Abstract
Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.
Highlights
In power machinery, the analysis and prediction of the temperature changes of multiple sensors from different parts of the equipment are important bases for the evaluation of its running state [1,2].Pumping stations are the most widely used water facilities
N) was developed on grey from a multivariable grey model (MGM) can describe different that affect the (1, operating state ofbased the system system theory [14] proposed by Deng (where (1, n) represents First order ordinary differential equation multidimensional degree, which can overcome the non-stationary signals limitations and effectively with n elements)
The results show that the MGM after optimization of the q parameters is better than the traditional the experimental results are compared
Summary
The analysis and prediction of the temperature changes of multiple sensors from different parts of the equipment are important bases for the evaluation of its running state [1,2]. N) was developed on grey from a MGM can describe different that affect the (1, operating state ofbased the system system theory [14] proposed by Deng (where (1, n) represents First order ordinary differential equation multidimensional degree, which can overcome the non-stationary signals limitations and effectively with n elements). It is a multidimensional generalization of the single variable grey model (GM) (1, 1). Improved the prediction accuracy by 0.01% and 2.02%, respectively
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