Failures in power transformers are one of the most serious occurrences in a power system. Thus, the monitoring of transformers and their ancillary equipment, such as bushings, is of great importance to improving the operational efficiency of these assets. In this context, this paper presents the development of a monitoring system for the measurement of partial discharges (PDs), which are a key parameter in the analysis of insulation condition. PD measurements were performed using the electrical method. For this purpose, a capacitive coupling device was developed for bushings that works as a sensor for high-frequency signals and also as a protection apparatus to guarantee the integrity of the bushings in cases of extreme events, such as lightning surges. In addition, a computational routine is presented that applies a digital filtering process followed by a proposed step for differentiating PDs from noises. For validation, the proposed system was subjected to laboratory tests and field applications, from which the viability of the project and the efficiency in detecting PDs were verified.
With the increasing demand in densely populated areas and the consequent lack of space for exclusive transmission line corridors, sharing structures between transmission and distribution lines has become a commonly used solution, especially in emerging countries. This practice causes the electromagnetic interaction between these lines. This paper evaluates the effect of the electromagnetic coupling between a conventional 69 kV line and a compact overhead distribution line (spacer cable system) rated 11.4 kV sharing the same structures. Simulations were performed using the software ATPDraw (Alternative Transient Program) considering steady state conditions, the occurrence of faults on the high voltage line and transmission line energization.
Surface pollution is a major cause of partial discharges in high voltage insulators in coastal cities, leading to degradation of their surface and accelerating their aging process, which may cause visible arcing, flashovers and system faults. Thus, this work provides a methodology for the assessment of the condition of insulators based on an instrument which generates a severity degree to help the electric utility team schedule maintenance routines for the structures that really need it. The instrument uses a Raspberry Pi board as the processing core, a PicoScope oscilloscope for the data acquisition and an antenna as a partial discharge sensor. The algorithms are implemented in Python, and use artificial intelligence tools, such as a convolutional network and a fuzzy inference system. Laboratory test methods for the simulation of the field pollution conditions were successfully used for the validation of the instrument, which showed a good correlation between the pollution level and the severity degree generated. In addition to that, field collected data were also used for the evaluation of the proposed severity degree, which is demonstrated to be consistent when compared with the utility’s reports and the history of the selected areas from where data were collected.
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