Steam turbine exhaust enthalpy predicating method based on PSO-SVR soft measurement model

A technology of PSO-SVR and prediction method, which is applied in the direction of prediction, data processing application, calculation, etc. It can solve the problems of lack of humidity measurement and control methods, and calculate exhaust enthalpy, etc., achieve good accuracy and generalization ability, and improve prediction accuracy Effect

Inactive Publication Date: 2018-12-07
SHANGHAI UNIVERSITY OF ELECTRIC POWER +1
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Problems solved by technology

In fact, in the structure of the steam turbine, the steam exhaust port is mainly located in the wet steam area, and the humidity measurement and control methods in this area are relatively lacking, and it is difficult to calculate the exhaust steam enthalpy through the pressure and temperature in this area.

Method used

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  • Steam turbine exhaust enthalpy predicating method based on PSO-SVR soft measurement model
  • Steam turbine exhaust enthalpy predicating method based on PSO-SVR soft measurement model
  • Steam turbine exhaust enthalpy predicating method based on PSO-SVR soft measurement model

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Embodiment

[0046] In order to verify the validity of the modeling method, this example takes the data of a 300MW unit under the following load conditions: maximum load, rated load, 85%, 70%, 60%, 50%, 40%, and historical The data were normalized as shown in Tables 1 and 2.

[0047]Table 1 Historical data input and output samples

[0048]

[0049] Table 2 Normalized input and output samples

[0050]

[0051]

[0052] In this embodiment, data under six load conditions are taken as training samples in this system. 50% load as a forecast sample. In this embodiment, MATLAB is used as the experimental platform, the hardware configuration is 2.4GHZ CPU, 8GB memory, and the operating system is Windows 10 64 bits. The PSO-SVR soft sensor prediction model is constructed and trained, and then the training samples are normalized to obtain the final result. The best combination of relevant parameters. The final parameter settings are as follows: local search capability c 1 =1.5, global ...

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Abstract

The invention relates to a steam turbine exhaust enthalpy predicating method based on a PSO-SVR soft measurement model. The method comprises the following steps of acquiring a sample data set; introducing a particle swarm intelligent algorithm, constructing a fusion type regression model based on a support vector machine for predicating the exhaust enthalpy, namely a PSO-SVR exhaust enthalpy softmeasurement model; training the PSO-SVR exhaust enthalpy soft measurement model based on the sample data set, performing solving for obtaining a best predication model, and establishing a corresponding exhaust enthalpy regression function; and performing steam turbine exhaust enthalpy predication based on the exhaust enthalpy regression function. Compared with the prior art, the steam turbine exhaust enthalpy predicating method has advantages of high predication capability, high predication precision, etc.

Description

technical field [0001] The invention relates to a method for predicting exhaust steam enthalpy of a steam turbine, in particular to a method for predicting exhaust steam enthalpy of a steam turbine based on a PSO-SVR soft sensor model. Background technique [0002] As of October 2017, the power generation capacity of my country's power plants was 5.1944 billion kwh, a year-on-year increase of 6.0%, and the growth rate increased by 2.1 percentage points over the same period of the previous year. Among them, the power generation of thermal power plants above designated size in the country was 3,799.3 billion kwh, a year-on-year increase of 5.4%, and the growth rate increased by 3.6 percentage points over the same period of the previous year. Thermal power is still the main form of power generation in my country. However, as the economy enters the new normal, facing the dual constraints of resources and the environment, the situation facing the thermal power industry is becomin...

Claims

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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 顾立群彭道刚于龙云李丹阳郑莉胡捷邓敏慧胡欢李嘉周彬严冬
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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