between the PV array and the load.
Design specifications include:
🔋 Input voltage (PV): 30.7 V
⚡ Output voltage: 36 V
🌞 Rated power: 250 W
⏱️ Switching frequency: 4 kHz
🔄 Coupled inductor (transformer) turns ratio
🧲 Load resistance
Using standard flyback converter design equations, the following parameters are calculated:
📐 Output filter capacitor
🧲 Primary inductance (L_p)
🧲 Secondary inductance (L_s)
📊 Duty cycle and voltage ripple
These values are then used directly in the Simulink model.
🎛️ PWM Generation and Switch Control
The duty cycle obtained from the P&O MPPT algorithm is fed to a PWM generator, which produces the switching pulses for the MOSFET of the flyback converter.
🔁 By varying the duty cycle:
The effective input impedance seen by the PV array is adjusted
Maximum power extraction is achieved
🧩 Simulation Model Description
The complete Simulink model consists of:
🟩 PV array with voltage and current sensors
🧠 P&O MPPT controller
🎚️ PWM generator
🔧 Flyback converter with coupled inductor
🔋 Resistive load
📊 Measurement blocks for PV-side and load-side parameters
🌤️ Irradiance Variation and System Response
To evaluate MPPT performance, irradiance is varied step-by-step:
☀️ 1000 W/m²
🌥️ 800 W/m²
☁️ 600 W/m²
🌫️ 400 W/m²
As irradiance decreases:
📉 PV current reduces
📐 PV voltage remains close to V_MPP
🔋 Output power follows available PV power
This confirms correct impedance matching between the source and load.
📊 Results and Discussion
The simulation results demonstrate that:
✅ The P&O MPPT algorithm accurately tracks the maximum power point
🔄 The flyback converter efficiently transfers power to the load
⚖️ Load voltage and current adjust automatically with irradiance changes
🛡️ Stable operation is maintained without oscillations
🏁 Conclusion
This blog presented a detailed explanation of the MATLAB implementation of a PV-fed flyback converter using the P&O MPPT algorithm. The results confirm reliable maximum power extraction, proper converter design, and stable system performance under varying irradiance conditions. This model is well suited for academic study, research applications, and low-power renewable energy systems.
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