A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefr...A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine(SVM)is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is1.3x10_5under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3dB,which is24times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is13times lower than the one using the global feature extraction method and24times lower than the one using the local feature extraction method.展开更多
In modem protection relays, the accurate and fast fault location is an essential task for transmission line protection from the point of service restoration and reliability. The applications of neural networks based f...In modem protection relays, the accurate and fast fault location is an essential task for transmission line protection from the point of service restoration and reliability. The applications of neural networks based fault location techniques to transmission line are available in many papers. However, almost all the studies have so far employed the FNN （feed-forward neural network） trained with back-propagation algorithm （BPNN） which has a better structure and been widely used. But there are still many drawbacks if we simply use feed-forward neural network, such as slow training rate, easy to trap into local minimum point, and bad ability on global search. In this paper, feed-forward neural network trained by PSO （particle swarm optimization） algorithm is proposed for fault location scheme in 500 kV transmission system with distributed parameters presentation, The purpose is to simulate distance protection relay. The algorithm acts as classifier which requires phasor measurements data from one end of the transmission line and DFT （discrete Fourier transform）. Extensive simulation studies carried out using MATLAB show that the proposed scheme has the ability to give a good estimation of fault location under various fault conditions.展开更多
This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and dis...This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and distributed delay. By the nonlinear transformation yi(t) = ui( et)(i = 1, 2,..., n), we transform a class of high-order neural networks with proportional delays into a class of high-order neural networks with constant delays and timevarying coefficients. With the aid of Brouwer fixed point theorem and constructing the delay differential inequality, we obtain some delay-independent and delay-dependent sufficient conditions to ensure the existence, uniqueness and global exponential stability of equilibrium of the network. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.展开更多
基金The National Key Technology R&D Program(No.2012BAH15B00)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX150076)
文摘A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine(SVM)is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is1.3x10_5under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3dB,which is24times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is13times lower than the one using the global feature extraction method and24times lower than the one using the local feature extraction method.
文摘In modem protection relays, the accurate and fast fault location is an essential task for transmission line protection from the point of service restoration and reliability. The applications of neural networks based fault location techniques to transmission line are available in many papers. However, almost all the studies have so far employed the FNN （feed-forward neural network） trained with back-propagation algorithm （BPNN） which has a better structure and been widely used. But there are still many drawbacks if we simply use feed-forward neural network, such as slow training rate, easy to trap into local minimum point, and bad ability on global search. In this paper, feed-forward neural network trained by PSO （particle swarm optimization） algorithm is proposed for fault location scheme in 500 kV transmission system with distributed parameters presentation, The purpose is to simulate distance protection relay. The algorithm acts as classifier which requires phasor measurements data from one end of the transmission line and DFT （discrete Fourier transform）. Extensive simulation studies carried out using MATLAB show that the proposed scheme has the ability to give a good estimation of fault location under various fault conditions.
基金Supported by National Natural Science Foundation of China under Grant Nos.61673008 and 11261010Project of High-level Innovative Talents of Guizhou Province([2016]5651)
文摘This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and distributed delay. By the nonlinear transformation yi(t) = ui( et)(i = 1, 2,..., n), we transform a class of high-order neural networks with proportional delays into a class of high-order neural networks with constant delays and timevarying coefficients. With the aid of Brouwer fixed point theorem and constructing the delay differential inequality, we obtain some delay-independent and delay-dependent sufficient conditions to ensure the existence, uniqueness and global exponential stability of equilibrium of the network. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.
