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International Conference on Trends in Electronics and Informatics (ICEI)

作者： J. Bennilo Fernandes K. Manideep M. Venkat Sainadh M. Lakshmi Sowmica Department of ECE Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of ECE Kalalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

As it can provide electricity for loads in dangerous areas without a power infrastructure, a back-up power delivery system is essential for military application and disaster relief. The advantages of the proton-exchange membrane electric cell over conventional energy sources including superior thermal efficiency, noise cancelling, key effect, and density, and zero greenhouse gas emissions gradually replace them as the significant supplier of power for the secondary energy provision system. A “dual electric cell and metallic element battery” backup power infrastructure is developed in this work, and fuzzy control-based energy management strategies are also investigated.

关键词： Software packages Microprocessors Hydrogen whales Computer architecture whale optimization algorithms Thermal noise

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Research on Wind Speed Forecasting Method of Wind Farm Based on whale optimization Algorithm-Hybrid Copula Function Correlation Analysis

Research on Wind Speed Forecasting Method of Wind Farm Based...

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IEEE International Conference on Power Data Science (ICPDS)

作者： Xuewei Tu Yafeng Zheng Wei Dan Shijie Sang Bingzhe Zhang Xinpeng Zhou State Nuclear Electric Power Planning Design & Research Institute Co. Ltd Beijing China North China Electric Power University Baoding China

ISBN: (纸本)9781665410533

Accurate wind speed prediction can effectively improve the power generation of wind turbines and realize the efficient use of wind energy. However, a large number of wind turbines exist in a wind farm, and complex temporal and spatial dependencies exist among the wind speeds of wind turbines, leading to difficulties in improving the accuracy of wind speed prediction. Therefore, this paper proposed a correlation analysis method by combining the whale optimization algorithm and hybrid copula function and constructed a wind speed prediction model based on spatiotemporal analysis. Mixing multiple single-copula functions and using a whale optimization algorithm for parameter solving can improve the level of correlation analysis and lay the foundation for obtaining accurate wind speed predictions. Taking the actual operation data of wind turbines in a certain area of My country as an example, the single copula prediction results are compared with the mixed copula prediction results. Experimental results show that the method proposed in this paper can effectively analyze the wind speed correlation of wind turbines and improve the accuracy of wind speed prediction.

关键词： Correlation Wind energy Wind speed Wind farms Prediction algorithms whale optimization algorithms Hybrid power systems

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Improved power quality in DFIG based wind energy conversion system using whale optimization technique

Improved power quality in DFIG based wind energy conversion ...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： T. Amar Kiran D. Ravi Kishore K. Charles Uggina Veera Naga Satish Dasari John Wesly Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology Rajahmundry India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In order for the globe to meet its energy requirements in the future, renewable energy sources (RES) are essential. Many dispersed generations like RESs and non-linear loads are features of the power electronics-based current power system design, which has caused a number of power quality (PQ) issues to arise. Regarding a double fed induction generator (DFIG) driven wind energy conversion system (WECS), this research suggests an optimal control mechanism. In this paper, the benefits of a metaheuristic optimizer, WOA, are applied to determine the optimal tuning of the PI controllers in the study's control system. The sophisticated controllers system is used for the adaptive modification of the discontinuity control gain, reducing the phenomenon of chattering in the stimulation network while maintaining the resilience of the closed-loop system. The PWM rectifier's highest possible voltage is tracked by the PI Controller, which also provides increased dependability. Initially, the DFIG and turbine modeling will be introduced. The rotor dimensions are then changed to achieve vector control over both the reactive and active power when utilizing the suggested whale optimization tuned PI controller. The converter's objectives are to deliver input currents with a reasonable harmonic content in order to run at a power factor of one. Extraction of maximum real power occurs at the interface between the DFIG and the powers electronic converter. When compared to other controlling schemes, the WOA shows highest accuracy of 94.34% and less THD value of 0.98%. Lastly, it's going to use the MATLAB 2021a / Simulink program to do a number of computational simulations in order to verify the suggested controls.

关键词： PI control Accuracy Heuristic algorithms Power quality Metaheuristics Doubly fed induction generators Rotors whale optimization algorithms MATLAB Wind energy conversion

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Research on selective harmonic elimination technology based on enhanced whale optimization algorithm

Research on selective harmonic elimination technology based ...

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IEEE International Conference on Mechatronics and Automation

作者： Weijie Li Wenyi Zhang College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China P.R

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

The key of inverter selective harmonic elimination technology lies in solving nonlinear transcendental equations. This paper takes a two-level three-phase bridge inverter circuit as an example, establishes its mathematical model from its output voltage, then constructs an evaluation function, and verifies the feasibility of the enhanced whale optimization algorithm in a selective harmonic elimination technology through MATLAB/Simulink simulation. At the same time, the simulation results also show that the DC voltage utilization rate of the three- phase inverter controlled by the selective harmonic elimination technology based on the enhanced whale optimization algorithm can reach 1.063, which is superior to the traditional Newton iterative method and SPWM technology in DC voltage utilization rate and harmonic elimination efficiency.

关键词： Software packages Simulation Bridge circuits Programming Harmonic analysis whale optimization algorithms Inverters

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A tandem white shark algorithm approach for optimizing drug-disease and drug-drug interactions in multimorbidity and polypharmacy

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期87卷

作者： Al Khatib, Sultan M. Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun Te Dept Software Engn Al Salt 19117 Jordan

Minimizing Drug-disease and drug-drug Interactions in Polypharmacy and Multimorbidity (DIPM) is a challenging problem. The challenge is caused by the large number of similar medications that can be prescribed for each disease and the adverse reactions that may be experienced by the selected medication(s) for one or more of the diseases and medications identified. Therefore, it is a selection problem as to which medication should be prescribed to the patient for each disease while maintaining the least drug-disease and drug-drug interactions. This is an optimization problem, and to the best of our knowledge, it has never been addressed before using search-based algorithms. The aims of this study are to explore and find the best performance among the employed algorithms in providing optimal or near-optimal solutions to this problem, and then to fine -tune an optimization approach to DIPM. Four algorithms are consequently employed, which are: Dragonfly Algorithm (DA), whale optimization Algorithm (WOA), Genetic Algorithm (GA), and White Shark optimization (WSO). To test the most suitable one among the algorithms, two synthetic datasets are used, representing simple and more complex problems. The results, considering only drug-disease interactions, show that GA is outperforming the others for a simple problem. However, WSO is found to outperform the others when the problem is more complex. The WSO is accordingly formed into a tandem approach, considering both drug-disease and drug-drug interactions for a real-world problem dataset. The approach indicates promising results with accuracy of (100%) for drug-drug interaction and (96.6%) for drug-disease interaction in (5.32) seconds.

关键词： Tandem White Shark optimization approach Drug-disease interactions Drug-drug interactions Multimorbidity and polypharmacy Search-based algorithms Dragonfly Algorithm whale optimization algorithms Genetic algorithms White Shark optimization

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Optimized Cross-Layer Cross-Domain Routing for Cluster-Based Dense Wireless Sensor Networks

Optimized Cross-Layer Cross-Domain Routing for Cluster-Based...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Subhashini Peneti Jitendra Prithviraj Deepak Patidar Sanjay Kumar Suman R. Poornima Gargi Verma Department of CSE MLR Institute of Technology Hyderabad Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur MP India Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P. India Dept. of ECE and Dean R&D St. Martin's Engineering College Secunderabad Telangana India Department of CSE-AI&ML School of Engineering Malla Reddy University GGITS MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

One of the most pressing issues in Wireless Sensor Networks (WSN) is finding a way to extend the life of the networks. In order to save the limited energy assets of Wireless Sensors Network, it is essential to model efficient approaches. The use of cross-layer procedures, which aim to optimize routing while also increasing the lifespan of the network, is a solution to these problems. In this paper, we provide a novel clustering-based Cross-Layer Cross-Domain Forwarding model. An innovative cross-layer cross-domain route methodology is used to discuss and improve a cluster-based heavy wireless network of sensors. To improve the functionality of complex wireless sensors, we presented a new hybrid method that combines elements of the Gray Wolf and whale Optimize algorithms. In the outcomes section, we compare the suggested model's performance to that of six additional typical models; we also look at the life span of the entire network as a whole and analyse the alive nodes for typical, developed, and absolutely advanced nodes in three separate test cases that vary in cluster size. Results for network lifetime and alive nodes assessment are better with the suggested technique compared to the current method. The suggested Adaptive Weighted whale optimization Algorithm (AW-WOA) consistently exceeds the other approaches, with an accuracy of 95% Grey Wolf optimization (GWO), whale optimization Algorithm (WOA), and Bat Search optimization (BSO). This is the case when compared to the alternative methods. On the other hand, AW-WOA retains outstanding outcomes, displaying improved stability and efficacy in optimisation throughout all phases, while all other approaches see a drop in accuracy with time.

关键词： Cross layer design Wireless sensor networks Analytical models Accuracy Clustering algorithms Routing whale optimization algorithms Sensors optimization Standards

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Ultra-Short-Term Prediction Method of Photovoltaic Power Generation Based on Improved GRNN-LSTM Combination Model

Ultra-Short-Term Prediction Method of Photovoltaic Power Gen...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yun Wu Tianyang Li Jieming Yang Dan Feng School of Computer Science Northeast Electric Power University China Chaoyang City Ecological and Environmental Affairs Service Center Chaoyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Ultra-short-term prediction of photovoltaic power generation is the prerequisite for real-time dispatch of the power system, in order to further improve the accuracy of ultra-short-term prediction of photovoltaic power, this paper proposes an ultra-short-term prediction method of photovoltaic power based on an improved Generalized Regression Neural Network(GRNN)-Long Short-Term Memory (LSTM) combination model. Firstly, in the GRNN model, an improved whale optimization algorithm based on grid constraints, quasi-reverse learning and local perturbations is proposed to solve the optimal smoothing factor of the model and improve the prediction accuracy. Secondly, in the LSTM model, a CNN convolutional layer and attention mechanism are introduced to better exploit temporal information and enhance the predictive capabilities for complex weather changes. Finally, a combination model threshold setting method based on an improved Dynamic Time Warping (DTW) algorithm is proposed. This method dynamically selects the best model's prediction results based on thresholds during different time periods, fully leveraging the advantages of each model to meet the diverse weather conditions in photovoltaic power prediction. Experimental results demonstrate that the improved GRNN model excels in predicting sunny weather power, the improved LSTM model excels in predicting complex weather power, and the combination model performs well across various meteorological conditions, providing a robust solution for photovoltaic power ultra-short-term prediction problems.

关键词： Photovoltaic systems Accuracy Heuristic algorithms Predictive models Power system stability Prediction algorithms whale optimization algorithms

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State of Health Estimation for Lithium-Ion Batteries Based on Improved Support Vector Regression

State of Health Estimation for Lithium-Ion Batteries Based o...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Xuelei Xia Yue Peng Jingyuan Hu Jiangwei Shen Zheng Chen Faculty of Transportation Engineering Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

To solve the problems with imprecise aging feature extraction and time-consuming parameter optimization in current data-driven lithium-ion battery state of health (SOH) estimation algorithms, an improved support vector regression (SVR) algorithm is proposed for accurate SOH prediction. First, the aging data of lithium-ion batteries are preprocessed using incremental capacity curves to extract key factors representing the battery’s aging state, which are then validated through grey relation analysis (GRA). Subsequently, the enhanced whale optimization algorithm (EWOA) is employed to optimize the kernel function parameters of the SVR algorithm. This enables the accurate estimation of the battery’s SOH based on the optimized SVR model. Finally, the model’s accuracy is validated by comparing it with traditional support vector machines (SVM), gaussian process regression (GPR), and extreme learning machine (ELM). The results indicate that the optimized SVR model developed in this study is capable of accurately estimating the SOH of the battery, with a maximum estimation error of less than 3%.

关键词： Support vector machines Lithium-ion batteries Accuracy Estimation Aging Feature extraction Prediction algorithms whale optimization algorithms Data models optimization

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Photovoltaic Power Generation Prediction Based on IWOA-TCN-Self-Attention-LSTM Neural Network

Photovoltaic Power Generation Prediction Based on IWOA-TCN-S...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Junming Wang Zhiheng Hu Zhenzhen Hu School of Communication Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Addressing the challenge of predicting photovoltaic power generation, which is hampered by various meteorological factors that disrupt the power output, remains a difficult task. This paper presents a fusion model (IWOA- TCN-Self-Attention-LSTM) based on improved whale optimization algorithm, self-attention mechanism, time convolution neural network and long short-term memory neural network. Firstly, Pearson correlation coefficient is used to select which are more important influencing factors from weather factors. Then, the optimized hyperparameters of the fusion model is carried out by using IWOA; Finally, the optimized hyperparameters are employed in the fusion model for training, leading to the generation of the final prediction result. And the prediction results show that the fusion model has better prediction accuracy.

关键词： Photovoltaic systems Training Analytical models Accuracy Neural networks Time series analysis Predictive models Prediction algorithms whale optimization algorithms Reliability

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Power-Efficient Cluster Head Selection in Wireless Sensor Networks using whale and Seagull algorithms

Power-Efficient Cluster Head Selection in Wireless Sensor Ne...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： P.Vinoth Kumar K. Venkatesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665462761

WSN includes deployed sensors. These small sensor nodes have energy limits since their batteries can't be replaced. Collect, transmit, and transfer data to the base station. Data transmission depletes WSN energy. Clustering is an efficient WSN data transport technique. whale optimization (WSOA) combines a seagull method with a global issue to meet global optimization concerns. The suggested technique helps choose based on residual node energy and nearby nodes, energy-aware cluster heads energy into consideration. Lifetime, energy economy, throughput, and stability of the recommended algorithm are tested. The hybrid metaheuristic's performance is compared to LEACH. The suggested approach outperforms others in residual energy, network longevity, and stability period.

关键词： Wireless sensor networks Metaheuristics Clustering algorithms whales Throughput Control systems whale optimization algorithms

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