Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System

Salarieh, Hassan; Badri, Abdelmajid; Zegrari, Mourad

doi:10.11648/j.ijiis.20160501.11

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…ABC can solve the problem of finding the MPP under different light intensities and it can search for the global optimum point, avoiding getting trapped in local optima. Bilal [26] first used ABC for MPPT of PV systems in 2013. The algorithm is compared with P&O algorithm to illustrate the superiority of ABC.…”

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

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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.

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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

“…Viewing Intelligent methods such as an Artificial Neural Network (ANN) [7][8][9], Fuzzy Logic (FL) [10][11][12] and the Artificial Bee Colony (ABC) [13,14], show that they are mostly digital complex methods, requires a lot of calculations and expensive as they need to use powerful microcontrollers due to the enormous computational load. They have fast converged speed and the highest accuracy for tracking the MPP.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic array maximum power point tracking via modified perturbation and observation algorithm

Numan

Shakir

Mahmood

2020

IJPEDS

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One drawback of PVs is their low efficiency. As the PVs have a unique maximum Power Point for a specified irradiation level, there must be an effective method for extracting maximum power from the PV module to raise the efficiency. Conventional Perturbation and Observation (P&O) is a simple algorithm for maximum power point tracking (MPPT) but it suffers from oscillation during steady state conditions and is deviated from the maximum power point during slow and rapid irradiation level change. This paper presents a modified P&O by adding variation in PV current as a third in addition to the voltage and power variation parameters. This new algorithm is capable of eliminating the MPPT deviation. To increase the perturbation speed, a double step is taken as the tracking is deviated from the MPP. The modified P&O algorithm is used to control the duty cycle of DC-DC buck converter. The simulation shows that the modified P&O is faster than the conventional. The power loss due to oscillation before attaining the steady state is less for modified P&O. For slow irradiation level change (ramp up 600 to 1000 and ramp down 1000-800) W/m2, the modified P&O shows less tracking diverge. As the irradiation level changes rapidly from 800 to 200 W/m2, it's shown that the modified algorithm attains the steady state faster than the conventional P&O and the average efficiency increased by 4.34%.

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“…Chen et al (2018) developed Hybrid Teaching Learning based totally Artificial Bee Colony (TLABC) to estimate the solar cell PV parameters SDM, DDM and TDM; Obtained outcomes whilst compared with different optimization techniques and located the proposed set of rules provides true accuracy and performance for estimation of various cell parameters. More works in this algorithm available in literaturesSalmi et al (2016),Pilakkat & Kanthalakshmi (2019),Hassan et al (2017),Oliva et al (2014).…”

mentioning

confidence: 99%

Brief Review on Solar Photovoltaic Parameter Estimation of Single and Double Diode Model Using Evolutionary Algorithms

Senthilkumar

Mohan

Krithuga

2023

Int. J. Eng. Tech. Mgmt. Res.

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In modern years several researchers contribute to renewable energy specifically solar as atmosphere responsive. Solar panels are an essential and chief constituent for solar energy selecting as they are active in transformation of solar radioactivity into electrical voltage correspondent. The principal concern in optimum generation of power from these solar panels is to be contingent on numerous characteristics mostly correlated to the sizing and modelling of photovoltaic (PV) panels for the essential presentations. An array of solar cells is castoff for generation of slight to average gauge power generation in numerous cases. Sizing of panels, the storing progression and application of electrical tracks in the procedure are some vital research qualities which together regulate and describe supreme power generation from solar panel. Parameter and circuit level modelling has been occupied as major problem of examination and several state of the art practices to govern the ideal sizing with respect to many circuit models such as single diode model (SDM) and double diode models (DDM) have been inspected in an widespread way in this review article. It offers the perceptions, features, and climaxes the strength and weaknesses of PV cell models. This article debates some algorithms and methods used in both SDM and DDM and a deep learning into the investigation of parameter assessments in each diode have been studied. Based on the showed evaluation, some commendations for upcoming research are provided.

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Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System

Cited by 11 publications

References 6 publications

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Photovoltaic array maximum power point tracking via modified perturbation and observation algorithm

Brief Review on Solar Photovoltaic Parameter Estimation of Single and Double Diode Model Using Evolutionary Algorithms

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