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In today’s competitive digital landscape, banking firms must leverage qualitative and quantitative analysis to enhance their website interfaces, ensuring they meet user needs and expectations. By combining detailed user feedback with data-driven insights, banks can create more intuitive and engaging online experiences, ultimately driving customer satisfaction and loyalty. Thus, the need for website customer behavior analysis to evaluate its interface is critical. This study focused on the five biggest banking firms and collected big data from their websites. Statistical analysis was followed to validate findings and ensure the reliability of the results. At the same time, agent-based modeling (ABM) and System Dynamics (SD) were utilized to simulate user behavior, thereby allowing for the prediction of responses to interface changes and the optimization of their website, and to obtain a comprehensive understanding of user behavior, thereby enabling banking firms to create more intuitive and user-friendly website interfaces. This interdisciplinary approach found that various website analytical metrics, such as organic and paid traffic costs, referral domains, and email sources, tend to impact banking firms’ purchase conversion, display ads, organic traffic, and bounce rate. Moreover, these insights into banking firms’ website visibility, combined with the behavioral data of the neuromarketing study, indicate specific areas for their website interface and performance improvement. Full article

(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, 2nd Edition)

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23 pages, 5970 KiB

Open AccessArticle

Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions

by Alain Aoun, Mehdi Adda, Adrian Ilinca, Mazen Ghandour and Hussein Ibrahim

Energies 2024, 17(16), 4075; https://doi.org/10.3390/en17164075 (registering DOI) - 16 Aug 2024

Abstract

The modern energy landscape is undergoing a significant transformation towards cleaner, decentralized energy sources. This change is driven by environmental and sustainability needs, causing traditional centralized electric grids, which rely heavily on fossil fuels, to be replaced by a diverse range of decentralized distributed energy resources. Virtual power plants (VPPs) have surfaced as a flexible solution in this transition. A VPP’s primary role is to optimize energy production, storage, and distribution by coordinating output from various connected sources. Relying on advanced communication and control systems, a VPP can balance supply and demand in real time, offer ancillary services, and support grid stability. However, aligning VPPs’ economic and operational practices with broader environmental goals and policies is a challenging yet crucial aspect. This article introduces a new VPP management and optimization algorithm designed for quick and intelligent decision-making, aiming for the lowest levelized cost of energy (LCOE), minimum grid technical losses, and greenhouse gas (GHG) emissions. The algorithm’s effectiveness is confirmed using the IEEE 33-bus grid with 10 different distributed power generators. Simulation results show the algorithm’s responsiveness to complex variables found in practical scenarios, finding the optimal combination of available energy resources. This minimizes the LCOE, technical losses, and GHG emissions in less than 0.08 s, achieving a total LCOE reduction of 16% from the baseline. This work contributes to the development of intelligent energy management systems, aiding the transition towards a more resilient and sustainable energy infrastructure. Full article

(This article belongs to the Section F2: Distributed Energy System)

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16 pages, 2860 KiB

Open AccessArticle

Attention-Enhanced Bi-LSTM with Gated CNN for Ship Heave Multi-Step Forecasting

by Wenzhuo Shi, Zimeng Guo, Zixiang Dai, Shizhen Li and Meng Chen

J. Mar. Sci. Eng. 2024, 12(8), 1413; https://doi.org/10.3390/jmse12081413 (registering DOI) - 16 Aug 2024

Abstract

This study addresses the challenges of predicting ship heave motion in real time, which is essential for mitigating sensor–actuator delays in high-performance active compensation control. Traditional methods often fall short due to training on specific sea conditions, and they lack real-time prediction capabilities. To overcome these limitations, this study introduces a multi-step prediction model based on a Seq2Seq framework, training with heave data taken from various sea conditions. The model features a long-term encoder with attention-enhanced Bi-LSTM, a short-term encoder with Gated CNN, and a decoder composed of multiple fully connected layers. The long-term encoder and short-term encoder are designed to maximize the extraction of global characteristics and multi-scale short-term features of heave data, respectively. An optimized Huber loss function is used to improve the fitting performance in peak and valley regions. The experimental results demonstrate that this model outperforms baseline methods across all metrics, providing precise predictions for high-sampling-rate real-time applications. Trained on simulated sea conditions and fine-tuned through transfer learning on actual ship data, the proposed model shows strong generalization with prediction errors smaller than 0.02 m. Based on both results from the regular test and the generalization test, the model’s predictive performance is shown to meet the necessary criteria for active heave compensation control. Full article

(This article belongs to the Section Ocean Engineering)

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31 pages, 14052 KiB

Open AccessArticle

Automated Safety Risk Assessment Framework by Integrating Safety Regulation and 4D BIM-Based Rule Modeling

by Dohyeong Kim, Taehan Yoo, Si Van-Tien Tran, Doyeop Lee, Chansik Park and Dongmin Lee

Buildings 2024, 14(8), 2529; https://doi.org/10.3390/buildings14082529 (registering DOI) - 16 Aug 2024

Abstract

Performing risk assessments in construction requires collecting and analyzing project data and historical safety accident data, which is challenging due to the inherent complexities and dynamic nature of construction projects. To address these challenges, building information modeling (BIM) has been leveraged as a centralized digital repository that integrates data and provides a holistic 3D view of a project. Previous studies have highlighted BIM’s significant functions for risk assessment, such as visualization, simulation, and clash detection. However, these studies often overlook the incorporation of temporal information, which is crucial for assessing risks accounting for the dynamic conditions of construction sites. This study develops a 4D BIM-based risk-assessment framework by integrating spatial and temporal data to respond to dynamic site changes. The framework leverages 4D BIM to combine 3D model data with time-, resource-, and logistics-related information, enhancing the tracking and evaluation of construction progress. The study involves investigating major construction accidents, classifying their risk factors, establishing risk-factor identification algorithms, and implementing the framework on a web-based platform for validation. This approach offers a comprehensive risk-identification strategy, applicable to multiple accident types, with intuitive visualization using BIM models, benefiting from managers’ experiential knowledge and enabling effective risk assessments and mitigation strategies. Consequently, potential safety risks at construction sites can be efficiently identified using interconnected spatial and temporal data while tracking changes in risk levels in real time and visualizing them on a web-based platform. Full article

(This article belongs to the Special Issue BIM-Based Construction Management)

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24 pages, 29740 KiB

Open AccessArticle

Research on eLoran Weak Signal Extraction Based on Wavelet Hard Thresholding Processing

by Langlang Cheng, Shougang Zhang, Zhen Qi, Xin Wang, Yingming Chen and Ping Feng

Remote Sens. 2024, 16(16), 3012; https://doi.org/10.3390/rs16163012 (registering DOI) - 16 Aug 2024

Abstract

As the eLoran signal propagates, its strength gradually diminishes with increasing distance, making subsequent signal capture and terminal development challenging. To address this phenomenon, this paper proposes an improved method based on wavelet hard thresholding. This method applies hierarchical processing to the coefficients obtained after wavelet decomposition, based on the signal’s center frequency. It effectively addresses issues like the disappearance of trailing edges and the presence of the noise with large coefficients. Simulation results show that the improved method has the largest output signal-to-noise ratio and effectively improves the problem of tailing vanishing and eliminates the noise with large coefficients. In analog source signal testing, the results show that the method can extract signals of 30 dBμv/m and above well. In actual signal testing, the improved method can extract eLoran signals transmitted over a distance of approximately 1000 km. Based on the results, it can be deduced that the input signal-to-noise ratio is −28.8 dB. Therefore, this method is a suitable and effective solution for extracting weak eLoran signals, providing strong support for signal monitoring in areas at the coverage boundaries of eLoran signals. Full article

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16 pages, 13739 KiB

Open AccessArticle

A Cationic Zn-Phthalocyanine Turns Alzheimer’s Amyloid β Aggregates into Non-Toxic Oligomers and Inhibits Neurotoxicity in Culture

by Abdullah Md. Sheikh, Shatera Tabassum, Shozo Yano, Fatema Binte Abdullah, Ruochen Wang, Takahisa Ikeue and Atsushi Nagai

Int. J. Mol. Sci. 2024, 25(16), 8931; https://doi.org/10.3390/ijms25168931 (registering DOI) - 16 Aug 2024

Abstract

Amyloid β peptide (Aβ) aggregation and deposition are considered the main causes of Alzheimer’s disease. In a previous study, we demonstrated that anionic Zn-phthalocyanine (ZnPc) can interact with the Aβ peptide and inhibit the fibril-formation process. However, due to the inability of anionic ZnPc to cross the intact blood–brain barrier, we decided to explore the interaction of cationic methylated Zn-phthalocyanine (cZnPc) with the peptide. Using a ThT fluorescence assay, we observed that cZnPc dose-dependently and time-dependently inhibited Aβ_1-42 fibril levels under in vitro fibril-formation conditions. Electron microscopy revealed that it caused Aβ_1-42 peptides to form small aggregates. Western blotting and dot immunoblot oligomer experiments demonstrated that cZnPc increased rather than decreased the levels of oligomers from the very early stages of incubation. A binding assay confirmed that cZnPc could bind with the peptide. Docking simulations indicated that the oligomer species of Aβ_1-42 had a higher ability to interact with cZnPc. ANS fluorescence assay results indicated that cZnPc did not affect the hydrophobicity of the peptide. However, cZnPc significantly increased intrinsic tyrosine fluorescence of the peptide after 8 h of incubation in fibril-formation conditions. Importantly, cell culture experiments demonstrated that cZnPc did not exhibit any toxicity up to a concentration of 10 µM. Instead, it protected a neuronal cell line from Aβ_1-42-induced toxicity. Thus, our results suggest that cZnPc can affect the aggregation process of Aβ_1-42, rendering it non-toxic, which could be crucial for the therapy of Alzheimer’s disease. Full article

(This article belongs to the Special Issue Neurodegenerative Diseases and Protein Quality Control System)

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28 pages, 909 KiB

Open AccessArticle

Optimizing AoI in IoT Networks: UAV-Assisted Data Processing Framework Integrating Cloud–Edge Computing

by Mingfang Ma and Zhengming Wang

Drones 2024, 8(8), 401; https://doi.org/10.3390/drones8080401 (registering DOI) - 16 Aug 2024

Abstract

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)

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18 pages, 9752 KiB

Open AccessArticle

Numerical Simulation of Fluid Flow Characteristics and Heat Transfer Performance in Graphene Foam Composite

by Jinpeng Bi, Rongyao Zhou, Yuexia Lv, Tingting Du, Juan Ge and Hongyang Zhou

Coatings 2024, 14(8), 1046; https://doi.org/10.3390/coatings14081046 (registering DOI) - 16 Aug 2024

Abstract

Graphene foam composite is a promising candidate for advanced thermal management applications due to its excellent mechanical strength, high thermal conductivity, ultra-high porosity and huge specific surface area. In this study, a three-dimensional physical model was developed in accordance with the dodecahedral structure of graphene foam composite. A comprehensive numerical simulation was carried out to investigate the fluid flow and convective heat transfer in open-cell graphene foam composite by using ANSYS Fluent 2021 R1 commercial software. Research results show that, as porosity increases, the pressure gradient for graphene foam composite with circular and triangular cross-section struts is reduced by 65% and by 77%, respectively. At a given porosity of 0.904, when the inlet velocity increases from 1 m/s to 5 m/s, the pressure gradient is increased by 11.3 times and 13.8 times, and the convective heat transfer coefficient is increased by 54.5% and 43% for graphene foam composite with circular and triangular cross-section struts, respectively. Due to the irregularity of the skeleton distribution, the pressure drop in Y direction is the highest among the three directions, which is 8.7% and 17.4% higher than that in the Z and X directions at the inlet velocity of 5 m/s, respectively. The convective heat transfer coefficient in the Y direction is significantly lower than that along the X and Z directions. Furthermore, triangular cross-section struts induce a greater pressure drop but offer less effective heat transfer compared to circular struts. The research findings may provide critical insights into the design and optimization of graphene foam composites, and promote their potential for efficient thermal management and gas/liquid purification in engineering applications. Full article

(This article belongs to the Special Issue Enhancing Machining Processes With Advanced Tool, Coating Materials and Cutting Technology)

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25 pages, 11985 KiB

Open AccessArticle

Plasma Dynamics and Electron Transport in a Hall-Thruster-Representative Configuration with Various Propellants: II—Effects of the Magnetic Field Topology

by Maryam Reza, Farbod Faraji and Aaron Knoll

Plasma 2024, 7(3), 680-704; https://doi.org/10.3390/plasma7030035 (registering DOI) - 16 Aug 2024

Abstract

We investigate the effects of the magnetostatic (

B

) field topology on the plasma behavior in a 2D collisionless simulation setup that represents an axial–azimuthal cross-section of a Hall thruster. The influence of the

B

-field topology is assessed in terms of [...] Read more.

We investigate the effects of the magnetostatic (

B

) field topology on the plasma behavior in a 2D collisionless simulation setup that represents an axial–azimuthal cross-section of a Hall thruster. The influence of the

B

-field topology is assessed in terms of two principal design properties of the field in a typical Hall thruster, i.e., the field’s peak intensity along the axial direction, and the field’s axial distribution. The effects of the field’s intensity are investigated for three propellants—xenon, krypton, and argon. Whereas, the effects of the axial profile of the magnetic field are studied only for the xenon propellant as an example. We primarily aim to understand how the changes in the

B

-field topology affect the spectra of the resolved instabilities as well as the electrons’ transport characteristics and the contributions of various momentum terms to transport. The numerical observations on the instabilities’ characteristics are compared against the relevant existing theories to determine the extent to which the simulated and the theoretically predicted characteristics are consistent across the studied parameter space. It was, most notably, found that modes related to ion acoustic instability are dominantly present across the simulation cases. The ion transit time instability additionally develops at the highest

B

-field intensities as a long-wavelength structure. The main influence of the axial profile of the

B

field on the plasma discharge was observed to be in terms of the electrons’ transport characteristics. Where possible, the insights from the simulations are discussed with respect to the relevant experimental observations available in the literature. Full article

(This article belongs to the Special Issue Feature Papers in Plasma Sciences 2023)

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7 pages, 231 KiB

Open AccessArticle

Simulation-Based Echocardiography Teaching in Medical Education: A Test-Based Pilot Study

by Michael Otorkpa, Alan Kirk, Nichola Philp and Asmaa Omran

J. Oman Med. Assoc. 2024, 1(1), 3-9; https://doi.org/10.3390/joma1010002 (registering DOI) - 16 Aug 2024

Abstract

Echocardiography is fundamental to diagnostic medicine, yet medical students seldom learn it. Simulation-based training to improve echocardiography learning is promising. This study examined how simulation-based echocardiography training affects final-year medical students’ knowledge and abilities. The study involved 16 medical students. Prior ultrasound experience and self-assessed competence were assessed using a pre-test that also had six multiple-choice questions on cardiac anatomy and physiology. The students went through an echocardiography teaching session using a simulator and a post-test with similar questions as the pre-test was administered thereafter. We compared both tests, and data analysis was performed using Microsoft Excel. Most students had little echocardiography experience before the class. After the teaching, scores averaged 5.07, up from 4.13 in the pre-test. Differences in pre-test and post-test scores were statistically significant (p = 0.007). The responses represented an improvement in self-assessed competence after the session. Simulation-based echocardiography improved medical students’ knowledge and skills. This study emphasizes the need for simulation-based training research to determine its long-term effects on clinical practice. Full article

16 pages, 8504 KiB

Open AccessArticle

Numerical Simulation of Flow Field around Jacket Foundations on Flat-Bed and Equilibrium Scour Bathymetry

by Dawei Guan, Yinuo Chu, Cheng Chen, Jingang Liu and Zishun Yao

J. Mar. Sci. Eng. 2024, 12(8), 1412; https://doi.org/10.3390/jmse12081412 (registering DOI) - 16 Aug 2024

Abstract

In recent years, jacket foundations have been increasingly employed in offshore wind farms. Their complex design comprising piles and trusses poses challenges for conducting comprehensive flow field measurements using physical experiments. Consequently, the influence of the flow field on local scour around these foundations remains unclear. Therefore, numerical simulation methods are essential to depict the surrounding flow characteristics. This study utilizes large eddy simulation (LES) turbulence models within OpenFOAM to simulate the flow field around jacket foundations on flat-bed and equilibrium scour bathymetry. A flume experiment was conducted for numerical model establishment and validation. The close agreement between experimental and numerical results indicates that the LES model accurately reflects the flow patterns around the jacket foundation. Time-averaged and instantaneous flow characteristics, average kinetic energy (AKE), turbulence structure, and bed shear stress were analyzed. The results indicate that flow intensity is reduced due to the shielding effect and energy dissipation by the truss structure of the jacket foundation. Furthermore, the AKE of the flow upstream of the rear piles decreases by 18.9% in the flat-bed state and 28.0% in the equilibrium state, indicating more energy dissipation and less scour at the rear piles in the equilibrium state. The research findings offer valuable insights into the design and scour protection strategies for jacket foundations. Full article

(This article belongs to the Section Coastal Engineering)

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27 pages, 5652 KiB

Open AccessArticle

Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo

by Hester Huijsdens, David Leeftink, Linda Geerligs and Max Hinne

Entropy 2024, 26(8), 695; https://doi.org/10.3390/e26080695 (registering DOI) - 16 Aug 2024

Abstract

Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situation, but its inference remains highly challenging. In this work, we introduce a Sequential Monte Carlo (SMC) sampler for the Wishart process, and show how it compares to conventional inference approaches, namely MCMC and variational inference. Using simulations, we show that SMC sampling results in the most robust estimates and out-of-sample predictions of dynamic covariance. SMC especially outperforms the alternative approaches when using composite covariance functions with correlated parameters. We further demonstrate the practical applicability of our proposed approach on a dataset of clinical depression (

n = 1

), and show how using an accurate representation of the posterior distribution can be used to test for dynamics in covariance. Full article

(This article belongs to the Special Issue Bayesian Inference and Mathematical Modeling in Complex Biological Systems)

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16 pages, 34662 KiB

Open AccessArticle

Mechanistic Insights into Effects of Perforation Direction on Thermal Hydraulic Performance of Ribs in a Rectangular Cooling Channel

by Weijia Qian, Ruiyang Shuai, Qingkun Meng, Subhajit Roy, Songbai Yao and Ping Wang

Aerospace 2024, 11(8), 675; https://doi.org/10.3390/aerospace11080675 (registering DOI) - 16 Aug 2024

Abstract

This study investigates the turbulent flow characteristics and heat transfer performance within a rectangular cooling channel with an aspect ratio of 5:3 and featuring perforated ribs, then explores the effects of the rib perforation directions on its thermal hydraulic performance. Through experimental tests (transient thermographic liquid crystal technique) and numerical simulations, it is demonstrated that horizontal perforated ribs can effectively reduce pressure loss at a high Reynolds number while maintaining notable heat transfer enhancement. Additionally, changing the rib perforation directions results in diverse effects on flow field and heat transfer. Our results show that horizontal perforated ribs can compress the recirculation vortex behind ribs, enhancing heat transfer by flow scouring, whereas upward-tilted perforated ribs increase flow friction and weaken heat transfer due to coupling of the airflow with the separation vortices behind the ribs. Downward-tilted ribs enhance local heat transfer by directing airflow behind the rib, and can also cause detachment of vortices and reduced friction. Our results indicate that introducing horizontal perforated ribs into a rectangular internal cooling channel can decrease pressure loss without significantly compromising heat transfer performance. Full article

(This article belongs to the Section Aeronautics)

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15 pages, 13260 KiB

Open AccessArticle

Refined Modeling of Heterogeneous Medium for Ground-Penetrating Radar Simulation

by Hai Liu, Dingwu Dai, Lilong Zou, Qin He, Xu Meng and Junhong Chen

Remote Sens. 2024, 16(16), 3010; https://doi.org/10.3390/rs16163010 (registering DOI) - 16 Aug 2024

Abstract

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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22 pages, 40834 KiB

Open AccessArticle

Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network

by Shengsheng Qin, Zhipeng Cao, Feng Wang, Sze Song Ngu, Lee Chin Kho and Hui Cai

Energies 2024, 17(16), 4076; https://doi.org/10.3390/en17164076 (registering DOI) - 16 Aug 2024

Abstract

To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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