Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications

Kalaiarasi, N.; Dash, Subhransu Sekhar; Padmanaban, Sanjeevikumar; Paramasivam, S.; Morati, Pandav Kiran

doi:10.3390/app8010145

Cited by 18 publications

(2 citation statements)

References 38 publications

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“…Solar insolation and temperature are employed as key features to train the model, while ref maximum panel current is the desired parameter. "Table 1" shows he MSX-60W solar module's PV model specifications [22].…”

Section: Design and Methodologymentioning

confidence: 99%

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Ishrat,

Babar Ali,

Vats

et al. 2023

IJRCS

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Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.

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Section: Design and Methodologymentioning

confidence: 99%

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Ishrat,

Babar Ali,

Vats

et al. 2023

IJRCS

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show abstract

“…However, the most commonly used are fractional short or open-circuit (FSC, FOC) [7,8], perturb and observe (P&O) [9][10][11], voltage-and current-based MPPT [12], incremental conductance (Inc-Cond) [13][14][15], extremum seeking control (ESC) [16][17][18][19], sliding mode control (SMC) [20][21][22][23], current sweep (CS) [24], and fuzzy logic control (FLC) [25][26][27][28][29][30]. Smart and advanced computing techniques such as eagle strategy control (ESC), particle swarm optimization (PSO), neural network control (NNC), and genetic algorithms (GAs) have also been commonly used in the last few years [35][36][37][38][39][40][41][42]. Each method of these existing algorithms is characterized by its complexity in hardware implementation, convergence speed, the sensors required, the sensed parameters, and the cost.…”

Section: Introductionmentioning

confidence: 99%

A Robust Maximum Power Point Tracking Control Method for a PEM Fuel Cell Power System

2018

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Taking into account the limited capability of proton exchange membrane fuel cells (PEMFCs) to produce energy, it is mandatory to provide solutions, in which an efficient power produced by PEMFCs can be attained. The maximum power point tracker (MPPT) plays a considerable role in the performance improvement of the PEMFCs. Conventional MPPT algorithms showed good performances due to their simplicity and easy implementation. However, oscillations around the maximum power point and inefficiency in the case of rapid change in operating conditions are their main drawbacks. To this end, a new MPPT scheme based on a current reference estimator is presented. The main goal of this work is to keep the PEMFCs functioning at an efficient power point. This goal is achieved using the backstepping technique, which drives the DC–DC boost converter inserted between the PEMFC and the load. The stability of the proposed algorithm is demonstrated by means of Lyapunov analysis. To verify the ability of the proposed method, an extensive simulation test is executed in a Matlab–Simulink T M environment. Compared with the well-known proportional–integral (PI) controller, results indicate that the proposed backstepping technique offers rapid and adequate converging to the operating power point.

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Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network

Chen

Zhang

2019

Neural Comput & Applic

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Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications

Cited by 18 publications

References 38 publications

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

A Robust Maximum Power Point Tracking Control Method for a PEM Fuel Cell Power System

Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network

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