The development of digital technologies stimulates widespread automation of processes in enterprises. This article discusses the problem of determining the values of the oil indicator of a transformer from the resulting image using computer vision. During the study, the device of the MS-1 and MS-2 oil indicators was studied and the features that must be taken into account when recognizing the device in the image and determining its value were considered. Based on the processed material, a method for recognizing device elements in an image has been developed using the OpenCV library and the Python programming language. The developed method determines instrument readings at different angles of rotation and in different weather conditions, which confirms the effectiveness of the proposed method.

Keywords: technical vision, oil indicator, contour recognition, OpenCV library

This study examines the combination of several non-destructive partial discharge (PD) detection methods to improve the accuracy of their detection. An analysis of methods for detecting PD in high-voltage insulation, a consideration of their features, and an analysis of the possibility of combining them to achieve the most accurate measurements were carried out. Analysis of the practical effectiveness of combining methods based on the developed variations of installations operating on the principles of two or more detection methods. Options for installations for PD detection that combine two or more detection methods are considered. A conclusion is given about the possibility of combining various methods of detecting partial discharges, taking into account the peculiarities of this type of combination. The simplest and most effective at the moment is the use of measuring cells that combine electromagnetic and acoustic detection methods.

Keywords: partial discharges; non-destructive testing of insulation; high voltage insulator; diagnostic methods for insulators