A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation

Messalti, Sabir; Harrag, Abdelghani; Loukriz, Abdelhamid

doi:10.1016/j.rser.2016.09.131

Cited by 232 publications

(125 citation statements)

References 63 publications

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“…In the hybrid ANN based fuzzy logic controller, the tracking accuracy is found to be better than the conventional INC algorithm [112]. Many improved MPPT methods have attained high tracking speed in the dynamics.…”

Section: Tracking Performancementioning

confidence: 99%

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

et al. 2018

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This paper elaborates a comprehensive overview of a photovoltaic (PV) system model, and compares the attributes of various conventional and improved incremental conductance algorithms, perturbation and observation techniques, and other maximum power point tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system. Following a discussion of the MPPT algorithms in each category, a table is drawn to summarize their key specifications. In the performance evaluation section, the appropriate PV module technologies, atmospheric effects on PV panels, design complexity, and number of sensors and internal parameters of the PV system are outlined. In the last phase, a comparative table presents performance-evaluating parameters of MPPT design criterion. This paper is organized in such a way that future researchers and engineers can select an appropriate MPPT scheme without complication.

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Section: Tracking Performancementioning

confidence: 99%

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

et al. 2018

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“…The optimization algorithm based on IPSM is the evolution of the Pattern Search Method (PSM) algorithm used by [11,47,48]. It is a search method with a finite population forming a mesh with geometric distances between each member.…”

Section: Mppt Algorithm Based On Ipsmmentioning

confidence: 99%

“…These facts have boosted the research on soft computing-based approaches to tackle the MPPT problem. Thus, in [11], an Artificial Neural Network (ANN) MPPT controller, based on fixed and variable step size, is proposed. In this work the data required to generate the ANN model are generated using P&O.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods

et al. 2017

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This paper deals with the optimization of maximum power point tracking when a photovoltaic panel is modelled as two diodes. The adopted control is implemented using a sliding mode control (SMC) and the optimization is implemented using an improved Pattern Search Method. Thus, the problem of maximum power point tracking is reduced to an optimization problem whose solution is implemented by Pattern Search Techniques, inheriting their convergence properties. Simulation examples show the effectiveness of the proposed technique in practice, being able to deal with different radiations. In addition, improved pattern search method (IPSM) is compared with other techniques such as perturb & observe and Particle Swarm optimization, after which IPSM presents lower energy losses in comparison with the other two algorithms, with the advantage of ensuring the location of the optimal power point in all cases.

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“…Also, algorithms based on artificial intelligence techniques such as fuzzy logic [13][14][15][16][17][18][19] and neural networks [20][21][22] have been used, as well as the implementation of optimization algorithms such as glowworm swarm [23], ant colony [24,25] and bee colony [26][27][28]. These algorithms are part of soft computing techniques and have the advantage of being easily implemented using embedded systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based MPPT Controller for a PV System

Robles-Algarín¹,

Giraldo²,

Álvarez³

2017

Energies

117

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Abstract:The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

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A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation

Cited by 232 publications

References 63 publications

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods

Fuzzy Logic Based MPPT Controller for a PV System

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