Boiler combustion condition identification method based on information entropy characteristics and probability nerve network

Abstract

The invention discloses a boiler combustion identification method based on information entropy characteristics and a probability nerve network. The method comprises steps of entering a data pretreatment procedure and obtaining typical load points and a characteristic sampling collection of corresponding exhaust smoke oxygen volume and furnace pressure signals through a data input interface, entering a sampling data entropy analysis process and calculating singular spectral entropy and power spectral entropy of the exhaust smoke oxygen volume and furnace pressure signals under the corresponding working condition, using the obtained entropy value signals and the corresponding load working condition point as a training data collection to construct a PNN boiler combustion working condition identification model and outputting the result to a client terminal to join the optimization operation guide and the condition detection. The invention not only solves procedure state characterization problem in the furnace but also reflects the attributes of the furnace operation performance timely and accurately, avoids fault guidance for the operation personnel caused by falsity data and wrong data, and provides a reference model to the boiler operation optimization, state monitor and failure diagnosis of a power plant monitor information system.

Description

technical field [0001] The invention relates to a boiler combustion working condition identification method based on boiler information entropy features and a probabilistic neural network, belonging to the field of machine learning modeling. Background technique [0002] Machine learning is a means and mechanism of acquiring knowledge from known sample data or information through mining, induction, deduction, analogy, etc. Its purpose is to learn from a given training sample according to a certain method or algorithm designed in advance. Then seek an estimate of the dependence between the input and output of a certain system, and make the estimate better predict the unknown output as accurately as possible or judge its nature. Probabilistic Neural Networks (PNN) was first proposed by Dr. D.F.Specht in 1989. It is a parallel algorithm developed based on the Bayesian classification rule and the probability density function estimation method of the Parzen window. It is also a c...

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Problems solved by technology

[0003] The combustion process of a large-scale pulverized coal boiler in a power station is a complex nonlinear time-varying process. The actual operation will be affected by factors such as the type of boiler, the type of coal used, the form and operation mode of the pulverizing system, and the mode of air distribution. Therefore, it is often difficult to Establishing an accurate process mechanism model brings difficulties to boiler combustion monitoring and performance optimization. It is often necessary to find an effective method to characterize the real-time state of the furnace in order to complete real-time monitoring, trend judgment and operation optimization of the furnace process

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