Elements of digital energy in the control of the state of the network, built on measurements of auxiliary parameters

Abstract

The article deals with the issue of transition of power engineering technologies to digital stations. An overview of the Siemens and ABB materials that de facto set standards for digital technology for energy. The creation of digital stations addresses issues that have not been addressed in this connection so far. Therefore, a triad was formed: digital control - equipment state monitoring - cybersecurity. Proceeding from such a triangle, energy in the digital incarnation acquires new faces, which opens up new possibilities for the use of diagnostics and data collection by sensors built on mediated data, and accordingly, the number of sensors increases significantly if we take into account the possibility of monitoring the primary technological processes. In this case, the amount of incoming data grows significantly. In accordance with current trends and increasing requirements for the quality of electricity in the network. One of the factors of the spoofed form of a grid on the network is the crown discharges that occur on the network and on the equipment, even during the interception to the digital station. Leading companies are developing devices and systems for diagnosing the presence of a corona discharge in the network according to non-electronics parameters. An example of such measurements is the ability to diagnose non-direct vemiers in the network. Such measurements should lead to an increase in data volume. Accordingly, the processing of such data arrays becomes possible with the use of digital technologies, which include the ability to process large volumes of data using the mathematical apparatus for processing large data - Data Mining. The results of processing by hardware (software) methods allows to determine or invent such data, which in direct measurements were not visible. Data Mining technologies allow expert estimates to be used on direct measurement data, which will inevitably lead to objectivity. Data Mining, in contrast to expert methods, finds objective patterns between different factors, thus minimizing the influence of the subjective human factor on decision making. The authors draw attention to the promising use of indirect measurement sensors in the power system, which, based on digital technologies, will provide an opportunity for a more profound analysis of real systems, which can lead to qualitative changes in the energy sector as a whole.