Thermal power plant time sequence variable abnormity monitoring method and system

Abstract

The invention discloses a thermal power plant time sequence variable abnormity monitoring method and system. The method comprises the steps of collecting time sequence data of thermal power plant analog signal monitoring variables; taking the whole time sequence data as an initial optimal PLR segment, calculating a decision coefficient of the optimal PLR segment, and taking the whole time sequencein the historical sample data as a first-layer original data segment; segmenting the original data segment to form two new data segments, and respectively calculating decision coefficients of the twonew data segments; judging whether the segments are allowed or not, and if yes, continuing to segment the segments; and judging whether the segmentation is finished or not to obtain the optimal PLR segmentation number. According to the method, the optimal segment number of the PLR is determined through a coefficient determination method, and the problems that an L method highly depends on a fitting error and left and right points in a time sequence are solved.

Description

technical field [0001] The invention relates to the technical field of variable monitoring of analog signals in thermal power plants, in particular to a method and system for monitoring abnormality of time series variables in thermal power plants. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Time series refers to the ordered collection of observation records arranged in chronological order, which widely exists in the fields of business, economics, scientific engineering and social sciences. In recent years, data mining research on time series data has received widespread attention, including association rule mining, similarity query, pattern discovery, anomaly detection, etc. [0004] The piecewise linear representation method (PLR) can extract intrinsic information from the historical time series data samples, and process the monitori...

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Problems solved by technology

[0005] However, the inventor found that when segmenting the time-series analog signal variables in a thermal power plant based on the segmented linear representation method, it is often impossible to select the best number of segments, and too large or too small a number of segments will lead to It is impossible to obtain the accurate trend of the data, and thus cannot accurately judge the abnormal operation of the thermal power plant system

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