Abstract
Power transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer’s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.
Highlights
Power transformers are considered to be the most important assets in power substations
Gas leakage between oil compartments may influence the Dissolved gas analysis (DGA) results and the identification of transformer insulation faults [6,7,8,9]. This case was shown in [10]: two faults were identified as low energy discharges (D1); later, it was determined by inspection of the equipment that it was oil contamination from the on-load tap-changer (OLTC) gases
This paper presents a method for determining the contamination of the transformer oil from the OLTC gases in a group of power transformers of a distribution system operator (DSO) based on the DGA results for the last two years, the application of the guides [6,7] and the knowledge of experts
Summary
Power transformers are considered to be the most important assets in power substations. Gas leakage between oil compartments may influence the DGA results and the identification of transformer insulation faults [6,7,8,9] This case was shown in [10]: two faults were identified as low energy discharges (D1); later, it was determined by inspection of the equipment that it was oil contamination from the OLTC gases. This paper presents a method for determining the contamination of the transformer oil from the OLTC gases in a group of power transformers of a distribution system operator (DSO) based on the DGA results for the last two years, the application of the guides [6,7] and the knowledge of experts. Based on this transformer oil contamination classification methodology, a DT algorithm is applied to develop a prediction model that automatically recognises transformer oil contamination from the OLTC gases in DGA samples
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