In an article recently published in the journal Biomimetics, researchers introduced the improved quantum-behaved particle swarm optimization (IQPSO) algorithm for maximum power point tracking (MPPT) in photovoltaic generation systems (PGSs). The study assessed how IQPSO enhances the accuracy and efficiency of the conventional QPSO approach.
Study: Enhancing Photovoltaic System Efficiency with IQPSO Algorithm. Image Credit: dalenandruleanu/Shutterstock.com
Limitations of Existing Approaches
In recent years, the rapid expansion of solar power has led to the widespread adoption of PGSs. For these systems, developing an efficient MPPT system is essential. MPPT ensures that the PGS operates at the Maximum Power Point (MPP) on the non-linear power-voltage (P-V) curve.
Many studies have treated MPPT as an optimization problem, employing biomimetic algorithms such as Particle Swarm Optimization (PSO) for control. These algorithms are generally more effective over the long term compared to other MPPT methods, offering improved tracking accuracy by minimizing feedback ratio distortion.
Among the bio-inspired algorithms, the PSO algorithm is more suitable for implementation within MPPT in PGS as it offers multiple advantages like easy programming, low memory requirements, and simple calculations. However, the PSO algorithm faces significant challenges, including a trade-off between efficiency and accuracy, as well as issues with premature convergence. These challenges must be addressed to optimize its performance.
To enhance the PSO algorithm, the Quantum PSO (QPSO) algorithm was developed. This algorithm incorporates quantum behaviors instead of traditional particle velocity and position strategies used in PSO, allowing particles to potentially occupy any position. This approach aims to strike a better balance between tracking accuracy and response time. Despite these improvements, issues like slow convergence and the occurrence of local optima continue to pose challenges.
The IQPSO and its Evaluation
In this study, researchers introduced the IQPSO algorithm tailored for the task of MPPT within PGSs. The proposed IQPSO addresses the premature convergence issue of conventional QPSO while enhancing tracking accuracy, convergence speed and reducing tracking time by estimating the MPP and adjusting the probability distribution.
IQPSO utilizes a natural exponential decay method for the contraction-expansion coefficient to expedite convergence. Moreover, accuracy improvements were achieved through the estimation of optimal solution positions and probability distributions evolving with each generation. In the MPPT system, the power stage comprises a series of buck-boost converters, while the control stage involves a microprocessor executing the biomimetic algorithm.
The system operates in both boost and buck modes by integrating the biomimetic algorithms with a series buck-boost converter. The IQPSO algorithm can be implemented within a chip for application in MPPT to enable the PGSs to operate at their MPP despite shading conditions and varying irradiance.
Researchers conducted MPPT experiments using a programmable DC power supply to simulate photovoltaic systems. These experiments included full-day observations from 8 am to 4 pm, irradiance-changing scenarios, multi-peak investigations, and single-peak experiments. The aim was to evaluate and compare the proposed IQPSO with other biomimetic algorithms, such as the Firefly Algorithm (FA), PSO, and QPSO, based on tracking time and accuracy under real-world conditions
Significance of the Study
The superiority of IQPSO in MPPT performance, both in terms of tracking speed and accuracy, was demonstrated through a series of rigorous experiments. These included irradiance variation experiments, multi-peak investigations, and single-peak analyses. Notably, when comparing the tracking responses of different MPPT algorithms using a single-peak P-V curve, IQPSO stood out with the highest tracking accuracy of 99.03 % and the fastest response time of 1.32 seconds, surpassing all other evaluated algorithms.
While the QPSO MPPT algorithm exhibited the second-best tracking accuracy of 98.48 %, its response time lagged significantly behind IQPSO, with a 3.93-second tracking duration. On the other hand, the FA MPPT algorithm demonstrated a faster response than QPSO but sacrificed accuracy, achieving only 96.31 %. The PSO MPPT algorithm fared the worst, displaying both lower accuracy compared to IQPSO and QPSO and the slowest response time.
Similarly, analysis of the tracking responses from various MPPT algorithms using the multi-peak P-V curve revealed that the IQPSO MPPT algorithm exhibited the fastest response time at 1.93 seconds, along with the highest accuracy of 98.81% among all algorithms. Following closely, the QPSO MPPT algorithm showcased the second-highest accuracy, trailed by the FA and PSO algorithms. Meanwhile, the FA MPPT algorithm demonstrated the second-fastest response time, with PSO and QPSO algorithms following suit.
Under changing irradiance conditions, the IQPSO MPPT algorithm displayed the fastest response, which was followed by the FA, PSO, and QPSO MPPT algorithms. The IQPSO MPPT algorithm's response also had the highest accuracy, which was followed by QPSO, FA, and PSO MPPT algorithms.
Ultimately, practical testing encompassing multi-peak, full-day, and single-peak assessments of photovoltaic arrays corroborated the superior performance of the IQPSO algorithm under real-world conditions. For instance, experimental results from both single-peak and multi-peak conditions showcased that the IQPSO algorithm achieved the fastest response time and highest tracking accuracy.
The IQPSO algorithm demonstrated tracking accuracies of 99.47 % and 99.91 %, with tracking times of 1.35 seconds and 1.73 seconds under single-peak and multi-peak conditions, respectively. Furthermore, IQPSO exhibited superior performance in terms of maximum power generation output from photovoltaic arrays over three days, generating a total of 26,607.04 Wh, followed closely by QPSO with 26,082.22 Wh.
In summary, the study's findings underscored the effectiveness of the proposed IQPSO MPPT algorithm in enhancing the power generation efficiency of photovoltaic systems under realistic operating conditions.
Journal Reference
Yu, G., Chang, Y., Lee, W. (2024). Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization. Biomimetics, 9(4), 223. https://doi.org/10.3390/biomimetics9040223, https://www.mdpi.com/2313-7673/9/4/223
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