Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system

R.Srinivasan.,; Balamurugan, C. R.

doi:10.1080/00207217.2021.1914192

Cited by 13 publications

(2 citation statements)

References 16 publications

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“…As a result, the BES algorithm is used to solve a variety of issues, including cluster head selection in wireless sensor networks (Shajin et al, 2020), routing problem of underwater wireless sensor networks (Kapileswar & Kumar, 2021), optimization of weight function in neural networks (Srinivasan & Balamurugan, 2021), optimal parameter estimation of the supercapacitors (Nassef et al, 2022), energy management problem of micro‐grids (Ferahtia et al, 2022), improvement of distributed systems (Eid et al, 2022), home appliances optimal scheduling in IoT Environment (Alhasnawi et al, 2022), wind speed maximum power estimation (Fathy et al, 2022), minimization of the power loss in distributed generation units and shunt reactive compensators (Eid et al, 2022), optimization of multi‐criteria decision making of blockchain‐based IoT healthcare industries (Qahtan et al, 2022), enhancement of variational mode decomposition technique (Li et al, 2022), estimation of unknown fitness function of the proton exchange membrane fuel cell models (Rezk et al, 2022), and long short‐term memory model optimization to prediction of a tea plantation (Huang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

2022

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Bald Eagle Search optimization (BES) is introduced recently, which mimics the bald eagles' hunting and food searching behaviour. The capability of a BES algorithm is enhanced in this paper by avoiding local optima stagnation and premature convergence problems. The BES algorithm is modified to enhance the performance of the algorithm and the modified algorithm is called the Self Adaptive Bald Eagle Search (SABES) algorithm. Dynamic‐opposite Learning (DOL) method is invoked in the initialization phase to increase the population diversity and convergence speed. To find better global solutions, the exploitation capability of the SBES algorithm is enhanced by considering the dynamic‐opposite solutions. In addition, the algorithmic parameter values of the BES algorithm have been determined using the linear and non‐linear time‐varying adaption strategy to create a balance between the search abilities which promotes the overall performance of the algorithm. The performance of the SABES algorithm is validated by comparing the results of 50 benchmark functions and CEC2017 functions with different erstwhile algorithms. The proposed algorithm achieves the best results in 80% of the benchmark functions, whereas the BES only gets the best results in 56% of functions. For the CEC2017, the SABES algorithm achieves optimal results for 20 functions which are highest in comparison to state‐of‐the‐art algorithms. The feasibility and effectiveness of the proposed algorithm are checked using the 15 CEC2020 competition real‐world single‐objective constrained optimization problems. The SABES achieves a 100% success rate and 100% feasibility rate in comparison to other well‐regarded algorithms. The statistical significance of the algorithm has been proved using Friedman's mean rank and Wilcoxon sign rank test.

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Section: Introductionmentioning

confidence: 99%

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

2022

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“…It usually consists of a three‐layer fully connected neural network which is fed by physical inputs measurable by small sensors 9,10 . Other approaches towards artificial neural network (ANN)‐based modeling of MPPT involve the application of radial basis functions (RBF), 11 fuzzy logic operators, 12 genetic algorithms, 13 heuristic search‐based algorithms, 14 and a combination with traditional MPPT techniques such as P&O 15 and incremental conductance 16 . Apart from the traditional fully connected ANN approach, some works have attempted the MPPT task using approaches such as reinforcement learning (RL) 17 and classical machine learning approaches 18 such as decision trees, k‐nearest neighbors, and recurrent neural networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

Transformer‐based time series prediction of the maximum power point for solar photovoltaic cells

Agrawal

Bansal

Gautam

et al. 2022

Energy Science & Engineering

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This paper proposes an improved deep learning-based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time seriesbased environmental inputs. Generally, artificial neural network-based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time-based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer-based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological-year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non-zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real-time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.

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Enhanced Re Boost-Luo with coupled inductors for efficient integration of PV systems in DC microgrids

Singh,

Faizal,

Praynlin

et al. 2024

Electr Eng

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Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system

Cited by 13 publications

References 16 publications

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

Transformer‐based time series prediction of the maximum power point for solar photovoltaic cells

Enhanced Re Boost-Luo with coupled inductors for efficient integration of PV systems in DC microgrids

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