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Research on Transformer Fault Diagnosis by WOA-SVM Based on Feature Selection and Data Balancing

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第1期20卷 41-49页

作者： Ding, Can Yu, Donghai Liu, Xiangdong Sun, Qiankun Zhu, Qingzhou Shi, Yiji China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

Oil-immersed transformers as one of the most important equipment in the power system, the fault prediction of it in advance can effectively reduce the subsequent harm. Aiming at the selection of input features and data sample imbalance in the transformer fault diagnosis model, this paper adopts the recursive feature elimination (RFE) method combined with SMOTETomek comprehensive sampling method to optimize the above problems. First, RFE is used to traverse all the features and filter the optimal combination of them as input features, then SMOTETomek is used to perform balancing operation on the samples of the train set, and finally, whale optimization algorithm (WOA) is used to find the best hyperparameters for support vector machine (SVM), and the results are compared with the diagnostic models operated without processing and after single processing operation, respectively. After several sets of experiments, it is proved that the optimized comprehensive fault diagnosis model performs better on the test set than both the untreated and the singly processed models, which proves the effectiveness of the methodology used in this paper. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： feature selection data balancing transformer whale optimization algorithm fault diagnosis

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Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 1755-1766页

作者： Zeng, Fan Nait Amar, Menad Mohammed, Ahmed Salih Motahari, Mohammad Reza Hasanipanah, Mahdi Zhengzhou Technol Coll Zhengzhou 450000 Henan Peoples R China Sonatrach Div Labs Dept Etud Thermodynam Boumerdes Algeria Univ Sulaimani Dept Civil Engn Coll Engn Sulaimaniyah Kurdistan Regio Iraq Arak Univ Dept Civil Engn Fac Engn Arak Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.

关键词： Safety factor LSSVM whale optimization algorithm Prediction models

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Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator algorithm with Enhanced Diversity

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Journal of Bionic Engineering 2024年第5期21卷 2497-2514页

作者： Yang Yang Yuchao Gao Jinran Wu Zhe Ding Shangrui Zhao Nanjing University of Posts and Telecommunications Nanjing210023China Australian Catholic University Banyo4014Australia Queensland University of Technology Brisbane4001Australia Wuhan University of Technology Wuhan430070China

Power systems are pivotal in providing sustainable energy across various ***,optimizing their performance to meet modern demands remains a significant *** paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power *** approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased *** incorporating the spiral model from the whale optimization algorithm(WOA)into the Multi-Objective Marine Predator algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation ***,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto *** applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.

关键词： Multi-objective optimization Automatic generation control PID controller Multi-objective marine predator algorithm whale optimization algorithm

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Fast Recognition Method of Driver Braking Intention Based on Partial Braking Pedal Signals

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2025年 1-13页

作者： Xiao, Songche Zhang, Xiangwen Tang, Mingbin Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Intelligence Integrated Automat Guangxi Un Guilin 541004 Peoples R China

The correct recognition of driver's braking intention is the foundation to realize the brake-by-wire system, and it is also the key to improving the energy recovery efficiency of electric vehicles with the hybrid braking system. However, the recognition speed and accuracy interact with each other, which increases the recognition difficulty. A fast recognition method is proposed based on partial brake pedal signals to improve the recognition speed and accuracy simultaneously. The brake pedal signal was decomposed into intrinsic mode function components with the whale optimization algorithm optimized variational mode decomposition (WOA-VMD), and the samples were extract from the components by the sample entropy. The effective features were selected with the grey relative analysis (GRA) algorithm. The braking intention recognition model was built with the whale optimization algorithm optimized support vector machine (WOA-SVM) algorithm and validated on the dSPACE semi-physical simulation platform. The results show that the noise in the signal was reduced effectively with the WOA-VMD algorithm, and the irrelevant features were filtered out effectively with the GRA algorithm, and the recognition speed and accuracy were improved significantly with the WOA-SVM algorithm. Compared with traditional algorithms, the accuracy increases by 12.67%, and the time is shortened to 0.223 s.

关键词： Braking intention whale optimization algorithm Variational mode decomposition Grey relation analysis Support vector machine

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Roughness prediction of asphalt pavement using FGM(1,1|sin) model optimized by swarm intelligence and Markov chain

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NEURAL NETWORKS 2025年 183卷 107000页

作者： Li, Zhuoxuan Cao, Jinde Shi, Hairuo Shi, Xinli Ma, Tao Huang, Wei Southeast Univ Sch Math Nanjing 210096 Peoples R China Minist Transport Key Lab Transport Ind Comprehens Transportat Theor Nanjing Modern Multimodal Transportat Lab Nanjing 211135 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Transportat Nanjing 210096 Peoples R China Southeast Univ Intelligent Transportat Syst Res Ctr Nanjing 210096 Peoples R China

The road traffic volumes are constantly increasing worldwide, leading to significant challenges in maintaining asphalt pavements. Vehicular loads and environmental changes impact asphalt pavements, necessitating suitable predictive models. The International Roughness Index (IRI), a key indicator of road smoothness, requires IRI prediction models for performance analysis. Using the fractional accumulation operator and sine term can improve the traditional grey model's low prediction accuracy. Then, the chaotic adaptive whale optimization algorithm and Markov chain are used to optimize the model. Based on the different asphalt pavement structures used by RIOHtrack as data for the experiments, the average RMSE, MAE, and MAPE reached 0.025, 0.020, and 1.392%, respectively. Compared with other grey models, it performs better in IRI multi-step prediction. Particularly, the proposed model can achieve compelling predictions in a small sample size only through the changes in IRI itself, which helps to evaluate road conditions and design maintenance plans.

关键词： IRI Grey model Fractional accumulated generating operation whale optimization algorithm Markov chain

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Optimal containment control for multi-agent systems using fast adaptive dynamic programming

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ASIAN JOURNAL OF CONTROL 2025年第3期27卷 1427-1441页

作者： Cao, Ao Wang, Fuyong Liu, Zhongxin Chen, Zengqiang Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

In this paper, an innovative adaptive dynamic programming (ADP) algorithm with fast convergence speed is designed for the optimal containment control problem of discrete-time linear multi-agent systems. Precisely, a quadratic input energy cost function, including local containment error information and actuator information in the neighborhood, is designed for each follower. Solving the stationary condition of the cost function, the optimal containment controllers are obtained. Traditional ADP methods use actor-critic neural networks to approximate optimal costs and control strategies, it is time-consuming to solve large-scale multi-agent problems due to the computational complexity of neural networks. In order to seek faster convergence speed of optimal containment control without knowing the model information, the fast ADP algorithm framework is designed, it is proved theoretically that the convergence speed is determined by some configurable parameters, and the whale optimization algorithm is employed to globally optimize the parameters of given spaces to derive the optimal configuration. Finally, numerical simulation results are given to verify the effectiveness of the designed algorithm.

关键词： adaptive dynamic programming faster convergence speed multi-agent system optimal containment control whale optimization algorithm

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Resilient multi-UAV path planning for effective and reliable information collection

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PHYSICAL COMMUNICATION 2025年 71卷

作者： Liu, Yunfei Chen, Yang Hu, Mian Zhang, Wenhao Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Minist Educ Wuhan Peoples R China UWE Bristol Ctr Machine Vis Bristol Robot Lab Bristol England

UAV technology provides opportunities for regional information collection in complex and dynamic environments. In many large-scale or intricate scenarios, such as widespread natural disasters, UAVs often encounter challenges in wirelessly transmitting collected data in real time, which may compromise the timeliness and relevance of the information. Additionally, UAV malfunctions or failures can result in the loss of critical data from specific target points. Therefore, when planning flight paths of multiple UAVs, it becomes essential to coordinate their flight paths to maintain data freshness while minimizing the risk of data loss. In this paper, we introduce a resilient path planning strategy to proactively respond to potential UAV failures. Our approach seeks to reduce discrepancies in the total volume of information carried by individual UAVs, therefore reducing the risk of substantial information loss. Given that traditional weighting methods often heavily rely on subjective coefficient settings, we have established a multi-objective resilient path planning model for multi-UAV information collection scenarios. To optimize UAV flight paths, we propose an improved non-dominated sorting whale optimization algorithm, which provides a more robust and adaptive solution to UAV coordination. Experimental results validated the effectiveness of the constructed mathematical model. Comparative analysis demonstrates that the Pareto front solutions generated by the proposed algorithm hold significant advantages in terms of distribution and uniformity.

关键词： Information collection UAV failure whale optimization algorithm Resilient path planning

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Analog Circuit Fault Diagnosis Model Based on WOA and Improved SDAE

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IEICE ELECTRONICS EXPRESS 2025年第1期22卷 20240633-20240633页

作者： Lu, Xinmiao Zou, Yixin Wu, Qiong Yang, Longyue Zhu, Yuna Harbin Univ Sci & Technol Sch Measurement Control Technol & Commun Engn Harbin 150000 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150000 Peoples R China Heilongjiang Network Space Res Ctr Harbin 150000 Peoples R China

With the advancement of integrated circuit technology, the stable operation of electronic devices is crucial. Targeting the issue that traditional analog circuit fault diagnosis models cannot simultaneously satisfy noise resistance, stability, and accuracy in real circuit environments, this research represents an analog circuit fault diagnosis model relied on the whale optimization algorithm and an improved SDAE. The model transfor ms fault signals into 2D time-frequency representations using VMD and CWT to achieve initial denoising;Utilizing DSDAE for further denoising and dimensionality reduction of feature vectors;finally, RF is used for classification. The results of the simulation demonstrate that even in noisy conditions, the model can maintain excellent diagnostic accuracy and stability. making certain improvements in enhancing the operational reliability of electronic devices.

关键词： Variational Mode Decomposition Continuous Wavelet Trans for m Stacked autoencoder whale optimization algorithm

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Fatigue crack growth rate prediction under single peak overload based on WOA-BP neural network

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INTERNATIONAL JOURNAL OF FRACTURE 2025年第1期249卷 1-19页

作者： Ye, Zihe Li, Haoran Li, Wenqi Wu, Yalian Xiang, Zhong Zhejiang Sci Tech Univ Sch Comp Sci & Technol Sch Artificial Intelligence Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China

This study is aimed at investigating the crack growth behavior of 2A12 aluminum alloy under constant amplitude loads and various single peak overload conditions, with a focus on the effects of different load ratios and overload ratios on the crack growth rate. Due to the complexity and numerous parameters involved in traditional physical models, we proposed a whale optimization algorithm-backpropagation neural network-based model for predicting crack growth rate. By comparing results on datasets of 2A12 aluminum alloy and QSTE340TM steel, including Wheeler, Huang, WOA-SVM, and WOA-RBF models, our study demonstrates that our model achieves higher predictive accuracy. Finally, the paper calculated the crack growth life using the cycle-by-cycle method and conducted a detailed comparison and analysis of the prediction errors of various models. This research holds significant theoretical and practical value for enhancing understanding of material crack behavior and developing more accurate models for predicting crack growth rates.

关键词： Single peak overload Fatigue crack growth rate BP neural network whale optimization algorithm

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Studyonthe optimizationfor Reactive Power Regulation of Synchronous Condenser Basedon Single Neuron Adaptive PID

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Chinese Journal of Electrical Engineering 2025年第1期11卷 184-193页

作者： Lin Wang Honghua Wang School of Electrical and Power Engineering Hohai UniversityNanjing 211100China College of Artificial Intelligence and Automation Hohai UniversityNanjing 213000China

A synchronous condenser(SC)isusedto maintain grid voltage stability owing to its bidirectional fast reactive power regulation ability and good dynamic *** address the issue of dynamic voltage instability inpower systemduring failuresor heavy inductive loads,an SC reactive power regulation optimization method based onsingle neuron adaptive PID(SNA-PID)combined with whale optimization algorithm(WOA)is *** approach aimsto overcome the limitationsof normal PID *** model of the SC reactive power regulation system,based on SNA-PID combined with the WOA,is established using *** parameters of the SNA-PID are optimizedbythe WOAwith the ITAE criterion under two typical operation situationsof the power system:one is to set three different degrees of short-circuit ground faults,and the other isto accessthree different three-phase resistive *** to conventional PID control,asthe degree of short-circuit ground faults increases and the three-phase resistive load resistance decreases,the SC reactive power regulation optimization method based on SNA-PID combined with the WOA can still reduce the voltage recovery time and voltage oscillation,while maintaining voltage *** results show that the proposed method exhibits better dynamic adjustment characteristics and adaptive ability.

关键词： Synchronous condenser reactive power regulation single neuron adaptive whale optimization algorithm PID control

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