The accident transition information is not only complex but also enrichment when single-phase-to-ground fault occurs in resonant earthed system. A new integration method of fault line detection based on instantaneous real power and instantaneous virtual power is proposed in this paper. Not as health lines, when single-phase-to-ground fault occurs in resonant earthed system, the grounded current of faulty line contains distinct transition damping oscillation direct current component with the compensation of arc suppressing. The damping oscillation direct current component shows the characteristics obviously of damping oscillation of industrial frequency through the analyses of instantaneous real power and instantaneous virtual power. Here, the characteristic frequency of 50Hz in instantaneous power theory is defined. By calculating the instantaneous real power and instantaneous virtual power respectively of each line after single-phase-to-ground fault occurred, the oscillator components can be decomposed. Through comprehensively analyzing the value of oscillator components with the characteristic frequency of instantaneous real power and instantaneous virtual power, the integration algorithm of fault line detection can be established. The new method makes full use of transition information without considering phase position and multiple distinguish. Large amount of simulation results shows that the proposed method is accurate and reliable.

The accident transition information is not only complex but also enrichment when single-phase-to-ground fault occurs in resonant earthed system. A new integration method of fault line detection based on instantaneous real power and instantaneous virtual power is proposed in this paper. Not as health lines, when single-phase-to-ground fault occurs in resonant earthed system, the grounded current of faulty line contains distinct transition damping oscillation direct current component with the compensation of arc suppressing. The damping oscillation direct current component shows the characteristics obviously of damping oscillation of industrial frequency through the analyses of instantaneous real power and instantaneous virtual power. Here, the characteristic frequency of 50Hz in instantaneous power theory is defined. By calculating the instantaneous real power and instantaneous virtual power respectively of each line after single-phase-to-ground fault occurred, the oscillator components can be decomposed. Through comprehensively analyzing the value of oscillator components with the characteristic frequency of instantaneous real power and instantaneous virtual power, the integration algorithm of fault line detection can be established. The new method makes full use of transition information without considering phase position and multiple distinguish. Large amount of simulation results shows that the proposed method is accurate and reliable.

This paper presents a fusion scheme for ground fault line detection in neutral compensated power networks. Fault characteristics as initial abrupt wave, real power and transient are employed in the proposed fusion method. The fusion is implemented with D-S evidence theory and information entropy. Algorithm of fault measures that representing the likelihood of a line tend to be the fault one is constructed. With fault measures and information entropy, the basic probability assignment of evidence theory is formulated for fusion strategy in this paper. Simulations and field fault data prove that the proposed fusion algorithm can improve the reliability and adaptability for line to ground fault detection.

This paper presents a fusion scheme for ground fault line detection in neutral compensated power networks. Fault characteristics as initial abrupt wave, real power and transient are employed in the proposed fusion method. The fusion is implemented with D-S evidence theory and information entropy. Algorithm of fault measures that representing the likelihood of a line tend to be the fault one is constructed. With fault measures and information entropy, the basic probability assignment of evidence theory is formulated for fusion strategy in this paper. Simulations and field fault data prove that the proposed fusion algorithm can improve the reliability and adaptability for line to ground fault detection.

As the small current grounding system fails, the zero-sequence current of each line appears complicated characteristics of non-stationary and nonlinear. A small current grounding system fault line selection method based on improved generalized harmonic wavelet packet decomposition and chaos oscillator detection algorithm is given in this paper. Through spread spectrum, the generalized harmonic wavelet packet decomposition algorithm could extract features of any frequency band and arbitrary bandwidth signal with less calculation, realizing fast and anti-aliasing separation to signals. Duffing oscillator system is unaffected by noise and presents highly sensitive to external signals which have the same frequency as the internal driving force. By time-scale transformation, Duffing oscillator could be adapted to the detection of different frequency signals. The fault line could be accurately selected by observing the changes in the system’s phase diagram. Numerical simulation showes the reliability and accuracy of the algorithm.

As the small current grounding system fails, the zero-sequence current of each line appears complicated characteristics of non-stationary and nonlinear. A small current grounding system fault line selection method based on improved generalized harmonic wavelet packet decomposition and chaos oscillator detection algorithm is given in this paper. Through spread spectrum, the generalized harmonic wavelet packet decomposition algorithm could extract features of any frequency band and arbitrary bandwidth signal with less calculation, realizing fast and anti-aliasing separation to signals. Duffing oscillator system is unaffected by noise and presents highly sensitive to external signals which have the same frequency as the internal driving force. By time-scale transformation, Duffing oscillator could be adapted to the detection of different frequency signals. The fault line could be accurately selected by observing the changes in the system’s phase diagram. Numerical simulation showes the reliability and accuracy of the algorithm.

According to the principles of harmonic-based earth fault protection（identification to the line with a single phase to earth fault） in non-solidly earthed network， the characteristics of non-linear load， system power， fault point as a harmonic source and the faulty harmonic current generated by them are analyzed. Through harmonic-based protection system， the influences of multi-harmonic sources are studied as well. It is discovered that the harmonic sources of system power and non-linear load play dominant roles which give their effect through the pre-fault harmonic voltage at the fault point. The influences of fault point can be neglected for it produced neglected harmonic current. The magnitude and phase of harmonic sources and the distance between fault point and harmonic sources are the main factors affecting the reliability of harmonics-based protections. The analysis results are proved by digital simulation.

According to the principles of harmonic-based earth fault protection（identification to the line with a single phase to earth fault） in non-solidly earthed network， the characteristics of non-linear load， system power， fault point as a harmonic source and the faulty harmonic current generated by them are analyzed. Through harmonic-based protection system， the influences of multi-harmonic sources are studied as well. It is discovered that the harmonic sources of system power and non-linear load play dominant roles which give their effect through the pre-fault harmonic voltage at the fault point. The influences of fault point can be neglected for it produced neglected harmonic current. The magnitude and phase of harmonic sources and the distance between fault point and harmonic sources are the main factors affecting the reliability of harmonics-based protections. The analysis results are proved by digital simulation.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

Empirical mode decomposition (EMD) was applied to deal with non-stationary signal, and can describe the characteristics of frequency. But EMD yields has its own interpretation of combinations of pure tones, and mode mixing is one of the problems. We give a solution involving an improved masking signal. The improved masking signal method is based on masking signal method, to change the way choosing the masking signal, we get the IMF1 without mode mixing. The method also allows EMD to be used to separate components that are similar in frequency that would be inseparable with standard EMD techniques. The experiment shows that the approach based on a improved masking signal to improve EMD is practical and effective.

Empirical mode decomposition (EMD) was applied to deal with non-stationary signal, and can describe the characteristics of frequency. But EMD yields has its own interpretation of combinations of pure tones, and mode mixing is one of the problems. We give a solution involving an improved masking signal. The improved masking signal method is based on masking signal method, to change the way choosing the masking signal, we get the IMF1 without mode mixing. The method also allows EMD to be used to separate components that are similar in frequency that would be inseparable with standard EMD techniques. The experiment shows that the approach based on a improved masking signal to improve EMD is practical and effective.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

A new method about dominant inertial mode identification for power systems was proposed．The method integrated empirical mode decomposition(EMD), Teager energy operator(TEO) and signal energy method. EMD expands the applied scene of signal energy method without considering the ordering issues．The fast-response ability and robustness of TEO leads to a higher accuracy for frequency identification．Based on the second-order model signal energy method, the algorithm for dumping ratio was simplified．The method was verified in WEPRI-36 and actual grid by Q-R eigenvalue analysis and Prony algorithm．Its fitting-curve adaption to unlinear system is better than Prony algorithm, and it can be effectively used to detect the dominant inertial mode．

A new method about dominant inertial mode identification for power systems was proposed．The method integrated empirical mode decomposition(EMD), Teager energy operator(TEO) and signal energy method. EMD expands the applied scene of signal energy method without considering the ordering issues．The fast-response ability and robustness of TEO leads to a higher accuracy for frequency identification．Based on the second-order model signal energy method, the algorithm for dumping ratio was simplified．The method was verified in WEPRI-36 and actual grid by Q-R eigenvalue analysis and Prony algorithm．Its fitting-curve adaption to unlinear system is better than Prony algorithm, and it can be effectively used to detect the dominant inertial mode．