A method is proposed for non-contact indication of the presence of operating voltage in disconnected sections of an alternating current contact network, based on the properties of liquid crystals and the effect of electrical influences. A procedure is given for selecting voltage indicator parameters when used on single-track sections of a traction network. The possibility of using sign-synthesizing liquid crystal indicators to create contactless indicators without an internal power source to indicate the presence of operating voltage in sections of the contact network is shown.

Keywords: AC contact network, single-track section, disconnected section, operating voltage, non-contact indication, liquid crystal indicator, properties, equivalent circuit, selection of parameters

Stochastic systems are systems in which changes are random. In which the predicted values depend on a probability distribution. An example of a stochastic system in a power system, the operation of which is affected by many random factors, their analysis and control will allow you to control the safe cycle and reliability of operation. Currently, there are methods and mathematical tools that allow us to estimate the likelihood of any phenomenon and situation with acceptable accuracy, however, they are practically not used to solve this problem on electrified railway transport. Therefore, the work summarizes the existing methods of probabilistic forecasting, and gives recommendations on their application to solve the existing problematic of the issue. Further, the paper shows a method for determining the probability of failure of a constituent structural element of any, arbitrarily complex, stochastic system, to which the contact network of electrified railways belongs. In the conclusion of the work, an elementary method-oriented software tool with the functional purpose of automating statistical methods for solving problems of primary data processing and calculating elementary statistics in the process of risk management is proposed, as well as loading and maintaining a database that is subsequently oriented to the principles of machine learning.

Keywords: electrified railways, stochastic systems, operational reliability, probability of failure, forecasting

Stable reliability characteristics are a guarantee of non-emergency operation of electrified railways, undoubtedly, the profitability of transportation processes is growing. The article proposes an innovative method for determining the characteristics of reliability, with a decrease in the risk of failure or emergency. Electric traction used in most of the railroad landfill. It is necessary to consider the issues of reliability of the power supply system to predict the state of the system and study the patterns of interaction that affect system and non-system communications. Therefore, we will pay special attention to the reliability of the power supply system; in order to predict failures, first of all, it is necessary to know the patterns of interaction and patterns that affect system and non-system communications. The danger of risk directly depends on the number and duration of failures. In the power supply system, a huge number of heterogeneous and differently distributed objects, nodes and elements operate in different modes and are susceptible to different operational influences.Therefore, the work summarizes the existing methods of probabilistic forecasting, and gives recommendations on their application to solve the existing problematic of the issue. Further, the paper shows a method for determining the probability of failure of a constituent structural element of any, arbitrarily complex, stochastic system, to which the contact network of electrified railways belongs. In the conclusion of the work, an elementary method-oriented software tool with the functional purpose of automating statistical methods for solving problems of primary data processing and calculating elementary statistics in the process of risk management is proposed, as well as loading and maintaining a database that is subsequently oriented to the principles of machine learning.

Keywords: electrified railways, predictive analysis, operational reliability, probability of failure, forecasting