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检索条件"主题词=whale optimization algorithms"

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Low-Voltage Series Arc Fault Detection Method Based on VMD

Low-Voltage Series Arc Fault Detection Method Based on VMD

引用

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-...

引用

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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WOA based Optimal TIDF Controller for Time Delayed System

WOA based Optimal TIDF Controller for Time Delayed System

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IEEE Power India Conference

作者： Vineet Kumar Renu Sharma Department of Electrical and Electronics Engineering MITS Madanapalle Department of Electrical Engineering SOA University Odissa

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This manuscript presents a novel methodology to effectively control time-delayed systems. In this work, authors have proposed a whale optimization Algorithm (WOA) tuned Tilt Integral Derivative Controller with Derivative Filter (TIDF) specifically designed for regulating time-delayed systems characterized by 4th order dynamics. The efficacy of the proposed method has been thoroughly verified by comparing its transient response specifications with those of existing approaches documented in the literature. This comparative analysis demonstrates that the WOA-tuned TIDF controller offers superior performance in terms of transient response, highlighting its suitability for the intended application. Additionally, the robustness of the proposed algorithm has been rigorously tested under conditions involving variable time delays, which are a common and challenging aspect of real-world systems. The results of these tests indicate that the WOA-tuned TIDF controller not only achieves a better transient response but also maintains stability and robustness across different scenarios. Consequently, the proposed methodology provides a reliable and effective solution for controlling time-delayed systems, ensuring both enhanced performance and resilience in various industrial and technological applications.

关键词： Transient response Heuristic algorithms Delay effects Filtering algorithms Control systems whale optimization algorithms Robustness Stability analysis Transient analysis Testing

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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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Intelligent Baijiu Blending Model Based on Meta-Goal Programming

Intelligent Baijiu Blending Model Based on Meta-Goal Program...

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Asian Control Conference

作者： Ding Wang Zhiyao Zhao Jiabing Yu Xin Zhang School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper proposes a new Baijiu blending optimization method, combining Meta-Goal Programming (MGP) and whale optimization Algorithm (WOA). The proposed method takes into account the complexity of Baijiu's composition and the significant influence of trace components on its flavor profile. To address this, a mathematical modeling framework that surpasses traditional linear and goal programming methods is introduced, which often neglects the nuances of liquor flavor in the pursuit of cost reduction. The study shows that the combination of MGP and WOA improves the accuracy of Baijiu blending by systematically balancing compound concentrations to achieve desired flavor profiles. The experimental results demonstrate the advantages of this approach over traditional methods, partic-ularly in reducing deviations in compound concentrations and better meeting the qualitative goals set by the blenders. This study contributes to the scientific and precise method for Baijiu blending and the intelligent automation of Baijiu production. It lays a theoretical foundation for automated blending.

关键词： Intelligent automation Process control optimization methods Production Programming Mathematical models whale optimization algorithms

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PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-VMD

PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-V...

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Electrical Energy Conversion Systems and Control（IEECSC), IEEE International Conference on

作者： Jinyong Zhang Dingguo Shao Yitong Wei School of Electromechanical Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350389357

ISBN: (纸本)9798350389364

The safety and reliability of the motor is the basis and guarantee of the stable operation of the motor, so it is of great significance to be able to quickly and accurately diagnose the early motor faults to avoid unnecessary downtime or even more serious consequences due to motor faults. In this paper, we diagnose the early inter-turn short-circuit fault of the motor from the two aspects of motor fault modeling and early fault signal processing. As the early fault signal of the motor is not obvious, we use the Variable Modal Decomposition (VMD) algorithm to process the fault signal, and use the whale optimization algorithm (WOA) to optimize the parameters of the VMD algorithm. After simulation verification results show that the best combination of parameters obtained after the whale optimization algorithm is applied to the VMD algorithm to decompose the fault signal, which can effectively eliminate the noise interference to extract the fault characteristics.

关键词： Fault diagnosis Torque Velocity control Signal processing algorithms Motors Feature extraction whale optimization algorithms Safety Circuit faults Spectrogram

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Rolling Bearing Fault Diagnosis Based on Improved Multi-Scale Dispersion Entropy and Deep Extreme Learning Machine

Rolling Bearing Fault Diagnosis Based on Improved Multi-Scal...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xu Chengjun Shu Sheng Wang Kun School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China Hainan Institute Wuhan University of Technology Wuhan China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In response to the challenges associated with the incomplete extraction of fault features in rolling bearings and the resultant low recognition accuracy, this study proposes a fault diagnosis model that integrates local mean decomposition, enhanced multi-scale dispersion entropy, and a whale optimization algorithm to refine the deep extreme learning machine. Initially, the model employs local mean decomposition to break down the original vibration signal into several product function components. Subsequently, spurious product function signal components are eliminated through the application of mutual information and kurtosis screening criteria. The multi-scale fault features of the signal components are then extracted using the improved multi-scale dispersion entropy method. Finally, the extracted fault features are fed into a deep extreme learning machine, which has been optimized via the whale optimization algorithm, for training and testing purposes. Experimental investigations conducted on rolling bearings, alongside comparisons with a control group, demonstrate that the proposed model effectively extracts fault features and enhances the identification accuracy of rolling bearing faults.

关键词： Fault diagnosis Accuracy Extreme learning machines Rolling bearings Feature extraction Entropy whale optimization algorithms Dispersion Monitoring Mutual information

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WOA-VMD and SVM Rolling Bearing Failure Diagnosis

WOA-VMD and SVM Rolling Bearing Failure Diagnosis

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yawen Zhang Guici Chen Wenbo Wang College of Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

To address the issue of low accuracy in extracting fault information during rolling bearing fault diagnosis, this study proposes a method using a combination of whale optimization Algorithm-Variational Mode Decomposition (WOA-VMD) and Support Vector Machine (SVM). The proposed method is aimed at improving the accuracy of fault information extraction in rolling bearings. Firstly, the whale optimization algorithm is used to optimize the parameters of variational mode decomposition. Then, the optimized VMD is used to decompose the bearing signal, calculate the steepness and energy entropy of the decomposed modal component, combine the steepness and energy entropy, and construct the feature matrix. Finally, the support vector machine model is used to identify and diagnose bearing faults. Compared with the whale optimization algorithm-empirical mode decomposition model (WOA-EMD), adaptive variational modal decomposition model (AVMD), gray wolf algorithm-variational modal decomposition model (GWO-VMD), the recognition accuracy of the four methods is 83.3333%, 89.7436%,93.5897%and100%, respectively, which is obviously the accuracy of the proposed method is higher.

关键词： Support vector machines Fault diagnosis Adaptation models Rolling bearings Information retrieval whale optimization algorithms Entropy

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Automatic Modulation Recognition Based on Features Fusion of IQH and AAH

Automatic Modulation Recognition Based on Features Fusion of...

引用

IEEE International Conference on Communications in China (ICCC)

作者： Pengwu Wan Xi Hui Dongrui Chen Siying Kang Xu Zhang Xi’an University of Posts & Telecommunications Xi’an China National Key Laboratory of Science and Technology on Space Microwave China Academy of Space Technology Xi’an China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

With the rapid development and popularization of the Internet of Things (IoT), its extensive applications make networks vulnerable to malicious attacks, leading to various security and privacy issues. Automatic modulation recognition (AMR) technology provides crucial support for the stable operation and security of the IoT. Based on this, this paper proposes a modulation recognition method based on histogram feature fusion. Firstly, signal sequences are transformed into asynchronous in-phase quadrature histogram (IQH) and asynchronous amplitude histogram (AAH) to represent signal features. While IQH exhibits a stronger advantage in certain conditions, AAH provides complementary information that can enhance the robustness and accuracy of the recognition process. Therefore, IQH and AAH are fused through discriminant correlation analysis (DCA) to obtain comprehensive feature vectors. Compared with single-input features, the combination of the two inputs will improve the final recognition accuracy. Secondly, the improved whale optimization algorithm (IWOA) is employed to optimize the radial basis function neural network (RBFNN), where the fused feature vector is utilized as input to recognize the modulation type of the input signal. Finally, seven modulation types are identified in simulation experiments. The experimental results indicate that under the additive white Gaussian noise channel model, a signal-to-noise ratio of 6 dB can achieve a recognition accuracy of $9 9 . 5 2 \%$. Compared with other experimental analyses, this method exhibits high performance and robustness.

关键词： Histograms Accuracy Correlation Modulation Robustness whale optimization algorithms Vectors

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