It is a known fact that the existing compressor stations on the sorting humps of
the railways have significant overconsumption of electricity. First, this is due
to the lack of automatic regulation of the compressed air capacity, which takes
into account the technological processes at the station and the weather
conditions within this section. In order to solve this problem, as a first
approximation, it is necessary to analyze all the factors affecting the energy
consumption of the compressor station and develop a mathematical model, which
will link these indicators. In this work, a correlation analysis of the
weather-related factors and specifics of the technological process (breaking up
of the train), which affect the energy consumption of the compressor unit (CU),
is carried out. Based on the analysis, there was a strong correlation between
the factors described and the power consumption at the station. A regression
model was developed. The issue of the distribution of energy levels as the cut
rolls down from the hump to the destination point is reviewed in detail.
Borrowing the principles of air navigation, the analysis of the effect of air
masses on a moving car is carried out. This made it possible to develop a
predictive multifactorial mathematical model of the power consumption of the
compressor plant depending on the type and weight-dimensional parameters of the
rolling car and the peculiarities of the sorting hump elements operating in
different weather conditions. This model makes it possible with a high
probability to predict the cost of electricity for the disassembly of one train
in order to supply a required level of system performance. This will reduce the
economic costs of energy consumption due to the optimal efficiency management of
the compressor stations through the implementation of variable frequency control
systems. In addition, this model makes it possible to build a fully automatic
system for regulating the efficiency of a CU, relying on the data of the
full-scale sheet, which can be obtained from the automated control system for
marshalling yards (ACS MY) and data received from meteorological stations. In
this way, incorrect operator actions are eliminated. The proposed model has been
tested on the example of an actual disassembly of a train. The obtained
simulation results showed significant savings in electricity during the period
of decoupling of the train.