An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model i...An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.展开更多
The converter is the core component of voltage source converter-high voltage direct current(VSC-HVDC),which is related to the stable operation of the system.The converter has a complex structure where the accuracy of ...The converter is the core component of voltage source converter-high voltage direct current(VSC-HVDC),which is related to the stable operation of the system.The converter has a complex structure where the accuracy of feature extraction is low,and the computation speed of traditional fault diagnosis strategies is slow.To solve this problem,a fault diagnosis strategy based on wavelet singular entropy(WSE)and support vector machine(SVM)was proposed.This method includes fault and label setting,converter fault feature extraction based on wavelet singular entropy,and converter fault classification based on support vector machine.The DC-side voltage signal was used as the detection signal,and the wavelet singular entropy was used for feature extraction to avoid noise interference.The classification is based on SVM.The experimental verification in PSCAD simulation proved that the method has better fault diagnosis ability for various faults and meets the needs of converter fault diagnosis.展开更多
Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavele...Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.展开更多
Secondary earth faults occur frequently in power distribution networks under harsh weather conditions.Owing to its characteristics,a secondary earth fault is typically hidden within the transient of the first fault.Th...Secondary earth faults occur frequently in power distribution networks under harsh weather conditions.Owing to its characteristics,a secondary earth fault is typically hidden within the transient of the first fault.Therefore,most researchers tend to focus on a feeder with single fault while disregarding secondary faults.This paper presents a fault feeder identification method that considers secondary earth faults in a non-effectively grounded distribution network.First,the wavelet singular entropy method is used to detect a secondary fault event.This method can identify the moment at which a secondary fault occurs.The zero-sequence current data can be categorized into two fault stages.The first and second fault stages correspond to the first and secondary faults,respectively.Subsequently,a similarity matrix containing the time-frequency transient information of the zero-sequence current at the two fault stages is defined to identify the fault feeders.Finally,to confirm the effectiveness and reliability of the proposed method,we conduct simulation experiments and an adaptability analysis based on an electromagnetic transient program.展开更多
文摘An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.
基金Supported by the National Natural Science Foundation of China(61673260)。
文摘The converter is the core component of voltage source converter-high voltage direct current(VSC-HVDC),which is related to the stable operation of the system.The converter has a complex structure where the accuracy of feature extraction is low,and the computation speed of traditional fault diagnosis strategies is slow.To solve this problem,a fault diagnosis strategy based on wavelet singular entropy(WSE)and support vector machine(SVM)was proposed.This method includes fault and label setting,converter fault feature extraction based on wavelet singular entropy,and converter fault classification based on support vector machine.The DC-side voltage signal was used as the detection signal,and the wavelet singular entropy was used for feature extraction to avoid noise interference.The classification is based on SVM.The experimental verification in PSCAD simulation proved that the method has better fault diagnosis ability for various faults and meets the needs of converter fault diagnosis.
基金supported by the Open Foundation of Jiangsu Engineering Center of Network Monitoring(Nanjing University of Information Science&Technology)(Grant No.KJR1509)the PAPD fundthe CICAEET fund
文摘Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.
基金This work was supported in part by National Science Foundation of China(No.51907097)National Key R&D Program of China(No.2020YFF0305800)+1 种基金the Full-time Postdoc Research and Development Fund of Sichuan University in China(No.2019SCU12003)the Applied Basic Research of Sichuan Province(No.2020YJ0012).
文摘Secondary earth faults occur frequently in power distribution networks under harsh weather conditions.Owing to its characteristics,a secondary earth fault is typically hidden within the transient of the first fault.Therefore,most researchers tend to focus on a feeder with single fault while disregarding secondary faults.This paper presents a fault feeder identification method that considers secondary earth faults in a non-effectively grounded distribution network.First,the wavelet singular entropy method is used to detect a secondary fault event.This method can identify the moment at which a secondary fault occurs.The zero-sequence current data can be categorized into two fault stages.The first and second fault stages correspond to the first and secondary faults,respectively.Subsequently,a similarity matrix containing the time-frequency transient information of the zero-sequence current at the two fault stages is defined to identify the fault feeders.Finally,to confirm the effectiveness and reliability of the proposed method,we conduct simulation experiments and an adaptability analysis based on an electromagnetic transient program.
