Journal of Engineering Research最新文献_第5页

Cyclical deep-sea pressure loading of cylindrical submersibles: Analytical solution with damage-scaling factors and application to the Titan submersible failure 圆柱形潜水器深海循环压力载荷：损伤比例因子解析解及其在泰坦潜水器失效中的应用

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-08-15 DOI: 10.1016/j.jer.2025.08.008

Ruud Weijermars

This study applies fundamental stress tensor analysis to quantify cyclic deep-sea pressure loading on cylindrical submersibles with end-caps. It examines differential stress distributions along the hull, highlighting how longitudinal stress shifts from tensile at mid-span to compressive near end-caps, while hoop stress varies due to superposition effects. The methodology is applied to the Titan submersible failure, incorporating strain gage data and adhesive bond degradation between its carbon-fiber hull and titanium end-rings. A damage-scaling factor is introduced to assess the impact of bonding failure on stress modifications. The analysis also considers stress accumulation leading to implosion once pressure-sealing breaches. Failure analysis suggests that repeated compression and decompression cycles contributed to progressive delamination and ultimate structural failure. While prior studies examined material damage, this research uniquely quantifies dynamic stress variations and their role in the submersible's collapse. The findings offer insights into differential stress concentrations in cylindrical pressure chambers, relevant to both passive submersibles and active submarines. The closed-form stress model may aid future investigations and inform submersible design improvements.

本研究采用基本应力张量分析方法，定量分析了带端盖的圆柱形潜水器的深海循环压力载荷。它检查了沿船体的不同应力分布，突出了纵向应力如何从跨中部的拉伸转移到端盖附近的压缩，而环向应力由于叠加效应而变化。该方法应用于Titan潜水器故障，结合应变计数据和碳纤维船体与钛端环之间的粘合剂降解。引入损伤标度因子来评估粘结破坏对应力变化的影响。分析还考虑了密封裂缝时应力积累导致的内爆。破坏分析表明，反复的压缩和减压循环导致了渐进式分层和最终的结构破坏。虽然之前的研究考察了材料损伤，但这项研究独特地量化了动应力变化及其在潜水器坍塌中的作用。这一发现为研究与被动潜艇和主动潜艇相关的圆柱形压力室的应力集中差异提供了洞见。封闭应力模型可能有助于未来的研究，并为潜水器的设计改进提供信息。

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引用次数: 0

Feature alignment LSTM for detecting anomalies in wind turbine main bearing temperature based on SCADA data 基于SCADA数据的风电主轴承温度异常检测特征对准LSTM

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-05-28 DOI: 10.1016/j.jer.2025.05.011

Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Zhao Rao, Haoxuan Luo

The main bearing is one of the core components of the wind turbine drivetrain. A novel anomaly detection method for wind turbine main bearing temperature based on long short-term memory (LSTM) networks with feature alignment (FA) is proposed and validated in this paper. A simplified physical mechanism model is used to analyze the factors affecting main bearing temperature, and feature variables are then selected from the supervisory control and data acquisition (SCADA) system. The FA technique is employed to synchronize the previous main bearing temperature with the current feature variables, capturing both historical and current information. The time step length for the FA-LSTM model samples is optimized through multi-metric comparisons. The performance of temperature evaluation using the selected variable combination coupled with FA-LSTM is optimal, compared to different methods and variable combinations. Compared to the standalone LSTM algorithm, the FA-LSTM model achieves reductions of 11.35 % in mean absolute error and 11.72 % in root mean square error. A normal operation model is established using historical health data to detect anomalies. Warning and alarm thresholds for anomaly detection are set based on the standard deviation of the residuals between the evaluated and actual temperatures, with a sliding window average used to suppress fluctuations. The proposed method is validated through real case studies of both faulty and healthy 6.45 MW semi-direct drive wind turbines, and the results demonstrate its significant effectiveness in detecting main bearing temperature anomalies. In the case studies, the proposed model issued alarms for main bearing temperature anomalies 30 days earlier than the SCADA system.

主轴承是风力发电机传动系统的核心部件之一。提出并验证了一种基于特征对齐的LSTM网络的风电机组主轴承温度异常检测方法。采用简化的物理机制模型分析影响主轴承温度的因素，并从SCADA系统中选择特征变量。利用FA技术将之前的主轴承温度与当前特征变量同步，同时获取历史和当前信息。通过多度量比较，优化FA-LSTM模型样本的时间步长。对比不同的方法和变量组合，所选择的变量组合与FA-LSTM相结合的温度评价效果最优。与独立LSTM算法相比，FA-LSTM模型的平均绝对误差降低了11.35 %，均方根误差降低了11.72 %。利用历史运行状况数据建立正常运行模型以检测异常。异常检测的警告和报警阈值是根据评估温度与实际温度之间的残差的标准偏差设置的，并使用滑动窗口平均值来抑制波动。通过故障和正常6.45 MW半直驱风机的实际案例研究，验证了该方法在检测主轴承温度异常方面的显著有效性。在案例研究中，所提出的模型比SCADA系统提前30天发出主轴承温度异常警报。

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引用次数: 0

Aerodynamic optimization of car top box design for drag reduction using CFD and Kriging modeling 基于CFD和Kriging模型的汽车顶箱减阻气动优化

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-06-16 DOI: 10.1016/j.jer.2025.06.003

Yingchao Zhang , Ruizhuo Zhou , Chang Su , Zhe Zhang , Xiaowen Zang

In this paper, the influence of the shape parameters and position parameters of the car top box on the aerodynamic performance is studied. The flow field numerical simulation software STAR-CCM+ was used to conduct CFD simulation of the vehicle outflow field. Through comparative analysis, it was found that the contribution rate of the top box to the resistance coefficient of the vehicle was 10.39 %, which proved the necessity of drag reduction optimization of the top box. Based on Isight optimization platform and aiming at reducing drag, the shape parameters and position parameters of the top box were optimized. The Kriging agent model is used to optimize the drag coefficient, and the post-processing diagrams of the outflow field before and after optimization are compared and analyzed, and the drag reduction mechanism is summarized. In addition, on the basis of the optimized vehicle model, the drag reduction slot is added to the bottom of the roof box, and the drag reduction is finally achieved by 5.2 %. The aerodynamic drag reduction of the top box can enhance the power performance and fuel economy of the vehicle and reduce carbon emissions.

本文研究了汽车顶盒的形状参数和位置参数对其气动性能的影响。利用流场数值模拟软件STAR-CCM+ 对车辆出流场进行CFD模拟。通过对比分析，发现顶箱对整车阻力系数的贡献率为10.39 %，证明了对顶箱进行减阻优化的必要性。基于Isight优化平台，以减小阻力为目标，对顶盒形状参数和位置参数进行了优化。采用Kriging agent模型对阻力系数进行优化，对比分析优化前后的出流场后处理图，总结减阻机理。此外，在优化车型的基础上，在车顶箱体底部增加减阻槽，最终实现了5.2% %的减阻。顶箱的气动减阻可以提高车辆的动力性能和燃油经济性，减少碳排放。

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引用次数: 0

A study of the effects of sky view factor on street pedestrians’ sensations & comforts against thermal environments and air qualities 天空景观因子对街道行人对热环境和空气质量的感觉和舒适度的影响研究

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-07-28 DOI: 10.1016/j.jer.2025.07.002

Siqi Wang , Shuangyan Feng , Shuang Liang , Li Yan , Hui Yin , Jian Zhang

The intensification of global warming and air pollution has increased the threat of diseases such as heat stress and respiratory ailments. This study aims to optimise the thermal-atmospheric environment of urban outdoor spaces from the perspective of urban street space design. Using field measurements, questionnaire surveys, and statistical analysis, this research verified the impacts of the sky view factor (SVF) on sensations of outdoor thermal environment (OTE) and air quality (AIQ). Local pedestrians’ neutral points (the point that people are perceptive free) towards OTE and AIQ were 28.15°C (neutral temperature, NT) and 16.95㎍/m³ (neutral PM2.5 concentration, NPM_2.5C). In addition, the NT decreased (27 – 24°C) as the SVF increase (0.1–0.7) while NPM_2.5C increased (13–33 ㎍/m³) with that. People’s comforts were affected by both temperature and PM2.5 C, the increase of either of them would cause comfort level reduction. Finally, subjective perceptions on OTE and AIQ were interactively impactive, their NT decreased as the rise of PM2.5 C. The findings demonstrated that local pedestrians exhibit poor tolerance to heat in regions with a high SVF and/or atmosphere is more severely polluted. This paper helps urban planners enhance their understanding of thermal comfort, air quality perception, and public health. Its findings expected to offer recommendations with significant reference value for urban design.

全球变暖和空气污染加剧，增加了热应激和呼吸系统疾病等疾病的威胁。本研究旨在从城市街道空间设计的角度，优化城市室外空间的热大气环境。通过实地测量、问卷调查和统计分析，验证了天空景观因子（SVF）对室外热环境（OTE）和空气质量（AIQ）的影响。当地行人对OTE和AIQ的中点（人们感知自由的点）分别为28.15°C（中性温度，NT）和16.95 /m³ （中性PM2.5浓度，NPM2.5C）。另外，随着SVF的增加（0.1 ~ 0.7），NT减少（27 ~ 24°C）， NPM2.5C增加（13 ~ 33‰/m³）。人们的舒适度同时受到温度和PM2.5 C的影响，两者中的任何一个升高都会导致舒适度降低。最后，对OTE和AIQ的主观感知是相互影响的，它们的NT随着PM2.5 C的上升而下降。研究结果显示，在SVF高及/或大气污染较严重的地区，行人对热的耐受性较差。本文帮助城市规划者提高他们对热舒适、空气质量感知和公共健康的理解。研究结果有望为城市设计提供具有重要参考价值的建议。

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引用次数: 0

Adjusting image stego practicality via YCbCr color space formation 通过形成YCbCr色彩空间来调整图像的渐近实用性

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-07-30 DOI: 10.1016/j.jer.2025.07.008

Shahad Alharthi , Adnan Gutub

Information technology has revolutionized the field of communication in the digital age. However, these advancements have also raised significant security concerns. Protecting classified information is critical, and steganography is widely used to ensure confidentiality by embedding secret messages into media files in a way that makes them nearly undetectable. Among the various steganographic techniques, the Least Significant Bit (LSB) method is a commonly used approach. However, determining the optimal color space that balances image quality and data hiding capacity remains an active area of research.

This study aims to bridge this gap by proposing a generalized evaluation framework that systematically analyzes ten color spaces RGB, HSV, HSI, YUV, YIQ, YCbCr, XYZ, LAB, LMS, and xyY using the LSB method. The framework involves applying LSB embedding across all color spaces, evaluating security using eight standard metrics (MSE, PSNR, SSIM, NAE, NCC, AD, MD, SC). The study further presented a normalized performance score for each space. Experimental results showed that YCbCr consistently outperforms others, achieving highest generalized score ranging between 0.875 and 0.890 for different testing images. The proposed framework provided attractive reproducible and scalable foundation for color space analysis within image steganography.

信息技术使数字时代的通信领域发生了革命性的变化。然而，这些进步也引起了重大的安全问题。保护机密信息至关重要，隐写术被广泛用于将秘密信息嵌入媒体文件中，使其几乎无法被发现，从而确保机密性。在各种隐写技术中，最低有效位（LSB）方法是一种常用的方法。然而，确定平衡图像质量和数据隐藏能力的最佳色彩空间仍然是一个活跃的研究领域。本研究旨在通过提出一个通用的评估框架来弥补这一差距，该框架使用LSB方法系统地分析了RGB， HSV， HSI， YUV， YIQ, YCbCr， XYZ， LAB， LMS和xyY十个颜色空间。该框架包括在所有色彩空间中应用LSB嵌入，使用八个标准指标（MSE， PSNR， SSIM， NAE， NCC， AD， MD， SC）评估安全性。该研究进一步给出了每个空间的标准化性能分数。实验结果表明，YCbCr在不同的测试图像上的广义得分最高，在0.875 ~ 0.890之间。该框架为图像隐写中的色彩空间分析提供了具有吸引力的可重复性和可扩展性的基础。

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引用次数: 0

Seismic behavior and numerical simulation of damaged beam-column joints retrofitted with viscoelastic steel-enveloped elements 粘弹性钢包络单元加固损伤梁柱节点的抗震性能及数值模拟

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-10-02 DOI: 10.1016/j.jer.2025.09.016

Xing-Huai Huang , Shang-Zhen Li , Yeshou Xu , Han-Jie Xiao , Li-Xin Wang , Aiguo Song , Jinde Cao

This study proposes an innovative retrofitting method to enhance the seismic performance of damaged reinforced concrete (RC) beam-column (B-C) joints by applying viscoelastic pads and steel plates. Five T-shaped RC joints were initially subjected to reverse cyclic loading tests to evaluate their seismic performance. Using limited experimental data, numerical simulations were utilized to capture the behavior of the T-shaped RC joints to assess how variations in the parameters of retrofitting devices influence the retrofitting effect. A detailed comparison between experimental tests and analytical examination of the experimental data revealed that the finite element model effectively replicated the seismic performance of the retrofitted damaged joints. Variations in the viscoelastic pad thickness significantly affected the joints' deformation capacity. Increasing the thickness of the steel plate substantially enhanced the initial stiffness, with a 14 mm thick viscoelastic pad and a 6 mm thick steel plate offering a balanced increase in seismic performance while avoiding excessive material usage.

本研究提出了一种创新的加固方法，通过粘弹性垫块和钢板来提高受损钢筋混凝土梁柱节点的抗震性能。对5个t形钢筋混凝土节点进行了反循环加载试验，以评估其抗震性能。利用有限的实验数据，采用数值模拟的方法对t型钢筋混凝土节点的行为进行了捕捉，以评估改造装置参数的变化对改造效果的影响。通过对试验数据的分析分析对比，表明有限元模型能够有效地模拟损伤节点的抗震性能。粘弹性垫层厚度的变化对节点的变形能力有显著影响。增加钢板的厚度大大提高了初始刚度，14 毫米厚的粘弹性垫和6 毫米厚的钢板提供了抗震性能的平衡增加，同时避免了过度的材料使用。

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引用次数: 0

Analysis of transient startup characteristics in a two-stage mixed-flow pump using a theoretical prediction model 用理论预测模型分析两级混流泵瞬态启动特性

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-05-24 DOI: 10.1016/j.jer.2025.05.008

Mingke Li , Jin Xu , Yuhu Wang , Tairan Chen , Xiaoping Zhang

In large-scale hydraulic engineering projects (such as China's South-to-North Water Diversion Project), the optimization of pumping station control strategies through studying the synchronization between starting flow rate and head can enhance operational efficiency and reduce energy losses during the startup phase. Currently, theoretical prediction models, owing to their computational efficiency and interpretability of underlying mechanisms, represent the most suitable approach for analyzing startup transient characteristics in engineering applications, but the transient prediction theory for multistage mixed flow pumps is overly simplified, making it difficult to accurately analyze the flow and head characteristics during startup, and also have significant errors in predicting multistage coupling losses. To address the above issues, a highly efficient and accurate transient prediction model for the startup process of a two-stage mixed flow pump was developed. A transient head model for the impeller was developed using torque-energy balance theory, incorporating a dynamic slip factor and corrected inflow angle under transient conditions. Subsequently, guide vane losses (diffuser, friction, pressure differential) and inter-stage losses (incidence, friction, pressure differential) were modeled. These components were integrated into a pressure balance equation, enabling iterative computation of transient head and flow rate. Then, the transient characteristics of the two-stage mixed flow pump under varying startup curves were analyzed, with a superior S-shaped curve proposed to minimize hydraulic shock during acceleration phases. Finaly, the transient prediction model's accuracy is validated through recirculation passage experimental during startup, and transient characteristics under different startup curves are analyzed.

在大型水利工程项目中（如中国的南水北调工程），通过研究启动流量与水头的同步来优化泵站控制策略，可以提高运行效率，减少启动阶段的能量损失。目前，理论预测模型由于其计算效率高、机制可解释性强，是工程应用中最适合分析启动瞬态特性的方法，但多级混流泵的瞬态预测理论过于简化，难以准确分析启动过程中的流量和水头特性，在预测多级耦合损失时也存在较大误差。针对上述问题，建立了一种高效、准确的两级混流泵启动过程瞬态预测模型。利用转矩-能量平衡理论建立了叶轮瞬态水头模型，该模型考虑了瞬态条件下的动态滑移系数和修正入流角。随后，对导叶损失（扩压、摩擦、压差）和级间损失（入射、摩擦、压差）进行了建模。将这些分量集成到压力平衡方程中，实现了瞬态水头和流量的迭代计算。在此基础上，分析了两级混流泵在不同启动曲线下的瞬态特性，提出了一种较优的s型曲线，以减小加速阶段的液压冲击。最后，通过启动过程中再循环通道试验验证了暂态预测模型的准确性，并分析了不同启动曲线下的暂态特性。

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引用次数: 0

Integrated multi-channel supply chain planning with human resource and multi-tier discount considerations: An RL-GWO optimization approach 考虑人力资源和多层折扣的综合多渠道供应链规划：一种RL-GWO优化方法

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-08-27 DOI: 10.1016/j.jer.2025.08.019

Qinyao Zhang , Bhishma Karki

This study addresses the complex challenge of optimizing a three-level, two-channel supply chain by integrating pricing, discounting, profit allocation, ordering, and human resource management under dynamic conditions. A mixed-integer nonlinear programming (MINLP) model is proposed, incorporating price-dependent demand, channel leakage rates, multi-tier discount structures for raw material suppliers and chain stores, and human resource capacity constraints. To tackle this intricate problem, a novel Reinforcement Learning-Enhanced Grey Wolf Optimizer (RL-GWO) algorithm is developed, utilizing Q-learning to dynamically balance exploration and exploitation. Computational results demonstrate RL-GWO's superior performance, achieving near-optimal solutions with error gaps below 0.8 % compared to the BARON solver while solving large-scale instances where BARON becomes computationally infeasible. Compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), RL-GWO consistently delivers higher accuracy and faster computation times across all tested problem sizes. Sensitivity analyses reveal critical insights into the impacts of transportation costs, potential demand, and demand leakage on profitability, highlighting the negative effect of increased transportation costs and demand leakage on margins, while higher retailer demand significantly boosts overall profit. This research provides a robust framework and an efficient, scalable solution methodology for optimizing multi-channel supply chains with integrated human resource considerations, offering valuable strategic guidance for practitioners.

本研究通过整合动态条件下的定价、折扣、利润分配、订单和人力资源管理，解决了优化三层双渠道供应链的复杂挑战。提出了一种混合整数非线性规划（MINLP）模型，该模型考虑了价格依赖性需求、渠道泄漏率、原材料供应商和连锁店的多层折扣结构以及人力资源能力约束。为了解决这个复杂的问题，开发了一种新的强化学习增强型灰狼优化器（RL-GWO）算法，利用q -学习动态平衡探索和开发。计算结果表明，与BARON求解器相比，RL-GWO具有卓越的性能，在求解大规模实例时，与BARON求解器相比，RL-GWO实现了误差小于0.8 %的接近最优解，而BARON在计算上是不可行的。与遗传算法（GA）和粒子群优化（PSO）相比，RL-GWO在所有测试的问题规模上都能提供更高的准确性和更快的计算时间。敏感性分析揭示了运输成本、潜在需求和需求泄漏对盈利能力影响的关键见解，强调了运输成本增加和需求泄漏对利润率的负面影响，而零售商需求的增加显著提高了整体利润。本研究为整合人力资源因素优化多渠道供应链提供了一个强大的框架和高效、可扩展的解决方案方法，为从业者提供了有价值的战略指导。

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引用次数: 0

Data-driven deep drawing optimization: Response surface methodology and machine learning 数据驱动的深拉深优化：响应面方法和机器学习

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-10-16 DOI: 10.1016/j.jer.2025.10.010

Lotfi Ben Said , Taoufik Kamoun , Jamel Mars , Sattam Alharbi , Wajdi Rajhi , Mohamed Turki , Mondher Wali

The deep drawing process is one of the most used techniques in cold forming process. Despite its importance in the mechanical industry, few predictive models exist for sheet metal formability. This study develops an experimental database from deep drawing cup tests on DD11 sheet steel and integrates it with advanced machine learning models, particularly XGBoost, to predict cup height (Hc) and maximum punch force (Fmax). Various tests were conducted considering punch diameter, sheet thickness, and lubricant, and the resulting dataset of 90 experiments was used to train and validate ML models. Response Surface Methodology (RSM) was also employed to support process optimization. The combination of systematic experimental characterization, RSM modeling, and ML prediction provides an accurate and efficient tool for deep drawing process optimization. The approach demonstrates strong predictive capability, outperforming conventional methods, and enables reliable data-driven design in sheet metal forming. While the study is limited to DD11 steel and cylindrical cup geometries with a relatively small dataset, it lays the groundwork for extending the methodology to other materials, geometries, and hybrid ML models to further improve predictive accuracy. The experimental and modelling study confirms the effectiveness of the proposed predictive model in accurately capturing the real behavior of the formed sheet metal.

深拉深工艺是冷成形中应用最广泛的工艺之一。尽管它在机械工业中的重要性，很少有预测模型存在板材成形性。本研究通过在DD11钢板上进行深冲杯试验开发了一个实验数据库，并将其与先进的机器学习模型（特别是XGBoost）相结合，以预测杯高（Hc）和最大冲压力（Fmax）。在考虑冲孔直径、板材厚度和润滑剂的情况下，进行了各种测试，并使用90个实验的结果数据集来训练和验证ML模型。采用响应面法（RSM）进行工艺优化。系统的实验表征、RSM建模和ML预测相结合，为深拉深工艺优化提供了准确、高效的工具。该方法具有较强的预测能力，优于传统方法，可实现可靠的数据驱动设计。虽然该研究仅限于DD11钢和圆柱杯几何形状，数据集相对较小，但它为将该方法扩展到其他材料、几何形状和混合ML模型奠定了基础，以进一步提高预测精度。实验和模型研究证实了所提出的预测模型在准确捕捉成形板的真实行为方面的有效性。

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引用次数: 0

Ensuring robust power tracking in microgrids: A new hybrid MPPT approach for improved dynamic behavior 确保微电网中强大的电力跟踪：一种新的混合MPPT方法，用于改善动态行为

IF 2.2 4区工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY

Journal of Engineering Research

Pub Date : 2026-03-01 Epub Date: 2025-09-09 DOI: 10.1016/j.jer.2025.09.002

Mohammed Yassine Dennai , Ali Benachour , Hamza Tedjini , Abdelfatah Nasri , El Madjid Berkouk

Efficient energy extraction in photovoltaic (PV)-grid connected microgrids demands robust MPPT algorithms capable of maintaining maximum power output amidst sudden solar irradiance fluctuations and rapid load switching. This paper presents a comparative study of various MPPT techniques, including Perturb and Observe (P&O), Grey Wolf Optimization (GWO), Particle Swarm Optimization-tuned Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS), hybrid Marine Predator Algorithm-Particle Swarm Optimization (MPA-PSO), Influential Flower Pollination Algorithm (IFPA), and a novel hybrid approach combining Artificial Ecosystem Optimization and P&O (AEO + P&O). To enhance tracking speed and system stability, the proposed AEO + P&O algorithm leverages the global search ability of AEO and the fast response of P&O. Results from dynamic microgrid operations reveal that AEO + P&O excels in performance, with a startup power output of 317.5 kW, settling at 670 kW during sudden irradiance fluctuations and returning to 487.3 kW after load changes. Under partial shading conditions, AEO + P&O outperforms other algorithms by maintaining the highest power output across varying irradiance levels, demonstrating superior convergence speed, stability, and robustness. Under full irradiance (1000 m²/s), AEO + P&O achieves 10.1 × 10⁵ W at the Point of Common Coupling (PCC), while under shaded conditions (Panel 1 at 800 m²/s and Panel 2 at 500 m²/s), it maintains 8.6 × 10⁵ W, outperforming other algorithms that show significant power drops. The proposed algorithm exhibits the lowest rise time (0.24 sec) and the minimum settling time (0.32 sec) compared to other MPPT techniques, offering faster tracking and higher accuracy. These results underscore two key objectives of the AEO + P&O algorithm: minimizing oscillations and convergence time while optimizing power extraction. As such, the proposed control solution is validated as a high-performance MPPT technique for the stable and reliable operation of PV systems within dynamic microgrids.

光伏（PV）并网微电网的高效能源提取需要强大的MPPT算法，能够在太阳辐照度突然波动和负载快速切换时保持最大输出功率。本文对扰动与观测（P&；O）、灰狼优化（GWO）、粒子群优化-调谐自适应神经模糊推理系统（PSO-ANFIS）、混合海洋捕食者算法-粒子群优化（MPA-PSO）、影响授粉算法（IFPA）以及人工生态系统优化与P&；O （AEO + P&；O）相结合的新型混合方法进行了比较研究。为了提高跟踪速度和系统稳定性，本文提出的AEO + P&；O算法利用了AEO的全局搜索能力和P&；O的快速响应能力。动态微网运行结果表明，AEO + P&；O性能优异，启动功率输出为317.5 kW，辐照度突然波动时稳定在670 kW，负荷变化后恢复到487.3 kW。在部分遮光条件下，AEO + P&；O通过在不同辐照度水平下保持最高功率输出，表现出卓越的收敛速度、稳定性和鲁棒性，优于其他算法。在全照度下（1000 m²/s）， AEO + P&；O在共耦合点（PCC）达到10.1 × 10 5 W，而在阴影条件下（面板1为800 m²/s，面板2为500 m²/s），它保持8.6 × 10 5 W，优于其他显示显着功耗下降的算法。与其他MPPT技术相比，该算法具有最低的上升时间（0.24 秒）和最低的沉降时间（0.32 秒），具有更快的跟踪速度和更高的精度。这些结果强调了AEO + P&；O算法的两个关键目标：最小化振荡和收敛时间，同时优化功率提取。因此，所提出的控制方案被验证为动态微电网中光伏系统稳定可靠运行的高性能MPPT技术。

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引用次数: 0