3篇
您的检索式：作者名="Kaining Hou"
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| 1 | A method for detecting two-dimensional plane stress distribution in basin-type insulator based on critically refracted longitudinal wave显示文摘Basin-type insulator often has small cracks due to stress concentration.The current method cannot accurately reflect the stress condition of the insulator to find the stress concentration areas.To solve these problems,a method for detecting two-dimensional plane stress(δ_(1) andδ_(2))within different depth ranges in a basin-type insulator is proposed based on critically refracted longitudinal(LCR)wave.First,the acoustoelastic equation characterising the relationship between the variation of LCR wave propagation time and the plane stress was derived.Next,the propagation characteristics of LCR wave in epoxy resin samples were investigated.Then,the stress distribution within different depth ranges of the insulator subjected to hydraulic load was measured using the proposed method,including direction(θ),δ_(1) andδ_(2).The results show that the magnitude of the stress alone cannot accurately characterise the stress state.Points with equal distances to the centre have similar stress magnitudes,but their directions are not the same.With increasing depth,θremains essentially unchanged at the same location,whileδ1 andδ2 decrease,and the rate of decrease varies at different locations.Comparing the measured and simulated data,the results showed that they were in good agreement,and the maximum errors of stress value andθwere 0.69 MPa and 2.97°,respectively,which confirmed the feasibility and accuracy of the stress detection in the proposed method. | Zhaoyang Kang Fuqiang Ren Hongru Zhang Jingjing Yang Kaining Hou Qingquan Li Dongxin He Hongshun Liu Yongzhi Zhao Huaxin Wen | 2024 | High Voltage2024，9，1： | 0 |
| 2 | Contamination degree prediction of insulator surface based on exploratory factor analysis-least square support vector machine combined model显示文摘This study presents a combined model based on the exploratory factor analysis(EFA)and the least square support vector machine(LSSVM)to predict the contamination degree of insulator surface.Firstly,EFA method is utilised to reduce numerous influence factor variables of the insulator contamination into a few factor variables,which could decrease the complexity of the model.Then,regarding the above factor variables as new input variables,LSSVM model is established to predict the insulator contamination de-gree.In order to obtain the optimal predictive value,the non-dominated sorting genetic algorithm II is applied on the optimization of LSSVM model parameters.The proposed EFA-LSSVM combined model is compared with the models of LSSVM,back propa-gation neural network,and multiple linear regression on the model performance.Results indicate that the EFA-LSSVM combined model in this study effectively overcomes the shortcomings of the other three models mentioned above in computational time,pre-diction accuracy and generalization ability.Finally,the feasibility of the proposed model in predicting contamination degree of insulator surface is verified by adopting the radar map of the evaluation indexes of model performance. | Jiaxiang Sun Hongru Zhang Qingquan Li Hongshun Liu Xinbo Lu Kaining Hou | 2021 | High Voltage2021，6，2： | 0 |
| 3 | Improved information entropy weighted vague support vector machine method for transformer fault diagnosis显示文摘A combined model based on improved information entropy and vague support vector machine(IVSVM)is introduced into transformer fault diagnosis using dissolved gas analysis in oil(DGA).The improved information entropy method is used to obtain the weights of each gas and to weight the raw data,and the processed training data and the corresponding fault types are inputted into the vague support vector machine(VSVM)model to obtain classifiers.Firstly,the training data are weighted by the improved information entropy method to discretise the original data from the mixed state for subsequent classifier training.Then,the vague set divides the events into true,false and unknown factors,which can optimise the sub-interface of SVM and improve the accuracy of the boundary point clas-sification.Finally,fault data from the literature and actual collections are selected for training and testing.By comparing with the widely used ratio method and artificial intelligence method,it can be concluded that the method described herein can effectively improve the accuracy of fault diagnosis.The result shows that this method has better applicability when facing actual fault type classification with higher data similarity. | Hongru Zhang Jiaxiang Sun Kaining Hou Qingquan Li Hongshun Liu | 2022 | High Voltage2022，7，3： | 0 |
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