Experimental Study of the Fundamental‐Frequency Component of a Plane, Finite‐Amplitude Wave

1966 ◽  
Vol 40 (4) ◽  
pp. 847-851 ◽  
Author(s):  
William W. Lester
Keyword(s):  
Experimental Study ◽  
Fundamental Frequency ◽  
Frequency Component ◽  
Finite Amplitude ◽  
Amplitude Wave ◽  
Fundamental Frequency Component

Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

2011 ◽  
Vol 382 ◽  
pp. 163-166
Author(s):  
Qing Xin Zhang ◽  
Jin Li ◽  
Hai Bin Li ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Wavelet Packet ◽  
Frequency Component ◽  
Wavelet Neural Network ◽  
Simulation Software ◽  
Monitoring Methods ◽  
Stator Current ◽  
Fundamental Frequency Component

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

Research on on-Load Current Method for Detecting Fault of Transformer Core Based on the Vibration Analysis Method

2012 ◽  
Vol 614-615 ◽  
pp. 1336-1339
Author(s):  
Xu Tao Wu ◽  
Pei Ding ◽  
Zhen Hua Yan ◽  
Sheng Chang Ji
Keyword(s):  
Fundamental Frequency ◽  
Load Condition ◽  
Current Method ◽  
Vibration Signal ◽  
Frequency Component ◽  
Analysis Method ◽  
Load Current ◽  
The Core ◽  
Transformer Core ◽  
Fundamental Frequency Component

When vibration signal analysis method is used to monitor the transformer, the core vibration signal is often obtained as the transformer runs under no-load condition. In this paper, a method extracting core vibration signal without operating transformer under no-load condition was introduced. The transformer vibration accelerative signals at different on-load current and no-load condition were measured. From the fitted curve of on-load current versus fundamental frequency component of vibration accelerated signal, the value of fundamental frequency component was obtained when the on-load current was zero, which was almost equivalent to the fundamental frequency component of vibration accelerative signal measured under no-load condition, thus the fundamental frequency component of core vibration can be obtained by using on-load current method. The core with fault was finally monitored by using the on-load current method compared with wavelet package transform method. From the result of experiment, it can be seen this method is very useful to estimate the condition of the transformer core.

On the inverse problem of reflection Green’s function seismograms over stratified acoustic media: Estimation of background models

Geophysics ◽  
2021 ◽  
Vol 86 (6) ◽  
pp. R849-R864
Author(s):  
Lasse Amundsen
Keyword(s):  
Least Squares ◽  
Fundamental Frequency ◽  
Green's Function ◽  
Low Frequency ◽  
Frequency Component ◽  
Weighted Sum ◽  
Levenberg Marquardt ◽  
Band Limited ◽  
Background Models ◽  
Fundamental Frequency Component

A theory is presented for estimating the background velocity and density of an acoustic stratified medium by iterative least-squares waveform inversion in the frequency-horizontal slowness domain of low-frequency precritical reflection incidence seismograms of time length [Formula: see text]. The initial model is constant. The prerequisites for the method are that the reflection seismograms should be Green’s function seismograms and that the fundamental frequency component [Formula: see text] is present. Then, the gradients of the objective function provide the low-wavenumber trend of the medium. A matrix formulation for the model update is expressed mathematically by the classic seismogram residual, Jacobian, gradient, and Hessian in the Levenberg-Marquardt approximation. The first iteration, which is equal to a constant-parameter migration inversion (CPMI), is thoroughly analyzed, and expressions for band-limited gradients and block Hessians are found. For primary precritical reflection incidence seismograms of infinite bandwidth, it is shown theoretically that the partial gradients in the CPMI model become a reflection strength-weighted sum of shifted discrete sign functions, typical of step or staircase functions, which provide interface locations in Born depth and amplitudes that can be mapped to velocity and density information. For frequency-band-limited primary reflection seismograms, the partial gradients become a reflection strength-weighted sum of wavenumber-band-limited discrete sign functions. When the fundamental frequency component in the seismograms is present, the band-limited discrete sign functions are oscillatory but keep the information of the step function characteristic of the partial gradient. When the fundamental frequency component in the seismograms is absent, the band-limited discrete sign functions keep information of where the steps are located but lose the information of the amplitudes of the steps. The Hessian elements are nonstandard with the Hessian modeled over a broader frequency range than the frequencies of the observed low-frequency seismogram to avoid it becoming close to singular. The main mathematical findings are illustrated by a simple model and seismograms, for which the background models are found after two iterations. For the sake of completeness, the background models are classically used as initial models in a Levenberg-Marquardt least-squares inversion scheme to estimate the layer velocities and densities from broadband seismograms.

Fault Detection & Classification in UPFC Integrated Transmission Line Using DWT

2017 ◽  
Vol 8 (4) ◽  
pp. 1793 ◽  
Author(s):  
Sanjay Kumar Mishra ◽  
Sarat Chandra Swain ◽  
Loknath Tripathy
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Transmission Line ◽  
Fundamental Frequency ◽  
Power Flow ◽  
Frequency Component ◽  
Spectral Energy ◽  
Discrete Wavelet ◽  
Non Stationary Signal ◽  
Fundamental Frequency Component

Fault detection and classification in UPFC (Unified Power Flow Controller) integrated transmission line using single terminal based DWT (Discrete Wavelet Transform) is proposed. The current is extracted from the sending end bus and processed through wavelet transform to evaluate the spectral energy (SE) using db4 mother wavelet. Three level decomposition is framed to extract the fundamental frequency component from non-stationary signal, considering sampling frequency of 2kHz system. The fundamental frequency component of respective phase currents are used to compute SE at sending end. The SE of individual phase current is the key factor for deciding the fault pattern detection and classification. The advantage of using this it requires less cost and protect entire transmission line with minimal fault detection time. The various types of fault (L-G, L-L, L-L-G, L-L-L) are simulated by considering the parameter like fault resistance, source impedance, fault inception angle, multi-location fault, reverse power flow and UPFC system parameter. The scheme works reliable and efficient to detect and classify the fault within a cycle of sample period 40 or a cycle of time period 20ms compared to other conventional relaying scheme.

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