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.
Highlights
Common energy consumption diagnosis methods are usually divided into two categories: the heat method and exergy method, based on the first law of thermodynamics and the second law of thermodynamics, respectively [1]
The Multivariate state estimation technique (MSET) model combined with the modified information entropy weight theory is proposed as a method for the steam turbine energy efficiency monitoring study
The MSET estimation model is established by characteristic parameters and the heat rate index under the selected steady 550 MW working condition in a 600 MW unit, with the recognition of the effectiveness in different operation levels being testified
Summary
Common energy consumption diagnosis methods are usually divided into two categories: the heat method and exergy method, based on the first law of thermodynamics and the second law of thermodynamics, respectively [1]. The matrix theory was used to deduce the main flow relations of the cyclic function method from the generalized mathematical model of the thermal system [5], which made the thermal economic analysis and diagnosis of the thermal system more effective. These methods cannot evaluate the system efficiency from energy quality aspect. Combined with the second law of thermodynamics and modern economic theory, researchers put forward the theory of thermal economic structure [8], which can locate the abnormal energy consumption position according to the change in economic consumption These methods need many thermodynamics parameters, and depend on simplification for the engineering process
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