Multivariate State Estimation Technique Combined with Modified Information Entropy Weight Method for Steam Turbine Energy Efficiency Monitoring Study

Abstract

An energy efficiency monitoring method of the steam turbine system is studied in this paper. Multivariate state estimation technique (MSET) is utilized to compare the actual monitoring parameters and the healthy data of the equipment in normal working condition with a multi parameter estimation model. Due to the limitation of a single heat rate index in evaluating energy efficiency variation, the energy efficiency deviation degree combined with improved information entropy weight is proposed to judge the steam turbine’s operation condition levels. The index value in the modified weight method has been searched for more steady weight values calculated by information entropy values with small variation. Taking a 600 MW unit as an example, the energy efficiency levels of the unit under a 550 MW normal working condition are clustered into four groups, testifying the MSET model correctness and calculating the deviation degree value. Then, the energy efficiency status monitoring model is utilized to record residuals of actual data and estimated data during abnormal energy efficiency period. The residuals over deviation degree are then marked and judged as related with the abnormal data. The results show that the MSET model can timely and accurately judge the change of unit operation state, and the deviation degree calculated by the modified information entropy weight method can provide earlier warnings for the abnormal energy efficiency working conditions.

A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

“…To avoid this problem, this study adopts the entropy weight method, an objective assignment method, to determine the weight of each index based on the amount of information provided by each index and the degree of variability. The smaller the entropy value, the greater the amount of information contained and it should be given a larger weight, and conversely the larger the entropy value, the smaller the weight should be given (Gu et al, 2021). The basic steps are as follows:…”