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Interconnected microgrids optimization via reconfiguration-based modular approach

APPLIED ENERGY 2024年 375卷

作者： Ayrir, Wiam Helmi, Ahmed M. Ramadan, Haitham S. Ibn Tofail Univ Natl Sch Appl Sci Elect Engn Networks & Telecommun Syst Dept Kenitra 14000 Morocco Zagazig Univ Fac Engn Comp & Syst Engn Dept Zagazig 44519 Egypt Buraydah Private Coll Coll Engn & Informat Technol Comp Engn Dept Buraydah 51418 Saudi Arabia Zagazig Univ Elect Power & Machines Dept Zagazig 44519 Egypt Inst Int Stockage Hydrogene ISTHY F-90400 Meroux Mova Belfort Territo France

This paper addresses the reconfiguration of two link power distribution networks (PDNs) under two scenarios: horizontal and vertical interconnections. Recent metaheuristic algorithms such as the Slime Mould algorithm (SMA) and artificial hummingbird algorithm (AHA) are used for identifying the optimal tie switches (TSs) to insert within each PDN upon interconnection. Both PDNs, of 33 bus each, are horizontally or vertically interconnected. Simulation results, using MATLABTM, demonstrate significant improvements in both power loss reduction and voltage profile improvement. Specifically, after the insertion of the fifth TS, a reduction of over 28% and 39% in power losses is achieved in the horizontal and vertical scenarios, respectively. Additionally, the voltage magnitude (V V m vm) ) reaches a value of 0.9423 pu and 0.9481 pu, respectively. Furthermore, the results show that AHA outperforms other techniques to efficiently reconfigure interconnected PDNs.

关键词： Smart grids Distribution networks Metaheuristics artificial hummingbird algorithm Voltage improvement Power losses minimization

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The determination of the strongest attributes of high-performance concrete featuring innovative admixtures via optimal regression-based methodologies

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 883-893页

作者： Zhang, Lianchao Guo, Shizhong Qi, Jianwei Tangshan Maritime Inst Tangshan 063000 Hebei Peoples R China

The acquisition of an optimal mix design for high-performance concrete (HPC) is known to present significant challenges due to its inherently intricate nature. Therefore, implementing machine learning techniques can effectively streamline the process, resulting in more efficient use of resources and a reduction in labor intensity. The present study has employed the support vector regression (SVR) methodology to forecast the compressive strength (CS) of HPC. In addition, the present investigation furnished two meta-heuristic techniques for attaining appropriate and suitable outcomes. Specifically, these techniques encompass the artificial hummingbird algorithm (AHA) and Flow Direction algorithm (FDA). The hybridized approach within the SVR-AHA and SVR-FDA frameworks was achieved by integrating algorithms with the model. Moreover, several criteria indicators have been employed to identify the optimal hybrid model. About 170 experimental data sets have been used to validate the performance of the developed method, which the samples split into two stages, including training and testing. Consequently, the utilization of the AHA algorithm in conjunction with the SVR model led to an enhanced association model, as evidenced by the following outcomes: RMSE = 2.00 (MPa), R2 = 98.59%, WAPE = 0.114 (MPa), MAPE = 1.22 (MPa), and. MAE = 0.717 (MPa).

关键词： High-performance concrete Flow Direction algorithm artificial hummingbird algorithm Support vector regression Compressive strength

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Vibration Control and Seismic Damages Reduction for Structural Buildings Based on Optimal Fractional-Order Controller and a Graphical User Interface Development

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2023年第8期11卷 4349-4370页

作者： Jaballah, Mohamed Seghir Harzallah, Salaheddine Nail, Bachir Ziane Achour Univ Djelfa Fac Sci & Technol Lab Dev Mech & Mat LDMM Djelfa Algeria Ziane Achour Univ Djelfa Civil Engn & Sustainable Dev Lab Djelfa Algeria Tissemsilt Univ Fac Sci & Technol Mech Engn Mat Struct Lab Tissemsilt Algeria

Introduction This paper proposes an optimal active vibration control system to control an active tuned mass damper (ATMD), which can help reduce the seismic structural damages caused by the earthquake effects. Methods The proposed controller exploits the features of the fractional-order calculus to improve the performances of the derivative and the integral actions of the conventional PID controller, the parameters gains (Kp, Ki, Kd), lambda and mu of the fractional-order PID controller (FO-PID) are selected optimally using the recent artificial hummingbird algorithm (AHA), A comparative study conducted against the classical PID controller in different earthquake excitations, to highlight the advantages of proposed FO-PID. Finally, A new graphical user interface (GUI) based on MATLAB called "SVCS Structural Vibration Control Simulator" is developed to facilitate the simulation of different vibration control systems using different earthquake excitations. Conclusion The results obtained reflect that using FO-PID controller improves the performance of active control and can

关键词： Structural vibration control PID Fractional order PID Metaheuristics optimization algorithm artificial hummingbird algorithm Active tuned mass damper Base isolation

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Optimizing energy efficiency: predicting heating load with a machine learning approach and meta-heuristic algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3993-4009页

作者： Feng, Yuan Baoji Univ Arts & Sci Baoji 721000 Shaanxi Peoples R China

This study responds to the urgent demand for accurate energy consumption forecasts and the strategic assessment of retrofit methodologies, aiming to achieve energy conservation and emissions reduction simultaneously. Amidst an escalating emphasis on energy-efficient practices, the research pioneers an integration of advanced optimization algorithms within a meticulous heating load (HL) prediction framework. Focused on the intricate domain of HL systems, this study leverages the Radial Basis Function (RBF) model, enhancing predictive precision through the integration of two meta-heuristic algorithms: the artificial hummingbird algorithm (AHA) and the Improved Grey Wolf Optimizer (IGWO). The validation process entails a comprehensive examination of HL data from diverse energy sources, subjected to stringent stability tests. This study introduces three distinctive models-RBF + AHA (RBAH), RBF + IGWO (RBIG), and an independent RBF model-each contributing unique insights for precise HL prediction. Remarkably, the RBAH model emerges as an exemplar, demonstrating an outstanding coefficient of correlation (R2) value of 0.996 and an exceptionally low root mean square error (RMSE) value of 0.605. These findings underscore the unparalleled predictive prowess of the RBAH model in forecasting heating load outcomes, marking it as a noteworthy advancement in the realm of energy prediction models. The study's contributions extend beyond the conventional, providing a robust framework and invaluable insights for advancing energy-efficient practices in building management, thereby addressing contemporary sustainability challenges.

关键词： Building energy Heating loud Radial basis function artificial hummingbird algorithm Improved grey wolf optimizer

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Estimation of pile-bearing capacity applying hybrid auto-regressed models based on regression decision tree

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2225-2239页

作者： Li, Yunpeng Dong, Shenghuan Jian, Yingbin Mei, Yuqian Shijiazhuang Railway Vocat & Tech Coll Acad Affairs Off Shijiazhuang 050062 Peoples R China Shijiazhuang Railway Tech Coll Integrat Ctr Sci & Technol Ind & Educ Shijiazhuang 050062 Peoples R China Shijiazhuang Railway Vocat & Tech Coll Personnel Dept Shijiazhuang 050062 Peoples R China

The utilization of machine learning (ML) approaches for predicting pile bearing capacity (PBC) has been a focal point in various research endeavors. Nevertheless, the inherent limitations of some ML methods, specifically their challenges in identifying global minima and their dilatory convergence rate, constitute the principal drawbacks when employing this methodology. This present investigation was dedicated to the development of an ML-based predictive model that has been enriched through optimization algorithms. Models were developed employing a Regression Decision Tree (DT) along with two optimizers, including the artificial hummingbird algorithm (AHA) and Coot Optimization algorithm (COA). The inputs encompassed mean cohesion, mean friction angle, mean soil-specific weight, mean pile-soil friction angle, flap number, pile area, and pile length. At the same time, the model's output was defined as the ultimate bearing capacity of the piles. The relationship between input parameters and the output was determined. The outcomes of this study demonstrated a significant alignment between the PBC predictions generated by the Regression Decision Tree optimized with artificial hummingbird algorithm (DTAH) and the actual measured bearing capacities. The high coefficient of determination and low Root Mean Square Error values, specifically 0.998 and 89.29 (KN), respectively, for the testing datasets, underscore the value of employing optimized DT models as robust, efficient, and pragmatic tools for predicting PBC, offering substantial advantages in the field of civil and geotechnique engineering.

关键词： Pile bearing capacity Machine learning methods Regression Decision Tree Hybrid models artificial hummingbird algorithm Coot Optimization algorithm

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The Utilization of a Multi-Layer Perceptron Model for Estimation of the Heating Load

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第6期15卷 116-127页

作者： Chen, Ken Zhu, Wenyao Zhejiang Rongqe Technol Co Ltd Lishui 323000 Zhejiang Peoples R China Lishui Univ Dept Comp Sci & Technol Lishui 323000 Zhejiang Peoples R China

The growing significance of energy-efficient building management techniques has led to research that combines precise heating demand predictions with sophisticated optimization algorithms. This research seeks a comprehensive solution to enhance building energy efficiency, addressing the growing concern for sustainability and responsible resource use in contemporary research and practice. In this research endeavor, the complex topic of energy optimization within the complex domain of heating, ventilation, and air conditioning (HVAC) systems is being tackled with a combination of creative problem- solving techniques and thorough examination. The significance of accurate heating load forecasts for raising HVAC system efficiency and cutting expenses is emphasized in this study. It introduces innovative methods by combining two advanced optimization algorithms, the artificial hummingbird algorithm (AHA) and the Improved Arithmetic Optimization algorithm (IAOA), with the Multi-Layer Perceptron (MLP) model. The main objective is to improve heating load forecast accuracy and expedite HVAC system optimization procedures. This study emphasizes how important precise heating load forecasts are to attaining energy efficiency, cost savings, and the ultimate objective of encouraging environmental sustainability in building management. The assessments unequivocally illustrate that the MLAH (Multi-Layer Perceptron with artificial hummingbird algorithm) model in the second layer emerges as the most exceptional predictor. It attains an impressive maximum Coefficient of Determination (R2) 2 ) value of 0.998 during the testing phase, reflecting a remarkable explanatory capacity and displaying remarkably low Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values of 0.43 and 0.337, indicating minimal prediction discrepancies compared to alternative models.

关键词： Heating energy consumption heating load Multi- Layer Perceptron artificial hummingbird algorithm Improved Arithmetic Optimization algorithm

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Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

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CHEMICAL PRODUCT AND PROCESS MODELING 2024年第5期19卷 737-760页

作者： Xu, Peng Zhang, Jidong Zhengzhou Coll Finance & Econ Sch Informat Engn Zhengzhou 450054 Henan Peoples R China

The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with artificial hummingbird algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

关键词： machine learning biomass gasification decision tree COOT optimization algorithm artificial hummingbird algorithm

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Triple-band Modified Printed Inverted-F Antenna Design for WI-FI-7 Applications 17

Triple-band Modified Printed Inverted-F Antenna Design for W...

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17th European Conference on Antennas and Propagation (EuCAP)

作者： Iliadis, Lazaros A. Rekkas, Vasileios P. Boursianis, Achilles D. Sarigiannidis, Panagiotis Karagiannidis, George K. Christodoulou, Christos Goudos, Sotirios K. Aristotle Univ Thessaloniki Sch Phys ELEDIA AUTH Thessaloniki Greece Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Univ New Mexico Dept Engn & Comp Albuquerque NM USA

ISBN: (纸本)9788831299077

WI-FI-6/6E is now commercialized and the WI-FI community is currently developing the IEEE 802.11be standard, namely WI-FI-7, which will offer enhanced throughput and higher data rate than its predecessors. In this article, a compact triple-band printed inverted-F (IF) antenna operating at 2.4 GHz, 5 GHz, and 6 GHz frequency bands is designed for WI-FI-7 applications. We design a novel antenna structure that is well-suited for triple-band operation. The core idea is to use a stripline as a feeder that also couples two modified IF designs. A nature-inspired optimization method, namely the artificial hummingbird algorithm (AHA), is used to achieve an optimal design solution for the triple-band IF antenna. Computed results demonstrate that the proposed antenna achieves satisfactory results regarding the reflection coefficient and the realized gain in all the frequency bands of interest.

关键词： WI-FI-7 inverted-F antenna artificial hummingbird algorithm antenna design

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Optimizing multi-objective construction management for resource-constrained project scheduling problems: a bio-inspired approach

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Asian Journal of Civil Engineering 2024年第6期25卷 4819-4837页

作者： Pham, Vu Hong Son Huynh, Loc Tin Faculty of Civil Engineering Ho Chi Minh City University of Technology (HCMUT) Vietnam National University (VNU-HCM) Ho Chi Minh Viet Nam

The optimization challenge of the multi-mode resource-constrained project scheduling problem (MRCPSP) involves balancing both project makespan (Cmax) and net present value (NPV). In this research, we propose utilizing the Multi-objective artificial hummingbird algorithm (MOAHA) for multi-objective optimization in handling the MRCPSP. This algorithm aims to minimize project makespan while maximizing net present value. By applying this approach, we identify the Pareto optimal solution set for the given problem. To assess the effectiveness of the MOAHA on two payment models, namely Payments of Activities (PAC) and Equal Time Interval (ETI), we conduct evaluations using practical construction case studies. These evaluations help determine the efficacy of the MOAHA in addressing challenges related to net present value and makespan within the context of the MRCPSP, considering problems of varying sizes sourced from the Project Scheduling Problem Library (PSPLIB). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： artificial hummingbird algorithm Multi-objective optimization Net present value (NPV) Resource-constrained

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Two-stage structural damage detection using modal strain energy and natural frequencies: Numerical and experimental study

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-25页

作者： Rokhsati, Moeinoddin Mohamadi Dehcheshmeh, Mohamad Ghodrati Amiri, Gholamreza Raissi Dehkordi, Morteza Department of Civil Engineering Shahrekord Branch Islamic Azad University Natural Disasters Prevention Research Center School of Civil Engineering Iran University of Science and Technology School of Civil Engineering Iran University of Science and Technology

The present study suggests a new damage index using natural frequency and modal strain energy called NFMSEBI for two-stage structural damage localization and quantification. The goal is to improve the performance of conventional modal strain energy-based index (MSEBI) for damage localization of different structures. For this purpose, three different structures including a planar frame, a planar truss, and an experimental 6-story shear frame were studied. For the optimization stage, three different objective functions are suggested using the combination of mode shapes and natural frequencies, generalized flexibility matrix and natural frequencies, and static displacements and natural frequencies. The performance of the mentioned objective functions is compared utilizing a nature-based metaheuristic algorithm named marine predator algorithm (MPA). Moreover, the performance of MPA has been compared to that of artificial hummingbird algorithm (AHA) and arithmetic optimization algorithm (AOA) using the most accurate objective function selected for each structure. The results showed that the influence of damage is more reflected in NFMSEBI than in MSEBI, whereas in some cases of the planar frame, MSEBI failed to identify damaged elements. Furthermore, the first objective function produces the most accurate results for the truss and frame models, while the second one produces the most reliable identifications for the experimental structure. For the experimental structure MPA and AHA have superior performance over AOA, while in numerical models MPA has the most accurate and stable results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Arithmetic optimization algorithm artificial hummingbird algorithm Experimental shear frame Finite element model updating Marine predator algorithm Modal strain energy Structural damage detection

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