An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Wang, Haixin; Shen, Jianxin

doi:10.1109/access.2018.2881888

Cited by 34 publications

(14 citation statements)

References 13 publications

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“…Given the excellent performance of BPNN, our methodology utilizes MEA to improve the BPNN to further improve prediction accuracy. [ 43 ] MEA sources to mimic the evolution of the human mind evolutionary process, where Figure presents the detailed schematic and flowchart. In particular, the group is randomly divided into several subgroups to launch a global competition.…”

Section: Resultsmentioning

confidence: 99%

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

et al. 2021

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Accurate phase constitution prediction is crucial for guiding the new steel design with desirable properties. This article uses three machine learning (ML) algorithms, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and fuzzy neural network (FNN), to compare the accuracy of predictive model. Toward the collected data, statistical measure of correlation is taken in the present work by determining Pearson correlation coefficient (PCC). The results show that the testing accuracy using BPNN is higher than the corresponding testing accuracy using RBFNN and FNN. With these understandings, the well‐known optimization algorithms, namely, mind evolutionary algorithm (MEA), are implemented to find optimal hyperparameters set that is able to induce a higher predictive capability. The resulting MEA‐BPNN further enhances the prediction results in the present dataset and efficiently explores the relationship between alloying elements and phase constitution. Finally, the reliability and practicability of the model are verified by experimental measurement on the ferrite content for prepared steels, and their mechanical properties are tested for engineering applications. As such, the work proposes a useful MEA‐BPNN model for predicting the phases of high carbon pearlite steel and provides an alternative route of designing new steels in a given system.

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Section: Resultsmentioning

confidence: 99%

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

et al. 2021

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“…Three aspects should be noticed in the training process: Firstly, the predicted heading angles are calculated in two coordinate axes. By subtracting from the optimal heading angles, the minimum deviation is chosen as the loss function value; Secondly, evolutionary algorithm [30] are applied to optimize the weights and thresholds of the network; Thirdly, in order to prevent over-fitting of the network, the network is tested by the training data set to judge whether the loss function value is continuously reduced and this can be one of the termination conditions in the training phase.…”

Section: Off-line Network Training Testmentioning

confidence: 99%

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

et al. 2019

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In this paper, the angle-of-arrival (AOA) measurements are adapted to locate a target using the UAV swarms equipped with passive receivers. The measurement noise is considered to be target-to-receiver distance dependent. The Cramer-Rao low bound (CRLB) of the AOA localization is calculated, and the optimal deployments are explored through changing angular separations and distances. Then, a distributed collaborative autonomous generation (DCAG) method is proposed based on the deep neural network (NN). The off-line training and on-line application rules are applied to generate the optimal heading angles for the UAV swarms in the AOA localization. The simulation results show that through the DCAG method, the generated heading angles for UAV swarms enhance the localization accuracy and stability. INDEX TERMS AOA localization, distributed collaborative autonomous generation (DCAG), Cramer-Rao low bound (CRLB), deep neural network (NN).

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“…To overcome the weaknesses of the conventional MPPT algorithms, several works have pointed out the advantages of using multistring topology with distributed dcdc converters based on global maximum power point tracking (GMPPT) algorithms [15]- [17]. For instance, advanced techniques based on artificial neural networks [18], [19], fuzzy logic [20], [21], dividing rectangle search control [22], sequential extremum seeking control [23], [24] have been used successfully in finding the global maximum power point (GMPP). In recent years, bioinspired optimization methods such as particle swarm optimization (PSO) [25]- [29], ant colony optimization (ACO) [30], [31], and artificial bee colony (ABC) [32], [33], [34] showed effectiveness in the determination of GMPP.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Harvesting From a Multistrings PV Generator Based on Artificial Bee Colony Algorithm

Motahhir¹,

Chouder

Hammoumi

et al. 2021

IEEE Systems Journal

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Photovoltaic (PV) systems based on multistring configuration are the best effective solution, given its advantages in terms of system availability, reliability, and energy efficiency. In this particular configuration each substring has its own dc-dc converter and a dedicated maximum power search algorithm which increase the cost and complexity. In this article, an efficient centralized global maximum power tracking (GMPPT) algorithm for multistring PV array subject to partial shading conditions is proposed. The algorithm is based on artificial bee colony (ABC) as an optimization approach to provide the optimal duty cycles allowing the extraction of the optimal global maximum power from each substring. In particular, the proposed approach allows significant reduction of the required sensors to only one pair of current and voltage sensors, at the common point of connection of the overall PV strings. The simulation study has been carried out under Cadence/Pspice and MATLAB/Simulink platforms on the I-V curves to confirm the effectiveness of the proposed algorithm when several shading patterns occur. In addition, complex shading pattern of a daily profile has been also carried out to demonstrate the GMPPT finding in dynamically variable conditions. Performance comparison against particle swarm optimization based maximum power point tracking algorithm and the traditional perturb and observe method has also been carried out. The obtained simulation and experimental results have shown the effectiveness and a good tracking capability of the proposed ABC algorithm in a multistring PV array configuration under uniform and nonuniform irradiance. Index Terms-Artificial bee colony (ABC) algorithm, digital signal processor (DSP), global maximum power tracking

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An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Cited by 34 publications

References 13 publications

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

Optimal Energy Harvesting From a Multistrings PV Generator Based on Artificial Bee Colony Algorithm

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