Using machine-learning methods in determination of the pipe line gas turbine plant effective power

Abstract

The paper considers methods of the gas turbine plant power designed for natural gas transportation and reveals their drawbacks. A program in the Python language was created to study applicability of the machine-learning methods to determine the plant power under operating conditions. Archival gas-dynamic parameters registered by the plant automatic control system were used as the initial data. Forecast quality of the machine-learning models was estimated depending on different sets of the feature parameters. Recommendations on the models use are provided; and the method error was determined. Hypothesis on applicability of models learned based on data of a single engine to estimate the power of the other engines of the same type was refuted. Machine-learning methods could be used to determine the gas turbine plant power even in the absence of part of the initial data, which is the main advantage over traditional methods.