Enhanced Bayesian Based MPPT Controller for PV Systems

Keyrouz, Fakheredine

doi:10.1109/jpets.2018.2811708

Cited by 41 publications

(15 citation statements)

References 14 publications

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“…An improved inverter optimizes the power extraction without adversely affecting the PV DC output to AC [98]. Proportional integral derivative (PID) controller is recommended to regulate D output to pulse width modulation from MPPT techniques due to its flexibility, stability, the minimum overshoot, fine-tuning characteristic, the minimum rise time for output voltage and performance optimization [85], [99]. Generally, AI-based MPPT techniques are applicable for grid-connected (ongrid), standalone (off-grid) and other specialized applications including solar vehicle, solar lamp, water heater, DC motor, and water pump.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

197

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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Section: Discussionmentioning

confidence: 99%

“…Bayesian ML is a method specialized in unsupervised classification, curve detection, and image segmentation. It is applicable in MPPT to achieve GMPP [85]. The real-time location-based weather forecasting is also applicable by using optimized modified ELM or Bayesian ML (BML).…”

Section: J MLmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

197

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“…The Gaussian process regression‐Jaya (GPR‐Jaya) PV arrays under PSC were developed by Huang et al 30 ; however, it has lowered the convergence speed and low solution accuracy. Keyrouz et al 31 introduced the enhanced Bayesian‐based MPPT PV system controller. It has drawbacks of raises the burden and oscillations emerge.…”

Section: Review Of Related Workmentioning

confidence: 99%

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Sundararaj

Anoop

Dixit

et al. 2020

Progress in Photovoltaics

176

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In general, the photovoltaic (PV) is considered as the best selection among renewable energy resources due to its nonpolluted operation and good flexibility condition. The PV system is affected because of the partial shading conditions (PSCs), which reduce the generated power. During steady-state operating conditions, there occurs a time delay in tracking the Global maximum power point (GMPP) and Local maximum power point (LMPP) under PSCs using the perturb and observe (P&O) method. In order to overcome such shortcomings, this paper proposed a hybrid algorithm with a P&O technique to improve the maximum power point tracking (MPPT) for the PV system under PSC. In addition to this, the P&O technique is utilized to achieve the LMPP in the first section, and the hybrid algorithm is utilized to achieve the GMPP in the second section. Here, the hybrid technique is the integration of Cauchy preferential crossover (CC) with the flower pollination algorithm (FPA). Furthermore, the exploitation ability of the FPA is enhanced by the CC, and the combined hybrid algorithm has the ability to produce the optimal duty cycle for the DC-DC boost converter for MPPT. Then the proposed method will be executed in MATLAB/Simulink model, and it is contrasted with the existing methods such as CC, current sensorless (CS), and FPA, respectively. The experimental results and analysis reveal that the proposed approach provides better performances when compared with several other metaheuristic algorithms. K E Y W O R D S crossover, DC to DC boost converter, global maximum power point, global pollination, local maximum power point, photovoltaic, PSC 1 | INTRODUCTION Due to the rapid growth and expansion in business as well as the consistently increasing refinement of existing ways of living, the world supply energy is exposed to a huge strain. This wonder creates uncertainties over energy security as well as environmental sustainability. 1,2 Besides, the problem of climatic changes and it is necessary to reduce carbon impressions has added to the solid force for organizations and countries to put resources into elective energy sources, especially renewable energy (RE). Solar energy is a standout among the most vital RE sources, rather than regular unrenewable assets, for example, fuel and coal. 3 Solar energy is spotless, limitless, and free. The primary utilization of photovoltaic (PV) frameworks is said to be a stand-alone (military, street and residential lighting, electric vehicles space applications, and water pumping). Despite these

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“…This paper proposes a Bayesian fusion technique (BFT) for tracking GMPP under PSCs. In this method, the Bayes' theorem is applied to obtain a proper GMPP solution by combining the information obtained from two methods [33][34][35], i.e., incremental conductance and Jaya algorithm for different partially shaded conditions. As the solutions from both conventional method and meta-heuristic algorithms are used, based on previous probabilities, the BFT technique can reduce the overall convergence time, increase tracking accuracy, and reduce steady-state oscillation.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian fusion technique for maximum power point tracking under partial shading condition

Gugulothu

Bhookya

2021

SN Appl. Sci.

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In this paper, a Bayesian fusion technique (BFT) based on maximum power point tracking (MPPT) is developed for the photovoltaic (PV) system that can exhibit faster and accurate tracking under partially shaded conditions (PSCs). Although the conventional hill-climbing algorithms have fast tracking capabilities, they are prone to steady-state oscillations and may not guarantee global peak under partially shaded conditions. Contrarily, the meta-heuristic-based techniques may promise a global peak solution, but they are computationally inefficient and take significant time for tracking. To address this problem, a BFT is proposed which combines the solutions obtained from conventional incremental conductance algorithm and Jaya optimization algorithm to produce better responses under various PSCs. The effectiveness of the proposed BFT-based MPPT is evaluated by comparing it with various MPPT methods, viz. incremental conductance, particle swarm optimization (PSO), and Jaya optimization algorithms in MATLAB/Simulink environment. From the various case studies carried, the overall average tracking speed with more than 99% accuracy is less than 0.25 s and having minimum steady-state oscillations. Even under the wide range of partially shaded conditions, the proposed method exhibited superior MPPT compared to the existing methods with tracking speed less than 0.1 s to achieve 99.8% tracking efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.

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Enhanced Bayesian Based MPPT Controller for PV Systems

Cited by 41 publications

References 14 publications

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

A Bayesian fusion technique for maximum power point tracking under partial shading condition

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