DEFINITION OF ENERGY EFFICIENCY INDICATORS OF GAS TRANSFER UNITS WITH APPLICATION OF NEURAL NETWORKS

摘要

When operating gas transfer units with gas turbine drive a significant share (about 9%) of natural gas is spent on compressor stations own needs.A considerable expense of pumped gas for gas transfer units own needs at compressor stations determines the actuality of realization of resources saving technologies. When performing energy saving measures it is required to control the resulting effect.Control of energy consumption is based on energy efficiency indicators of gas transfer units which include efficiency and gas fuel rate.The aim of this paper was to develop a universal method of calculating energy efficiency indicators using intelligent methods which exclude risks of reducing accuracy of results.In this paper we proposed and justified method of calculating energy efficiency indicators on the base of monitored parameters of gas transfer units work with application of neural networks. The research made it possible to receive and to substantiate the method of determining the specific consumption of fuel gas by the parameters of HPA controlled standard automation system based on the application of intelligent neural networks. Neural network model can be integrated in the station monitoring system compressor station need only advance to pick up army of weights to train a neural network) for a particular type HPA. When training a neural network according to the variation of the parameters in the training set and 20% teaching error does not exceed 1%. When using neural networks for the calculation of energy efficiency indicators for the unit for which there was training for other units of the same type, the mean square error does not exceed 5%.