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Partial Discharge (PD) measurement is one of the best solutions for condition assessment of Gas Insulated Switchgears (GISs). For having Condition-based maintenance of GIS, online PD monitoring is of great importance. For this aim, Ultra High Frequency (UHF) PD sensors should be installed inside the GIS during the installation. However, in most installed GISs in industries, the internal UHF PD sensors are not installed. In this paper, a new method for online defect type recognition according to external UHF PD sensors and based on the time-frequency representation of PD signal is proposed. In this case, four artificial defect types named protrusion on the main conductor, protrusion on the enclosure, free moving metal particle, and metal particle on spacer are implanted inside the 132 kV L-Shaped structure of one phase in enclosure GIS. The signal energy at each level of the decomposed signal by Discrete Wavelet Transform (DWT) is applied for features of each defect type. The trends of signal energy variations at each frequency range of signal are applied for discriminating between each defect type. The Deep Feed Forward Network (DFFN) classifier is applied for PD pattern recognition. The results show the benefits and simplicity of the proposed method for PD signal classification, independent from the position of the PD sensor, especially in the case of online PD monitoring of GIS. [ABSTRACT FROM AUTHOR]

Change in customers' consumption patterns due to the introduction of new electricity consuming technologies into human consumption practices has impacted the existing optimal loading profile of the grid facilities. Although some MV/LV distribution transformers (DTs) are operating under a light load profile, this does not guarantee that they are in a healthy condition, since necessary maintenance procedures are required. On the other hand, many unplanned overloading conditions occur on the other DTs and causes accelerated failures which contributes to very high operational costs. This paper presents the outcome of a study on how the planned DTs relocation can save asset managers against their huge DTs replacement costs developed due to the highly overloaded DTs failures in a utility, and how it will improve the overall reliability of the DTs. A mixed-integer linear programming (MILP) solution is proposed to formulate the introduced economic relocation-based asset management (AM) framework while minimizing total cost (TC) of the network. The results show that incorporating relocation techniques into the AM formulation ends in reduction of the distribution network TC and a decrease in asset failure rates if compared with application of traditional techniques. [ABSTRACT FROM AUTHOR]

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]