Cheng-Tao Hsieh

Application of Morlet wavelets to supervise power system disturbances

A wavelet transform approach using a Morlet basis function is proposed to supervise power system disturbances in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring various disturbances as time varies. The method has been tested on the detection of various simulated disturbances including voltage sag, voltage swell, momentary interruption and oscillatory transients and on the harmonic analysis of the arc furnace from the field test data. Testing results demonstrated the practicality and advantages of the proposed method for the applications.

High-impedance fault detection utilizing a Morlet wavelet transform approach

An application of Morlet wavelets to the analysis of high-impedance fault generated signals is proposed in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. The proposed method has been applied to discriminate the high-impedance faults from the normal switching events, and to examine the faults under various grounds including Portland cement, wet soil and grass. Testing results have demonstrated the practicality and advantages of the proposed method for the applications.

Enhancement of damage-detection of wind turbine blades via CWT-based approaches

In this paper, a continuous wavelet transform-based approach is applied to enhance the damage-detection capability of wind turbine blades. With the time--frequency localization features embedded in wavelets, the time and scale information of the acquired signals can be presented as a visualization scheme, where the condition monitoring of turbine blades can be better realized. Based on these sensor signals, this proposed approach was applied to discriminate the damaged structure from the healthy one under several scenarios. Test results have demonstrated the practicality and advantages of the proposed method for the application considered

Application of wavelets to classify power system disturbances

The wavelet transform approach is proposed in this paper to classify various power system disturbances. On encountering various disturbances, the proposed method is useful to categorize these shortfalls into different groups so that operators can decide on strategies to suppress or eliminate them effectively. Different from those Fourier-based transforms, the wavelet transform approach is more efficient in tracking signal dynamics as time varies. The method has been tested through the classification of various simulated disturbances, including voltage sag, voltage swell, momentary interruption, oscillatory transients and flat-tops. Testing results showed the feasibility and practicality of the method for the applications.

Feasibility of fractal-based methods for visualization of power system disturbances

Abstract In this paper, an application of a fractal-based approach is proposed to detect power quality disturbances. In the proposed method, a fractal number computation is embedded within the detection procedure. These computed fractals are refined through the moving average technique, and then compared with the predetermined threshold in order to signal encountered events. In this way, the characteristics of non-stationary disturbances can be more closely monitored. The transient behavior, cavities and discontinuities in the signals can be all investigated. The proposed method has been tested on various disturbances, including voltage sag, voltage swell, momentary interruption, oscillatory transient and harmonic distortion scenarios. The results have demonstrated the feasibility of the approach for visualization of power system disturbances.

Application of continuous wavelet transform for study of voltage flicker-generated signals

An application of continuous wavelet transform (CWT) for the analysis of voltage flicker-generated signals is proposed. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be integrally presented, thereby enhancing the monitoring of voltage flicker-generated signals at different time intervals. The Morlet wavelet has been selected as the basis function for the CWT in the proposed method. The merit of the method lies in that the signal component at any frequency of interest can be more easily monitored than by discrete wavelet transform (DWT). The high frequency required in the voltage flicker study can be also comfortably accomplished. This approach has been applied to investigate various simulated voltage flicker-generated signals, and inspect the data recorded from the actual are furnace operation. Test results help solidify the practicality and advantages of the proposed method for the applications.

Data reduction of power quality disturbances : a wavelet transform approach

In this paper, application of a wavelet transform approach for the data reduction of power quality disturbances is proposed. In this approach, we extracted the difference signals between the original signal and the reference waveform by a subtractor. These extracted signals were then compressed through wavelet transform methods. In this way, the amount of data was shown significantly reduced. The data transmission and storage capability can be also enhanced. We have investigated towards the reduction of various power quality disturbance data by the proposed method. Test results demonstrated the effectiveness of the method for the applications.

Visualizing time-varying power system harmonics using a Morlet wavelet transform approach

Abstract In this paper, a Morlet wavelet transform approach for the study of time-varying power system harmonics is proposed. By this method, because the time–frequency localization characteristics are embedded in wavelets, the time and frequency information of a waveform can be simultaneously presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring the signals of interest when time varies. The proposed method has been applied to visualize inrush current harmonics of transformers and to investigate arc-furnace-generated signals. From the test results, the merits of the proposed method have been demonstrated for applications.

Computation of continuous wavelet transform via a new wavelet function for visualization of power system disturbances

In this paper, a computation of continuous wavelet transform via a new wavelet function is proposed for the visualization of electric power system disturbances. The proposed wavelet function is derived from the B-spline function. In the employment of the method, the ratio of the frequency bandwidth to the center frequency depends only on the parameter settings, which can be easily adjusted for different resolution needs. Because this new function is a piecewise polynomial, the wavelet transform can be less computation-intensive. Besides, it is seen compactly supported, the integration process can be thus faster along with a higher accuracy. In the paper, this proposed wavelet function is embedded into the wavelet transform scheme, and applied to investigate the disturbances recorded in a steel plant located at southern Taiwan. Computation results reveal that the proposed approach can be a useful aid in the monitoring of power system disturbances. It is also suitable to extend this approach to other industry applications where electric power quality is crucially required.

Inspection enhancement of wind turbine blades using novel signal-processing approaches

In this paper, a signal-processing approach is applied to enhance the inspection of wind turbine blades. This method starts with a time-frequency representation of signals, where a generalized time-bandwidth product method integrated with short-time Fourier transform is developed and applied. It is followed by the utilization of convex set to help justify the contour map such that the blade-condition monitoring can be better realized. Through test signals received from the sensors, it was found that the proposed approach would discriminate the damaged structure from the healthy one under several scenarios, thereby ensuring a higher efficiency of wind power generation.