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检索条件"主题词=Whale Optimization Algorithms"

共 393 条记录，以下是241-250 订阅

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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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Reliability Dispatching of Distributed Energy Resource Based on Improved whale Algorithm for Distribution Networks

Reliability Dispatching of Distributed Energy Resource Based...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Wei Chai Ruimeng Xue Fan Yang Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai China Shanghai Proinvent Information Technology Company Shanghai China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

In an effort to boost the reliability of power supply in distribution systems, this study introduces an optimized scheduling model for distributed resources in distribution networks. This model is grounded on an enhanced whale optimization algorithm, designed to counteract the algorithm's tendencies toward local optima and its insufficient convergence precision, with the objective of maximizing power supply reliability. Initially, the model accommodates the large-scale integration of distributed resources into the distribution network. Subsequently, the whale optimization algorithm is refined by adjusting the convergence factor and inertia weight. The enhanced algorithm is then utilized to solve the model. The outcomes demonstrate the efficacy of the proposed improved whale optimization algorithm in significantly enhancing the reliability of power supply in advanced distribution systems.

关键词： Power supplies Energy resources Distribution networks Reliability engineering whale optimization algorithms Scheduling Large scale integration Particle swarm optimization optimization Convergence

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Performance Assessment of Meta-Heuristic and WOA algorithms for End-to-End Connection Weights Enhancement of (DeepLABNet)

Performance Assessment of Meta-Heuristic and WOA Algorithms ...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Mustapha Abdulrahman Lawal Okere Chidiebere Emmanuel Fatima Umar Zambuk Emmanuel Ramson Nannin Abdulmutalib Abdullahi Tapidi Blessing Abu Department of Computer Science Abubakar Tafawa Balewa University Bauchi Nigeria Department of Computer Science Federal Polytechnic Kaltungo Gombe Department of Computer Science University of Calabar Cross River Nigeria

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Radial Basis Function (RBF) neural networks are widely used in artificial intelligence applications. However, traditional training algorithms often suffer from local optima stagnation and slow convergence, reducing network efficiency. This study investigates using the whale optimization Algorithm (WOA) to optimize RBF neural networks for improved cancer classification accuracy. WOA addresses the limitations of traditional algorithms by avoiding local optima and ensuring global nonlinear parameter optima. We evaluated WOA-RBF on cancer datasets and compared its performance with conventional algorithms, including Genetic Algorithm, Particle Swarm optimization, Ant Colony optimization, and Differential Evolution. Results indicate WOA-RBF outperforms other algorithms in precision and convergence, achieving 97% average classification accuracy for ovarian tumours and 96% for pancreatic tumours. WOA-RBF demonstrated faster convergence and better generalization capabilities. This study fills the gap in optimizing RBF neural networks using WOA, providing a reliable solution for cancer classification tasks. This research contributes to developing more accurate cancer diagnosis systems using RBF neural networks optimized with WOA.

关键词： Training Accuracy Machine learning algorithms Neural networks whale optimization algorithms Classification algorithms Reliability Cancer Convergence Tumors

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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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Classification of Students’ Classroom Learning States Based on Physiological Information and Classroom Behavior

Classification of Students’ Classroom Learning States Based...

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

作者： Yifeng Zhu Qi Zhao Xu Yang Xiaofeng An School of Electronic Information Engineering Changchun University of Science and Technology Changchun China Education Quality Monitoring Center Jilin Engineering Normal University Changchun China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In classroom teaching, artificial intelligence technology can automate the analysis of students' classroom status. This enables teachers to efficiently and intuitively grasp students' responses to the classroom content and their level of understanding, thus enhancing the teaching program. Additionally, it can remind students to regulate their behavior in the classroom, ultimately aiding students in improving their learning performance. Currently, most research on students' learning status in the classroom relies on cameras, sometimes supplemented with other devices. However, this approach presents challenges such as complex equipment, limited recognition due to camera angles, and the inability to analyze the specificity of students' differences. This paper combines classroom behavior data with bracelet physiological data, which is convenient and enhances the comprehensiveness of the analysis simultaneously. The dataset on students' classroom status is established to serve as the foundation for the following research. The classification performance of KNN, RF, GBDT, and XGBoost is enhanced through Grid Search (GS). Furthermore, the XGBoost algorithm is further optimized using the whale optimization Algorithm (WOA). Experimental results demonstrate that the WOA-XGBoost algorithm chosen in this study can achieve an accuracy rate of 94.62%. This algorithm can significantly assist students in enhancing the quality of their classroom learning. Helping students improve the quality of classroom learning and assisting teachers in enhancing teaching programs.

关键词： Radio frequency Performance evaluation Machine learning algorithms Education Machine learning Nearest neighbor methods Cameras Physiology whale optimization algorithms Classification algorithms

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An Intrusion Detection in IoT Using Bidirectional Gated Recurrent Unit with Self Attention Network

An Intrusion Detection in IoT Using Bidirectional Gated Recu...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： S Vijayaraghavan V. Srinath Sheeba Armoogum R. Archana Reddy S Divya Department of ECE SCSVMV University Kanchipuram India Department of ECE Kongunadu College of Engineering and Technology Tholurpatti India Department of Information & Communication Technologies (ICT) University of Mauritius Mauritius School of Computer Science and Engineering SR University Warangal India Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In recent years, Internet of Things (IoT) is one of the most significant trends where cyberattacks with integrated servers, communications networks, and applications are increasing at high speed. Protection of cyber-threat is one of the most challenging tasks in information technology. The problem caused by the IoT network remains unsolved for longer periods. In this research, to detect IoT based attacks a small intrusion detection system is implemented on UNSW-NB15 dataset. To secure the networks, an Intrusion Detection System (IDS) is proposed which is a popular types of network security technology. Initially, the pre-processing is done using min-max normalization and those normalized features are processed into feature selection, where whale optimization Algorithm (WOA) is employed to select the optimal features. Finally, Bidirectional Gated Recurrent Unit (Bi-GRU) with Self-Attention Network (SAN) architecture is introduced to easily recognizes the hackers from the received optimal features. The suggested BiGRU with SAN detect attacks and additionally offers user-centric cyber security solutions which is receiving greater focus. From the result analysis, it clearly shows that Bi-GRU with SAN successfully decreases detection time and enhances detecting precision. The suggested Bi-GRU with SAN achieved higher accuracy of 98.72% when compared to existing Deep Neural Network (DNN) based IDS and Ensemble-based Learning Method (ELM) methods.

关键词： Accuracy Intrusion detection Logic gates Feature extraction whale optimization algorithms Internet of Things Servers Information technology Computer crime Protection

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Reliability-Driven Strategic Allocation of Renewable Integrated EV Charging Infrastructure in Radial Distribution System

Reliability-Driven Strategic Allocation of Renewable Integra...

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Silchar Subsection Conference (SILCON), IEEE

作者： Raj Chakraborty Priyanath Das Diptanu Das Punam Das Sadhan Gope Arup Ratan Bhowmik Department of Electrical Engineering National Institute of Technology Agartala Agartala India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Electric Vehicles (EVs) are vital for the progress of sustainable transportation, as they substantially lower greenhouse gas emissions and curtail the reliance on fossil fuels. The unplanned deployment of EV Charging Stations (EVCS) can degrade the reliability indices and disrupt distribution network parameters, resulting in diminished customer satisfaction. This work presents a reliability-focused method for the strategic allocation of EVCS integrated with Type 1 Distributed Generation (DG) in the IEEE 33 bus Radial Distribution System (RDS). The DG is combined with EVCS to improve the performance of distribution network parameters. The IEEE 33 bus RDS was divided into three distinct zones, with each zone equipped with an EVCS and a DG unit to improve charging accessibility throughout the network. The problem has been structured as an optimization task to enhance reliability indices. The Symbiotic Organisms Search (SOS) tool is used to determine the optimal solution. The obtained results have been validated by comparing it with the Grey Wolf Optimizer (GWO) and whale optimization Algorithm (WOA). The analysis highlights that the strategic integration of EVCS with DG is vital for improving the reliability and robustness of power distribution networks.

关键词： Symbiosis Transportation Distribution networks Electric vehicle charging whale optimization algorithms Robustness Organisms Resource management Vehicle dynamics optimization

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Speed Sensorless Method of Bearingless Synchronous Reluctance Motor Based on WOA-Elman Neural Network

Speed Sensorless Method of Bearingless Synchronous Reluctanc...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Enxiang Xu Ruijie Zhao National Research Center of Pumps Jiangsu University Zhenjiang China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

In the control of bearingless motors, it is essential to install sensors for detecting rotor position and speed signals. However, the conventional mechanical sensor installation increases motor size and costs, limiting its operational scope at high speeds. In order to realize the ultra-high speed, low cost, and practical operation of bearingless synchronous reluctance motor, a novel rotor position and speed estimation scheme base on the Elman neural network is proposed and designed. The Elman neural network's initial weights and thresholds are optimized using the whale optimization algorithm, which improved the prediction accuracy of the method. Subsequently, utilizing Matlab/Simulink software and an experiment platform, a bearingless synchronous reluctance motor control system is built to validate the proposed theory. Simulation and experimental results collectively demonstrate the correctness, anti-interference ability and feasibility of the proposed methodology.

关键词： Mechanical sensors Costs Neural networks Software algorithms Rotors Motors whale optimization algorithms

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Enhanced Sugarcane Leaf Disease Detection via Hybrid Deep Capsule Autoencoder CNN Optimized by Improved whale Algorithm

Enhanced Sugarcane Leaf Disease Detection via Hybrid Deep Ca...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Raj Gaurang Tiwari Satvik Vats Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

This research uses a Hybrid Deep Capsule Autoencoder-based Convolutional Neural Network and an Improved whale optimization (IWO) model to classify sugarcane leaf diseases. It starts with public dataset images that are pre-processed using an adaptive intensity-limiting histogram equalization technique. This preprocessing technique improves data quality for analysis by increasing input image contrast and drastically reducing noise. Hybrid wavelet-Walsh transform and Laplacian edge detection with a Gaussian operator capture leaf image properties during feature extraction. Adaptive Horse Herd optimization (AHO) decreases dimensionality while keeping crucial information for accurate classification to improve the feature set. The suggested Hybrid model employs capsule networks and convolutional neural networks to classify sugarcane leaf diseases, with the IWO model improving performance. Testing shows that the framework works, with 97.213% accuracy. This finding outperforms previous methods and shows hybrid deep learning strategies' promise in agricultural disease identification. In precision agriculture, the findings provide a powerful tool for early sugarcane leaf disease detection and control, improving crop output and sustainability.

关键词： Wavelet transforms Adaptation models Accuracy Laplace equations Image edge detection Autoencoders Feature extraction whale optimization algorithms Convolutional neural networks Diseases

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Optimal Sizing of Isolated Hybrid Microgrid for Residential Community in Saudi Arabia

Optimal Sizing of Isolated Hybrid Microgrid for Residential ...

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Australasian Universities Power Engineering Conference, AUPEC

作者： Ahmed S. Menesy Hamdy M. Sultan Waleed M. Hamanah Ibrahim O. Habiballah M. A. Abido Salah Kamel Department of Electrical Engineering King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia Egypt Applied Research Center for Metrology Standards and Testing Research & Innovation KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Saudi Arabia Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

This paper mainly focuses on the optimal design of microgrid hybrid renewable energy system (RES). The developed system in this work includes photovoltaic (PV), wind turbine (WT), diesel generator (DG), along with an energy storage component comprising a battery system. The optimal components sizes of the proposed hybrid generating system has been achieved using a recent metaheuristic optimization technique called artificial rabbits optimization (ARO). The presented algorithm aims to address a multi-objective optimization challenge, seeking to minimize the cost of energy (COE), the reliability index characterized by the loss of power supply probability (LPSP), and excess energy within specified constraints. The hybrid system is developed for deployment in the coastal residential community of Yanbu, located in the Medina province of Saudi Arabia (at 24.16° north latitude and 37.32° east longitude), with a planned operational lifespan of 25 years. To ensure the precision, stability, and robustness of the developed algorithm in addressing the optimization problem, a comprehensive comparison is conducted with the results generated by well-established optimization algorithms, including the Grey Wolf Optimizer (GWO) and whale optimization Algorithm (WOA). This comparative study assesses the impact of reliability indices on the optimal sizing of the proposed isolated hybrid RES, highlighting that the ARO algorithm excels in identifying the most efficient system configuration to be the most cost-effective and eco-friendly option.

关键词： Rabbits Wind Uncertainty Power supplies Microgrids Robustness whale optimization algorithms Batteries Wind turbines optimization

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