检索结果-内蒙古大学图书馆

Frequency regulation by optimized fuzzy based self-adaptive virtual inertia control for microgrid with variable renewable penetration

引用

JOURNAL OF ENERGY STORAGE 2024年 104卷

作者： Mohanty, Debidasi Giri, Ankita Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

The increasing use of renewable energy sources are solving environmental issues, energy shortage problems and result in economic growth. Microgrid can provide a framework for connecting RESs to the utility grid, but high penetration of RESs make it difficult to lower the Rate of Change of Frequency (RoCoF). In this research, a selfadaptive Virtual Inertia Control (VIC) technique is proposed to tackle this instability issue. VIC method simulates virtual inertia, enhancing inertia of the overall system and frequency stability. VIC technique links energy storage systems to the utility and helps with power supply. However, an accurate analysis is necessary to regulate the Virtual Inertia (VI) constant value, which could adequately eradicate the adjustment mistakes & system instability. Fuzzy logic controllers can tackle non-linear problems and provide robustness, and reliability. This research presents a fuzzy based self-adaptive VIC system for stable load frequency regulation in low-inertia microgrid. In addition to this, a fuzzy based secondary frequency control is also proposed in this study. A fuzzy based self-adaptive VIC is developed for VI constant emulation using real power fluctuations of RESs and frequency regulations. The arithmetic optimization algorithm technique is used to obtain the optimized controller parameters. Efficacy of the proposed self-adaptive VIC with fuzzy based secondary thermal controller is validated against no VIC with integral and fuzzy based secondary frequency controller, standard VIC with integral as well as fuzzy based secondary frequency controller and self-adaptive VIC with integral secondary controller for high/low renewable sources penetrations and system parameter variations. Finally, real time validation of the proposed VIC approach has conducted with the help of OPAL-RT 5700 and the experimental results are compared with conventional VIC method.

关键词： Load frequency regulation Self-adaptive virtual inertia control Energy storage system Fuzzy logic controller arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid densenet with long short-term memory model for multi-modal emotion recognition from physiological signals

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期83卷 35221-35251页

作者： Pradhan, Anushka Srivastava, Subodh Natl Inst Technol Patna Dept ECE Patna 800005 Bihar India

Recognition of emotions from multi-modal physiological signals is one among the toughest tasks prevailing amid the research communities. Most existing works have focused on emotion recognition (ER) from single modal signals, which is now ineffective. Certain models considered multiple modalities, but the results obtained are not satisfactory, and there is still a possibility for improvement in accuracy. Therefore, this work introduces a novel and effective mechanism by embedding multiple techniques to achieve the required task. The projected approach includes stages like pre-processing, signal-to-image conversion, feature extraction, feature selection and classification. Each signal modality is separately pre-processed, and the results are provided to a complex dual tree with fast lifting wavelet transform (CTFL-WT) to convert the signals into images. The converted images are sent to the channel attentive squeezenet (CASN) model for feature extraction. The obtained features are then reduced with the help of an adaptive arithmetic optimization algorithm (AAOA). The reduced features are then provided to the hybrid densenet with long short term memory (DLSTM) for accurate labelling. The projected work resulted in the classification of three different emotions such as neutral, stress and amusement. The implementations are performed in the Python platform, and the evaluations are done using the wearable stress and affect detection (WESAD) dataset. In comparison, the proposed work resulted in an overall accuracy value of 99% and an overall F1-score value of 97.84%.

关键词： Multi-modal signals Emotion recognition Channel attention Squeezenet arithmetic optimization algorithm Densenet Long short term memory network

来源：评论

学校读者我要写书评

暂无评论

Prediction of variables involved in TEG Dehydration using hybrid models based on boosting algorithms

引用

COMPUTERS & CHEMICAL ENGINEERING 2024年 188卷

作者： Wang, Fangxiu Zhao, Jiemei Van Hoang, Vo Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Hubei Peoples R China Bialystok Tech Univ Fac Elect Engn Wiejska 45C PL-15531 Bialystok Poland

The extraction of gas from fields often involves impurities, necessitating natural gas processing to separate these impurities. This process typically entails the removal of acid gases (such as carbon dioxide and hydrogen sulfide) and dehydration, commonly achieved through absorption using triethylene glycol (TEG). Efforts to minimize BTEX emissions and maintain optimal dry gas water content are pivotal for enhancing the economic and environmental sustainability of natural gas processing. In this study, the accurate prediction of BTEX and dry gas water contents is aimed by boosting-based methods and hybridization techniques. Four hybrid models, including XGBoost, CatBoost, HGBR, and LightGBoost, were developed in conjunction with the arithmetic optimization algorithm (AOA) meta-heuristic algorithm to optimize and fine-tune hyperparameters. Through a comprehensive case study and comparison of various evaluation indices, the XGBoost-AOA hybrid model emerged as the most accurate in predicting both outputs. Hence, we recommend the XGBoost algorithm optimized with the AOA algorithm as the preferred approach for predicting BTEX and dry gas water contents in natural gas processing.

关键词： Tri ethylene glycol Hybrid Boosting XGBoost arithmetic optimization algorithm Dehydration

来源：评论

学校读者我要写书评

暂无评论

Estimating pile setup parameter using XGBoost-based optimized models

引用

GEOMECHANICS AND ENGINEERING 2024年第3期36卷 259-276页

作者： Du, Xigang Ma, Ximeng Dong, Chenxi Nikkhoo, Mehrdad Sattari Hebei Univ Water Resources & Elect Engn Dept Transportat Engn Cangzhou 061001 Hebei Peoples R China Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q(t)), average wall friction (f(s)), and effective vertical stress (sigma(vo)), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S-u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm optimization (PSO), Social Spider optimization (SSO), arithmetic optimization algorithm (AOA), and Sine Cosine optimization algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the arithmetic optimization algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the

关键词： arithmetic optimization algorithm cone penetration test extreme gradient boosting model particle swarm optimization pile set-up parameter A social spider optimization

来源：评论

学校读者我要写书评

暂无评论

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

引用

IEEE ACCESS 2022年 10卷 11110-11137页

作者： Ridha, Hussein Mohammed Hizam, H. Mirjalili, Seyedali Othman, Mohammad Lutfi Ya'acob, Mohammad Effendy Abualigah, Laith Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Adv Lightning Power & Energy Res ALPER Serdang 43400 Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad 10001 Iraq Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Univ Putra Malaysia Fac Engn Dept Proc & Food Engn Serdang 43400 Selangor Malaysia Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia

Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized arithmetic Operation algorithm based on Efficient Newton Raphson (HAOA(ENR)) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Levy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOA(ENR) outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.

关键词： optimization Mathematical models Heuristic algorithms Photovoltaic systems Education Renewable energy sources Prediction algorithms Photovoltaics model parameters extraction arithmetic optimization algorithm Newton Raphson method Lambert W function optimization

来源：评论

学校读者我要写书评

暂无评论

Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 8711-8724页

作者： Weng, Peng Xie, JingJing Zou, Yang Changzhou Univ Huaide Coll JingJiang Peoples R China Shanghai Construct 2 Grp Co Ltd Shanghai Peoples R China

The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R-2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices.

关键词： HPC concrete compressive strength deep learning arithmetic optimization algorithm grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Zhou, Jing Wang, Qingdong Khajavi, Hamed Rastgoo, Amir Huanggang Normal Univ Coll Comp Huanggang 438000 Hu Bei Peoples R China KN Toosi Univ Technol Dept Civil Engn Tehran Iran

This research focuses on the crucial task of accurately forecasting electricity consumption, a key concern in modern societies where electricity is essential for industries, healthcare, and transportation. The study explores the complex factors influencing electricity usage, aiming to improve the accuracy of consumption forecasts. Recognizing the limitations of current forecasting techniques, which often fail to account for the detailed interplay of variables affecting consumption, it introduces innovative methods to address these challenges. By employing sensitivity analysis and advanced hybrid boosting algorithms, such as LightGBM and CatBoost optimized with arithmetic optimization and Fruit Fly optimization algorithms, the research analyzes time-series data on electricity use, weather conditions, and usage timing across three power distribution networks. The sensitivity analysis reveals the significant impact of customer behavior and temperature on consumption patterns. The predictive models, especially the optimized LightGBM, demonstrate remarkable accuracy, with rates of 0.9996 and 0.9968 for training and testing datasets, respectively. These results highlight the effectiveness of the proposed methodologies in providing accurate and reliable forecasts of electricity demand, offering valuable insights for the energy sector to manage resources more efficiently and meet consumption needs effectively.

关键词： Forecasting power consumption Time -series prediction of electricity consumption Sensitivity analysis LightGBM arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing sparrow search algorithm with hybrid multi-strategy and its engineering applications

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 5601-5632页

作者： Zhu, Xuemin Liu, Sheng Zhu, Xuelin You, Xiaoming Shanghai Univ Engn Sci Sch Management Shanghai Peoples R China Univ Int Business & Econ Sch Informat Beijing Peoples R China Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai Peoples R China

An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems.

关键词： Sparrow optimization algorithm (SSA) arithmetic optimization algorithm golden levy flight distribution t-distribution perturbation engineering design problems

来源：评论

学校读者我要写书评

暂无评论

Enhanced lithium-ion battery state-of-charge estimation for Electric Vehicles using the AOA-DNN approach

引用

OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第6期45卷 2856-2873页

作者： Thangaraj, Kokilavani Indiran, Rajarajeswari Ananth, Vasantharaj Raman, Mohan Sri Krishna Coll Engn & Technol Autonomous Dept Elect & Elect Engn Coimbatore Tamil Nadu India Adithya Inst Technol Autonomous Dept Elect & Elect Engn Coimbatore Tamil Nadu India KIT Kalaignar Karunanidhi Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Sona Coll Technol Autonomous Dept Mech Engn Salem Tamil Nadu India

Electric vehicles (EVs) battery management systems (BMSs) rely on exact state of charge (SoC) estimations to guarantee efficient and safe operation. Lithium-ion batteries (LIBs) are favored for EVs due to their extended lifespan, high energy density, and minimal self-discharge and high voltage. To address these issues, this research propose a LIB SoC prediction based on an actual BMS in EVs. The main objective is improving SoC of LIB. The proposed hybrid strategy is the combined performance of both the dynamic neural networks (DNN) and arithmetic optimization algorithm (AOA). Commonly it is named as DNN-AOA technique. The SoC of Lithium-ion batteries are predicted using the DNN approach. The proposed AOA is used to optimize the weight parameter of DNN to enhance prediction accuracy and reliability. By then, the operational MATLAB platform has adopted the proposed framework, and existing procedures are used to compute its execution. The proposed method demonstrates superior existing like Bayesian network (DBN), random vector functional link neural network (RVFLNN) and Gaussian progress regression (GPR). The proposed method yields a lower error value of 0.1 and a higher accuracy value of 98% compared with other existing methods. This research introduces a hybrid lithium-ion battery State of Charge (SoC) prediction method combining Dynamic Neural Networks (DNN) with the arithmetic optimization algorithm (AOA). The DNN-AOA technique enhances SoC prediction accuracy and reliability by optimizing DNN weight parameters. Results show that the proposed method outperforms existing techniques, such as Bayesian Network, Gaussian Process Regression, and Random Vector Functional Link Neural Network, achieving an error of 0.1 and accuracy of 98%. image

关键词： arithmetic optimization algorithm current dynamic neural network electric vehicle lithium-ion battery state of charge temperature voltage

来源：评论

学校读者我要写书评

暂无评论

Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters

引用

WATER RESOURCES MANAGEMENT 2022年第10期36卷 3901-3927页

作者： Kadkhodazadeh, Mojtaba Farzin, Saeed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran

For the first time, a novel hybrid machine learning model named the least-squares support vector machine-arithmetic optimization algorithm (LSSVM-AOA) was proposed. The performance of LSSVM-AOA was checked on six benchmark data sets (BDSs) to showcase its applicability. After testing the performance of the novel hybrid machine learning model, its performance in electrical conductivity (EC) and total soluble solids (TDS) estimating was developed at six stations in the Karun river basin. For this purpose, effective parameters were selected by the principal component analysis (PCA) method. The results of the technique for order of preference by similarity to ideal solution (TOPSIS) method showed that the LSSVM-AOA has promising results in modeling BDSs and estimating water quality parameters (WQPs) in comparison with classical and hybrid algorithms (artificial neural network (ANN), adaptive neural fuzzy inference system (ANFIS), LSSVM, LSSVM-particle swarm optimization (LSSVM-PSO) and LSSVM-whale optimization algorithm (LSSVM-WOA)). The average values of correlation coefficient (R) in EC and TDS estimates were 0.969 and 0.950, respectively. Eventually, the Monte Carlo method (MCM) showed that the LSSVM-AOA has the lowest uncertainty among other algorithms.

关键词： New hybrid algorithm LSSVM arithmetic optimization algorithm Water quality parameters Benchmark data set TOPSIS

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：