检索结果-内蒙古大学图书馆

Presenting an innovative single-setting adaptive protection method for AC microgrid with wind turbine

ELECTRICAL ENGINEERING 2024年 1-16页

作者： Jahanbani, Hassan Razavi, Farzad Nafisi, Hamed Islamic Azad Univ Dept Elect Engn Qazvin Branch Qazvin Iran Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran

Today, despite numerous studies which have been conducted on the subject of adaptive protection, there are still many shortcomings in this field. For example, the presence of a wind turbine in the network changes the short-circuit currents value and might result in the malfunction of overcurrent relays. In addition, changes in the microgrid topology will create many protection settings, which will create problems for saving the settings or changing them. For this reason, in this paper, a new adaptive protection method based adaptive linear prediction, whale optimization algorithm is proposed. In this method, first, the overcurrent protection relay is replaced by an algorithm based on adaptive linear prediction for fault detection, and then a single setting required by this method is pre-calculated in different microgrid conditions and topologies and updated using a central protection system. The proposed method is tested in different microgrid topologies and under different fault conditions. The results prove the efficiency and the superiority of the proposed method over existing adaptive protection methods.

关键词： Microgrid protection Adaptive linear prediction Fault detection whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Rapid Prediction of Cutterhead Torque in Hard-Rock Tunneling Using IEWOA-TSVD-ITELM

引用

IEEE ACCESS 2024年 12卷 88658-88680页

作者： Li, Long Liu, Zaobao Shandong Technol & Business Univ Sch Management Sci & Engn Yantai 264005 Shandong Peoples R China Northeastern Univ Coll Resources & Civil Engn Key Lab Minist Educ Safe Min Deep Met Mines Shenyang 110819 Peoples R China Northeastern Univ Key Lab Liaoning Prov Deep Engn & Intelligent Tech Shenyang 110819 Peoples R China

Predicting cutterhead torque is essential for optimizing TBM construction strategies and minimizing jamming risks. This study presents a novel hybrid model (IEWOA-TSVD-ITELM), developed using data from 7635 tunneling cycles from the Yinsong Project, to enhance cutterhead torque prediction accuracy. The EWOA enhances its global search capability by introducing new position updating and adaptive adjustment strategies (IEWOA). In addition, by leveraging the Softsign function for the nonlinear transformation of the expected output matrix of the TELM, a third hidden layer is added to enhance the feature extraction capabilities (ITELM), whereas truncated singular value decomposition (TSVD) is employed to reduce the noise in the output matrix of the third hidden layer of the ITELM (TSVD-ITELM). Furthermore, the IEWOA optimized the number of neurons and randomly generated weights and biases in the TSVD-ITELM. This study comprehensively evaluates and compares six optimization algorithms using 25 standard test functions. Additionally, the IEWOA-TSVD-ITELM is compared with eight classical machine learning models. This study examines the impact of different timing lengths of the rising phase and rock mass grades on model performance. The results demonstrate the outstanding performance of the IEWOA as an optimization algorithm. The IEWOA-TSVD-ITELM model achieves an R-2 value of 0.644 on the test set, with an MAE of 326.623 and an RMSE of 435.821, outperforming the other algorithms. Increasing the timing length from 30 to 60 s reduces the MAE and RMSE by 11.82% and 9.56%, respectively, but the gains diminish when the timing length increases from 60 to 90 s.

关键词： Torque Prediction algorithms Predictive models Tunneling Rocks optimization Adaptation models whale optimization algorithms Extreme learning machines Machine learning TBM cutterhead torque prediction whale optimization algorithm truncated singular value decomposition two-hidden-layer extreme learning machine extreme hybrid model

来源：评论

学校读者我要写书评

暂无评论

A ship motion forecasting approach based on Fourier transform, regularized Bi-LSTM and chaotic quantum adaptive WOA

引用

OCEAN ENGINEERING 2024年 313卷

作者： Li, Ming-Wei Xu, Rui-Zhe Geng, Jing Hong, Wei-Chiang Li, He Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Heilongjiang Peoples R China State Key Lab Hydropower Equipment Harbin 150040 Peoples R China Harbin Engn Univ Sanya Nanhai Innovat & Dev Base Sanya 572024 Hainan Peoples R China Asia Eastern Univ Sci & Technol Dept Informat Management Taipei 22046 Taiwan Yuan Ze Univ Dept Informat Management Taoyuan 320315 Taiwan Univ Lisbon Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Lisbon Portugal

Ship motion forecasting is of great significance to assist ship navigation and ensure ship operation. However, due to the coupling influence of wind, waves, currents and other factors, the ship motion has a strong time variation, which brings great challenges to the improvement of the ship motion forecasting accuracy. This paper proposes a novel approach for ship motion forecasting. Firstly, Fourier Transform (FT) is used for data pre-processing to deal with the data noise. Secondly, the Bi-LSTM is applied to simulate the nonlinear dynamical system of ship motion, and the Dropout mechanism and l2 regularization method are used to improve the generalization performance of the network, and a novel ship motion prediction model (FRB model) is constructed. Thirdly, in view of the inherent defects of whale optimization algorithm (WOA), based on chaotic mapping and quantum computing, a new Chaotic Quantum Adaptive whale optimization algorithm (CQAWOA) is proposed to construct a new ship motion forecasting approach, namely FRB&CQAWOA. Finally, based on the measured pitch and roll data of ship, the feasibility and superiority of the established FRB model and the proposed CQAWOA are tested. The test results indicate that the proposed model have better prediction accuracy than other algorithms selected in this paper.

关键词： Ship motion forecast Deep learning model Fourier transform Bi-LSTM whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid evolutionary approach for multi-objective unit commitment problem in power systems

引用

ENERGY REPORTS 2024年 11卷 2439-2449页

作者： Singh, Amritpal Khamparia, Aditya Al-Turjman, Fadi Lovely Prof Univ Sch Comp Sci & Engn Phagwara Punjab India Babasaheb Bhimrao Ambedkar Univ Dept Comp Sci Amethi India Near East Univ AI & Robot Inst Artificial Intelligence Dept Mersin Turkiye Near East Univ AI & Robot Inst Software Dept Mersin Turkiye Near East Univ AI & Robot Inst Informat Syst Engn Dept Mersin Turkiye Univ Kyrenia Fac Engn Res Ctr AI & IoT Mersin Turkiye

The operating strategy of the power system is changing from the minimum cost to the minimum cost with a minimum emission approach as a result of growing worries about global warming and environmental change brought on by the combustion of fossil fuels. The generating scheduling problem becomes a multi-objective one as a result of the economic and emission objectives' inherent conflict. In this study, a multi-objective whaledifferential evolution-genetic algorithm (WODEGA) is employed to produce an optimal solution that illustrates a potential trade-off between the unit commitment (UC) problem's emission and cost objectives. The environmental and economic dispatch problem with system and unit constraints is solved using the multi-objective WODEGA method. Main goal of research is to explore the problem of unit commitment and sub-problem of unit commitment i.e. economic dispatch.

关键词： Unit commitment Economic dispatch Multi-objective unit commitment whale optimization algorithm Differential evolution Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

ATLHCAF and DMNGI-PLL Based Control Techniques for a Dual-Stage SPV Array and BES in Shipboard Microgrid

引用

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第3期60卷 4338-4349页

作者： Semwal, Purusharth Narayanan, Vivek Singh, Bhim Panigrahi, Bijaya Ketan Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

An integration of renewable energy sources (RESs) in the maritime industry reduces fuel emissions. This work assesses the performance of a shipboard microgrid (SMG) in the context of a zero-carbon, all-electric ferry. Nonlinear service loads, RESs with power-electronic converters, and propulsion motor give rise to severe power quality issues. Thus, an arctangent logarithmic hyperbolic cosine adaptive filter (ATLHCAF) is used to mitigate power quality issues. An inclusion of power quality control is imperative to ensure that total harmonic distortion (THD) adheres to IEEE 519-2022 and IEC 60092-101 standards. A dual-modified novel generalized integrator-phase locked loop (DMNGI-PLL) enables synchronization, facilitating power transfer between shore grid and SMG. It also employs MNGI for sensorless speed and position estimation of propulsion motor, thereby obviating a need for mechanical position sensors. The dependability of SMG is contingent upon the stability of the DC bus. Consequently, a meta-heuristic-based whale optimization algorithm (WOA) is implemented to guarantee DC bus voltage stability. SMG is modelled in MATLAB and validated on a real-time testbed.

关键词： Adaptive filter cold ironing shipboard microgrid synchronization whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel intervention effect-based quadratic time-varying nonlinear discrete grey model for forecasting carbon emissions intensity

引用

INFORMATION SCIENCES 2024年 675卷

作者： Ye, Li Fang, Liping Dang, Yaoguo Wang, Junjie Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Hubei Peoples R China Toronto Metropolitan Univ Dept Mech & Ind Engn 350 Victoria St Toronto ON M5B 2K3 Canada Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211100 Jiangsu Peoples R China

In the context of severe global warming, accurately exploring the trend of carbon emissions intensity (CEI) changes is of great significance for mitigating climate change issues. The implementation of China 's Carbon Emissions Trading Scheme (ETS) in 2013 is a policy intervention aimed at influencing CEI. The impact of intervention events makes forecasting a complex problem, which poses significant challenges to the construction of forecasting models. We first develop a quadratic time -varying nonlinear discrete grey model (QDNDGM(1,1)) to assess the intervention effect of the ETS policy. Then, a novel intervention effect -based quadratic time -varying nonlinear discrete grey model (IE-QDNDGM(1,1)) is developed to conduct the prediction under intervention effect, including an intervention term. The whale optimization algorithm (WOA) is used to calculate a nonlinear parameter. We assess the intervention effect of the ETS policy in China and find that it can indeed reduce CEI. We verify the IE-QDNDGM(1,1) model 's superiority by comparing its predictive performance with that of three grey models, one statistical technique, and one artificial intelligence model. The comparative study shows the proposed model 's excellent fitting and prediction performance. An ablation experiment is conducted to validate the design of the IE-QDNDGM(1,1) model. Policy implications of the ETS intervention effect are discussed.

关键词： Grey forecasting model Intervention effect Carbon emissions intensity Carbon Emissions Trading Scheme whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Transient pressure prediction in large-scale underground natural gas storage: A deep learning approach and case study

引用

ENERGY 2024年 311卷

作者： Chu, Hongyang Zhang, Liang Lu, Huimin Chen, Danyang Wang, Jianping Zhu, Weiyao Lee, W. John Univ Sci & Technol Beijing Sch Civil & Resources Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Texas A&M Univ Harold Vance Dept Petr Engn College Stn TX 77843 USA Univ Sci & Technol Beijing Shunde Grad Sch Beijing 528399 Guangdong Peoples R China

Large-scale underground natural gas storage (UNGS) facilities typically allocate only one month annually for well shut-in to facilitate pressure build-up. This limited well shut-in period affects the subsequent pressure measurements and evaluation of gas storage capacity. This study proposes a novel workflow to directly predict the transient pressure behavior of UNGS during pressure build-up without requiring additional well shut-in operations. The proposed workflow uses the gas injection-withdrawal rate as a dynamic input feature for a deep learning model, with reservoir pressure as the output feature. An enhanced WA-BiLSTM model integrates multiple physical mechanisms and advanced optimization algorithms. The model achieves mean squared error (MSE) of 2 x 10-3, which is less than 5 % of traditional model's results. Field data from the largest Hutubi UNGS exhibit a prediction accuracy of 99.0 % during the well shut-in phase. High-precision prediction results with low MSE ensure the reliability of pressure derivative data. By analyzing the predicted pressure data, the actual gas storage volume is 104.75 x 108 m3, accounting for 97.9 % of the facility's designated storage capacity.

关键词： Underground natural gas storage Transient pressure behavior Case study BiLSTM whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

引用

APPLIED MATHEMATICAL MODELLING 2024年 133卷 474-490页

作者： Hao, Tengteng Xu, Kaili Zheng, Xin Liu, Bo Li, Jishuo Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series;whale optimization algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the whale optimization algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

关键词： Tailings dam safety trend prediction Feature extraction whale optimization algorithm Long short term memory Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Chicwhale optimization algorithm for the VM migration in cloud computing platform

引用

EVOLUTIONARY INTELLIGENCE 2020年第4期13卷 725-739页

作者： Venkataswamy, Srinivas Byatarayanapura Mandal, Indrajit Keshavarao, Seetharam Atria Inst Technol Informat Sci & Engn Bangalore Karnataka India MINA Inst Engn & Technol Comp Sci & Engn Nalgonda Telangana India Anveshana Res & Dev Hyderabad Telangana India

Nowadays, Virtual Machine (VM) migration becomes very popular in the cloud computing platform. Various VM migration based mechanisms are designed for optimal VM placement but remain a challenge due to improper energy consumption in the cloud model. This paper proposes an approach for VM migration in the cloud using an optimization algorithm, Chicken-whale optimization algorithm (Chicwhale), which is developed by integrating the whale optimization algorithm in Chicken swarm optimization. In the developed approach, a local migration agent is utilized for monitoring the memory and resources utilization in the cloud continuously, and the VM is migrated using the service provider based on the requirement of the VMs to complete a task assigned. At first, the cloud system is designed, and then the proposed Chicwhale is employed by moving the VMs optimally, and the fitness function for best VM migration is carried out by considering several parameters, like load, migration cost, resource availability, and energy. The performance of the VM migration strategy based on Chicwhale is evaluated in terms of energy consumption, resource availability, migration cost, and load. The proposed Chicwhale method achieves the maximal resource availability of 0.989, minimal migration cost of 0.0564, the minimal energy consumption of 0.481, and the minimal load of 0.0001.

关键词： Virtual machine migration Chicken swarm optimization Cloud computing whale optimization algorithm Resource availability

来源：评论

学校读者我要写书评

暂无评论

Application of Improved whale algorithm to Optimize Dephosphorization Process Parameters in Converter Steelmaking

引用

APPLIED SCIENCES-BASEL 2025年第8期15卷 4277-4277页

作者： Wu, Congrui Kong, Yueping Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China

Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. The methodology constructs a physics-informed objective function linking process parameters to optimization targets, thereby resolving the disconnect between mechanistic and data-driven modeling approaches. The algorithm innovatively combines Sobol quasi-random sequences with grey wolf social hierarchy strategies to prevent premature convergence in high-dimensional search spaces while maintaining Pareto front diversity, supplemented by a reward mechanism to ensure strict adherence to multi-objective constraints. Experimental validation using steel plant production data demonstrates IMOWOA's efficacy, achieving a 10.8% reduction in endpoint phosphorus content and a 5.79% decrease in production costs per ton of steel. Comparative analyses further confirm its superior feasibility and stability in quality-cost co-optimization, evidenced by a 12.6% improvement in hypervolume (HV) over conventional swarm intelligence benchmarks, establishing a robust framework for industrial metallurgical process optimization.

关键词： process parameter optimization endpoint phosphorus content production costs whale optimization algorithm converter steelmaking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：