Academics have developed a maximum power point tracking (MPPT) algorithm for PV systems that combines incremental conductance with fuzzy logic control. The inputs used are the sum of conductance and incremental conductance and its rate of change. It had an average efficiency of 97.7%.
Research led by scientists at Morocco’s Cadi Ayyad University of Marrakech has proposed a new MPPT algorithm for PV systems under fluctuating conditions. Their method combines incremental conductance (InC) with fuzzy logic control (FLC), using the sum of conductance and incremental conductance (SInC) and its rate of change (CSI) as input variables.
“This work contributes to the ongoing efforts to improve the efficiency and adaptability of PV systems, offering a robust solution for maximizing power extraction under diverse and dynamic operating conditions with a lower cost,” said the team. “By combining the strengths of incremental conductance and fuzzy logic control, our proposed approach addresses the limitations of traditional MPPT methods, providing a significant advancement in the field of renewable energy systems.”
In the proposed system, InC is an MPPT method that uses conductance and its change, while FLC applies rule-based logic rather than mathematical modeling. The rule-based logic in this model is based on the values of SInC and CSI.
“The rules enhance MPP tracking under low irradiance conditions,” the team explained. “They also allow for fine-tuning all the membership function boundaries, leading to improved overall system performance.”
The new method was tested in a MATLAB/Simulink simulation of a 210 W PV panel with a DC-DC boost converter. The converter was controlled by a 10 kHz pulse width modulation (PWM) signal and connected to a 50 Ω resistive load. The method was compared with three competing MPPT algorithms. The first used the perturb and observe (P&O) method with FLC, taking the slope of the power-voltage curve and power variation as inputs. The second also combined P&O with FLC but used the power-voltage slope and the change in that slope as inputs. The third was InC-FLC, which used only SInC as input.
All four MPPT techniques were tested under dynamic irradiance conditions, with temperature fixed at 25 C. Irradiance increased every 0.2 seconds from 200 to 1,000 W/m². They were also tested under dynamic load conditions, with irradiance fixed at either 500 W/m² or 1,000 W/m². In those cases, the load changed from 50 Ω to 20 Ω at 0.2 seconds, then from 20 Ω to 35 Ω at 0.4 seconds.
“The proposed approach using SInC and CSI as fuzzy inputs achieves the best performance, with an average efficiency of 97.7%, a convergence time of 53 ms, and an RMS of 97.8%,” said the scientists. “This method maintains stable electrical quantities across varying irradiance levels and under load variations, effectively eliminating oscillations and ensuring reliable operation under dynamic conditions.”
The researchers noted that during simulations, the proposed method showed a slight delay (under 5 milliseconds) in reacting to sudden load changes. While this had little effect on efficiency or stability, they said it points to the need for refining the algorithm’s responsiveness. They concluded that, overall, the method’s low root mean square error and high performance metrics demonstrate its robustness and potential for real-world PV systems under dynamic conditions.
The scientists presented their results in “Hybrid fuzzy logic approach for enhanced MPPT control in PV systems,” which was recently published in Scientific Reports. Scientists from Morocco’s Cadi Ayyad University of Marrakech, Mohammed First University, Sidi Mohamed Ben Abdellah University, Saudi Arabia’s Al-Baha University, and the United Kingdom’s Cardiff University contributed to the study.