Estimating the photovoltaic MPPT by artificial neural network

Farhat, Sadik; Alaoui, Rachid; Kahaji, Abdelilah; Bouhouch, Lahoussine

doi:10.1109/irsec.2013.6529641

Cited by 13 publications

(2 citation statements)

References 11 publications

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“…They can be computationally intensive, requiring significant processing power and often more complex hardware. The need for large amounts of training data can be a limitation as well, especially in contexts where data may be difficult to collect [21][22][23][24][25][26]. These challenges highlight the need for alternate strategies that can address the shortcomings of AI, particularly in the context of PVSs and GMPPT.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

et al. 2023

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The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.

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Section: Introductionmentioning

confidence: 99%

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

et al. 2023

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“…Although the INC approach can solve the difficulties of the P&O algorithm in terms of decreasing the oscillation around the MPP, this fact leads to lower energy efficiency but poses a challenge in determining the right step and threshold (Deniz, 2017; Roy et al, 2017). Several artificial intelligence (AI) based MPPT controllers have been presented in the literature as a result of the lack of accuracy in the conventional methods brought on by perturbations around the MPP (Farhat et al, 2013; Reisi et al, 2013). As a result, those algorithms are presented as a solution to such problems as predicting and controlling renewable energy systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system

Mbarki,

Fethi,

Chrouta

et al. 2023

Transactions of the Institute of Measurement and Control

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The non-linear power-current characteristics of a photovoltaic generator (GPV) present a challenge in maximizing power production. To address this issue, Maximum Power Point Tracking (MPPT) methods, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), are commonly used due to their quick response and minimal oscillation. As well, sliding mode control (SMC) is a popular method for controlling linear and nonlinear systems because of its high robustness. In this study, the principal purpose is to develop a new approach based on the combination of ANFIS and Terminal Robust Sliding Mode Control (ANFIS-TRSMC) to resist the PV system against uncertain conditions and track the optimal power point. The simulation results show that the ANFIS-TRSMC controller has an accurate, fast, and robust response in comparison with other algorithms such as perturb and observe and the maximum power voltage–based TRSMC controller.

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Design and Development of Photovoltaic Power Meter

Pérez

García

2019

Communications in Computer and Information Science

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Estimating the photovoltaic MPPT by artificial neural network

Cited by 13 publications

References 11 publications

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system

Design and Development of Photovoltaic Power Meter

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