Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose t...Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.展开更多
This paper first suggests the use of the Fourier frequency transmission method of two dimensions function （ 2D FFT） to analyze radial rotating errors that occurred in a rotor. Based on this method a magnetic rotor i...This paper first suggests the use of the Fourier frequency transmission method of two dimensions function （ 2D FFT） to analyze radial rotating errors that occurred in a rotor. Based on this method a magnetic rotor is measured. The authors point out that the main cause to affect radial rotating accuracy of the rotating shaft at a high speed is the dynamic imbalance of the shaft itself. Finally the feedforward control scheme is suggested to improve the accuracy of the shaft in an active magnetic bearing （ AMB ） system.展开更多
We studied the muscle fatigue and recovery of thirty male sprinters(aged 18–22 years)using the Frequency Analysis Method(FAM).The interferential currents(ICs)with different thresholds for sensory,motor and pain respo...We studied the muscle fatigue and recovery of thirty male sprinters(aged 18–22 years)using the Frequency Analysis Method(FAM).The interferential currents(ICs)with different thresholds for sensory,motor and pain responses,the maximal voluntary contraction(MVC),and the amplitude of the surface EMG(aEMG,sEMG)were assessed prior to and immediately after an acute explosive fatigue training session,and during one-week recovery.We found that IC increased on average from 32.38.9 mA to 37.57.5 mA in sensory response at 10 Hz immediately post training(p=0.004)but decreased at 24-hr post training(p=0.008)and returned to pre-levels thereafter.Motor and pain response patterns at 10 Hz were similar(motor:p=0.033 and 0.040;pain:p=0.022 and 0.019,respectively).The change patterns of ICs were similar to but prior to the changes of sEMG.The agreement between IC assessment and amplitude of sEMG(aEMG)/MVC ratio was good(>95%).The present study suggested that the changes in ICs were prior to the changes in both the aEMG and force during fatigue.These changes may reflect the physiological sensory change due to peripheral fatigue.FAM may be useful as an effective early detection and simple tool for monitoring muscle fatigue during training and recovery in athletes.展开更多
基金supported in part by Shaanxi Natural Science Foundation Project (2023-JC-QN-0438)in part by Fundamental Research Funds for the Central Universities (2452021050).
文摘Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.
文摘This paper first suggests the use of the Fourier frequency transmission method of two dimensions function （ 2D FFT） to analyze radial rotating errors that occurred in a rotor. Based on this method a magnetic rotor is measured. The authors point out that the main cause to affect radial rotating accuracy of the rotating shaft at a high speed is the dynamic imbalance of the shaft itself. Finally the feedforward control scheme is suggested to improve the accuracy of the shaft in an active magnetic bearing （ AMB ） system.
基金The study was funded by National Key Research and Development Program(2018YFF0300904,2019YFF0301700)from Ministry of Science and Technology of the People's Republic of China.
文摘We studied the muscle fatigue and recovery of thirty male sprinters(aged 18–22 years)using the Frequency Analysis Method(FAM).The interferential currents(ICs)with different thresholds for sensory,motor and pain responses,the maximal voluntary contraction(MVC),and the amplitude of the surface EMG(aEMG,sEMG)were assessed prior to and immediately after an acute explosive fatigue training session,and during one-week recovery.We found that IC increased on average from 32.38.9 mA to 37.57.5 mA in sensory response at 10 Hz immediately post training(p=0.004)but decreased at 24-hr post training(p=0.008)and returned to pre-levels thereafter.Motor and pain response patterns at 10 Hz were similar(motor:p=0.033 and 0.040;pain:p=0.022 and 0.019,respectively).The change patterns of ICs were similar to but prior to the changes of sEMG.The agreement between IC assessment and amplitude of sEMG(aEMG)/MVC ratio was good(>95%).The present study suggested that the changes in ICs were prior to the changes in both the aEMG and force during fatigue.These changes may reflect the physiological sensory change due to peripheral fatigue.FAM may be useful as an effective early detection and simple tool for monitoring muscle fatigue during training and recovery in athletes.
