CN112131780A - Thermal power plant circulating water system control method based on data mining - Google Patents
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Abstract
The invention provides a thermal power plant circulating water system control method based on data mining, which carries out early prediction on operation. The method has the advantages that the neural network modeling is carried out on the condenser vacuum system by utilizing a data mining technology and a large amount of historical data, so that the change condition of the vacuum system after the operation is predicted more accurately, the quantitative calculation accuracy is high, an enthalpy value table look-up function is compiled by utilizing a computer, the interplant economic benefit can be calculated rapidly, accurately and quantitatively by matching with a corresponding algorithm, the early prediction is carried out for starting/stopping the circulating water pump, and meanwhile, the change of the economy of a unit is calculated accurately and quantitatively by utilizing the algorithm rule and the influence of comprehensive load.
Description
Technical Field
The invention relates to a thermal power plant circulating water system control method based on data mining.
Background
The economy of the condensing thermal power generating unit is closely related to the vacuum of the condenser. The steam condenser has high vacuum degree, high steam making function and high economy, and otherwise, the economy is low. In summer, the environmental temperature rises, the temperature of circulating water rises, the effect of cooling steam turbine exhaust is reduced, and the vacuum degree is reduced. At this time, in order to improve the vacuum degree and the unit efficiency, a thermal power plant generally adopts a plurality of circulating water pumps to operate. After the circulating water pump is started, the power generation coal consumption is reduced, the fuel cost is reduced, but the active power P of the circulating water pump is reduced before the power supply is started, and the total power revenue is reduced. How to evaluate the change of the overall economy of the unit after the circulating water pump is started/stopped is the key for determining the starting/stopping of the circulating water pump.
In the prior art, after a circulating water pump is started or stopped, the economic benefit of starting or stopping the circulating water pump is evaluated by measuring the change amplitude of condenser vacuum on the spot, and the early prediction of operation is lacked. In assessing the effect of vacuum changes on coal consumption, empirical values are still used, such as how many g/kWh changes in coal consumption per 1kPa change in vacuum. The method uses the empirical value, the influence of the current load on the coal consumption for power generation is eliminated, the error changes along with the load, accurate calculation cannot be achieved, and the investment is overlarge if the relation between the whole set of coal consumption for power generation and the vacuum is established through experiments.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a thermal power plant circulating water system control method based on data mining aiming at the defects in the prior art, which carries out early prediction for starting/stopping a circulating water pump, and accurately and quantitatively calculates the change of the economic efficiency of a unit by utilizing algorithm rules and the influence of comprehensive loads.
The technical scheme is as follows: in order to achieve the purpose, the invention provides a thermal power plant circulating water system control method based on data mining, which is characterized by comprising the following steps of: the method comprises the following steps:
(1) data acquisition:
taking one year as a window, acquiring operation data of the condenser every other one hour, wherein the acquired operation data comprises circulating water temperature T, circulating water flow F, unit load L and condenser vacuum V, and storing the data in a server;
(2) screening collected data:
screening the collected circulating water temperature T, the collected circulating water flow F, the unit load L and the condenser vacuum V, and eliminating a shutdown value, a startup value and an abnormal value;
(3) normalization of the collected data:
standardizing the acquired data by the following formula 1 to eliminate the influence of dimension;
(4) modeling a BP neural network of a condenser vacuum system of a unit:
dividing collected data into a training set, a test set and a test set, training a model by using the training set, selecting a BP neural network model as an input layer with 3 variables through repeated tests, selecting two hidden layers with 6 nodes and an output layer with 1 output node, selecting Relu as a hidden layer activation function, selecting Adam as a gradient algorithm, selecting MSE as an evaluation function, and establishing and storing the model;
(5) predicting the vacuum of a condenser of a unit:
and predicting the vacuum of the unit behind the started/stopped circulating water pump by using the model. When the double units are operated, after the circulating water pump is started/stopped, the pressure P of the circulating water is changed into 3/2 times or 2/3 times of the original pressure of the circulating water. Substituting the pressure after starting/stopping the circulating water into the model to obtain the predicted vacuum x' after starting/stopping the circulating water pump, and obtaining a predicted vacuum value by using the following formula 2; x' × std (x) + mean (x);
(6) the coal consumption of the unit for generating electricity is changed;
and establishing a table look-up function f of the steam saturation enthalpy value. The unit saves/increases the coal generation amount delta bHair-like deviceAs followsThe calculation of formula 3 and formula 4 shows (the negative value is the coal consumption increase);
E＝f(x)
(7) and (3) measuring and calculating the economic efficiency of the unit:
when the double-machine running is carried out, after the circulating water pump is started/stopped, the coal price of the current standard coal is set as PSign boardOn-line electricity price PElectric powerStarting a standby circulating water pump of the machine No. 1, and calculating the economical efficiency of the machine No. 1 by using the following formula 5;
ΔC1＝Δbhair 1×PLabel 1-ΔPInternet access×PElectric power；
The plant economy is calculated by the following equation 6:
ΔC＝ΔC1+ΔC2＝(Δbhair 1+ΔbHair 2)×PSign board-ΔPInternet access×PElectric power
When the running conditions of the machine No. 1 and the machine No. 2 are completely the same, the operation can be simplified.
ΔC＝2×ΔbHair 1×PSign board-ΔPInternet access×PElectric power。
As an improvement of this solution, the amount of steam entering the condenser in said formula 3 is equal to the flow F of condensateCondensed waterThe numerator is the variable quantity of the steam work of the condenser after the vacuum change, eta in the denominatorThermal efficiencyThe overall thermal efficiency of the unit.
As an improvement of the scheme, the reduction/increase of the unit online electric quantity is the active power P of the circulating water pump.
As an improvement of the scheme, when the Delta C is larger than zero, the plant-to-plant economic benefit of starting/stopping the circulating water pump is improved, otherwise, the plant-to-plant economic benefit is reduced.
Compared with the prior art, the invention has the following beneficial effects:
1. and carrying out advanced prediction on the running operation. The neural network modeling is carried out on the condenser vacuum system by utilizing a data mining technology and a large amount of historical data, so that the change condition of the vacuum system after the operation is more accurately predicted.
2. The quantitative calculation accuracy is high. The enthalpy value table look-up function is written by a computer, and the interplant economic benefit can be quickly, accurately and quantitatively calculated by matching with a corresponding algorithm.
3. The investment is small. A large amount of existing data in the power plant are fully transferred, comprehensive analysis is carried out, and a corresponding algorithm is compiled to achieve the prediction effect without additional investment.
Drawings
FIG. 1 is a schematic diagram of the steps employed in the present invention;
fig. 2 is a trend chart of the unit price of the standard coal and the price of the on-grid electricity in the invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
A thermal power plant circulating water system control method based on data mining collects a group of data every hour by collecting annual operation parameters of a unit circulating water system and taking one year as a window. In a condensing steam turbine, factors affecting the vacuum of the condenser are the circulating water temperature, the water circulating water amount, the amount of steam discharged into the condenser, and the operation mode (constant) of the vacuum system. Therefore, the data acquisition variables include the circulating water temperature T, the circulating water flow F (which can be replaced by the circulating water pressure P under the condition that the pipeline conditions are not changed), the unit load L, and the condenser vacuum V.
And screening the collected data of the unit. And screening the acquired data of one year, and removing the shutdown value, the startup value and the abnormal value.
And (5) standardizing the data collected by the unit. To eliminate the influence of dimension, the collected data is normalized by equation 1.
And (4) modeling a BP neural network of a condenser vacuum system of the unit. The collected data is divided into a training set, a testing set and a testing set. Training the model using a training set. Through repeated tests, a BP neural network model is selected as an input layer with 3 variables, two hidden layers with 6 nodes and output layers with 1 output node are selected, the hidden layers are activated by a function Relu, a gradient algorithm is selected by Adam, and an evaluation function is selected by an MSE. And establishing and storing the model.
And predicting the vacuum of the condenser of the unit. And predicting the vacuum of the unit behind the started/stopped circulating water pump by using the model. When the double units are operated, after the circulating water pump is started/stopped, the pressure P of the circulating water is changed into 3/2 times or 2/3 times of the original pressure of the circulating water. And substituting the pressure after the circulation water is started/stopped into the model to obtain the predicted vacuum x' after the circulation water pump is started/stopped. Using equation 2, a predicted vacuum value is obtained.
x′＝x′×std(x)+mean(x)
The coal consumption of the unit for generating electricity is changed. And establishing a table look-up function f of the steam saturation enthalpy value. The unit saves/increases the coal generation amount delta bHair-like deviceAs shown by the following calculations of equation 3 and equation 4 (negative values are coal consumption increases).
E＝f(x)
In equations 3 and 4, the amount of steam entering the condenser is equal to the condensate flow FCondensed water. The molecule is the variable quantity of the work done by the steam entering the condenser after the vacuum change. In the denominator, ηThermal efficiencyThe overall thermal efficiency of the unit.
The reduction/increase of the unit online electric quantity is the active power P of the circulating water pump.
And (4) measuring and calculating the economy of the unit. And (5) running the double machines, and starting/stopping the circulating water pump. Assuming that the coal price of the time standard coal is PSign boardOn-line electricity price PElectric powerStarting the standby circulating water pump of the No. 1 machine, and calculating the economical efficiency of the No. 1 machine by using a formula 5
ΔC1＝ΔbHair 1×PLabel 1-ΔPInternet access×PElectric powerThe plant economy is calculated by equation 6.
ΔC＝ΔC1+ΔC2＝(ΔbHair 1+ΔbHair 2)×PSign board-ΔPInternet access×PElectric power
When the running conditions of the machine No. 1 and the machine No. 2 are completely the same, the operation can be simplified.
ΔC＝2×ΔbHair 1×PSign board-ΔPInternet access×PElectric power
A value of ac greater than zero indicates that the plant-wide economic efficiency of starting/stopping the circulating water pump is improved, and conversely, the plant-wide economic efficiency is reduced. This can then be the most important condition for starting/stopping the circulating water pump without taking into account the fault situation.
As shown in fig. 1, the application process of the present invention includes: acquiring unit operation data of a year, wherein the acquisition state comprises the starting and stopping states of a standby circulating water pump; screening a start-up value, a stop value and an abnormal value; standardizing the acquired data and eliminating dimension influence; carrying out BP neural network modeling on the acquired data, and establishing a prediction model of the vacuum system; looking up a table of enthalpy values, and calculating the change of work load of the current value and the predicted value; converting the change of the work done amount to the change of the standard coal consumption; and calculating the difference between the lower fuel cost and the reduced revenue by taking the current coal marking unit price and the power price on the internet as statistical standards.
As shown in fig. 2, Δ b is a unit price of coal and electricity price on the internet for different prices of coal and electricityHair-like deviceThe critical values are different. The trend reflected from the graph is along with the rise of the coal price, and the critical value is reduced; the critical value increases with the increase of the price of the on-line electricity. When predicted Δ bHair-like deviceIn the upper right region, it is contemplated to start/stop the circulating water pump to obtain positive unit revenue. On the contrary, when falling in the lower left region, the circulating water pump is not started/stopped.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (4)
1. A thermal power plant circulating water system control method based on data mining is characterized in that: the method comprises the following steps:
(1) data acquisition:
taking one year as a window, acquiring operation data of the condenser every other one hour, wherein the acquired operation data comprises circulating water temperature T, circulating water flow F, unit load L and condenser vacuum V, and storing the data in a server;
(2) screening collected data:
screening the collected circulating water temperature T, the collected circulating water flow F, the unit load L and the condenser vacuum V, and eliminating a shutdown value, a startup value and an abnormal value;
(3) normalization of the collected data:
standardizing the acquired data by the following formula 1 to eliminate the influence of dimension;
(4) modeling a BP neural network of a condenser vacuum system of a unit:
dividing collected data into a training set, a test set and a test set, training a model by using the training set, selecting a BP neural network model as an input layer with 3 variables through repeated tests, selecting two hidden layers with 6 nodes and an output layer with 1 output node, selecting Relu as a hidden layer activation function, selecting Adam as a gradient algorithm, selecting MSE as an evaluation function, and establishing and storing the model;
(5) predicting the vacuum of a condenser of a unit:
and predicting the vacuum of the unit behind the started/stopped circulating water pump by using the model. When the double units are operated, after the circulating water pump is started/stopped, the pressure P of the circulating water is changed into 3/2 times or 2/3 times of the original pressure of the circulating water. Substituting the pressure after starting/stopping the circulating water into the model to obtain the predicted vacuum x' after starting/stopping the circulating water pump, and obtaining a predicted vacuum value by using the following formula 2;
x′＝x′×std(x)+mean(x)；
(6) the coal consumption of the unit for generating electricity is changed;
establishing a table look-up function f of the steam saturation enthalpy value, and saving/increasing the coal generation quantity delta b of the unitHair-like deviceAs shown by calculation using the following formula 3 and formula 4 (negative values are coal consumption increases);
E＝f(x)
(7) and (3) measuring and calculating the economic efficiency of the unit:
when the double-machine running is carried out, after the circulating water pump is started/stopped, the coal price of the current standard coal is set as PSign boardOn-line electricity price PElectric powerStarting a standby circulating water pump of the machine No. 1, and calculating the economical efficiency of the machine No. 1 by using the following formula 5;
ΔC1＝Δbhair 1×PLabel 1-ΔPInternet access×PElectric power；
The plant economy is calculated by the following equation 6:
ΔC＝ΔC1+ΔC2＝(Δbhair 1+ΔbHair 2)×PSign board-ΔPInternet access×PElectric power
When the running conditions of the machine No. 1 and the machine No. 2 are completely the same, the operation can be simplified.
ΔC＝2×ΔbHair 1×PSign board-ΔPInternet access×PElectric power。
2. The thermal power plant circulating water system control method based on data mining as claimed in claim 1, wherein: the steam amount entering the condenser in the formula 3 and the formula 4 is equal to the condensate flow FCondensed waterThe numerator is the variable quantity of the steam work of the condenser after the vacuum change, eta in the denominatorThermal efficiencyIs made into a machineThe overall thermal efficiency of the stack.
3. The thermal power plant circulating water system control method based on data mining as claimed in claim 1, wherein: the reduction/increase of the unit online electric quantity is the active power P of the circulating water pump.
4. The thermal power plant circulating water system control method based on data mining as claimed in claim 1, wherein: when Δ C is greater than zero, it means that the plant-wide economic efficiency of starting/stopping the circulating water pump is improved at this time, and conversely, it is reduced.
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Cited By (3)
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| CN114198978A (en) * | 2021-04-22 | 2022-03-18 | 安徽新力电业高技术有限责任公司 | Optimization design method for self-control test scheme of circulating water system |
| CN113671830A (en) * | 2021-08-10 | 2021-11-19 | 浙江浙能技术研究院有限公司 | Thermal power generating unit cold end optimization closed-loop control method based on intelligent scoring |
| CN113671830B (en) * | 2021-08-10 | 2024-04-02 | 浙江浙能数字科技有限公司 | Cold end optimization closed-loop control method for thermal power generating unit based on intelligent scoring |
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