Emission monitoring time series data abnormal value detection method for coal-fired unit

Abstract

The invention relates to an emission monitoring time series data abnormal value detection method for a coal-fired unit. Specifically, all-working-condition historical sample data set of a measured point is subjected to working condition division by applying a k-means clustering algorithm according to unit operation states and environment-protection facility operating ways; then specific to each working condition historical sample data set, an abnormal value detection model is established based on a statistical hypothesis test method; and the time series data of the measured point is subjected to online identification by selecting different hypothesis test methods according to data distribution characteristics of the working condition sample set, and then abnormal points which are inconsistent with historical distribution and current change tendency can be positioned in time. Abnormal value online detection on the field monitored data is performed by the hypothesis test methods, and the abnormal points which are inconsistent with historical distribution or current change tendency can be dynamically identified, so that early warning for more complex fault monitoring is provided, refine degree of alarming is improved, and online monitoring management level and rapid response capability are improved.

Description

technical field [0001] The invention belongs to the field of energy, and in particular relates to a method for detecting an abnormal value of time-series data of emission monitoring of a coal-fired unit. Background technique [0002] With the continuous deepening of energy conservation and emission reduction work in my country, the requirements for pollutant emission standards for coal-fired units are becoming more and more stringent. In 2014, the National Development and Reform Commission, the Ministry of Environmental Protection and other departments jointly formulated the "Coal Power Energy Conservation and Emission Reduction Upgrading and Transformation Action Plan (2014-2020)", which put forward the requirements for coal-fired units to meet the pollutant emission standards of gas turbine units, the so-called Ultra-low emissions, requiring that under the condition of a reference oxygen content of 6%, the emission concentrations of soot, sulfur dioxide, and nitrogen oxide...

AI Technical Summary

Problems solved by technology

[0006] 1) The operating conditions and operating modes of the units and ultra-low emission facilities are constantly changing, and the measuring points will show different changing characteristics under different conditions, which will lead to the average value and method of the measuring point data under different conditions showing The difference makes the fixed threshold based on the sample set of all working conditions not well adapted to the continuously changing operating conditions;

[0007] 2) Since the fixed threshold is obtained on the sample set of all working conditions, in order to adapt to all working conditions, its value range must be relatively loose, which makes it impossible to effectively identify data points that fall within the threshold range but have abnormal trends , the export has missed detection;

[0008] 3) The 3 Sigma method is based on the assumption that the data is normally distributed, and the monitoring data of the ultra-low emission measuring points of coal-fired units may not necessarily obey the normal distribution even under steady-state conditions, so the 3 Sigma method is not fully applicable For ultra-low emission applications in coal-fired units

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