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Partial Discharge (PD) measurement is one of the main methods for condition monitoring of Gas Insulated Switchgears (GIS). Internal Ultra High Frequency (UHF) sensors can be applied for capturing PD propagated electromagnetic signals within the GIS. PD sensor placement inside the GIS is one of the main challenges for designing an online PD measuring system. For this aim, the impacts of different GIS components on propagated PD electromagnetic waves should be studied. In this paper, different PD sensor position angles (with respect to PD sensor) are used to investigate their sensitivity to measuring PD electromagnetic waves. Two distinguishable parameters from the calculated PD electromagnetic waves, the first rated electric field and the signal's power over the two frequency ranges (0.3-2 GHz and 0.3-3 GHz) are used to analyze and quantify the calculation results. The impacts of different enclosure diameters, different types of spacers, and various disconnector contact gap distances (under different voltage levels) on this wave propagation are studied. Additionally, the two standard GIS busbar profiles, named L-shape and T-shape, are discussed in this paper. The results show that the attenuation degree of the measured PD EM waves is strongly influenced by the busbar dimensions and the configuration of its components. The GIS busbar designer can employ these results to select the proper PD sensors and their installation locations. [ABSTRACT FROM AUTHOR]

The general method for identifying the partial discharge type in a power transformer is based on their fingerprints in the form of phase‐resolved discharge patterns. In the case of multiple defects, traditional clustering methods can be applied for separation of active sources. However, such an approach is impractical for online real‐time monitoring due to the very large data size. In this paper a new method using stream clustering is introduced. The method separates the active sources by processing the signal once it is captured, then only a synopsis of the discharge data is stored. Two stream clustering algorithms: Density Grids and DenStream are employed. Through measurements obtained from laboratory experimental setups (corona, surface discharge, transformer defect model) performance of the proposed algorithms are evaluated. It is shown that stream clustering method is able to separate the constituent components involved in the stream of a multi‐source discharge signal without the need to store a large amount of information. The performance of the Density Grids method depends on a limited number of features that it can accommodate. In comparison, the DenStream method can capture more features which enable better separation of active sources at the expense of longer processing time. [ABSTRACT FROM AUTHOR]

Summary: Partial discharge (PD) measurement is one of the best methods for condition monitoring of transformers. In this paper, we use 5 different types of defects as follows: scratch on winding insulation, bubble in oil, moisture in insulation paper, a very small free metal particle in the transformer tank, and a fixed sharp metal point on the transformer tank, for our PD‐related studies. Each type of defect is implemented into 1 of the 5 identical transformer models, which had been developed in the authors' recent work. The continuous wavelet transform is applied to each related measured time‐domain PD signals. This process results in an image, for each PD pulse in the time‐frequency domain. Using these images, a gray‐level covariance matrix is constructed. The texture features are extracted from the constructed gray‐level covariance matrix of each PD signal. Principal component analysis is applied on the recorded PD data to reduce its dimension, and then support vector machine is used to classify the computed first 6 principal components of those defects' PD signals. The accurate outcome of defects identification in this work verifies efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the presence of excessive noise in the recorded partial discharge (PD) current signals, de-noising of these signals is a crucial task for performing any investigation on the subject. Meanwhile, to accelerate this de-noising process a single PD pulse can be extracted from the train of those recorded pulses, followed by its de-noising. In this paper a single PD pulse is extracted from the train of recorded PD pulses, using noisy recorded data cumulative energy. A de-noising technique based on adaptive neuro-fuzzy inference systems is proposed. To verify the validity of the proposed method, four different sources of PD signals are physically simulated. The proposed method is applied on laboratory recorded signals and is compared with a conventional method which is based on discrete wavelet transform (DWT) and linear adaptive filtering method. Furthermore, the proposed algorithm is used for extraction and de-noising of single PD activities of an on-site recorded data. This method introduces an efficient pulse distortion mitigation tool which requires no adjustment of any pre-set parameter-neither for different noise levels, nor for different kind of PD pulses. This is an important feature of the proposed technique. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Laplacian score is an effective method to select high performance features in terms of discrimination of multiple PD source types. • Similar features of PD signals do not add much more information in discrimination of multiple PD sources. • Correlation coefficient is a method to evaluation of similarity between different features. • Different sets of features in different case of multiple PD source types presence, are selected by feature selection algorithm. Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of them may be effective. Therefore, among the extracted features, those useful for discrimination of the multiple PD sources are studied. Then, a method, using laplacian score, and the correlation coefficient algorithms; is developed for feature selection. In order to discriminate among the multiple partial discharge sources, a density-based algorithm spatial clustering of applications with noise (DBSCAN) have been employed to cluster among available PD sources and the noise. The results of some case studies demonstrated the great ability of this method in proper discrimination of multiple PD sources. [ABSTRACT FROM AUTHOR]

Power transformers are considered to be one of the most essential and costly pieces of equipment in a power system. Identifying insulation faults in the shortest possible time prevents the occurrence of irreparable damage. Partial discharge (PD) is one of the most significant insulation faults. The first step in the study of PD is the precise modeling of transformer winding at high frequencies. In this paper, the finite element method is used to calculate the parameters for a detailed model of transformer winding. For this reason, a detailed model of transformer winding and the analytic formulations are first presented for the calculation of the parameters of the model. Using two-dimensional finite element methods, the 20-kV transformer winding is then simulated according to exact technical specifications and designed using Maxwell software. After that, the parameters for the detailed model presented in this paper are derived and calculated. Finally, the validity of the model in the frequency range is determined by applying a similar PD pulse on the real and simulated models. [ABSTRACT FROM AUTHOR]

Accurate Partial Discharge (PD) measurement requires the expansion of the measurement frequency range to Ultra High Frequency (UHF) band. For this it is needed to implement modern techniques when monitoring an important element of power system, such as power transformer. Different kind of experimental models, such as insulation defect models, and the complete transformer windings models are used to simulate the partial insulation failure situations which occur in a real transformer. However, since in these models, some kind of simplifications are employed which may result in an electromagnetic waves (EM) propagation pattern different from what exists in a real transformer and encounter errors in its PD measurement results. In this paper, the transformer core effects on PD EM wave propagation are investigated. To address this concern, different core designs are simulated. Finite Integration Time (FIT) method is used for electric field calculation through application of Microsoft CST microwave studio Software. The probes recorded electric fields are analyzed using wavelet transform (WT). A new approach based on energy scale coefficient (for each decomposition level) is used for mother wavelet selection. Results of these simulations demonstrate how existence of core and its design affect the characteristics of measured PD signal. [ABSTRACT FROM AUTHOR]