## 1 Introduction  
The reliability of large power transformers is a key element in ensuring stable electricity supplies to consumers. A significant problem that affects many countries around the world is the high percentage of units that have exceeded their designed service life, usually estimated at around 30-35 years [1]. Due to the high costs of purchase, transport, and insurance, as well as the long order lead time, power transformers are not immediately replaced with new ones after their service life is exceeded. The high costs of replacing the device encourage the operators of the power system to extend its service life as much as possible, unfortunately resulting in an increased risk of failure [2, 3]. According to a report prepared by the CIGRE association, PDs are one of the most common insulation degradation mechanisms leading to transformer failures [4]. The dynamics of the development of a defect caused by PDs depends on factors such as the type of discharge and its energy, the time of the impact of the discharge on the insulation system, the degree of aging and moisture of the insulation system, the oil temperature, and the moisture migration process [5, 6]. In some cases, the defect may develop over many years of transformer operation without leading to its failure. In other cases, the defect may develop extremely quickly, most often as a result of random events, such as overvoltages or a sudden increase in oil temperature caused by unit overload or cooling system failure. Often, the dynamics of the development of the defect accelerates in its final stage, just before failure. For this reason, in recent years, online PD monitoring systemshave gained importance [7; 8; 9]. The main advantages that give online PD monitoring an advantage over periodic tests are the following:  
* The ability to immediately detect PD ignition and a sudden, sharp increase in their intensity. * The ability to detect an upward trend in the values of parameters describing PD pulses, thanks to which the maintenance department can quickly develop and implement procedures that minimize the risk of failure. * The ability to adjust the schedule of periodic diagnostic tests (e.g., accelerating or postponing them) and to plan downtimes and maintenance work based on data recorded by the PD monitoring system. * The ability to detect the relationship between PD activity and transformer operating parameters, such as voltage, oil temperature, or on-load tap changer position, which in turn opens the possibility of forecasting PD activity based on statistical models and artificial intelligence methods. * The ability to estimate the approximate location of the PD source based on the analysis of the activity of acoustic emission transducers and then precisely determine the location of the defect using the time difference of arrival (TDoA) technique. Currently, the most popular direct PD detection methods adapted for online monitoring include the acoustic emission (AE) method [10; 11] and the ultra-high-frequency (UHF) electromagnetic method [12; 13]. Both methods have their strengths, but also limitations, which is why neither of them can be considered an ideal and sufficient technique to monitor PDs. For this reason, in the aspect of advanced transformer diagnostics, the simultaneous use of both methods is increasingly suggested [14; 15; 16; 17; 18; 19]. The main advantage of this strategy is primarily the increased reliability of discharge detection. This results from the fact that the methods discussed are based on different physical phenomena and are susceptible to different types of interference. Sources of acoustic noise usually do not generate electromagnetic signals, while sources of electromagnetic interference generally do not generate acoustic signals. During transformer monitoring using the AE method, potential sources of acoustic interference include oil pumps, fans, core (the so-called Barkhausen noise), intensive weather phenomena (e.g., heavy hailstorms), and tap changer operations.