Maximum power point tracking based on adaptive neuro‐fuzzy inference systems for a photovoltaic system with fast varying load conditions

Mohammed, Karam Khairullah; Buyamin, Salinda; Shams, Immad; Mekhilef, Saad

doi:10.1002/2050-7038.12904

Cited by 11 publications

(7 citation statements)

References 24 publications

(23 reference statements)

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“…For non-standard operating conditions, voltage source converters, or VSCs, are frequently recommended. On the other hand, adaptive neuro-fuzzy inference systems (ANFIS) may be directly in charge of solar panel-fed power modules that are capable of producing alternating current [15]. The management of today's increasingly common building-integrated photovoltaic modules may also fall under the purview of ANFIS [16].…”

Section: Simulation and Analysis Of Optimal Power Injection System Ba...mentioning

confidence: 99%

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

Assaf,

Kurnaz

2024

IJEER

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Many countries are seeing significant improvements in the fields of building, urban planning, technology, network management, and the need for diverse forms of energy and different generating techniques, as well as the necessity for low and middle distributing voltage in all areas. Depending on the needs of the user, starting needs, capacity, intended usage, waste output, and economic efficiency, many methods are used to generate this energy. To solve the problems brought on by the suggested excessive voltage of the provided system, energy collection devices can be used, and they can be used efficiently with smart grid intelligent control systems. A mathematical model was developed with four main components: simulation, correlation, and evaluation following the solar the program was set of photovoltaic panels solar panels, An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Maximum Power Point Tracking (MPPT), as well as 600-volt electric network, in order to examine and analyze the viability of the proposed network collaboration and storage of electricity in private photovoltaic networks based on solar energy. This phase next looks at the output power impact on the network, as well as the influence of network temperature and coincident radiation. An analysis was conducted to ascertain the impact of these basic limitations on actual use. This section covers the computer simulation of the proposed system. The final section contains the created system's block diagram. The system's input light is transformed into electricity that circulates in this system's power. The main electrical system with a 600-volt capacity can use this energy. The suggested system was evaluated using MATLAB simulation tapes and graphing for each system component, and the simulation outcomes of the entire system were considered.

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Section: Simulation and Analysis Of Optimal Power Injection System Ba...mentioning

confidence: 99%

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

Assaf,

Kurnaz

2024

IJEER

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“…Elobaid et al described the ANN MPPT with several off-line preparation features, nonlinear mapping, higher speed answer, and lower calculation effort [53]. The authors in [30] suggested a new neural network (NN) MPPT controller for PV systems. Data were collected from the P&O system using MATLAB/Simulink to train and test the NN model.…”

Section: State Of the Art Of Ann And Mpptmentioning

confidence: 99%

“…ANFIS creates a fuzzy inference system (FIS) employing input-output datasets. The model calculates the membership function parameters that provide the greatest fit for tracking the input-output data [30]. The parameters of the fuzzy membership function are changed using a hybrid learning technique that incorporates backpropagation and least squares algorithms [31].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

et al. 2021

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In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.INDEX TERMS Solar photovoltaic (PV), energy harvesting (EH), maximum power point tracking (MPPT), artificial neural network (ANN), Levenberg-Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG).

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“…To improve the output power of PV modules in PSC, various optimization algorithms have been proposed for tracking the GMPP amidst rapidly changing environmental factors. Researchers have introduced algorithms like Particle Swarm Optimization (PSO) [9], Enhanced Dandelion Optimizer [10], Bat algorithm [11], and Neuro Fuzzy inference system [12] due to their effectiveness in GMPP tracking. However, these algorithms often exhibit significant oscillations and struggle with convergence issues around GMPP.…”

Section: Introductionmentioning

confidence: 99%

Advanced Control Strategies for Photovoltaic Power Quality and Maximum Power Point Tracking Optimization

Nkambule,

Nabil Hasan,

Shongwe

2024

IEEE Access

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This paper introduces an innovative Expectation-Conjugate-Gradient (ECG) approach designed for the management of the interfacing inverter in a grid-connected fixed tilt bifacial Photovoltaic (PV) system. The focus of this algorithm is to address current harmonics issues and elevate power quality. Additionally, the proposed control strategy integrates with an Enhanced Dual second-order generalized integrator phase-locked loop (EDSOGI-PLL), enhanced with a Savitzky-Golay Filter (SGF) to achieve grid synchronization, mitigate voltage harmonics, and estimate symmetrical components during unbalanced grid conditions. The state-of-art Double deep Q-network (DDQN) maximum power point tracking algorithm is introduced, integrating centralized start-up condition computation to stabilize voltage levels at the shared DClink bus terminal. Key benefits include reduced harmonics, enhanced stability, adaptive control, and minimal computational load. Simulation confirms compliance with IEEE 519 Standards, THD < 3%, PF ≈ 1, and reveal a significant reduction in CO2 emissions.

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Maximum power point tracking based on adaptive neuro‐fuzzy inference systems for a photovoltaic system with fast varying load conditions

Cited by 11 publications

References 24 publications

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

Advanced Control Strategies for Photovoltaic Power Quality and Maximum Power Point Tracking Optimization

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