On-Line Detection And Measurement Of Partial Discharge Signals In A Noisy Environment

In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity. One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi-resolution analysis (WMRA). However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks. This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels. The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window. Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal. A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction. The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise

Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods

This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines (a novel method) as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge; however, its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation, and feature extraction are discussed. Segmentation of a sequence of data recording is preprocessing to partition the data into segments each representing a duration containing either an event or a transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating the effectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions