MATLAB Simulation of Fuzzy MPPT and P&O MPPT Battery Charging Control for Solar Systems
We will explore the comparison between two battery charger controllers for solar systems: the Fuzzy Maximum Power Point Tracking (Fuzzy MPPT) and Perturb and Observe Maximum Power Point Tracking (P&O MPPT). We will break down the MATLAB simulation models, their functioning, and the performance differences between the two controllers in real-time battery charging scenarios.
Introduction to MPPT Controllers
The primary goal of Maximum Power Point Tracking (MPPT) controllers is to extract the maximum power from the solar panels to charge the battery efficiently. Two commonly used MPPT algorithms are:
Fuzzy MPPT: A fuzzy logic-based controller that adapts to various environmental conditions without complex mathematical models.
P&O MPPT: A classical MPPT algorithm that perturbs the operating point of the solar panel and observes the resulting changes in power to converge on the maximum power point.
In this study, we simulate both controllers using MATLAB and compare their performance in terms of power extraction, battery charging, and system stability.
Simulation Model Setup
The simulation models use a 2,000 W solar PV array, which consists of four panels connected in series. Each panel has the following specifications:
Voltage at Maximum Power Point (Vmp): 30.9 V
Open-Circuit Voltage (Voc): 36.6 V
Current at Maximum Power Point (Imp): 8.1 A
Short Circuit Current (Isc): 8.75 A
The power output varies with irradiation levels, ranging from 2 W at low irradiation (200 W/m²) to 2,200 W at full irradiation (1,000 W/m²). The battery used in the simulation has a 48V rating and a capacity of 20Ah.
The primary components involved in the simulation include:
PV Panels
Battery
Buck-Boost Converter (BU Converter)
MPPT Controllers (Fuzzy MPPT and P&O MPPT)
How Fuzzy MPPT Works
In the Fuzzy MPPT controller model, the system uses two main inputs: the PV panel's voltage (Vpv) and current (Ipv). The controller adjusts the duty cycle of the BU converter to extract the maximum power from the PV panels by using fuzzy logic to calculate the optimum duty cycle based on the change in power and voltage.
Key Steps in the Fuzzy MPPT Algorithm:
Measure Voltage and Current: PV panel voltage and current are continuously measured.
Calculate Power Change: The difference in power (P) and voltage (V) is calculated at each time step.
Fuzzy Logic Decision Making: A fuzzy logic system evaluates the change in power and voltage to adjust the duty cycle.
Duty Cycle Adjustment: The controller adjusts the duty cycle of the BU converter to maintain maximum power extraction.
Battery State Monitoring: The battery's voltage is monitored, and charging only occurs if the battery voltage is below its full charge level (52.26V).
This fuzzy logic-based approach ensures smoother and more stable operation under varying solar irradiance conditions.
How P&O MPPT Works
The P&O MPPT model works by periodically adjusting the operating point of the PV panel by perturbing the voltage and observing the resulting power changes. When the system detects that the power has increased, it continues perturbing in that direction; if the power decreases, it reverses the perturbation.
Key Steps in the P&O MPPT Algorithm:
Measure PV Voltage and Current: Similar to the fuzzy model, voltage and current from the PV panel are measured.
Power Perturbation: The system perturbs the voltage and monitors the resulting power.
Duty Cycle Adjustment: Based on the power changes, the duty cycle is adjusted to extract maximum power.
Oscillation Detection: P&O MPPT tends to cause oscillations in power and current, especially during transitions in irradiation conditions.
Though effective, the P&O algorithm can result in higher oscillations and slower convergence to the maximum power point, especially under varying light conditions.
Simulation Results and Comparisons
Fuzzy MPPT Performance:
Smooth Battery Charging: The Fuzzy MPPT controller provides a smooth and consistent battery charging curve with minimal oscillations.
Quick Response to Irradiation Changes: Even when the irradiation changes from 1,000 W/m² to 500 W/m², the Fuzzy MPPT adjusts quickly without causing any instability in the PV voltage, current, or battery charging.
Stable Power Extraction: Power extraction remains stable with less fluctuation in panel output and battery charging during irradiance changes.
P&O MPPT Performance:
Oscillations in Power and Current: The P&O controller exhibits oscillations in both the PV current and power output, particularly at the start-up phase and when irradiance conditions fluctuate.
Slower Convergence: The P&O algorithm reaches its maximum power point more slowly compared to Fuzzy MPPT. It takes about 7.18 seconds to reach the maximum power point, whereas the Fuzzy MPPT reaches it in approximately 1 second.
Instability During Transitions: Irradiation changes lead to significant oscillations in PV voltage and current, as well as transitions in battery current, resulting in less efficient charging.
Conclusion
The MATLAB simulation results clearly highlight the differences between Fuzzy MPPT and P&O MPPT for battery charging in solar systems:
Fuzzy MPPT offers smoother operation, faster convergence to the maximum power point, and better stability during irradiation transitions.
P&O MPPT, while effective, suffers from slower response times and greater oscillations in power and battery charging, especially under dynamic conditions.
For solar battery charging applications, the Fuzzy MPPT algorithm demonstrates superior performance, making it a more reliable choice for systems that experience frequent fluctuations in solar irradiance.
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