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Enhancing sustainable urban economies: A statistical investigation of transitioning from fossil fuel-dependent industries to green innovation hubs

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Liu, Fengyu Chen, Yuxiang Zhu, Jiulong Zhai, Ge Nanyang Inst Technol Fanli Business Sch Nanyang 473000 Peoples R China Zhongnan Univ Econ & Law Sch Finance Wuhan 430073 Peoples R China Zhejiang Univ Sch Publ Affairs Hangzhou 310058 Peoples R China Zhejiang Univ Inst Fiscal Big Data & Policy Hangzhou 310058 Peoples R China

As a result of the evolution of the smart grid, the new energy market has been restructured in urban systems, whereby domestic users play a more active role in the demand response (DR) programs with a view to balancing production and demand. DR incentive signals, though, are difficult to respond to due to limited understanding by users. As a result, an Energy Management Controller (EMC) has been expanded for responding to DR signals and solving energy management problems. The EMC employs a whale optimization algorithm (WOA) for automatically responding to the DR signal and solving energy management issues by planning 3 kinds of domestic load: non-interruptible, interruptible, and hybrid. With the WOA, the EMC generates the best home load plan for reducing power costs, carbon emissions, PARs, and customer dissatisfaction. Simulations were accomplished in order to evaluate the offered WOA against current algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and teaching learn optimization algorithm (TLBO). The outcomes indicated that the suggested WOA reduces electricity costs, carbon emissions, PARs, and consumer dissatisfaction better than other algorithms.

关键词： whale optimization algorithm Demand response Electric vehicles Batteries Smart grid Renewable energy sources Scheduling

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Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2022年 270卷

作者： Peng, Lemin He, Caitou Heidari, Ali Asghar Zhang, Qian Chen, Huiling Liang, Guoxi Aljehane, Nojood O. Mansour, Romany F. Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Dept Informat Technol Wenzhou Polytech Wenzhou 325035 Peoples R China Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimi-zation algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photo-voltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal pa-rameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA out-performs similar algorithms currently available.

关键词： whale optimization algorithm optimization Swarm -intelligence Parameter estimation Photovoltaic models Solar energy

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Study on the Modeling and Compensation Method of Pose Error Analysis for the Fracture Reduction Robot

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MICROMACHINES 2022年第8期13卷 1186-1186页

作者： Liu, Minghe Li, Jian Sun, Hao Guo, Xin Xuan, Bokai Ma, Lifang Xu, Yuexuan Ma, Tianyi Ding, Qingsong An, Baichuan Hebei Univ Technol Sch Artificial Intelligence & Data Sci & Engn Res Minist Educ Tianjin 300130 Peoples R China Beijing Univ Posts & Telecommun Sch Automat Beijing 100876 Peoples R China Natl Res Ctr Rehabil Tech Aids Beijing Key Lab Rehabil Tech Aids Old Age Disabil Beijing 100176 Peoples R China Natl Res Ctr Rehabil Tech Aids Key Lab Neurofunct Informat & Rehabil Engn Minist Civil Affairs Beijing 100176 Peoples R China

Background: In the process of fracture reduction, there are some errors between the actual trajectory and the ideal trajectory due to mechanism errors, which would affect the smooth operation of fracture reduction. To this end, based on self-developed parallel mechanism fracture reduction robot (FRR), a novel method to reduce the pose errors of FRR is proposed. Methods: Firstly, this paper analyzed the pose errors, and built the model of the robot pose errors. Secondly, mechanism errors of FRR were converted into drive bar parameter's errors, and the influence of each drive bar parameter on the robot pose error were analyzed. Thirdly, combining with Cauchy opposition-based learning and differential evolution algorithm (DE), an improved whale optimization algorithm (CRLWOA-DE) is proposed to compensate the end-effector's pose errors, which could improve the speed and accuracy of fracture reduction, respectively. Results: The iterative accuracy of CRLWOA-DE is improved by 50.74%, and the optimization speed is improved by 22.62% compared with the whale optimization algorithm (WOA). Meanwhile, compared with particle swarm optimization (PSO) and ant colony optimization (ACO), CRLWOA-DE is proved to be more accurate. Furthermore, SimMechanics in the software of MATLAB was used to reconstruct the fracture reduction robot, and it was verified that the actual motion trajectory of the CRLWOA-DE optimized kinematic stage showed a significant reduction in error in both the x-axis and z-axis directions compared to the desired motion trajectory. Conclusions: This study revealed that the error compensation in FRR reset process had been realized, and the CRLWOA-DE method could be used for reducing the pose error of the fracture reduction robot, which has some significance for the bone fracture and deformity correction.

关键词： fracture reduction robot error model whale optimization algorithm differential evolution algorithm opposition-based learning error compensation

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Optimal operation of multi-source electric vehicle connected microgrid using metaheuristic algorithm

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JOURNAL OF ENERGY STORAGE 2022年 52卷

作者： Jayalakshmi, N. S. Jadoun, Vinay Kumar Gaonkar, D. N. Shrivastava, Ashish Kanwar, Neeraj Nandini, K. K. Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India NITK Dept Elect & Elect Engn Mangalore 575025 India Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Gurugram 122003 Haryana India Manipal Univ Jaipur Dept Elect Engn Jaipur 303007 Rajasthan India

In this paper, a multi-source microgrid (MG) has been considered which inducts power from solar photovoltaic (PV), wind turbine, pumped hydro storage system (PHSS) and diesel generator (DG). A problem formulation is proposed on a multi-source MG considering an electric vehicle (EV) as source and load demand. A modified operation strategy is proposed to achieve the lowest possible fuel usage of DG and to optimize the operation of multi-sources used in the MG. When the sum of PV, wind power production and EV discharge is less than the load requirement, the required deficit power should be delivered by DG and PHS. This work considers PV and wind as the primary energy supplying sources, while DG, EV and PHS as the additional energy suppliers with EV and PHS as energy storage systems. By properly coordinating EVs, they can become a major contributor to the successful execution of the MG concept. In this work, a modified charging/discharging algorithm is presented to check the effect of EVs to supply a portion of peak loads with PHS to reduce the fuel consumption of DG in three diverse modes of operation. A modified whale optimization algorithm (WOA) and teaching learning-based optimization (TLBO) are applied to effectively solve this proposed complex problem using the MATLAB platform. The optimum solutions obtained after different independent trials by both the techniques are compared with the latest published techniques. It can be observed that modified WOA performs better than TLBO and other recently published methods on the base case and proposed multi-source MG case in three diverse modes of operation. The outcomes of the simulation confirm the effectiveness of modified WOA in reducing fuel consumption.

关键词： Multi-source microgrid Electric vehicle whale optimization algorithm Teaching-learning based optimization Pumped hydro storage Wind power Solar power Diesel generators

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Hybrid optimal design method for magnetic field coils under magnetic shielding boundary

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SENSORS AND ACTUATORS A-PHYSICAL 2023年第1期359卷

作者： Wu, Zhihong Pang, Haoying Wang, Zhuo Fan, Wenfeng Liu, Feng Pei, Hongyu Quan, Wei Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Ultraweak Magnet Field Measurement Technol Beijing 100191 Peoples R China Hangzhou Innovat Inst Beihang Univ Zhejiang Prov Key Lab Ultraweak Magnet Field Space & Appl Technol Hangzhou 310000 Peoples R China

A novel strategy for designing uniform magnetic field and gradient magnetic field coils considering the boundary effect of magnetic shielding is proposed in this study. This strategy uses intelligent optimization algorithm to skillfully transform the complex coil design problem into the spatial optimization problem of individual population. The mirror method is combined with Green's function to accurately calculate the magnetic field, and the target field method (TFM) is combined with the whale optimization algorithm to optimize the stream functions using adaptive ridge regression. The minimum fitness function is defined as the real-time magnetic field relative error, and the optimal ridge coefficient is continuously obtained by determining the current density coefficient. The proposed method significantly reduces the magnetic field deviation in the target area compared to the conventional TFM, with reductions ranging from 12.2% to 0.11% (�� coil), 0.3% to 0.02% (�� coil), 20% to 10% (d ��/d �� coil) and 2.9% to 2.4% (d ��/d �� coil). The strategy has been effectively validated by analytical simulations and experimental results, and is expected to be applied to the design of uniform field coils and gradient coils for ultra-high precision atomic sensors.

关键词： Uniform field and field gradient coils Target field method Magnetic shielding whale optimization algorithm

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Intelligent Fault Diagnosis Method for Gearboxes Based on Deep Transfer Learning

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PROCESSES 2023年第1期11卷 68页

作者： Wu, Zhenghao Bai, Huajun Yan, Hao Zhan, Xianbiao Guo, Chiming Jia, Xisheng Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter alpha corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis.

关键词： whale optimization algorithm variational mode decomposition deep transfer learning gearbox fault diagnosis

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A novel framework for integrating solar renewable source into smart cities through digital twin simulations

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SOLAR ENERGY 2023年第1期262卷

作者： Li, Bohan Tan, Weijun Jilin Jianzhu Univ Sch Architecture & Urban Planning Changchun 130119 Peoples R China Tongji Architecture Design Grp Co Ltd Shanghai 200092 Peoples R China

In this study, the challenges of incorporating PV systems and wind energy sources into smart city power grids are addressed. The fluctuating nature of renewable energy is considered, and microgrid energy management is investigated using probabilistic and deterministic assumptions. To enhance energy management, digital twin simulations are employed, providing a virtual representation of the power grid. This enables accurate analysis and prediction of system behavior in different scenarios. Digital twins help simulate the effects of load profiles on microgrids, facilitating better energy planning and decision-making. The proposed framework considers objective functions related to cost reduction, voltage profile improvement, and voltage stability enhancement. The whale optimization algorithm (WOA) is applied to solve the complex and nonlinear energy management problem, aiming to minimize costs and enhance voltage stability. Simulation results show that an optimally installed PV system and wind turbines reduce energy costs and improve overall system efficiency in smart cities. Furthermore, the objective function of WOA outperforms particle swarm optimization (PSO) and differential evolution algorithm (DEA) in achieving the desired outcomes.

关键词： Smart city microgrid Energy management Digital twin whale optimization algorithm Stochastic loads

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One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods

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ENERGIES 2023年第6期16卷 2688页

作者： Huang, Chao-Ming Chen, Shin-Ju Yang, Sung-Pei Chen, Hsin-Jen Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan Kun Shan Univ Dept Green Energy Technol Res Ctr Tainan 710 Taiwan

This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the salp swarm algorithm (SSA) and the whale optimization algorithm (WOA) to optimize the weight distribution for each single model. The final predicted value is the weighted sum of the integral for each individual model. The proposed method is applied to a 3.6 MW wind power generation system that is located in Changhua, Taiwan. The results show that the proposed optimal ensemble model gives more accurate forecasts than the single prediction models. When comparing to the other ensemble methods such as the least absolute shrinkage and selection operator (LASSO) and ridge regression methods, the proposed SBI algorithm also allows more accurate prediction.

关键词： wind power forecasting ensemble method particle swarm optimization salp swarm algorithm whale optimization algorithm

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Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104193-104193页

作者： Yousri, Dalia Mirjalili, Seyedali Machado, J. A. Tenreiro Thanikanti, Sudhakar Babu Elbaksawi, Osama Fathy, Ahmed Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Polytech Porto Dept Elect Engn Inst Engn Rua Dr Antonio Bernardino de Almeida 431 P-4249015 Porto Portugal Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Jouf Univ Fac Engn Elect Engn Dept Al Jouf Saudi Arabia Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt Zagazig Univ Fac Engn Elect Power Machine Dept Zagazig Egypt

An effective harmony between the exploration and exploitation phases in meta-heuristics is an essential design consideration to provide reliable performance on a wide range of optimization problems. This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer (HHO) based on the fractional calculus (FOC) memory concept. In the proposed variant of the HHO, a hawk moves with a fractional order velocity, and the rabbit escaping energy is adaptively tuned based on FOC parameters to avoid premature convergence. As a result, the fractional-order modified Harris hawks optimizer (FMHHO) is proposed. The sensitivity of the algorithm performance vis-a-vis the FOC parameters is addressed, and the best variant is recommended based on twenty-three benchmarks. For validating the quality of the proposed variant, twentyeight benchmarks of CEC2017 are tested. For evaluating the proposed variant against the other present-day techniques, several statistical measures and non-parametric tests are performed. Moreover, to demonstrate the applicability of the proposed technique, the proton exchange membrane fuel cell (PEMFC) model parameters estimation process is handled based on several measured datasets. In this series of experiments, the FMHHO variant is compared with the standard HHO and the other techniques based on intensive statistical metrics, mean convergence curves, and dataset fitting. The overall outcome shows that the FOC memory property improves the performance of the classical HHO and leads to accurate and robust solutions fitting the measured data.

关键词： Fractional-order modified HHO whale optimization algorithm Particle Swarm optimization Harris hawks optimization Salp Swarm algorithm Parameters estimation Grey Wolf Optimizer Fractional calculus Genetic algorithm optimization Fuel cell

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Design of uniform field coils coupled with a magnetic shield cylinder?

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年第1期583卷

作者： Huang, Binyue Cao, Qian Chen, Zhengyu Wang, Yuhao Yue, Ziqian Zhai, Yueyang Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing 100191 Peoples R China

Active magnetic field coils and magnetic shields are widely employed to achieve uniform and low magnetic fields, and the coil generating magnetic field would be influenced as a result of the coupling with the shields. It turns out to increase the magnetic field strength and shrink the uniform area, which is detrimental to atomic devices. An analytical solution that takes into account the coupling effect of shields and coils is established by using the magnetic vector potential. Analyses for three different configurations are first performed, including mu-metals, 2K-MnZn ferrites and 10K-MnZn ferrites. Analytical calculations reveal that the optimal radii of the standard Lee-Whiting coils with high uniformity are all around 0.81 of shield radii. Additionally, adopting the whale optimization algorithm (WOA), a modified coil with improved uniformity and comparable size is proposed. Experimental results show that a better uniformity of 2.68e-5 and a 34% longer uniform area is obtained when compared with the standard coil. The accuracy of the internal magnetic field distributions is compared and tested by simulations and experiments. Situations utilizing shields and coils will be facilitated by this effective application of WOA in uniform field coil design.

关键词： Uniform magnetic field coil Magnetic shield Shield-coil coupling whale optimization algorithm Atomic magnetometer

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