检索结果-内蒙古大学图书馆

Enhancing the small-signal stability of the island microgrids under the virtual impedances-gray wolf optimization model: two distinct case studies

引用

INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2025年 20卷 1024-1035页

作者： Shahravi, Hamidreza Olamaei, Javad Kheradmandi, Morteza Sadr, Ali Islamic Azad Univ Dept Elect & Engn South Tehran Branch Tehran Iran Shahid Beheshti Univ Fac Elect Engn Daneshjou Blvd Velenjak Tehran Iran Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

The objective of this study is to oversee the operation of several converter-based distributed generations in order to assure efficient power distribution inside an island-microgrid (MG). The study commences by introducing a MG model that integrates virtual impedances with a phase-locked loop. It subsequently presents a unique method for analyzing small-signal stability in islanded MGs. A virtual impedance setting strategy is created using the gray wolf optimization algorithm. It was found that the voltage and frequency of the MG stay within acceptable boundaries. The MG stability index was much increased and reactive power imbalances were eliminated.

关键词： small-signal stability island MGs virtual impedances distinct case studies gray wolf optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

引用

SOFTWARE-PRACTICE & EXPERIENCE 2021年第8期51卷 1745-1772页

作者： Salimian, Mahboubeh Ghobaei-Arani, Mostafa Shahidinejad, Ali Islamic Azad Univ Qom Branch Dept Comp Engn Qom Iran

Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end-users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state-of-the-art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near-optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

关键词： autonomic computing fog computing gray wolf optimization algorithm IoT applications service placement

来源：评论

学校读者我要写书评

暂无评论

Prediction of landfill gases concentration based on Grey wolf optimization - Support Vector Regression during landfill excavation process

引用

WASTE MANAGEMENT 2025年 198卷 128-136页

作者： Liu, Zhansheng Zhang, Zehua Zhang, Qingwen Zhao, Linlin Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China

In some areas, there is a phenomenon that the landfill is full or even over-capacity with the extension of the service period. With the aging and damage of the protective facilities, this phenomenon may have a more serious impact on the surrounding environment. It is necessary to excavate and transport the waste beyond the part to control it. This process will inevitably produce many landfill gas emissions, which will pollute the air. Therefore, it is necessary to predict and control the landfill gas. This study utilizes the Grey wolf optimization (GWO) algorithm to optimize Support Vector Regression (SVR). It establishes prediction models for various LFG concentrations based on previous LFG concentration data and real-time environmental monitoring data. The models are compared with traditional Support Vector Regression and Random Forest (RF) algorithms, predicting the concentrations of odor, ammonia, hydrogen sulfide, methane, and nitrogen oxides. The results indicate that GWO-SVR demonstrates more stable and accurate predictions across various LFG, with the coefficient of determination R2 approximately 10% higher than that of SVR and RF, and most other error metrics significantly lower. In contrast, SVR and RF show substantial errors in predicting odor, hydrogen sulfide, and nitrogen oxides. Thus, the GWO-SVR algorithm substantially improves the performance in predicting LFG concentrations, meeting the needs of on-site management.

关键词： Landfill governance Landfill gas prediction gray wolf optimization algorithm Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Lithium battery state of health estimation based on PSO-GWO algorithm optimization under chaotic mapping with hybrid kernel extreme learning machine model

引用

JOURNAL OF APPLIED ELECTROCHEMISTRY 2025年第6期55卷 1383-1398页

作者： Ding, Xvqiang Ni, Yiwei Zhu, Dandan Li, Zhiwei Jiao, Yunxiao Wang, Qi Nantong Inst Technol Sch Automot Engn Nantong 226001 Peoples R China Jiangsu Univ Sci & Technol Zhenjiang 212003 Peoples R China

In battery management systems, the health status of lithium batteries constantly affects the accurate estimation of their charging status and energy status, making the health status particularly important. However, in energy storage systems, rapidly and accurately estimating the health status of lithium batteries, as well as estimating it at an appropriate proportion, has always been a challenge. Therefore, to improve the stable operation of energy storage systems, this paper proposes a model that optimizes a hybrid kernel extreme learning machine using the circle mapping chaotic method and particle swarm optimization algorithm to improve the gray wolf algorithm. First, to address the issues of initialization instability and slow convergence speed in the gray wolf optimization algorithm, the ideas of circle mapping chaos and particle swarm optimization are proposed to replace the position update formula of the gray wolf optimization algorithm, thereby enhancing the algorithm's stability and convergence speed. Second, to address the limitation of the single learning capability of the kernel extreme learning machine model, a hybrid kernel extreme learning machine model is employed. The generalization and learning capabilities of the entire model are verified through simulation experiments. Finally, simulation predictions are conducted again under different data-splitting proportions to obtain an optimal training proportion, providing additional reference directions for practical application. Experimental results demonstrate that the proposed model maintains a fit above 0.99 for four sets of batteries, and ensures the reliability and reasonableness of the model when using a 50% training set proportion.

关键词： Lithium battery state of health gray wolf optimization algorithm Hybrid kernel extreme learning machine Particle swarm optimization algorithm Circle chaos mapping

来源：评论

学校读者我要写书评

暂无评论

Battery Health Prediction with Singular Spectrum Analysis and Grey wolf Optimized Long Short-Term Memory Networks

引用

ENERGIES 2025年第9期18卷 2401-2401页

作者： Huang, Chengti Li, Na Zhu, Jianqing Shi, Shengming Huaqiao Univ Coll Engn Quanzhou 362021 Peoples R China Huaqiao Univ Business Sch Quanzhou 362021 Peoples R China Quanzhou Normal Univ Coll Transportat & Nav Quanzhou 362000 Peoples R China

To tackle the intricate challenges of nonlinearity and non-stationarity in lead-acid battery degradation data, this paper introduces the SG-LSTM model, an innovative approach to battery health prediction. This model uniquely integrates Singular Spectrum Analysis (SSA) and Grey wolf optimization (GWO) with Long Short-Term Memory (LSTM) networks, forming a sophisticated predictive framework. By targeting key degradation features, such as the charging time of multiple voltage rise segments from the charging curve, the model effectively captures critical battery health dynamics. SSA plays a vital role by filtering outliers from these feature sequences, ensuring high-quality data for analysis and enhancing the robustness and accuracy of predictions. The refined data are then processed by a GWO-optimized LSTM network, where GWO's bio-inspired optimization fine-tunes the LSTM parameters for optimal performance. Experimental results demonstrate that the SG-LSTM model outperforms existing models in prediction accuracy and stability;specifically, SG-LSTM achieves 0.27 RMSE, outperforming LSTM (0.84), SSA-LSTM (0.4), and SSA-BP (0.6).

关键词： lead-acid batteries state-of-health estimation singular spectrum analysis gray wolf optimization algorithm long short-term memory network

来源：评论

学校读者我要写书评

暂无评论

GWO-elman based composite detection of transformer oil flow temperature

引用

OPTICAL FIBER TECHNOLOGY 2025年 89卷

作者： Du, Jiwei Zhao, Yi Lan, Binhuan Huang, Liming Sun, Shizheng Suining Power Supply Co State Grid Sichuan Elect P Suining 629000 Sichuan Peoples R China Chongqing Jiaotong Univ Sch Elect & Mech Engn & Vehicle Engn Chongqing 400074 Peoples R China

This paper addresses the issue of nonlinear coupling error caused by cross-sensitivity of strain and temperature in the composite detection of flow rate and temperature in transformer oil using a Fiber Bragg Grating (FBG) sensor. This paper focuses on the FBG sensor as the research object, elucidating the underlying principles of composite detection. Subsequently, a composite detection experimental platform is established for the purpose of analyzing the coupling error of flow rate and temperature. Ultimately, a nonlinear decoupling algorithm based on the Grey wolf Optimizer (GWO) is proposed to enhance the nonlinear decoupling algorithm of the Elman neural network (simple recurrent neural network, Elman). The findings demonstrate that within the flow rate range of 0-5 m/s and the temperature range of 30 degrees C-150 degrees C, the maximum error is reduced by 72.0 % and 81.3 %, and the average error is reduced by 74.4 % and 79.4 %. This significantly enhances the precision and reliability of the sensor's detection capabilities.

关键词： Fiber Bragg grating Flow rate temperature complex detection Strain-temperature cross-sensitivity gray wolf optimization algorithm Elman neural network

来源：评论

学校读者我要写书评

暂无评论

Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models

引用

ENERGY 2025年 316卷

作者： Yang, Shaomei Luo, Yuman North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071000 Peoples R China

Accurate photovoltaic (PV) power prediction can support optimal scheduling and decision-making in energy systems. An innovative hybrid prediction model efficiently predicts complex time series data by integrating multiple advanced algorithms in a deep fusion approach. First, Random Forest (RF) is employed for feature screening and optimization to eliminate redundant features, thereby enhancing model training efficiency and prediction accuracy when dealing with complex environmental data. Subsequently, the symplectic geometry model decomposition (SGMD) technique was utilized to break down historical power signals, extracting information from various frequency components and enhancing the input features for the Bidirectional Long and Short-Term Memory Network (BiLSTM) model. Thereafter, aiming at the problem of hyperparameter tuning of the BiLSTM model, the gray wolf optimization algorithm (GWO) was used for automatic optimization to improve prediction stability. In the case studies, the proposed model exhibited impressive performance metrics: in Case Study 1, it achieved an RMSE of 1.351, an MAE of 0.666, and a MAPE of 3.786, while in Case Study 2, it recorded an RMSE of 0.487, an MAE of 0.265, and a MAPE of 6.304. The model also shows scalability and robustness across diverse climatic conditions and PV technologies, confirming its applicability to real-world scenarios.

关键词： Power prediction Feature selection gray wolf optimization algorithm Bidirectional long and short-term memory networks

来源：评论

学校读者我要写书评

暂无评论

Research on Mobile Robot Path Planning Based on MSIAR-GWO algorithm

引用

SENSORS 2025年第3期25卷 892-892页

作者： Chen, Danfeng Liu, Junlang Li, Tengyun He, Jun Chen, Yong Zhu, Wenbo Foshan Univ Coll Mech Engn & Automat Foshan 528000 Peoples R China

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO a

关键词： path planning gray wolf optimization algorithm reinforcement learning parameter selection detour foraging

来源：评论

学校读者我要写书评

暂无评论

A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

引用

ACTA GEOPHYSICA 2024年第3期72卷 1847-1866页

作者： Zhou, Jian Yang, Peixi Yong, Weixun Khandelwal, Manoj Huang, Shuai Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

关键词： Tunnel boring machine Lithology prediction Random forest Particle swarm optimization algorithm Ant-lion optimization algorithm gray wolf optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Digital watermarking algorithm for QR code based on hybrid PSOGWO optimization

引用

PHYSICA SCRIPTA 2024年第11期99卷

作者： Han, Yue Lv, Yuanjian Wang, Zhifang Heilongjiang Univ Dept Elect Engn Xuefu Rd Harbin 150080 Peoples R China

With the rapid development of digital media, QR codes have become widely used as an important medium for information transmission. However, this also presents serious security challenges, such as frequent image infringement and inevitable compression and scaling issues during data transfer. These challenges directly threaten the effectiveness and integrity of image information, and effective protection measures must be taken. Under this background, digital watermarking technology comes into being as an innovative method to enhance security. This paper presents an innovative Hybrid Particle Swarm optimization and gray wolf optimization (HPG) algorithm, which combines the advantages of PSO and overcomes the limitations of traditional GWO algorithm. The algorithm is further improved by using Bernoulli chaotic mapping and nonlinear convergence factor. The experiments focus on the global and local search balance of the algorithm and its ability to resist repeated scaling attacks. The results show that the nonlinear convergence factor of HPG algorithm slows down the iteration speed at the initial stage, strengthens the global search, and avoids falling into the local optimal. In the later stage, the iteration is accelerated to improve the local search speed and convergence efficiency, thus optimizing the performance of the algorithm and ensuring the safety and quality of image information. In addition, the algorithm has better performance in balancing imperceptibility and robustness of digital watermarking, and its peak signal-to-noise ratio (PSNR) is higher. Even with many scaling iterations, the normalized correlation (NC) value is still close to 1. Compared with the existing typical algorithms, HPG algorithm is more robust to repeated scaling attacks.

关键词： watermark embedding algorithm gray wolf optimization algorithm nonlinear control parameters particle swarm algorithm

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：