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检索条件"主题词=whale optimization algorithms"

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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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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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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...

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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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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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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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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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Investigation of Water Cycle Algorithm in Renewable Based Microgrid

Investigation of Water Cycle Algorithm in Renewable Based Mi...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasantini Samal Niranjan Nayak Sujit Kumar Bhuyan Department of EE ITER SOA Deemed to be University Bhubaneswar Resource Assessment & Asset Analysis Manikaran Analytics Limited New Delhi India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study explores load frequency control (LFC) in a two-area power system, focusing on three controllers: a standard PID, a PID tuned with Particle Swarm optimization (PSO-PID), and a PID optimized using the whale optimization Algorithm (WCA-PID). By comparing these controllers, the study evaluates their effectiveness in maintaining stability and minimizing frequency and tie-line power deviations under varied load conditions. While the basic PID controller is robust, PSO and WCA optimizations enhance its performance. Simulation results show that both PSO-PID and WCA-PID offer improvements in settling time, overshoot, and steady-state error, with WCA-PID achieving the best overall performance. The study underscores the value of optimization techniques in advancing LFC capabilities, offering insights for future power system control strategies.

关键词： Simulation Signal processing algorithms Power system stability whale optimization algorithms Stability analysis Steady-state Frequency control optimization Standards Water cycle

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Short Term Forecast of Photovoltaic Power Generation Based on WOA-LSTM

Short Term Forecast of Photovoltaic Power Generation Based o...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Quan Chen Ao Chu Jiayu Du Mingze Wang China University of Mining and Technology-Beijing Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Short-term prediction of photovoltaic (PV) power is vital for ensuring the safe and Uninterrupted functioning of the power system and optimizing resource allocation. In this study, we propose a short-term PV power prediction method based on WOA-LSTM. To accurately capture the dynamic performance of PV power generation systems, We carefully selected the sample data set, conducted data analysis on it, and utilized the Spearman correlation coefficient to identify valuable features for training the prediction model. The proposed WOA-LSTM model utilizes the whale optimization algorithm to determine optimal hyperparameters for LSTM, including neuron count, learning rate of the model, and batch size. A comparative analysis with other prevalent models, including LSTM, GRU, BP, RNN, WOA-GRU, WOA-RNN, and PSO-LSTM, demonstrates the superiority of our proposed model in terms of power prediction accuracy. Furthermore, our integrated model surpasses individual models in prediction precision: The whale optimization algorithm effectively reduces model errors during LSTM hyperparameter search. Therefore, our WOA-LSTM model holds significant promise for short-term PV power prediction research.

关键词： Photovoltaic systems Training Analytical models Renewable energy sources Accuracy Predictive models Prediction algorithms Data models whale optimization algorithms Long short term memory

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