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检索条件"主题词=Whale Optimization Algorithms"

共 393 条记录，以下是231-240 订阅

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Hazardous Gas Concentration Monitoring in Factories Based on FTIR Combined with WOA-BP Algorithm

Hazardous Gas Concentration Monitoring in Factories Based on...

引用

System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Yuhong Liu Xu Ma School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

With the intensification of air pollution problems, real-time monitoring of harmful gas concentration is very important. Fourier Transform Infrared Spectroscopy (FTIR) has been widely used in the field of gas testing with its advantages of its fastness and accuracy. Standard BP neural network models will have a low accuracy and slower speed when detecting gas concentrations, so this paper uses the whale optimization algorithm to optimize the BP neural network, which will be used to detect the concentration of harmful gases in the factory. The whale optimization algorithm can avoid being in a local optimal problem during the training process, and the training speed and accuracy can be improved. This article uses the absorbance of the gas and the temperature of the current environment as the input of the system and the concentration value of the gas is used as the output. The experimental results show that the error between the gas concentration predicted by the BP neural network model and the actual value is about 6%, and the error of the WOA-BP model is about 3%. The root mean square error (RMSE) is also improved, verifying the accuracy of the WOA-BP model.

关键词： Training Accuracy Temperature Neural networks whales Predictive models whale optimization algorithms Production facilities Systems simulation Testing

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Less: Large-scale Workload Forecasting Model Based on Multiple Sequence Compression

Less: Large-scale Workload Forecasting Model Based on Multip...

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IEEE International Conference on Web Services (ICWS)

作者： Zeyuan Ding Binbin Feng Wangyang Yu Bolan Zhang School of Computer Science Shaanxi Normal University Xian China Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

As application migration to the cloud becomes the mainstream way of application deployment, application runtime management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a largescale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group; second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the whale optimization Algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.

关键词： Training Accuracy Runtime Predictive models Containers Logic gates Prediction algorithms whale optimization algorithms Forecasting optimization

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Optimal Sizing of Hybrid Renewable System for Residential Appliances

Optimal Sizing of Hybrid Renewable System for Residential Ap...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Abdulgader Alsharif Emad Almabsout Abdussalam Ali Ahmed Mohamed Khaleel Yasser F. Nassar Hala J. El-Khozoadar Department of Electrical & Electronic Engineering College of Technical Sciences Sabha Sebha Libya Department of Electrical Engineering Higher Institute of Engineering Technology Benghazi Benghazi Libya Department of Mechanical Engineering Faculty of Engineering Bani Waleed University Bani Waleed Libya Department of Electrical-Electronic Engineering Faculty of Engineering Karabuk University Karabuk Turkey Mechanical & Renewable Energy Engineering Wadi Alshatti University Brack Libya Electrical Engineering and Smart System Department The Islamic University of Gaza Gaza Palestine

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Hybrid Renewable Power Systems (HRPSs) have been extensively employed at regional, national, and different levels due to their capability of decreasing Carbon Dioxide (CO2) emissions and reducing energy consumption amount. However, the main challenge of Renewable Energy Sources (RESs) is the uncertainties of the demand and source integration that may be addressed by nature-inspired metaheuristic algorithms. The optimal operation of a Microgrid (MG) includes Photovoltaic (PV), Wind Turbine (WT), and Battery (BT), as integrated sources for the distributed generation system. This paper proposes the utilization of the whale optimization Algorithm (WOA) in order to overcome sizing limitations. The aforesaid optimization algorithm benchmarked with the Antlion optimization (ALO) algorithm that coupled with a supervisory control algorithm called energy management strategy to meet the objective functions. The main objectives of this study are to obtain a hybrid system with lower cost and lower reliability. The results of the system show better performance on WOA as the main findings in terms of cost ($0.01/kWh) and LPSP (0.08%). The aforementioned result contributes a cost-effective system with lower loss, and providing sustainable living which means electricity is available most of the time.

关键词： Renewable energy sources Home appliances Costs Uncertainty Metaheuristics Supervisory control whale optimization algorithms

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Condition Monitoring of Capacitor Aging in MMC Based on Improved Convolutional Neural Networks

Condition Monitoring of Capacitor Aging in MMC Based on Impr...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zehua Huang Ruochen Huang Qiongbin Lin Chuanwei Lin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Power China Fujian Electric Power Engineering Co. LTD. Any Fuzhou China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

This article introduces a new fault detection method by integrating Convolutional Neural Networks (CNN) and Gated Recursive Units (GRUs) for sub-module capacitors in Modular Multilevel Converters (MMCs). Considering the aging fault characteristics of these capacitors, the proposed method extracts feature from the sub-module capacitor voltages and their associated differential voltage waveforms, which are used as inputs to the proposed model. To improve the selection of hyper-parameters of the CNN-GRU neural network, the whale optimization Algorithm (WOA) is utilized, then the aging condition of sub-module capacitors can be evaluated. Simulation results show that the proposed method is valid in monitoring capacitor aging in the MMC sub-module, with an overall accuracy of more than 98.2% in detecting and identifying capacitor aging.

关键词： Accuracy Simulation Capacitors Aging Feature extraction whale optimization algorithms Convolutional neural networks Monitoring Robots optimization

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Active Fault Diagnosis Method Based on Deep Forest and Its Industrial Application

Active Fault Diagnosis Method Based on Deep Forest and Its I...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Dong Li Kaixun He Lili Tao Hanlong Liu College of Electrical and Automation Engineering Shandong University of Science and Technology Qingdao China Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology China College of Intelligent Manufacturing and Control Engineering Shanghai Second University of Technology China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

Wind turbine equipment, as a key component in the process production control of thermal power plants, directly affects the power generation efficiency of the plant. Due to the non-linear and non-stationary nature of the signals acquired from wind turbine sensors, this paper presents a novel active fault diagnosis approach for wind turbine systems based on deep forest techniques. Initially, multi-dimensional time-domain features are extracted from the raw fault data. Simultaneously, important features are selected from multiple angles by integrating Pearson correlation coefficient and Borutashap algorithm, following the principles of active fault diagnosis. Additionally, the whale optimization algorithm (WOA) is utilised to search the optimal sinusoidal auxiliary signal parameter, thereby highlighting specific fault manifestations and improving the model’s diagnostic capability. The deep forest, with its strong classification ability and noise resistance, overcomes the overfitting issue in shallow machine learning algorithms and the complex parameter tuning in deep learning. Then, the designed sinusoidal auxiliary signal is used as an additional input to the deep forest model, enabling the final fault classification outcomes. Finally, experiments are conducted using a wind turbine power transmission system fault simulator. The experimental results demonstrate an improvement in fault diagnosis accuracy, confirming the effectiveness of the proposed method.

关键词： Fault diagnosis Deep learning Machine learning algorithms Feature extraction whale optimization algorithms Regression analysis Wind turbines Random forests Tuning Power generation

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Integration of Deep Learning and Time Series Decomposition for Enhanced Short-Term Load Forecasting

Integration of Deep Learning and Time Series Decomposition f...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhiqiang Xi Xiaotong Ye Jibo Yu Department of Computer Science and Engineering Sichuan University of Science and Engineering Guang 'an China Department of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This study aims to improve the accuracy of short-term power load forecasting through deep learning to ensure the dynamic balance and stable operation of the power grid system. In response to the poor prediction accuracy of traditional serial combination models and the issue of data leakage in modal decomposition prediction models, this paper proposes a parallel combination model based on deep learning. The model integrates a self-developed time series decomposition (TD) module, Bidirectional Gated Recurrent Unit (BiGRU), Temporal Convolutional Network (TCN), and Multi-Head Self-Attention (MHSA). First, the time series decomposition module decomposes power load data into trend and periodic components to extract long-term and short-term features. These features are then fed into parallel BiGRU and TCN deep networks for processing. The multi-head self-attention mechanism is incorporated to further enhance the model’s expressive capabilities. Additionally, the model leverages the Beluga whale optimization (BWO) algorithm to optimize hyperparameters, fully exploiting the complex correlations within the data. Experimental results show that the proposed BWO-TD-BiGRU-TCN-MHSA deep learning model significantly outperforms baseline models in terms of MAPE, RMSE, MAE, and R 2 . This verifies the model's high accuracy and practicality in power load forecasting, providing reliable support for the planning and stable operation of power systems.

关键词： Deep learning Accuracy Load forecasting Time series analysis Predictive models Prediction algorithms Feature extraction Data models whale optimization algorithms Load modeling

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Sub-optimal Perceptron Controller Using WOA Algorithm for Farming Watering Mobile Robot

Sub-optimal Perceptron Controller Using WOA Algorithm for Fa...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana L. H. Oliveira J. Bautista Jesus Pinto-Lopera Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the whale optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.

关键词： Software algorithms Signal processing algorithms Software Hardware whale optimization algorithms Steady-state Mobile robots MATLAB Farming Python

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Optimal Scheduling of Electric Vehicles Charging/Discharging Considering V2G and PV System Using Three Different Inventive algorithms

Optimal Scheduling of Electric Vehicles Charging/Discharging...

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International Middle-East Power System Conference, MEPCON

作者： Merna Ali Ghoneem Walid Atef Omran Zinab E.Afifi Walaa Ibrahim Gabr Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt Department of Mechatronics Engineering Faculty of Engineering German University in Cairo Cairo Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

This paper introduces optimal coordination approach for electric vehicles (EVs) charging/discharging in EV charging station integrated with photovoltaic system using energy management strategies. Three inventive metaheuristics optimization algorithms are implemented and studied to determine day-ahead optimal scheduling for maximization the total profits of EV charging station. These proposed algorithms are whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO) and hunger games search algorithm (HGS). A comparative study is done among the three optimization algorithms with aid of using MATLAB software program considering both modes of operation for EVs: grid-to-vehicle mode when EV is charged from the grid and the other mode is vehicle to grid mode when EV is discharged into the grid. The day-ahead profit is simulated for the three case studies passing through different demand periods; off-peak, mid-peak and on-peak. The simulation results show that HGS algorithm surpasses WOA by 4.72% in total profits and GWO by 7.87%.

关键词： Vehicle-to-grid Simulation Software algorithms Optimal scheduling Games Charging stations Electric vehicle charging whale optimization algorithms MATLAB Energy management

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Enhancing Voltage Stability in Distribution Networks through Metaheuristic Algorithm-Based Reactive Power Compensation from MDHD EVs

Enhancing Voltage Stability in Distribution Networks through...

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Innovative Smart Grid Technologies (ISGT)

作者： Chen Zhang Kourosh Sedghisigarchi Rachel Sheinberg Shashank Narayana Gowda Rajit Gadh Smart Grid Energy Research Center University of California Los Angeles Los Angeles USA Electrical and Computer Engineering Department California State University Northridge Los Angeles USA

ISBN: (数字)9798350313604

ISBN: (纸本)9798350313611

Medium and heavy-duty (MDHD) electric vehicles (EVs) with sizable batteries and high charging power pose voltage stability risks in distribution networks. This paper introduces a novel approach using reactive power compensation from MDHD EVs through metaheuristic algorithms. Genetic algorithms, particle swarm optimization, moth flame optimization, salp swarm algorithm, whale optimization, and grey wolf optimization are assessed for their performance in voltage stability, power loss reduction, and computational efficiency. The proposed approach, primarily utilizing the salp swarm algorithm, optimizes power flow to mitigate voltage deviations and enhance stability. Simulations on a modified IEEE 33 bus system validate the algorithm’s effectiveness in improving voltage stability and reducing power losses, facilitating the integration of MDHD EVs into distribution networks.

关键词： Reactive power Metaheuristics Voltage Distribution networks Power system stability Stability analysis whale optimization algorithms

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Combined Capacity and Internal Resistance Estimation of Lithium-Ion Batteries Based on Voltage Recovery

Combined Capacity and Internal Resistance Estimation of Lith...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Qianyuan Dong Jinglin Liu School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

Accurate estimation of a battery's aging state is a critical prerequisite for ensuring the safety of electric vehicles. Currently, the prevailing data-driven methods for estimating battery states prioritize capacity estimation, often disregarding internal resistance. This focus may precipitate safety risks arising from battery inconsistencies. Consequently, it is essential to accurately estimate both the capacity and internal resistance of batteries in tandem to ensure reliable and consistent battery testing. Hence, a novel method leveraging the WOA-ELMAN neural network for the simultaneous estimation of battery capacity and internal resistance has been proposed to tackle the aforementioned challenge. This approach identifies voltage recovery as a key health indicator, gleaned from the historical performance data of the battery. Subsequently, an integrated model for estimating both capacity and internal resistance is developed using the ELMAN neural network architecture. The whale optimization algorithm is then engaged to fine-tune the initial parameters, thereby enhancing the estimation accuracy. To validate the efficacy of this method, the “Path Dependent Battery Degradation Dataset” from Oxford-a widely recognized public dataset-is employed. The findings reveal that the average root mean square error (RMSE) for capacity and internal resistance estimation stands at 0.89% and 7.14%, respectively.

关键词： Resistance Accuracy Correlation Neural networks Estimation Voltage whale optimization algorithms Batteries Safety Integrated circuit modeling

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