Thermal power generating unit load (quasi) steady-state working condition clustering algorithm based on data smoothness functions

Abstract

The invention discloses a thermal power generating unit load (quasi) steady-state working condition clustering algorithm based on data smoothness functions. The algorithm comprises the steps that 1, a comprehensive application statistics method is used for carrying out data pretreatment on collected unit load data, and the accuracy and the uniformity of a researched data object are improved so that the operation working condition of a unit can be reflected truly; 2, with the data smoothness functions as a criterion, a monotonous data region larger than a preset critical threshold value is cut off, and the (quasi) steady-state working condition of unit load data is determined; 3, the unit load data meeting the requirement for the data smoothness and meeting the shortest (quasi) steady-state working condition time are selected, and the effective clustering algorithm is used for obtaining and storing a typical working condition data set. The method is flexible, applicable, suitable for determining the thermal power generating unit load (quasi) steady-state working condition, capable of obtaining the typical working condition data of unit operation, and the effective data support is provided for work such as follow-up unit diagnostic service and condition analysis and optimization of the unit operation.

Description

technical field [0001] The invention belongs to the technical field of safety and economic remote diagnosis and service of a steam turbine unit in a thermal power plant, and in particular relates to a clustering algorithm of unit load (quasi) steady-state working conditions based on a data smoothness function. Background technique [0002] At the present stage, relying on the rapid development and application of modern information technology, especially computer network technology, the Energy Conservation Technology Laboratory has realized the integration and utilization of the information infrastructure of power generation enterprises and power generation groups, through the centralized report of real-time primary operation data of the unit site A large energy consumption big data support platform integrating real-time data collection, transmission, storage and calculation of thermal power units has been gradually established. Through the big data support platform of unit e...

AI Technical Summary

Problems solved by technology

[0004] When performing equipment state diagnosis and analysis, it is first necessary to obtain the stable working conditions of the historical data set. Only under (quasi) steady working conditions can the parameters have a strong state consistency. Under unsteady working conditions, the data cannot truly reflect The relationship between system input and output will bring identification and modeling errors

If the unsteady-state data is directly extracted from the historical database of the unit and used for calculation and analysis, the results will have large fluctuations and deviations, which cannot reflect the actual operating status and performance of the unit equipment. It has no practical guiding significance and application value

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