Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Villegas-Mier, César G.; Rodríguez‐Reséndiz, Juvenal; Álvarez-Alvarado, José Manuel; Rodriguez-Resendiz, Hugo; Herrera-Navarro, Ana M.; Rodríguez-Abreo, Omar

doi:10.3390/mi12101260

Cited by 81 publications

(11 citation statements)

References 80 publications

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“…The neural network solution is classified as an intelligent control solution, which can learn from an existing database for making the best decision for any condition (Villegas-Mier et al, 2021). The performance of any kind of neural network controller depends basically on the database size and events.…”

Section: Neural Network Mppt Algorithmmentioning

confidence: 99%

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

et al. 2022

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One of the most reliable and advanced renewable energy sources is wind energy. It is critical to harness as much wind energy as possible and maintain wind turbines operating at full capacity. Maximum power point tracking (MPPT) is a cutting-edge study that incorporates a variety of approaches. Because each MPPT technique has its own set of advantages and disadvantages, developing an accurate maximum power point tracking methodology for a certain case necessitates understanding. As a result, they must be checked thoroughly. This research tries to examine many algorithms that can be used to improve the wind energy system’s global MPPT performance. The traditional “Perturb and Observe” tool, the optimization method based on the “particle swarm optimization algorithm,” the neural network, and the “fuzzy logics” as intelligent tools are these techniques. The main objective of this research is to define and evaluate four different flexible algorithms that achieve the fundamental objective of this optimization. The advantages, drawbacks, and thorough analysis of MPPT systems are highlighted in terms of initial investment, responsiveness, and capacity to create maximum energy output. All of this comparison was made through simulation software, which is the MATLAB Simulink tool. The conclusions are supported by a comprehensive discussion and presentation of the results for a variety of situations and tests that reflect real-world behavior in any wind system.

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Section: Neural Network Mppt Algorithmmentioning

confidence: 99%

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

et al. 2022

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“…Some researchers have proposed intelligence based MPPT techniques to handle the drawbacks of conventional method. The techniques used are artificial intelligence (AI) [7], fuzzy logic control (FLC) [8], [9], machine learning (ML) [10], and deep learning [11]. Artificial neural network (ANN) works based on its ability to learn from data set input.…”

Section: Introductionmentioning

confidence: 99%

Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading

Windarko

Sholikhah²,

Habibi³

et al. 2022

IJECE

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<span>Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.</span>

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“…CNNs is a variation on the multilayer perceptron, uses two-dimensional matrices and is very effective in computer vision, such as the application of electroencephalogram signals in medical area [24,25]. RNNs are suitable for processing sequential data and has been widely used in Maximum Power Point Tracking, parameter estimators for induction motors and so on [26,27]. Moreover, they both can better learn sentence and document representation.…”

Section: Introductionmentioning

confidence: 99%

A Sequential Graph Neural Network for Short Text Classification

et al. 2021

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Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and most of the existing graph-based models ignore sequential information (e.g., word orders) in each document. In this work, we propose an improved sequence-based feature propagation scheme, which fully uses word representation and document-level word interaction and overcomes the limitations of textual features in short texts. On this basis, we utilize this propagation scheme to construct a lightweight model, sequential GNN (SGNN), and its extended model, ESGNN. Specifically, we build individual graphs for each document in the short text corpus based on word co-occurrence and use a bidirectional long short-term memory network (Bi-LSTM) to extract the sequential features of each document; therefore, word nodes in the document graph retain contextual information. Furthermore, two different simplified graph convolutional networks (GCNs) are used to learn word representations based on their local structures. Finally, word nodes combined with sequential information and local information are incorporated as the document representation. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of our method.

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Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Cited by 81 publications

References 80 publications

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

Comparative analysis of MPPT techniques for enhancing a wind energy conversion system

Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading

A Sequential Graph Neural Network for Short Text Classification

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