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A method for predicting variable load rate of thermal power units based on wavelet neural network

A technology of wavelet neural network and thermal power unit, which is applied in the direction of instruments, adaptive control, control/regulation system, etc., can solve the problems of unfavorable intelligent control of thermal power plants and unsatisfactory precise control, and achieve intelligent prediction methods, Active prediction and high prediction accuracy

Active Publication Date: 2017-11-03
SOUTHEAST UNIV
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Problems solved by technology

[0002] For a long time, the research on the variable load rate of thermal power plants is some passive measurement. When encountering unit variable load, we can only estimate the possible load variable rate of the thermal power unit in this state. This estimated value cannot meet the more precise requirements. control, which is not conducive to further intelligent control of thermal power plants

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  • A method for predicting variable load rate of thermal power units based on wavelet neural network
  • A method for predicting variable load rate of thermal power units based on wavelet neural network
  • A method for predicting variable load rate of thermal power units based on wavelet neural network

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Embodiment Construction

[0038] The technical solution of the present invention will be further introduced below in combination with specific embodiments.

[0039] The invention provides a method for predicting variable load rate of thermal power units based on wavelet neural network, comprising the following steps:

[0040] S1: Select the variable load target instruction, current load, current main steam pressure value and BTU coal quality correction coefficient at a load change moment from the DCS system of the thermal power plant as input data, select the time interval as 10s, and According to the actual load curve of the thermal power unit at this load change moment, the variable load rate d is obtained 1 As the predicted output data, and R=200 groups (x 1 , x 2 , x 3 , x 4 , d 1 ) 1 ,(x 1 , x 2 , x 3 , x 4 , d1 ) 2 ,...,(x 1 , x 2 , x 3 , x 4 , d 1 ) R As learning samples, Q=20 groups (x 1 , x 2 , x 3 , x 4 , d 1 ) 1 ,(x 1 , x 2 , x 3 , x 4 , d 1 ) 2 ,...,(x 1 , x 2...

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Abstract

The invention discloses a method for predicting the variable load rate of a thermal power unit based on a wavelet neural network. By establishing a wavelet neural network, the timely, effective and active prediction of the variable load rate of a thermal power unit is realized. High precision.

Description

technical field [0001] The invention relates to a method for predicting variable load rate of thermal power units, in particular to a method for predicting variable load rate of thermal power units based on wavelet neural network. Background technique [0002] For a long time, the research on the variable load rate of thermal power plants is some passive measurement. When encountering unit variable load, we can only estimate the possible load variable rate of the thermal power unit in this state. This estimated value cannot meet the more precise requirements. Control is not conducive to the further intelligent control of thermal power plants. In terms of large power grids, due to the rapid increase in the scale of modern power grids, the addition of various distributed power sources, UHV DC transmission, and the construction of smart grids. These factors will put forward higher requirements for the current power grid control, and the modern power grid must develop along the...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
Inventor 吕剑虹岑垚崔晓波周帆
Owner SOUTHEAST UNIV
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