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检索条件"主题词=Whale optimization algorithms"

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Research on Multi-source Heterogeneous Data Preprocessing Based on Improved Crow Search Algorithm

Research on Multi-source Heterogeneous Data Preprocessing Ba...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Shuai Yuan Xiaolu Pei Shulin Guo Yu Long Ke Li Beijing CLP Feihua Communication Co. Ltd Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The integrity of multi-source heterogeneous data in vehicle-grid interaction is crucial to the security of the vehicle-grid system. Aiming at the problem that the missing of multi-source heterogeneous data can easily lead to inaccurate analysis results, which in turn affects the charging scheduling of electric vehicles and the load management of the power grid, this paper proposes a multi-source heterogeneous data filling algorithm based on the improved crow search algorithm. First, based on the characteristics of multi-source heterogeneous data in vehicle-grid interaction, a data filling model is constructed to clarify the correspondence between parameters and data filling models. Secondly, in view of the problem of insufficient search accuracy of the crow search algorithm, an optimal solution solidification mechanism and an adaptive step size strategy are proposed to improve the algorithm. Then, the improved crow search algorithm is used to find the optimal solution for the data filling model. Finally, the filling model optimized by the algorithm is used to construct a heterogeneous data set. Taking accuracy as the performance index, the proposed algorithm is compared with the unimproved crow search algorithm and whale optimization algorithm, and the results show that the data accuracy of the proposed algorithm is increased by more than 20%, which effectively improves the data integrity.

关键词： Adaptation models Accuracy Stability criteria Search problems Filling Data models whale optimization algorithms Scheduling Security Load modeling

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State-of-Health Estimation for Lithium-ion Batteries Based on An Improved Gated Dual Attention Unit Neural Networks

State-of-Health Estimation for Lithium-ion Batteries Based o...

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International Conference on Power System Technology (POWERCON)

作者： Zhenglu Shi Jiazhu Xu Linjun Zeng Yang He College of Electrical and Information Engineering Hunan University ChangSha China School of Energy and Powe Engineering Changsha University of Science and Technology ChangSha China

ISBN: (数字)9798350351286

ISBN: (纸本)9798350351293

State of health (SoH) as a crucial parameter plays a vital role in battery management system, which can assess the degree of aging in lithium-ion batteries. However, the SoH values cannot be directly obtained through sensors. To solve the issue and obtain precision SoH values, an improved gated dual attention unit (IGDAU) neural network is proposed. Firstly, the moving average filter is applied to smooth the differential thermal voltammetry curve and health indicators with six are derived. Subsequently, the pearson correlation coefficient is utilized to analysis the relationship between health features and SoH. Then, the whale optimization algorithm method is employed to optimize the hyperparameters of the gated dual attention unit model. Finally, the proposed methods are validated by using open sources dataset provided by the University of Oxford. The results indicate that the IGDAU model can precisely estimate the SoH values. In comparison to the SVM, GPR and IGRU methods, the values of the four evaluation indexes are reduced, showing good estimation ability.

关键词： Lithium-ion batteries Support vector machines Neural networks Estimation Logic gates Filtering algorithms whale optimization algorithms Sensors Power systems Indexes

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Utility-Scale Bifacial Solar Photovoltaic System: Optimum Sizing and Techno-Economic Evaluation

Utility-Scale Bifacial Solar Photovoltaic System: Optimum Si...

引用

IEEE General Meeting Power& Energy Society

作者： Sharaf K. Magableh Caisheng Wang Feng Lin Department of Electrical and Computer Engineering Wayne State University Detroit United States

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introduces a multifaceted comparative analysis including various aspects like energy generation, reliability, environmental effect, economic viability, and footprint area. Real measured data, including ambient temperature, solar irradiance, and a utility-scale load, were used for studying both systems in the City of Detroit. The optimal system sizing and energy management strategy are attained using the whale optimization algorithm. Minimizing the loss of power supply probability and sizing the number of photovoltaic panels (N PV ) are carried out for both cases. Results revealed that the bifacial solar system generates more power with a lower N PV , a smaller installation area, and hence a lower levelized cost of energy for the entire project lifetime compared to the monofacial system. Accordingly, the bifacial system outlined in this paper is recommended and can be implemented in various locations to establish a sustainable solar energy system that is economically feasible with clean energy production for the entire project's lifespan.

关键词： Photovoltaic systems Temperature measurement Solar irradiance Temperature distribution Costs Power supplies Urban areas whale optimization algorithms Solar system Reliability

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Electricity theft detection model based on feature engineering and integrated classifier

Electricity theft detection model based on feature engineeri...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Huijie Liu Qingsheng Zhao Dingkang Liang Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Focused on tackling the issue because the electricity theft detection model has low applicability and the recognition rate is greatly affected by subjective factors like manual extraction features, a power-stealing user recognition model based on feature engineering and integrated classifier is proposed. The model uses Tsfresh and whale optimization Algorithm (WOA) as the main feature extraction and selection tool. Then we use an integrated classifier to identify the abnormal users. Firstly, we collected the electricity consumption data of 600 different users for 583 consecutive days. Secondly, the rough feature extraction is performed by Tsfresh and the variance-based method. Thirdly, the whale optimization Algorithm is used to finely select the features, getting the most suitable feature parameters. Finally, the integrated classifier is used to classify abnormal users. After evaluating the performance of different classifiers ensemble, an integrated classifier is composed of a Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). Meanwhile, in terms of feature extraction, we compared the results of three single classifiers which consist of the integrated classifier, also compared with other feature selection and classification methods. The experimental results show that the average accuracy of this method is 96.11%, which is higher than other methods.

关键词： Support vector machines Accuracy Electricity Computational modeling Manuals Feature extraction whale optimization algorithms Classification algorithms Random forests Classification tree analysis

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Multiple Fixed-wing UAV Cooperative Path Planning for a Mission in Complex Terrain

Multiple Fixed-wing UAV Cooperative Path Planning for a Miss...

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IEEE International Conference on Robotics and Biomimetics

作者： Umair Javaid Sami Shahid Robotic Institute Ningbo University of Technology Zhejiang China College of Automation Nanjing University of Aero-nautics and Astronautics Jiangsu China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Cooperative path planning (CoPP) is pivotal in the success of multiple fixed-wing UAV (multi-FW-UAV)-based missions. The CoPP for multi-FW-UAVs is challenging due to environment, UAV system, and mission constraints. In addition, the mission-specific constraints twofolds the CoPP problem difficulty. In this paper, we devise a CoPP problem for multi-FW-UAV systems considering change in heading to destination, speed, altitude, flight path slope, and turn angles. In addition, each FW-UAV must simultaneously reach the destination while avoiding collision with other FW-UAVs during the mission. To get an optimal feasible flying path for each FW-UAV in the system during the mission, we propose a two-step optimization algorithm using a hybrid improved whale optimization algorithm (HI-WOA). Initially, each FW-UAV computes its best feasible flying path, which is later used to develop a consensus on arrival time and collision avoidance mechanism in the system. Comparative simulation results show the effectiveness of the proposed HI-WOA for FW-UAVs CoPP.

关键词： Chaotic mapping Simulation Biomimetics Metaheuristics Autonomous aerial vehicles Cost function whale optimization algorithms Path planning Collision avoidance Robots

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Open-Circuit Fault Diagnosis of NPC Three-Level Inverter Based on WOA-VMD

Open-Circuit Fault Diagnosis of NPC Three-Level Inverter Bas...

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Semiconductor and Electronic Technology (ISSET), International Symposium on

作者： Binbin Zhang Jiafan Wang Changhong Li Chaobo Chen School of Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798350390285

ISBN: (纸本)9798350390292

Due to the problems of poor feature extraction and low accuracy of fault diagnosis in traditional fault diagnosis methods for inverters, the whale optimization algorithm combined with variational modal decomposition is proposed as an open circuit fault diagnosis method for inverters. The VMD is performed on the output voltage to obtain multiple IMFs, and the energy entropy value of each mode is extracted, which is used as the input of SVM and verified in the simulation platform. The simulation results show that the proposed method can effectively extract fault features with high classification accuracy.

关键词： Fault diagnosis Support vector machines Accuracy Voltage Feature extraction Inverters Entropy whale optimization algorithms Vectors Circuit faults

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Low-Voltage Series Arc Fault Detection Method Based on VMD

Low-Voltage Series Arc Fault Detection Method Based on VMD

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International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST)

作者： Qiaojia Tang Li'an Chen Wenxuan Li School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Electrical Engineering and Automation Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen China

ISBN: (数字)9798350388947

ISBN: (纸本)9798350388954

Aiming at the current situation that the traditional overcurrent protection device is difficult to detect the low-voltage series arc fault, a detection model for low-voltage series fault arc based on Variational Mode Decomposition (VMD) was proposed, utilizing a VMD method optimized by the whale optimization Algorithm (WOA). Firstly, WOA was used to optimize the VMD parameters of different loads to decompose the signal. Then, a multi-feature data set was constructed by combining Pearson correlation coefficient and random forest importance assessment. Finally, combined with support vector machine, a low-voltage series arc fault detection model was built. The results show that the accuracy of the proposed arc fault detection method is more than 99 %, and it is suitable for a variety of typical loads.

关键词： Support vector machines Low voltage Accuracy Fault detection Arc discharges Feature extraction Mathematical models whale optimization algorithms Random forests Load modeling

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Automatic Control of Microwave Heating System Based on PID and Evolutionary Computation

Automatic Control of Microwave Heating System Based on PID a...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohaiyedin Idris Ahmad Zuhaily Anuar Zamani Muhammad Khusairi Osman Ahmad Asri Abd Samat Hasnain Abdullah Idris Mohd Mydin M. Abdul Kader Esa Apriaskar Mohd Fauzi Abu Hassan Centre for Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Malaysia Faculty of Electrical Engineering Studies Universiti Malaysia Perlis Arau Perlis Malaysia Department of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Semarang Indonesia Intelligent Automotive Systems Research Cluster Electrical Electronic and Automation Section Universiti Kuala Lumpur Kulim Kedah Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The food and beverage industry has progressed in tandem with the evolution of communication technologies. Researchers are drawn to the developing stream of modern technology, particularly microwave heating technology, which offers good temperature control. In order to optimise system efficiency, it is important to adjust the controller parameters precisely. However, the system poses a bit of a challenge to control and stabilize the optimum temperature. This is due to the generated temperature is affected by several factors such as load variations, changes in material properties, and electromagnetic field distribution, making the conventional control process ineffective. This paper proposed the whale optimization algorithm (WOA) and particle swarm optimization (PSO) to determine the ideal tuning parameters for Proportional–Integral–Derivative (PID) controllers. An evaluation and comparison of the method's performance is conducted against genetic algorithm (GA). The results demonstrate that the proposed PID controller with WOA achieved superior performance compared to the GA and PSO. The WOA obtained the optimal values for the proportional gain (Kp), integral gain (Ki), and derivative gain (Kd) as 1000, 80, and 80, respectively, with the lowest integral absolute error (IAE) of 214.79 and fastest convergence rate compared to the GA and PSO.

关键词： Electromagnetic heating Temperature distribution Process control Microwave communication whale optimization algorithms Particle swarm optimization Tuning

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Research on Task Assignment of Firefighting UAVs Based on E-CARGO Model

Research on Task Assignment of Firefighting UAVs Based on E-...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jin Xu Zhiyu Xiang Senyue Zhang Yue Sun Beihang Gao school of Artificial Intelligence Shenyang Aerospace University Shenyang china

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Firefighting UAV technology has become one of the core tools of modern firefighting operations, and its mission execution is constantly expanding in scale and complexity. In the face of this development, it has become even more critical to find an efficient way to ensure that drones can be assigned to perform their most suitable tasks. In this study, we used the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model to systematically analyze the task assignment (FDTA) problem of firefighting drones, and introduced an enhanced whale optimization algorithm (EWOA) to optimize the path planning in the FDTA problem. Finally, simulation experiments are carried out under diverse terrain conditions to demonstrate the efficiency and fast response ability of the improved algorithm under different workloads and environmental conditions.

关键词： Mathematical models whale optimization algorithms Stability analysis Path planning Planning Resource management Thermal stability optimization Drones Qualifications

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Research on big data processing framework algorithm optimized by deep learning

Research on big data processing framework algorithm optimize...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Shuo Ma Business School Shandong University of Political Science and Law Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a deep learning optimization big data processing framework based on Hadoop distributed architecture, Convolutional Neural Network (CNN) and whale optimization Algorithm (WOA). First, the Hadoop distributed file system (HDFS) and MapReduce technologies are used to store and preprocess large-scale data in parallel, and reduce data redundancy and processing delay through efficient data distribution and cleaning. Secondly, as a feature extraction module, the CNN model extracts the spatial features of the data layer by layer by using multi-layer convolution and pooling operations to provide reliable feature representation for subsequent classification and prediction tasks. In order to further improve the performance of the model, this paper introduces whale optimization algorithm (WOA) to optimize the hyperparameters of the CNN model (such as learning rate, convolution kernel size, etc.). By simulating the hunting behavior of whales, WOA automatically adjusts the combination of hyperparameters during model training, thus significantly improving the accuracy and training efficiency of the model. The experimental results show that the proposed Hadoop-CNN-WOA framework has higher accuracy than the traditional single model in processing large-scale image classification tasks.

关键词： Training Deep learning Convolution Computational modeling Distributed databases Big Data Feature extraction whale optimization algorithms Classification algorithms Convolutional neural networks

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