A floating threshold quantization algorithm to suppressmixing interferences in partial discharge

Due to the obvious difference of energy distribution frequencies from partial discharge (PD) signal and its mixing interferences (white noise and narrow brand), we uses the characteristic that node decomposition coefficients of wavelet packet transform can effectively show the energy change of signals to build up a floating threshold quantization algorithm (FTQA) varying with the noise energy of PD decomposition coefficients. It makes the node thresholds under the optimal base various with the noise strength in decomposition coefficients to self adaptively reality the choice of optimal threshold to finely partition PD decomposition coefficients. For simulated and real PD signals with mixing interferences, the conditional global threshold quantization algorithm (GTQA) and the proposed floating threshold quantization algorithm are employed to suppress the mixing interferences in PD signals and compared, and the results show that the proposed algorithm has the stronger suppression ability to mixing interference on PD signal and keeps perfect PD waveform via suppression.     

A Practical Method of Eliminating the White Noise of Partial Discharge with Wavelet

The white noise of PD(partial discharge) signal brings great difficult to the PD signal's processing, so eliminating the white noise is a necessary section. There are many methods of eliminating white noise, but none of them are suitable to the PD signal processing. Because PD signal and white noise have different Lipschitz exponents and different wavelet transform features in time-scales, a new eliminating white noise method has been brought forward that has simple operation and meets the timing need of PD signal's processing. After the processing with this method, the PD signal is not distortion and the effect of eliminating white noise is very good. This method can be applied to the processing of PD signal gotten from the site.     

Suppression of Periodic Narrowband Interferences in Cable Partial Discharge Detections

There are background noises and interferences in the signal acquired,due to partial discharge(PD) detection system covers a broad frequency band.To suppress periodic narrowband signal which is a quite serious interference in PD measurement,the existing suppression method is introduced,the new method of wavelet packet transform is mainly studied to de-noise the periodic narrowband in XLPE cable PD detection system,which is based on the db4 basic wavelet using soft threshold.The results of the experiment show that wavelet packet transform is effective in restraining the periodic narrowband interferences to extract the PD pulses in XLPE cable PD monitoring system.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

FAST ADAPTIVE FILTERING ( FAF ) FOR THE PROCESS DETECTION CONTROL SYSTEMS

The limitation of LMS adaptive noise cancelling (ANC ) is analyzed here. Based on the discussing of general signal and noise model of process detection control system, a fast adaptive filtering(FAF) is proposed on the basis of modelling the correlativity between the noise and it's correlative noise with the view of large noise. Fast transversal filtering ( FTF ) adaptive, algorithms is used to imitate the correlative modelling. Both simulation and experiments show that the method presented in this paper is suitable for the process detection control system , and it exhibts better results than the LMS noise cancelling method.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Effective Coefficient Threshold for Complex Wavelet Transform

This paper constructs complex wavelet which used for suppressing white noise in PD,and then analyses some disadvantages of several existing wavelet threshold and presents a method of Effective Wavelet Coefficient(EWC) according to the characteristic of modulus maximum of PD signals.Then it is compared with maximin theory threshold selection and Stein unbiased risk estimate theory threshold selection.The findings suggest that the method of EWC is adaptive,and it can suppress noise completely with small distortion of PD signal.     

The frequency detection model with anti-noise in the autonomous chaotic system

A method of detecting the signal frequency which is disturbed by the noise based on the autonomous chaotic system is proposed. A signal with noise was added to an autonomous chaotic system, and then a negative feedback was imposed to it. The feedback gain was appropriately adjusted, so as to let the noisy system orbit be a limit cycle. The number of times that the orbit went directly through a plane was calculated by loop phase technology, and the vibration frequency was found according to it. The frequency of system is determined by the frequency of the test signal, yet free from noise, so the frequency of the test signal is the vibration frequency of the system. Simulation results further demonstrate the effectiveness of the method.     

Study on the New Technology of Extracting PD in Strong Noise Environment With Complex Wavelet

Based on existing research, this paper analyzes and compares the phase information characters between partial discharge signal (PD) and its disturbance, and also extracts the feature of Partial Discharge with complex wavelets in noise environment. The research demonstrates that complex wavelets are more superior to original real wavelets in identifying Partial Discharge signal, and those simulated applications and the datum from laboratory can guarantee the effectiveness of the extraction of the feature of Partial Discharge and the restraint of the strong electromagnetic disturbance.     

Wavelet denoising method of temperature signal in the process of coal gas absorption and desorption

The study on the temperature evolutionary trend under both the ground stress and gas pressure is significant in the process of coal gas absorption and desorption. However, the valid signal is difficult to gain because of much noise in the experimental data, so according to the characteristics of the temperature signal gained under different experimental conditions of coal gas absorption and desorption, this essay filtrates noise and restores the valid signal by using the wavelet denoising of MATLAB to obtain the valid temperature evolutionary curve. Through contrasting and analyzing wavelet basis function and different decomposition levels, the essay chooses the sym8 wavelet basis function which has a higher order of vanishing moments and better smoothness. The reconstructed signal filtrates most of high frequency signal, and keep low frequency, as well as smooth signal. The result shows that the de-noising effect is good and the temperature evolution rule can be well reflected in the process of the coal gas absorption and desorption.     

Application of Wavelet & Multi-Resolution Analysis to Detection of the Time of PN Code Acquisition

In the field of CDMA system, DS-SS technology has been used widely. Thereby, a great deal research on acquisition method of PN code is based on DS-SS. In the traditional way, the power detection method of judgment is used widely. Based on the characteristic of PN code acquired signal (namely BPSK signal or QPSK signal) and characteristic of un acquired signal (namely white Gaussian noise), this paper introduces the wavelet detection method of PN code acquisition time. Meanwhile, the performance of wavelet threshold is also studied. In the end, the statistics of parameters in this detection method is made. The result indicates that the wavelet & multi resolution has practical value in the signal processing.     

The Filtering Character of Hilbert-Huang Transform and Its Application

Hilbert-Huang transform(HHT) is a new two-step time-frequency analytic method to analyze the nonlinear and non-stationary signal. The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decompose into a finite and often small number of intrinsic mode functions(IMF). Using Hilbert transform to those IMF components can yield instantaneous frequency, the final presentation of this results is a energy frequency-time distribution, designated as the Hilbert spectrum. Examples from the numerical results of signal de-noising are given to demonstrate the power of this new method, those results can clarity the advance and efficient of this method.     

Fetal electrocardiogram extraction based on radial basis function neural networks

A novel method for extracting fetal electrocardiogram (FECG) from the abdominal composite signal of a pregnant woman is proposed. The maternal component in the abdominal electrocardiogram (ECG) signal is a nonlinearly transformed version of the mother's ECG (MECG). This nonlinear relationship was identified using radial basis function (RBF) neural networks. The FECG is extracted by subtracting the nonlinearly transformed version of the MECG from the abdominal ECG signal. The baseline shift and noise in the FECG are suppressed by wavelet packet denoising technique. Experimental results obtained from the actual ECG signals demonstrate the effectiveness of the proposed method in extracting FECG even when it is totally embedded within the maternal(QRS) complex.     

Relationship between Partial Discharge and Dissolved Gases Conentration in Oil

Long time partial discharge is bad for transfomers inside insulation and now partial discharge monitoring is not satisfactory because of electromagnetic interference and noise interference .This is a new way via dissolved gases concentration analysis to find partial discharge and acconding to diagnosis system to confirm partial discharge level. A basic research has been carried out to discover the correspondence relationship between partial discharge level and dissolved gases concentration,some valuable laws are gained for the first time.     

Application of Wavelet Transform to De-noising Method

The wavelet transform is a new subject developed quickly in the past ten years. Compared with the Fourier transform, the wavelet transform is a part of time-frequency transform. The most important character is that it can be used to transform a signal into basic units at different scales and location, each unit represents a component of original signal difference from others. The wavelet transform has been proven to be a powerful and efficient tool for processing signal due to this character. This paper introduces the de-noising principles of the wavelet transform. It is proved to be an effective method by the simulating analysis.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

A spread spectrum steganalysis algorithm based on discontinuity detection

A novel audio steganalysis method is proposed.. the audio signal is denoised with wavelet transform. Then, a part of noise signal with different length is intercepted circularly and is used to calculate the cross correlation sequence with the rest of the noise signal. With the wavelet discontinuity detection technique, the feature is extracted from the cross correlation sequence for steganalysis and find out the steg audio. The detection rate is determined by the embedding strength of the secret message other than the embedding capacity. Experimental results show that the more embedding intensity of PN sequence is, the higher the detection rate will be. The detection rate of the algorithm is above 80% when the strength of the PN sequence is about 0.002, which demonstrates that the proposed algorithm has good detection performance.     

Image denoising based on adaptive graph regularization

Adaptive regularization can select different parameters based on the features of local areas in an image, which can differentiate the edges and noise in an image flexibly. An adaptive graph regularization is proposed based on graph spectral theory and adaptive regularization, which uses the Non local means to generate the weighting function of graph. The adaptive graph regularization equation is used to filter the noisy image. Simulation results show that the proposed method can effectively remove the noise and is superior to other graph theory based partial differential equation methods.     

PD Online Measurement of Transformers with Wide Frequency Band System and the Noise Eliminating Method by Cascaded IIR Lattice Notch Digital Filter

The frequency band of conventional partial discharge (PD) measurement systems is selected just within several ten kilohertz. With the development of computer and digital signal processing techniques, wide frequency band (WFB) measurement systems have been used to detect PD in electrical equipment for its higher sensitivity. Nevertheless, the WFB measurement systems are easy suffered from the interference of strong field electromagnetism noises. A set of PD online monitoring system with frequency band from 50 kHz to 1 megahertz used for transformer is developed, in which pre-processing device and cascaded 2nd order IIR lattice notch digital filter are mainly used to suppress periodic narrow-band interference. With analysis of field monitored signal at a 220 kV substation, it is shown that the monitoring system not only can detect and extract the discharge pulse signal effectively, but also has the powerful capability of suppressing interference.     

Analysis of UHF Method Used in Partial Discharge Detection in GIS

The possibility of UHF (ultra high frequency) method used in partial discharge detection in GIS is analyzed, and the UHF sensor, simulating passive filter, detecting the method of wide-band and narrow-band are studied, in order to avoid interferes of low-frequency. The authors develop an inner loop sensor in GIS model and investigate UHF method detecting partial discharge in GIS. The results indicate that the UHF system of detecting PD can effectively detect partial discharge and avoid outer corona interfere and Periodic pulse interfere. At the same time, narrow-frequency PD detection is an effective detection method to restrain UHF interferes.