Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems

Watanabe, Rodrigo Bairros; Ando, Oswaldo Hideo; Leandro, Paulo G. M.; Salvadori, Fabiano; Beck, Marlon Felipe; Brandt, Marcelo Henrique Manzque; Oliveira, Fernando Marcos de

doi:10.3390/en15155338

Cited by 16 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experiment conducted under 1000 W/m 2 lighting conditions and 50 ℃ environment, the results show that the improved FA method track MPP in 0.72 s, which increase the tracking time by 35% compared to the unmodified FA. Watanabe et al [25] proposed an efficient FA method, in the experiment conducted at an intensity of 1000 W/m 2 and a temperature of 50 ℃, the results show that the efficiency of FA method is 99.93%, which is 30% higher than the traditional P&O method.…”

Section: Meta-heuristic Algorithmmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

View full text Add to dashboard Cite

Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.

show abstract

Section: Meta-heuristic Algorithmmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

View full text Add to dashboard Cite

show abstract

“…The power converter and the tracking algorithm are primarily linked through the duty cycle (δ). The tracking algorithm assesses voltage and current data from the system, dynamically adjusting the duty cycle to optimize extracted power [26]. Figure 5 depicts the boost converter models utilized for the P&O and GA tracking algorithms.…”

Section: Pv System Modelmentioning

confidence: 99%

“…To address challenges related to the partial shading of strings, intelligent or bioinspired algorithms offer effective solutions [26]. Among these, GAs stand out, developed based on Darwin's theory of the evolution of species.…”

Section: Introductionmentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

Self Cite

View full text Add to dashboard Cite

Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.

show abstract

“…This reduces steady-state oscillation, but it requires limiting the maximum velocity of particles and may still get stuck in local maximums in some situations. In reference [37], MPPT is operated using FA. Although FA has high-speed convergence, it must balance tracking time and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization

Yu,

Chang,

Lee

2024

Biomimetics

View full text Add to dashboard Cite

This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs). The proposed IQPSO addresses the premature convergence issue of QPSO, enhancing tracking accuracy and reducing tracking time by estimating the maximum power point and adjusting the probability distribution. Employing exponential decay, IQPSO facilitates a reduction in tracking time, consequently enhancing convergence efficiency and search capability. Through single-peak experiments, multi-peak experiments, irradiance-changing experiments, and full-day experiments, it is demonstrated that the tracking accuracy and tracking time of IQPSO outperform existing biomimetic algorithms, such as the QPSO, firefly algorithm (FA), and PSO.

show abstract

Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems

Cited by 16 publications

References 21 publications

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization

Contact Info

Product

Resources

About