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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Cheng, Long Ma, Hongzhong Xiao, Yusong Chen, Yuelin College of Energy and Electrical Engineering Hohai University Nanjing211100 China

ISBN: (纸本)9798350382426

Aiming at the problem that it is difficult to accurately identify the mechanical fault of the transformer core, a method of transformer core mechanical fault diagnosis based on variational mode decomposition (VMD)-multi-scale permutation entropy (MPE) and a gray wolf optimization algorithm to optimize the parameters of hybrid kernel extreme learning machine (GWO-HKELM) is proposed. Firstly, VMD is used to decompose the transformer vibration signal and select multiple IMF components that are highly correlated with the original vibration signal. Secondly, we calculate the MPE values of the IMF components and use them to construct a feature dataset. Furthermore, GWO is used to optimize the key kernel parameters of HKELM and establish a GWO-HKELM diagnostic model. Then, the feature dataset is imported into the GWO-HKELM diagnostic model for recognition. Finally, the effectiveness and superiority of the suggested method are verified through experimental data and comparison with other methods. The experimental results show that the suggested method can effectively identify different types and degrees of mechanical faults caused by transformer core looseness, with a diagnostic accuracy of over 98.0%. It can provide theoretical guidance for the formulation of on-site maintenance strategies for transformers. © 2023 IEEE.

关键词： Fault diagnosis grey wolf optimization algorithm Hybrid kernel Extreme learning machine Iron core Power transformer

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Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

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BUILDINGS 2024年第2期14卷 396页

作者： Wang, Ranran Zhang, Jun Lu, Yijun Huang, Jiandong Guangzhou Univ Sch Fine Arts & Design Guangzhou 510006 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China

Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the grey wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy through the creation of four hybrid ensemble learning models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, and GWO-CatBoost. A comparative analysis was conducted between the results obtained from these hybrid models and their conventional counterparts. The evaluation of these models is based on five key indices: R2, RMSE, VAF, MAE, and bias, addressing an objective assessment of the predictive models' performance and capabilities. The outcomes reveal that GWO-XGBoost, exhibiting R2 values of (0.971 and 0.978) for the train and test stages, respectively, emerges as the best predictive model for estimating the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) compared to other models. Consequently, the proposed GWO-XGBoost algorithm proves to be an efficient tool for anticipating CSFrRNSC.

关键词： fiber-reinforced concrete Durable Sculptural Elements AdaBoost CatBoost LightGBM XGBoost grey wolf optimization algorithm machine learning

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An adaptive deep transfer learning method for bearing fault diagnosis

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MEASUREMENT 2020年 151卷 107227-000页

作者： Wu, Zhenghong Jiang, Hongkai Zhao, Ke Li, Xingqiu Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Bearing fault diagnosis has made some achievements based on massive labeled fault data. In practical engineering, machines are mostly in healthy and faults seldom happen, it's difficult or expensive to collect massive labeled fault data. To solve the problem, an adaptive deep transfer learning method for bearing fault diagnosis is proposed in this paper. Firstly, a long-short term memory recurrent neural network model based on instance-transfer learning is constructed to generate some auxiliary datasets. Secondly, joint distribution adaptation, a feature-transfer learning method, which is used to reduce the differences in probability distributions between an auxiliary dataset and target domain dataset. Finally, grey wolf optimization algorithm is introduced to adaptively learn key parameters of joint distribution adaptation. The proposed method is verified with two kinds of datasets, and the results demonstrate the effectiveness and robustness of the proposed method when the labeled fault data are scarce. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Feature-transfer learning Instance-transfer learning Long-short term memory recurrent neural network Joint distribution adaptation grey wolf optimization algorithm

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Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷 106211-106211页

作者： Dong, Jianhua Liu, Xiaogang Huang, Guomin Fan, Junliang Wu, Lifeng Wu, Jie Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Kunming Univ Sci & Technol Fac Agr & Food Kunming 650500 Yunnan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Wuhan Polytech Univ Sch Civil Engn & Architecture Wuhan 430023 Peoples R China

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7

关键词： Reference evapotranspiration Climatic zones Grasshopper optimization algorithm grey wolf optimization algorithm Machine learning

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Metaheuristic optimization algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System

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PROCESSES 2024年第2期12卷 291页

作者： Zafar, Farhan Malik, Suheel Abdullah Ali, Tayyab Daraz, Amil Alamri, Atif M. Alqahtani, Salman A. Bhatti, Farkhunda Int Islamic Univ Fac Engn & Technol Dept Elect & Comp Engn Islamabad 44000 Pakistan NingboTech Univ Sch Informat Sci & Engn Ningbo 315100 Peoples R China King Saud Univ Coll Comp & Informat Sci Software Engn Dept Riyadh 11495 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11495 Saudi Arabia Mehran Univ Engn & Technol Jamshoro Dept Elect Engn Jamshoro 76062 Pakistan

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (PIDD2-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the grey wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base optimization (GBO), and Whale optimization algorithm (WOA). The effectiveness of proposed controllers PIDD2-PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative (PIDD2-PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA-PIDD2-PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results sh

关键词： underactuated system ball and balancer optimization PIDD2-PI TID-F grey wolf optimization algorithm cuckoo search algorithm gradient based optimization and whale optimization

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A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 139卷 112824-000页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim de Albuquerque, Victor Hugo C. Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept Zagazig Egypt Univ Fortaleza Fortaleza Ceara Brazil Torrens Univ Australia 90 Bowen Terrace Fortitude Valley Qld 4006 Australia

Because of their high dimensionality, dealing with large datasets can hinder the data mining process. Thus, the feature selection is a pre-process mandatory phase for reducing the dimensionality of datasets through using the most informative features and at the same time maximizing the classification accuracy. This paper proposes a new grey wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods. The sigmoid function is used to transform the continuous search space to the binary one in order to match the binary nature of the feature selection problem. The two-phase mutation enhances the exploitation capability of the algorithm. The purpose of the first mutation phase is to reduce the number of selected features while preserving high classification accuracy. The purpose of the second mutation phase is to attempt to add more informative features that increase the classification accuracy. As the mutation phase can be time-consuming, the two-phase mutation can be done with a small probability. The wrapper methods can give high-quality solutions so we use one of the most famous wrapper methods which called k-Nearest Neighbor (k-NN) classifier. The Euclidean distance is computed to search for the k-NN. Each dataset is split into training and testing data using K-fold cross-validation to overcome the overfitting problem. Several comparisons with the most famous and modern algorithms such as flower algorithm, particle swarm optimization algorithm, multi-verse optimizer algorithm, whale optimization algorithm, and bat algorithm are done. The experiments are done using 35 datasets. Statistical analyses are made to prove the effectiveness of the proposed algorithm and its outperformance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Feature selection grey wolf optimization algorithm Wrapper method Classifier, accuracy Cross-validation Mutation

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Remote Sensing Inversion of Typical Offshore Water Quality Parameter Concentration Based on Improved SVR algorithm

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REMOTE SENSING 2023年第8期15卷 2104-2104页

作者： Ren, Jianghua Cui, Jianyong Dong, Wen Xiao, Yanfang Xu, Mingming Liu, Shanwei Wan, Jianhua Li, Zhongwei Zhang, Jie China Univ Petr Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Technol Innovat Ctr Maritime Silk Rd Marine Resour Qingdao 266580 Peoples R China Chinese Acad Sci Acad Aerosp Informat Innovat Beijing 100094 Peoples R China Minist Nat Resources Inst Oceanog 1 Remote Sensing Off Qingdao 266061 Peoples R China

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-grey wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO

关键词： chlorophyll a suspended matter SVR differential evolution algorithm grey wolf optimization algorithm

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Digital twin-based job shop anomaly detection and dynamic scheduling

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2023年 79卷

作者： Li, Yibing Tao, Zhiyu Wang, Lei Du, Baigang Guo, Jun Pang, Shibao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China KTH Royal Inst Technol Dept Prod Engn Stockholm Sweden Wuhan Univ Technol Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.

关键词： Digital twin (DT) Multi-level production process monitoring model Real-time scheduling optimization strategy Rolling window mechanism grey wolf optimization algorithm

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Prediction of high-temperature flow stress of HMn64-8-5-1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

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MATERIALS RESEARCH EXPRESS 2022年第9期9卷 096506页

作者： Liang, Qiang Zhang, Xianming Liu, Xin Li, Yongliang Minist Educ Engn Res Ctr Waste Oil Recovery Technol & Equipme Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Mech Engn Chongqing 400067 Peoples R China Chongqing Changjiang Electrician Ind Grp Co Ltd Chongqing 401336 Peoples R China

An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64-8-5-15 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 001-10 s(-1), the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3139 %, 2448 % and 1265 %, and the values of RMSE were calculated to be 1804, 1482 and 0467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64-8-5-15 alloy.

关键词： HMn64-8-5-1 5 brass alloy flow stress constitutive model BP neural network grey wolf optimization algorithm

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Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期224卷

作者： Zhang, Qizhao Liu, Hongshun Guo, Jian Wang, Yifan Liu, Luyao Liu, Hongzheng Cong, Haoxi Shandong Univ Sch Elect Engn Shandong Prov Key Lab UHV Transmiss Technol & Equi Jinan Peoples R China State Grid Qingdao Power Supply Co Qingdao Peoples R China North China Elect Power Univ Sch Elect Engn Beijing Peoples R China

The continuous and reliable operation of the transformer is the basis to ensure the normal operation of the power system. Relevant departments collect multi-dimensional and multi-source heterogeneous parameter data during the operation, maintenance and repair of transformers. The effective information contained in the parameter data can directly reflect the current operating status of the transformer. On the basis of support vector machine and grey wolf algorithm, an improved GWO-MCSVM algorithm based on nonlinear convergence factor and Tent chaotic mapping is proposed. The algorithm parameters are optimized through training samples, and the results are evaluated and verified in the algorithm itself, so as to improve the accuracy of the status assessment results. Finally, the accuracy of assessment results of the algorithm proposed in this paper, existing genetic algorithms and particle swarm optimization algorithms are compared by evaluating multiple sets of measured samples. By comparison, the effectiveness of the algorithm proposed in this paper for transformer condition assessment has been verified.

关键词： Transformer Condition assessment Support vector machine grey wolf optimization algorithm

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