Background. Compressor stations of main gas pipelines are classified as high-risk facilities. Gas turbines, piston and electric drive units are used as drives of gas pumping units at compressor stations. But it is the automated electric drive with a capacity of 4–25 MW that is the most promising system due to low capital and operating costs, high energy performance combined with high reliability and environmental friendliness. However, the requirements to ensure trouble-free operation of compressor station units are constantly being tightened in accordance with the industry regulatory framework. In the domestic electrical engineering market today, there are no reliable and adequate technical means and methods of preventive forecasting of the condition of electric drive gas pumping units. In this regard, the aim of the study is to provide a theoretically sound methodology to assess the technical condition of an electric drive in the on-line mode and a medium-term forecast of its operational parameters.
Materials and methods. Data on the condition of the electric drive is taken from the sensors and fed to the subsystem for predicting the technical condition. In case, a decision is made about an upcoming failure, the maintenance subsystem prevents failures.
Results. The authors have proposed a method of technical condition management based on Bayesian models of condition forecasting based on controlled parameters and their compliance with the embedded knowledge base. An automated system for forecasting the condition of an electric drive of gas pumping units is developed and studied. It is shown that a reduction in downtime and an increase of the utilization factor of the system is achieved due to forecasting as part of the electric drive technical condition management system, which allows us to initiate preventive actions or to prepare for repair.
Conclusions. The use of automated systems for predicting the technical condition of the electric drive of the gas pumping unit allows us to plan capital and routine repairs based on the actual condition, to eliminate the thermal effect of currents and reduce the cost of overhauls, to monitor the operation of the cooling system of powerful machines and maintain optimal modes that increase the insulation life; with the combined use of vibration analysis and FFT analysis of power consumption, accurately identify the causes of increased vibration levels, as well as to lower overall operating costs.