MPPT algorithms comparison in PV systems: P&amp;O, PI, neuro-fuzzy and backstepping controls

Martín, Aránzazu D.; Vázquez, Jesús R.

doi:10.1109/icit.2015.7125517

Cited by 32 publications

(15 citation statements)

References 14 publications

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“…R. Vazquez, [6]. Being a robust non-linear controller, Lyapunov functions also guarantees the system stability along with reaching its control objective, to track optimum power under variable atmospheric conditions.…”

Section: Backstepping Control Strategymentioning

confidence: 99%

MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

Sriharibabu¹,

Rao²

2018

IJET

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It is very important to have maximum power point trackers for photo voltaic systems to improve their efficiency. This paper deals with the converter based maximum power point tracking by robust backstepping controller along with the neural network. The neural network provides the output reference PV voltage to the backstepping controller. Back propagation neural network is used for a standalone photovoltaic system under robust environmental conditions. Unlike Conventional solar-array mathematical model, neural network does not require any physical data for modeling since it has the superior potential to derive non-linear models without requiring the physical data of the models. In this paper the maximum power point of photovoltaic module is predicted with the simulation trained back-propagation neural network using a random set of data collected from a real photovoltaic array. The neural network based PV system with backstepping controller is modeled in MATLAB/Simulink. At different atmospheric conditions the developed model is simulated. The simulation results of PV system depict that with the proposed converter based controller, the maximum power is tracked accurately and successfully.

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Section: Backstepping Control Strategymentioning

confidence: 99%

MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

Sriharibabu¹,

Rao²

2018

IJET

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“…The main objective of the first stage (boost converter) is allowing the PV array to generate the maximum power using the MPPT technique [9]. There are several algorithms used to track the MPP effectively; the authors of [10] showed that the back-stepping algorithm gives good results. Several publications explore two broad categories of MPPT techniques: indirect MPP tracking like the fractional open circuit voltage method [11], direct MPP tracking like the incremental conductance [12,13], or the Perturb and Observe (P&O) method that is implemented in this work.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the rise time of the back-stepping control of MPPT and the integral back-stepping were 2.42 ms and 2.17 ms, respectively. In [10], efficiencies of 96%, 96.5%, 98.2%, and 99.1% were obtained by using P&O algorithm, PI, neuro-fuzzy, and back-stepping, respectively. Our main objectives are to achieve a lower response time and higher efficiency in the MPPT stage.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Design of Single-Phase PV Inverter with MPPT Algorithm Applied to the Boost Converter Using Back-Stepping Control in Standalone Mode

Diouri

Es-Sbai

Errahimi

et al. 2019

International Journal of Photoenergy

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We propose a high-performance and robust control of a transformerless, single-phase PV inverter in the standalone mode. First, modeling and design of a DC-DC boost converter using a nonlinear back-stepping control was presented. The proposed converter uses a reference voltage that is generated by the Perturb and Observe (P&O) algorithm in order to extract the maximum power point (MPP) by responding accurately to varying atmospheric conditions. Another goal for using the boost converter is to raise the voltage at the input of the inverter without using a transformer in this system, thus making the system more compact and less expensive. Secondly, the single-phase H-bridge inverter was controlled by using back-stepping control in order to eliminate the error between the output voltage of the inverter and the desired value, even if there is acute load variation at the output of the inverter. The stability of the boost converter and H-bridge inverter was validated by using Lyapunov’s stability theory. Simulation results show that the proposed PV system with back-stepping controllers has a good extraction of the MPP with an efficiency of 99.93% and 1 ms of response time. In addition, the sinusoidal form of the output voltage of the inverter is fixed to 220 V and the total harmonic distortion of the output voltage was found to be less than 1%.

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“…To address these issues, the non-linear commands, such as the backstepping [6] and the sliding mode [7], and the artificial intelligence controls, such as the Artificial Neural Network (ANN) [8] and the fuzzy logic [9], have been designed. Indeed, despite of the implementation complexity of these techniques, they have better performance criteria [10,11].…”

Section: Introductionmentioning

confidence: 99%

MPPT Design Using Artificial Neural Network and Backstepping Sliding Mode Approach for Photovoltaic System under Various Weather Conditions

Boudaraia¹,

Mahmoudi²,

Abbou³

2019

IJIES

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This paper presents a novel hybrid technique for tracking the maximum power point of the photovoltaic panel. This approach includes two loops: The first one is the ANN (Artificial neural network) loop that is used to estimate and generate the reference of optimal voltage. While, the second loop consists of the BSM (Backstepping Sliding Mode) controller. Effectively, the proposed controller is designed to track the signal of the desired voltage, which is generated using the first loop, by adjusting the duty cycle of the boost converter. Thus, this loop allows the DC/DC converter to product the maximum power at the terminals of the PV module and the load. Indeed, in contrary to the traditional backstepping, the proposed ANN-BSM technique guarantee zero steady-state error. On the one hand, by using the ANN, the system can quickly predict the desired optimal voltage, also it allows the system to avoid the unnecessary calculations and research of the maximum point of power. On the other hand, the sliding mode and the backstepping controller are used to provide the good performances and robustness against the rapid changes of insolation. In addition, the asymptotic stability of this system is made by applying the Lyapunov approach. To show the effectiveness and the tracking performances of the proposed ANN-BSM technique, a comparative study with the classical methods, P&O and incremental conductance algorithms, and hybrid approaches such as the ANN-Backstepping controllers and the ANN-Integral Sliding mode, is investigated in MATLAB/Simulink software.

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MPPT algorithms comparison in PV systems: P&O, PI, neuro-fuzzy and backstepping controls

Cited by 32 publications

References 14 publications

MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

Modeling and Design of Single-Phase PV Inverter with MPPT Algorithm Applied to the Boost Converter Using Back-Stepping Control in Standalone Mode

MPPT Design Using Artificial Neural Network and Backstepping Sliding Mode Approach for Photovoltaic System under Various Weather Conditions

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