MPPT for Photovoltaic Modules via Newton-Like Extremum Seeking Control

Zazo, H.; Castillo, Esteban; Reynaud, J.F.; Leyva, R.

doi:10.3390/en5082652

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…If precise quantification is necessary, the memory space increases, but a higher resolution can be obtained. (18) The text-based maximum and minimum values will be equally divided over 16 regions from 0 to 15. Any value above the maximum will go into region 15 and any value lower than the minimum will go into region 0.…”

Section: Cmac Algorithmmentioning

confidence: 99%

Smart Maximum-power-point Tracking Method for Photovoltaic Module Arrays Using Irradiation and Temperature Sensors

Chao¹,

Liao²,

Tseng³

2018

Sensors and Materials

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The purpose of this study was to develop a maximum-power-point tracking (MPPT) method for photovoltaic (PV) module arrays using a cerebellar model articulation controller (CMAC). Eighteen Sharp NT-R5E3E modules were used in a 9 series 2 parallel configuration. The power generation data at different sunlight intensities and array temperatures were used for obtaining training data. The slope of the power-voltage (P-V) characteristic curves was used for tuning the weight of the CMAC during MPPT. This allowed the quantification and adjustment of the system and improved the tracking accuracy. The CMAC uses an algorithm that mimics the neural structure of the human cerebellum, and it has the ability to learn quickly, associate, and summarize. The time required for training was therefore relatively short, and this can increase the power generation of the PV module array. Some test results showed that tracking with this maximum-power-point (MPP) CMAC was perfectly feasible over a range of different temperatures and sunshine intensities.

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Section: Cmac Algorithmmentioning

confidence: 99%

Smart Maximum-power-point Tracking Method for Photovoltaic Module Arrays Using Irradiation and Temperature Sensors

Chao¹,

Liao²,

Tseng³

2018

Sensors and Materials

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“…Over recent years, there has been a large volume of published studies on smart MPP trackers [6][7][8][9][10][11][12] in an attempt to precisely locate the MPP and improve the tracking performance, both dynamic and static. Yet, these tracking strategies fail to track the global MPP as intended in the case of any partially shaded PV modules in an array, simply due to the multiple peak problems commonly found in practical operations.…”

Section: Open Accessmentioning

confidence: 99%

A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System

Chao

2015

Energies

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This paper aims to present an improved version of a typical particle swarm optimization (PSO) algorithm, such that the global maximum power point (MPP) on a P-V characteristic curve with multiple peaks can be located in an efficient and precise manner for a photovoltaic module array. A series of instrumental measurements are conducted on variously configured arrays built with SANYO HIP2717 PV modules, either unshaded, partially shaded, or malfunctioning, as the building blocks. There appear two, triple and quadruple peaks on the corresponding P-V characteristic curves. Subsequently, the tracking performance comparisons, made by some practical experiments, indicate the superiority of this improved MPP tracking algorithm over the typical one.

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“…This method needs to build a set of fuzzy rules, and creates the library by rule of thumb, but also needs a longer calculation. Zazo et al [27] proposed an effective solution called NLESC for ESC transient derivative applications of photovoltaic modules' transient response. This method explored how to make the transient response of the system more stable, reducing the vibration, and does not discuss how to eliminate the vibration of steady response.…”

Section: Introductionmentioning

confidence: 99%

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

2014

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This paper proposes a sliding mode extremum seeking control (SMESC) of chaos embedded particle swarm optimization (CEPSO) Algorithm, applied to the design of maximum power point tracking in wind power systems. Its features are that the control parameters in SMESC are optimized by CEPSO, making it unnecessary to change the output power of different wind turbines, the designed in-repetition rate is reduced, and the system control efficiency is increased. The wind power system control is designed by simulation, in comparison with the traditional wind power control method, and the simulated dynamic response obtained by the SMESC algorithm proposed in this paper is better than the traditional hill-climbing search (HCS) and extremum seeking control (ESC) algorithms in the transient or steady states, validating the advantages and practicability of the method proposed in this paper.

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MPPT for Photovoltaic Modules via Newton-Like Extremum Seeking Control

Cited by 14 publications

References 14 publications

Smart Maximum-power-point Tracking Method for Photovoltaic Module Arrays Using Irradiation and Temperature Sensors

Smart Maximum-power-point Tracking Method for Photovoltaic Module Arrays Using Irradiation and Temperature Sensors

A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

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