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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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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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Comprehensive Study of Nonlinear Maglev System Utilizing COOT Optimized FOPID Controller

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IEEE Access 2025年 13卷 102234-102250页

作者： Marabathina Maheedhar T. Deepa School of Electrical Engineering Vellore Institute of Technology Chennai India

To improve the performance of the magnetic levitation system, the most recent metaheuristic COOT algorithm was first employed in this study to tune the Fractional Order Proportional Integral and Derivative (FOPID) controller. Because of its intrinsic instability and nonlinearity, the magnetic levitation system requires accurate and reliable control strategies to stay stable and reach the required position. Conventional PID controllers often fail to address the complexity of magnetic levitation systems due to their higher-order nature. The FOPID controller, with its fractional calculus-based design, offers greater flexibility and superior control performance by extending the integral and derivative orders to non-integer values. The objective function that encapsulates the magnetic levitation system’s performance requirements is minimized during the tuning phase. Integral Square Error (ISE) serves as the objective function in this case. The novelty of the proposed work is that the COOT algorithm is used to tune the FOPID controller by using the ISE as the objective function and examined by the servo and square responses in simulation and real-time. The contribution of the proposed work is two different algorithms, including the Genetic Algorithm and whale optimization Algorithm, were used to contrast the effectiveness of the FOPID controller based on the COOT method. In comparison to the other two algorithms, the simulation results and hardware results show that the FOPID controller tuned with the COOT algorithm performed substantially better. Additionally, the FOPID controller outperformed the PID controller tuned with the COOT algorithm.

关键词： whale optimization algorithms optimization Magnetic levitation Genetic algorithms Frequency control Linear programming Approximation algorithms Accuracy Servomotors Prediction algorithms

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Optimal Placement and Sizing of Capacitor Banks in Radial Distribution Systems Using the whale optimization Algorithm

Optimal Placement and Sizing of Capacitor Banks in Radial Di...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Adham Osama Hatem H. Zeineldin El-Fouly Tarek H.M. Ehab F. El-Saadany EESC Department Advanced Power and Energy Center (APEC) Khalifa University Abu Dhabi UAE

Capacitor banks (CBs) are widely utilized in electrical distribution networks for reactive power compensation. This study introduces a two-step approach for identifying the optimal location and sizing of CBs in a 34-bus radial distribution network. The proper allocation of CBs in distribution networks provides several benefits such as power loss reduction, boosting net savings, and improving feeders’ voltage profiles. The swarm optimization algorithm selected in this study is the whale optimization Algorithm (WOA) to find the optimal sizing and allocation of capacitors in the proposed system. The first step in this study will find the fittest buses for capacitors allocation through the loss sensitivity indices and then the second step comes to optimally identify the best locations and sizes of CBs using the whale optimization algorithm. The optimal capacitor placement (OCP) problem considers minimizing the total cost comprising the active power loss cost, the capacitors purchase, and capacitors installation costs. Three cases are studied and compared, the uncompensated case (without reactive power compensation), the fixed capacitors, and the switched capacitors. The results showed the capability of WOA to improve the overall performance in terms of the annual cost, the percentage of energy loss reduction, and the net saving in ${\$}$/year.

关键词： Reactive power Costs Capacitors Distribution networks Voltage Switches whale optimization algorithms

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Long Short-Term Memory for Short Term Load Forecasting with Singular Spectrum Analysis and whale optimization Algorithm

Long Short-Term Memory for Short Term Load Forecasting with ...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ruixiang Zhang Meng Yuan Zhaorui Jin Ziyu Zhu Yuanhui Chen Yu Wang Yaojie Sun Longjun Zhao School of Information Science and Technology Fudan Univesity Shanghai China Institute for Six-Sector Economy Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System Shanghai China China Xiongan Group Digial City Technology Co. Ltd. Beijing China

Short-term load forecasting is necessary for the safety and high efficiency of power system. In recent years, a large number of artificial intelligence-based methods have been used for short-term load forecasting by researchers, among which deep learning methods such as long short-term memory (LSTM) has been proved to be better at load forecasting. However, in the process of constructing LSTM models, data processing and hyperparameter selection tend to depend on experience, resulting in relatively slow modeling speed. This study proposes a short-term load forecasting method with singular spectrum analysis (SSA), LSTM network and whale optimization algorithm (WOA). SSA is used to decompose and reorganize the load sequence, and the WOA is used to optimize the hyperparameters of the LSTM network. The results show that the proposed method performs better than the benchmark in the three counties of the United States used for verification and comparison, that is, SSA-WOA-LSTM can be used to improve the accuracy of the short-term load forecasting.

关键词： Load forecasting Predictive models Hyperparameter optimization Data models whale optimization algorithms Forecasting optimization

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Reconfiguration of the Distribution Network using a whale optimization Algorithm

Reconfiguration of the Distribution Network using a Whale Op...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Akash Solanki Shweta Mehroliya Anoop Arya Shilpi Tomar Karuna Nikum Department of Electrical Engineering Maulana Azad National Institute of technology Bhopal India Department of Electrical Engineering Samrat Ashok Technological Institute Vidisha India Department of Electrical Engineering Thakur College of Engineering and Technology Mumbai India

The most prevalent and crucial approach to energy system reconfiguration can be used to enhance the power system for distribution, which is covered in this paper. Reconfiguration entails modifying the status of switches in the network's radial distribution topology. The aim is to enhance the power profile while lowering the complete loss of power. This problem was solved using a variety of optimization strategies, including heuristic methods, which are more effective in this case, and classical optimization, which is a time-consuming methodology. To find the best case in the reconfiguration problem, the whale optimization algorithm (WOA), a modern heuristic optimization approach, is used. When solving discrete optimization issues, the WOA is quite effective. The outcomes of WOA are contrasted with those of several heuristic methods (33 bus systems and 69 bus systems).

关键词： Network topology Heuristic algorithms Power distribution Voltage Distribution networks whale optimization algorithms Power system reliability

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Enhancement of Network Security in MANET By Using Guided whale optimization Algorithm (GWOA) for Solving Multiobjective optimization

Enhancement of Network Security in MANET By Using Guided Wha...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： N. Siva Naga Malleswari Dingari Kalpana S. Marlin N. Bala Sundara Ganapathy M. Arun P. Satyanarayana Department of ECE V. R. Siddhartha Engineering College Vijayawada India Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of EEE Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of ECE Panimalar Engineering College Chennai Tamil Nadu India

Recently, wired networks have become more popular than wireless ones. Mobile Ad hoc Network, also used in the military (MANET). As the name suggests, military applications are the primary users of military or conventional MANET, which is designed to protect networks from intruders. The MANET is deployed for rescue missions during times of emergency, such as natural catastrophes or law enforcement activities. Each mobile node in a MANET is outfitted with both a Wi-Fi transmitter and receiver. In this paper we present that the Guided whale optimization Algorithm for Solving Multiobjective optimization for wireless security in MANET, Within the MANET, the best secured routing route is chosen with the assistance of a whale optimization algorithm (WOA), which is used within this article. When calculating the appropriateness of a routing route, the WOA algorithm takes into account both the trust factor and the physical separation among the nodes. The suggested routing technique has three steps: detecting the faith and distance-based statistics for each node, identifying a k-disjoint route, and establishing the optimum path based on those metrics. The indicators such as overhead as well as packet delivery rate are used in the examination of the effectiveness of the trust-based GWOA. When compared to other authentication methods, ours is the most efficient in terms of computing complexity.

关键词： Wireless communication Transmitters Authentication Routing whale optimization algorithms Complexity theory Communication system security

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Feature Selection and Parameter optimization of Optimized Extreme Learning Machine for Motor Fault Detection

Feature Selection and Parameter Optimization of Optimized Ex...

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International Conference on CAD Systems in Microelectronics (CADSM)

作者： Dade Wu Hubei Mechanical and Electrical Research and Design Institute Co. Ltd. Wuhan China

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and non-linear fault categories and the accuracy is relatively low., we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model., SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method., it is applied to the real motor fault detection example., and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.

关键词： Solid modeling Extreme learning machines Fault detection Simulation Simulated annealing Feature extraction whale optimization algorithms

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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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Metaheuristic-Based Cepstral Sensing Technique for Prolonging Network Lifetime and Mitigating Primary User Emulation Attacks in Cognitive Radio Sensor Network

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IEEE Access 2025年 13卷 100370-100391页

作者： V. Abilasha A. Karthikeyan School of Electronics Engineering Vellore Institute of Technology Vellore India

Cognitive Radio Sensor Network (CRSN) impart adaptive access to inactive radio bands, optimizing spectrum utilization. Conversely, by manipulating secondary users, Primary User Emulation Attack (PUEA) create severe threats to security and cause improper spectrum allocation and higher energy usage. To overcome this issue, we present a Cepstral Spectrum Sensing Multi-Hop whale optimization Algorithm (CSSMWA) and the proposed cepstral sensing uses a Logarithmic Spectral Derivative algorithm that aids in identifying authentic primary users from mimicked signals due to its feature-revealing properties, elevating the ability to identify the precision of PUEAs, perform better than other methods with low, moderate to high noise levels. whale optimisation algorithm maintains network performance and energy efficiency while refining sensing parameters and enhancing decision-making. Extensive simulations are used to analyze the proposed algorithm, incorporating important performance parameters including error probability, energy efficiency, residual energy, data gathering efficiency, network longevity, and throughput. According to simulation findings, the proposed algorithm surpasses other methods in terms of privacy, network surveillance capabilities, and spectrum sensing accuracy. For future wireless networks, the work assists in developing CRSN that are resilient, trustworthy, and energy-efficient.

关键词： Sensors Clustering algorithms Energy efficiency Cepstral analysis Routing Cognitive radio Energy consumption Computational complexity Accuracy whale optimization algorithms

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