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检索条件"主题词=Whale optimization algorithms"

共 386 条记录，以下是221-230 订阅

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Research on Optimizing the Controller Configuration for High-Power Supply Control System in Nuclear Fusion Based on Hybrid Algorithm WOA-IAPSO

Research on Optimizing the Controller Configuration for High...

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IEEE International Conference on Plasma Science (ICOPS)

作者： L. Zhang L. Jiang G. Gao Chinese Academy of Sciences Institute of Plasma Physics Hefei Institutes of Physical Science Hefei China University of Science and Technology of China Hefei China

ISBN: (数字)9798350360912

ISBN: (纸本)9798350360929

With the development of nuclear fusion technology, high power supply control system plays crucial role in the operation of nuclear fusion reactor, and PID controller is widely used in power supply control field. In this paper, a novel hybrid algorithm combining whale optimization Algorithm (WOA) and Improved Adaptive Particle Swarm optimization (IAPSO) is proposed for PID parameter tuning in high-power supply, which combines the two organically to form a new optimization strategy, and is different from the simple weighted average method. Through comparing the performance of WOA, IAPSO, weighted average WOA-IAPSO algorithm and hybrid WOA-IAPSO algorithms on step response and Bode graph, the simulation results show the proposed algorithm can realize a more sensitive, stable and efficient response in the high power operation. This research is not only of great significance to the advancement of nuclear fusion technology, but also provides a useful reference for the application of adaptive optimization methods in other fields.

关键词： Power supplies Simulation optimization methods Fusion reactors Control systems whale optimization algorithms Plasmas

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Mitigating the impact of EV charging stations on RDS through optimized allocation of RDGs and capacitor

Mitigating the impact of EV charging stations on RDS through...

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International Conference on Electrical Energy Systems (ICEES)

作者： Yuvaraj. T Thirumalai. M R. Essaki Raj V. L. Raja Balamurugan P Centre for Smart Energy Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Rajalakshmi Engineering College Chennai India Department of Mechanical Engineering Loyola Institute of Technology Chennai India Department of Electrical and Electronics Engineering Loyola Institute of Technology Chennai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The global surge in electric vehicle (EV) usage demands more electricity for charging, owing to EVs' cheap maintenance, improved performance, and environmental benefits. However, this surge poses problems to distribution system efficacy and reliability, emphasizing the importance of accurate EV charging station siting. To lessen the effect of EVCS, renewable distributed generation (RDG) based on solar and wind, as well as shunt capacitors, play an important role in radial distribution systems (RDS). Incorrect location and sizing can severely reduce RDS performance. This study employs the whale optimization algorithm (WOA) in conjunction with other optimization methods to identify the ideal locations and sizes of RDGs, capacitors, and EVCS within an Indian 28-bus RDS. The goal is to minimize power losses, boost voltage stability, and enhance bus voltage. The results show that the suggested method effectively lowers power losses, raises bus voltage levels, and enhances overall system stability.

关键词： Renewable energy sources Capacitors Power distribution Voltage Power system stability Stability analysis Electric vehicle charging whale optimization algorithms Resource management Surges

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The Study on Settlement Prediction Model of Earth-Rock Dams Based on EMD-LSTM

The Study on Settlement Prediction Model of Earth-Rock Dams ...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Zongqi Li Chenglin Yao Zhongjun Deng Jiangrong Pei Zhiming Ren Chunfeng Li China Institute of Water Resources and Hydropower Research Beijing China Beijing IWHR Corporation Beijing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

In addressing the difficulties encountered by settlement prediction models for earth-rock dams—such as the susceptibility of regression models to multicollinearity, as well as challenges like overfitting, local minima traps, and the complexity of tuning hyperparameters in neural network models—this study introduces an enhanced approach that incorporates Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) neural networks. To begin with, EMD is applied to decompose the time series data obtained from Global Navigation Satellite System (GNSS) measurement points at multiple scales, allowing for the extraction of both trend and periodic components. After that, Principal Component Analysis (PCA) is conducted to select key factors influencing the model, thus reducing the data’s dimensionality and improving its ability to generalize. Lastly, an LSTM-based time series model is developed, and its hyperparameters are optimized through the whale optimization Algorithm (WOA), which enhances both the model’s accuracy in prediction and its convergence speed. The experimental outcomes demonstrate that the proposed model significantly outperforms traditional settlement prediction methods for earth-rock dams, with a Mean Squared Error (MSE) of 7.0701 and a Mean Absolute Error (MAE) of 1.8859, alongside a coefficient of determination (R 2 ) of 99.83%. Compared to existing techniques, this model exhibits considerable improvements in noise reduction, feature extraction, and hyperparameter tuning, offering a highly accurate and dependable approach for predicting settlement in earth-rock dams.

关键词： Analytical models Accuracy Dams Time series analysis Predictive models Feature extraction Data models whale optimization algorithms Long short term memory Principal component analysis

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Research on Electric Vehicle Charging Load Forecasting Method Based on Variable Mode Decomposition and one Dimensional Convolutional Neural Network

Research on Electric Vehicle Charging Load Forecasting Metho...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Chengyu Wang Weiwei Kang Fenglin Deng State Grid Anhui Electric Power Co. Ltd Xuancheng Power Supply Company Xuancheng China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With the popularity of electric vehicles(EVs), the demand for charging infrastructure is also increasing. To enhance the precision of short-term load forecasting for EVs, a hybrid model has been developed that integrates a one-dimensional Convolutional Neural Network (1DCNN) with a Gated Recurrent Unit (GRU). This model uses channel fusion to extract time series features from different dimensional directions for model learning and fusion. At the same time, the whale optimization Algorithm (WOA) is used to optimize the model hyperparameters to avoid the inaccuracy of artificially set hyperparameters. This model’s performance is further enhanced by utilizing the Variable Mode Decomposition (VMD) technique to decompose the experimental data. Our proposed WOA-1DCNN-GRU model is then trained on these decomposed modal components, with the final predictions being reconstructed from them. Utilizing real-world charging pile data from a specific region, our simulation experiments confirm that the WOA-1DCNN-GRU model, which leverages VMD, demonstrates superior forecasting accuracy and stability compared to existing methods.

关键词： Load forecasting Neural networks Time series analysis Predictive models Feature extraction Prediction algorithms Data models whale optimization algorithms Electric vehicle charging Load modeling

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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...

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

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

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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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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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