The paper presents the results of using the fundamental and the low frequency harmonic components of leakage current to study aging of silicone ruhher in saltfog. Experiments have been conducted on RTV and HTV coated rods at different fields (0.25-0.6 kV/cm) and conductivities (1000 to 2500 pS/cm). The onset of dry-hand arcing on samples could he determined hy measuring the low frequency harmonic components. A correlation has been found between the fundamental and low harmonic components of leakage current and different forms of aging. Where erosion could be associated with an increase in the level of both the fundamental and low frequency harmonic components of leakage current. For example, surface damage for HTV rods occurred when the fundamental component of leakage current was greater than 2 mA. On the other hand, when the samples approached failure, the fundamental component of leakage current reached relatively high values ( > 6 mA for HTV rods and > 2 mA for RTV rods) and the low frequency harmonic components of the leakage current tended to decrease. The results suggest that both the fundamental and low frequency harmonics of leakage current can be used as a tool to determine both the beginning of aging and end of life of silicone rubher in salt-fog.

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This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved.

The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction. current health condition, and all related financial costs (maintenance, operation, and failure) would result in an economic risk management plan with adequate subsequent decisions.

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