Integrated DWT–FFT approach for detection and classification of power quality disturbances

This paper presents a wavelet transform based multiresolution signal decomposition technique for monitoring power system load switching The proposed method depends on the standard deviations of wavelet coefficients calculated at different resolution levels Power system signals are decomposed using discrete wavelet transform based filter bank The simulation results of the study clearly demonstrate that proposed method can efficiently detect and localize power system load switching in time domain The method can also give information on the magnitude of the load switched and its distance from the observation point

Power system load switching monitoring using wavelet transform based multiresolution signal decomposition

An idea of empirical mode decomposition (EMD) with Hubert Transform (HT) as well as cross-section of smoothed pseudo Wigner-Ville distribution (SPWVD) and instantaneous distortion index (IDIN) are investigated in point of detection of high frequency inrush power quality disturbances. Application and assessment of mentioned techniques are performed using disturbances recorded by wide area power quality monitoring system installed in the three substations connected to the same medium voltage line i.e. two substations of small hydro power plants (SHP) and one urban substation. Dynamic aspect of SHP integration with power systems are tracked using mentioned techniques in point of local and area mutual influence.

Detection of power system transients disturbances in distributed generation systems using Hubert transform and signal decomposition

In this paper the Discrete Wavelet Transform (DWT) concept using multi-resolution signal decomposition (MSD) technique with Daubechies family db4 second order vanishing moment mother wavelet for detection of power quality (PQ) disturbances has been proposed and presented. The PQ disturbances are also classified by extracting energy distribution features from multiresolution analysis (MRA) curve based on detailed energy distribution pattern (DEDP). The information obtained from detail squared wavelet transform coefficients (SWTC) of the disturbance signal at each wavelet decomposition level is utilized for plotting MRA curve. The proposed method algorithm has been verified by simulating various PQ disturbances and results are analyzed using Mathworks Matlab/Simulink. The classification and detection sensitivity of the proposed method is also verified and presented when signal to noise ratio (SNR) of 20 dB is superimposed during the duration of PQ disturbances. The proposed technique appears to be effective and accurate tool for the detection, localization and classification of PQ disturbances.

http://nopr.niscair.res.in/bitstream/123456789/15891/1/JSIR%2072%282%29%2092-100.pdf

Discrete wavelet transform based classifier for PQ disturbance detection

The quality of service indicators in distribution networks represents a main concern in the power industry nowadays, and the power quality measurements (PQ) are an important part to obtain these indicators. When a PQ disturbance is detected and measured it has to be characterized to obtain the parametric data related with it; this way, it can be classified according to international standards. To make the characterization process it has been relevant the use of digital signal processing techniques like digital filtering and shifting functions. In this paper digital filters, cancelation filters and delay functions are proposed to characterize PQ disturbances. The results suggest that the developed algorithms can be easily implemented in field instruments due to their simplicity and low computational effort required for his operation. All he developments were made using National Instruments LabVIEW® and conventional PC architectures.

http://ieeexplore.ieee.org/iel7/6472813/6478876/06478882.pdf

Characterization of power quality disturbances using digital filtering techniques

Deregulation and embedded generation are two important reasons for the recent interest in power quality. Other important reasons are the increased emission of disturbances by equipment and the increased susceptibility of equipment, production processes, and manufacturing industry to voltage disturbances. Thus it is essential to establish a real time power quality monitoring system to detect power quality disturbance. Wavelet Transform (WT) is a mathematical tool, which provides an automatic detection of Power Quality Disturbance (PQD) waveforms, especially using Daubechies family. Several types of Wavelets Network algorithms have been considered for detection of power quality problems. But both time and frequency information are available by Multi Resolution Analysis (MRA) alone. This paper presents the application of wavelet transform to detect, localize, and extract power switching disturbance. A power system switching transients have been simulated using MATLAB-7.01. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. The proposed technique appears to be robust for detection and localization of power quality disturbances created due to capacitor switching and load switching.

M.D. Takale

Papers

Power system switching transients analysis using multiresolution signal decomposition