An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

Amara, Karima; Bakir, Toufik; Malek, A.; Hocine, D.; Bourennane, El-Bay; Fekik, Arezki; Zaouia, Mustapha

doi:10.15676/ijeei.2019.11.4.3

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…Next, a linear model is constructed in this feature space. As a result, the SVMR function for approximating nonlinear training data is as follows, (7) (8) (9) The kernel function in linear form is given by Eq. ( 9).…”

Section: Fig 3 Svm For Linear Regression Problem On Twomentioning

confidence: 99%

“…Each technique has its own set of strengths and weaknesses and its own method of tracking the MPP. The conventional methods are the Perturb and Observe (P&O) [1] and incremental conductance (IC) [2] methods, mathematical methods such as curve fitting [3] and beta MPPT [4], measurement-based methods such as look-up table [5] and current sweep [6], constant parameter methods such as fractional open circuit voltage [7] and fractional short circuit current [8] methods, trial and error methods such as gradient descent method [9] and variable inductance method [10], optimization techniques like genetic algorithm [11], ant colony optimization [12], practical swarm optimization [13], gray wolf optimiza-tion [14], and cuckoo search optimization [15], intellectual methods like an artificial neural network [16], fuzzy logic control [1], and ANFIS [1,9] are listed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…In literature, DC-DC converters such as the boost [2][3][4][5][6], buck-boost [7,8], buck [10], and SEPIC [14] are employed. In addition, an inverter [9] can also be used to drive the ac loads or to supply the grid. This study proposes a unique method for tracking the MPP of a solar module using support vector machine regression learning.…”

Section: Introductionmentioning

confidence: 99%

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Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Mahesh

Meyyappan

Alla

2023

Int. j. electr. comput. eng. syst. (Online)

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Photovoltaic panels use the sun’s radiation on their surface to convert solar energy into electricity. This process is dependent on the temperature of the surface and the intensity of the sun's radiation. To escalate the energy transformation, the solar system must be functioned at its maximum power point (MPP). Every maximum power point tracking (MPPT) technique has a distinct mechanism for tracking maximum power point (MPP). The support vector machine (SVM) regression algorithm is used in this work to develop a novel method for tracking the MPP of a PV panel. The solar panel technical parameters were used to prepare the data for training and testing the SVM model. The SVM algorithm predicts the PV panel's maximum power and relevant voltage for specific irradiation and temperature. The duty cycle of the boost converter corresponding to the maximum power was evaluated using the predicted values. The result of the simulation shows that the proposed control strategy forces the solar panel to work near the predicted MPP. The SVM regression control strategy gives the MPP tracking efficiency of more than 94% for the solar PV system despite variable climatic conditions during its stable state operation. In addition, a comparative analysis of the proposed method was carried out with the existing approaches to confirm the effective tracking of the proposed technique.

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Section: Fig 3 Svm For Linear Regression Problem On Twomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Mahesh

Meyyappan

Alla

2023

Int. j. electr. comput. eng. syst. (Online)

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“…It was proposed that an optimization technique based on mixed-integer nonlinear programming be used, and that the goal functionality be used to lower the daily operating costs. In [33] and [34], the authors demonstrated how a realtime power management application may be constructed. A complete, incomplete, and accurate forecast were made for each of the three scenarios under consideration.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cooperative Control Technique for Hybrid AC/DC Smart Microgrid Converters

Jasim

Bureš

et al. 2023

IEEE Access

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This study proposes a novel technique of cooperative control for a distributed hybrid DC/AC Microgrid (MG) by designing a digital Infinite Impulse Response (IIR) filter-based Proportional-Resonant (PR) current controller. This controller adopts an Adaptive Neuro Fuzzy Inference System (ANFIS) trained by Particle Swarm Optimization (PSO) to control inverter output current while tracking Maximum Power Point (MPP). A hybrid ANFIS-PSO extracts maximum power from both inverter and boost converter-based solar Photovoltaics (PVs) systems quickly and with zero oscillation tracking. The proposed PR controller cancels harmonics while achieving high gain at the resonant frequency (grid frequency). The PR controller offers quick reference signal tracking, grid frequency drift adaptation, easy system design, and no steadystate error. Moreover, this investigation features a PR controller frequency-domain analysis. The proposed technique smooths voltage and improves steady-state and transient responses. Cooperative control is implemented on an IEEE 14-bus MG with distributed communication. The findings indicate that the proposed control technique can regulate MG voltage to obtain a more stable voltage profile. The adopted MG, made up of dispersed resources, is crucial for assessing power flow and quality indicators in a smart power grid. Finally, numerical simulation results are utilized to verify the recommended technique.

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“…Similarly, PI is used with P&O algorithm [21] to improve the efficiency of the PV system. Furthermore, gradient descent also known as steepest descent method [22] is used in which local minimum of the function is found by tracking the steps which are proportional to the negative of the gradient while MPPT occurs when dP dV is minimum. The major limitation in all of those linear controllers is that they use the linearized model and they cannot deal with nonlinear dynamics of a system.…”

Section: Introductionmentioning

confidence: 99%

Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation

et al. 2020

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The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system’s dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness.

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An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

Cited by 11 publications

References 31 publications

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

A Novel Cooperative Control Technique for Hybrid AC/DC Smart Microgrid Converters

Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation

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