Experimental investigation and artificial neural network prediction of small-scale compressed air energy storage system based on pneumatic motor

Abstract

Compressed air energy storage (CAES) possesses the advantages of high reliability, good economic performance, longer discharge time, extended service life, and comprehensive utilization of heat, cold, and electricity. In this study, a CAES test bench based on a pneumatic motor (PM) is built, and the output performance of the CAES system is investigated under the variable working conditions represented by the change of the compressed air pressure and the fluctuation of the load demand. The effects of major parameters like torque, voltage, current, rotation speed, and regulated pressure on the power output and energy efficiency of the PM and generator are analyzed. By the evaluation of learning rate difference, number of hidden layer neural networks, and training function, a prediction model of CAES based on artificial neural network (ANN) is developed. By checking the mean-square error and related coefficient, the ANN model is validated by subsequent experimental results. Finally, to predict the maximum power output and energy efficiency of the PM and generator, genetic algorithm is supplemented for the parameters optimization. From the result, it can be found that the high power output range of the PM is concentrated in the low voltage, high current and medium-to-high rotation speed regions. Likewise, the low compressed air consumption rate of PM is concentrated mainly in the regions of low voltage, low rotation speed, high current and high torque. When the regulated pressure reaches 10.5 bar and the current value sync reaches 13 A, PM can generate a maximum output power of approximate 658 W.