CN113591388A - Steam turbine heat rate optimization method based on industrial data and process mechanism - Google Patents
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Abstract
The invention provides a steam turbine heat rate optimization method based on industrial data and a process mechanism, which comprises the following steps of: step S100: preprocessing historical production data based on a thermal mechanism of a steam engine, removing abnormal samples, and using health data for model training; step S200: training a steam turbine heat consumption rate model based on process mechanism analysis; step S300: establishing an optimized control model based on a high-dimensional approximate model; step S400: solving the heat rate nonlinear optimization problem. According to the invention, through establishing a steam turbine production optimization model, an optimal algorithm is provided to find the optimal solution of each control parameter to guide production, so that the optimal heat consumption rate is ensured, and the dependence on the experience of workers is eliminated.
Description
Technical Field
The invention belongs to the technical field of steam turbine control, and particularly relates to a steam turbine heat rate optimization method based on industrial data and a process mechanism.
Background
The steam turbine is high-speed rotating work-doing equipment which converts internal energy of steam into mechanical energy, and the steam releases energy in the steam turbine to push the steam turbine to drive a generator to generate electric energy. The steam turbine body system is divided into three typical objects of a high-pressure cylinder, an intermediate-pressure cylinder and a low-pressure cylinder.
The control method of the existing steam turbine is that on the premise of meeting the production safety of a boiler, an engineer writes a control parameter range in a centralized control operation rule and specifies the control range of key operation parameters (such as enthalpy, temperature, pressure and other thermodynamic parameters). When the production working condition changes, the operation workers adjust according to the actual running condition to meet the condition that the parameters are in the control range, so that the optimal steam turbine heat consumption rate is achieved as the optimization target.
At present, the method for adjusting production based on the range of key control parameters only often encounters the following problems in the actual operation process:
1. the internal performance of equipment related to a steam turbine unit in a power plant, the performance of the equipment, and the change of loss can also greatly influence the heat consumption rate of the drive unit directly or indirectly.
2. The operation of the steam turbine belongs to a dynamic continuous process, and is influenced by various uncertain factors. The most economic parameters exist among the exhaust steam temperature of the steam turbine, the frequency of the circulating water pump and the condenser vacuum, so that the heat consumption and the service power of the steam turbine are optimal economically, but operators of different teams can adjust the parameters according to personal experience. The production line is not operated in the optimal process. Therefore, the production cost is closely related to the conditions of skill, experience, concentration degree and the like of operators, the operation of the operators belongs to years of accumulated experience, the operators are not the optimal dynamic combination of all production parameters of the process, and the production benefit has higher optimization and promotion space.
Disclosure of Invention
Aiming at the technical problems, the invention provides a steam turbine heat consumption rate optimization method based on industrial data and process mechanisms. And performing joint debugging optimization on key parameters of each steam turbine based on a trained steam turbine heat consumption rate prediction model and real-time production data, and realizing a cost control target with optimal heat consumption rate on the premise of ensuring process safety.
The method comprises the steps of preprocessing massive industrial historical data and removing abnormal samples in the historical data. And then, selecting key influence parameters of the steam turbine process, providing a high-dimensional data training method, and establishing a heat consumption rate prediction model highly consistent with the actual steam turbine operation. By establishing a steam turbine production optimization model, an optimal algorithm is provided to find the optimal solution of each control parameter to guide production, the optimal heat consumption rate is ensured, and the dependence on the experience of workers is eliminated.
The specific technical scheme is as follows:
the method for optimizing the heat rate of the steam turbine based on industrial data and a process mechanism comprises the following steps:
step S100: preprocessing historical production data based on a thermal mechanism of a steam engine, removing abnormal samples, and using health data for model training;
the data preprocessing process mainly comprises the following steps:
in the actual production process, the accident that the sensor fails or the data acquisition device fails in a short time may occur, and at this time, the acquired data may be larger than the actual signal, and the error is also large, so that the outliers are removed, otherwise, the accuracy of the model is affected.
Because the value ranges of the input parameters of the model are greatly different, if the original data is directly used, the output error of the model is large. Therefore, the maximum and minimum values of the normalization of each parameter are manually determined by combining the possible maximum variation range of each parameter in the unit load lifting process, and all data are subjected to normalization processing.
The key control points of the steam turbine are often provided with a plurality of measuring points (such as main steam pressure, main steam temperature and the like), and the measuring points are required to be averaged.
And (4) preprocessing historical production data and then using the preprocessed historical production data for training a steam turbine heat consumption rate model.
Step S200: training a steam turbine heat consumption rate model based on process mechanism analysis;
training a high-dimensional approximate model of heat consumption rate y, power generation load x1, main steam pressure x2, main steam temperature x3, reheater outlet steam pressure x4, reheater outlet steam temperature x5, reheater inlet steam temperature x6, reheater inlet steam pressure x7, reheated desuperheating water flow x8, superheated desuperheating water flow x9, turbine backpressure x10, circulating water inlet temperature x11 and water supply flow x12 on the basis of the production data sample cleaned in the step S100;
the high-dimensional approximation model of the output variable y is:
in formula (1), K is the maximum order of the input variable x, i and i 'represent each specific variable x, K and K' represent the order of each variable x, and the model parameters include: C. a. thei,kAnd Bi,i’,k,k’Where C represents the zeroth order response to the output variable y; a. thei,kFinger input variable xiThe effect on the output variable y when acting alone; b isi,i’,k,k’Is an input variable xiAnd xi’The effect of the coupling on the output variable y.
Further comprising:
step S210: establishing a calculation relation between an output variable predicted value y and an input variable x based on a high-dimensional approximation model of an expression (1), wherein a subscript M represents each group of data, M is the group number of the data, and other symbols are the same as the expression (1):
step S220: constraining the error range sigma of the high-dimensional approximate model, and introducing two variables ya not less than 0mAnd ybmBuilding type (3) - (6), y* mIs a sample value of the output variable;
0≤yam≤σ，m∈M (5)
0≤ybm≤σ，m∈M (6)
step S230: establishing a target value r for linear optimization, and making the error ya between the predicted value and the data sample valuem+ybmMinimum, as in formula (7);
step S240: setting an error range sigma, and setting an initial order K of an input variable x to be 1;
step S250: and solving a linear optimization problem. Aiming at the linear optimization problem established in the steps S210-S240, the mathematical programming technology is utilized, and a classical dual simplex algorithm is adopted to efficiently solve the problem;
step S260: judging whether the linear optimization problem has a solution; if the solution exists, outputting the result, and stopping the algorithm; if no solution exists, the step S270 is executed;
step S270: increasing the order of an input variable x, wherein K is K + 1; returning to the step S250, solving the linear optimization problem after the variable x order is updated; repeatedly executing the steps S250-S270 by continuously increasing the order K of the variable x to obtain all the parameters C, A of the high-dimensional approximate model within the error range sigmai,kAnd Bi,i’,k,k’。
Step S300: establishing an optimized control model based on a high-dimensional approximate model;
the control target of the heat consumption rate is Max (y) (8);
a heat rate optimization mathematical programming model is formed by a heat rate formula (1) training equation and a heat rate formula (8) objective function.
Step S400: solving the heat rate nonlinear optimization problem.
Aiming at the nonlinear mathematical programming problem established in step S300, the mathematical programming technique is utilized, and a classical Successive Convex Approximation (Successive Convex Approximation) algorithm is adopted to efficiently solve the problem.
The invention has the technical effects that:
(1) based on the thermal principle of the steam engine, the industrial historical production data is analyzed and cleaned, abnormal data samples are removed, and healthy training sample data are provided for subsequent process model training.
(2) Based on the thermal principle of the steam engine, key production parameters influencing the heat consumption rate are determined. And a high-dimensional characterization approximate model training algorithm is provided, and the steam turbine heat consumption rate model is trained to accurately predict the heat consumption rate by adopting the cleaned industrial historical production data.
(3) And establishing a heat consumption rate optimal control mathematical model by adopting the steam turbine heat consumption rate prediction model obtained by training, searching the optimal heat consumption rate control production condition, and finally making a production parameter joint debugging strategy with optimal production cost.
Drawings
FIG. 1 is a schematic diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the inventive method for solving the heat rate high-dimensional approximation model parameters;
FIG. 3 is a diagram illustrating the prediction results of the approximation model according to the embodiment.
Detailed Description
The specific technical scheme of the invention is described by combining the embodiment.
A steam turbine of a certain thermal power plant is of a subcritical, intermediate reheating type, high-middle pressing cylinder and double-cylinder double-exhaust single-shaft condensing type, main steam of a boiler of the plant is changed into two paths for supplying air to customers after the steam turbine does work to generate electricity, primary air supply is 2-stage air extraction of a high-pressure cylinder of the steam turbine, and secondary heat supply selects one of four-stage air extraction supply or reheating air supply to supply air during normal operation. The requirements of the steam turbine centralized control regulation are taken as constraint conditions one, and are shown in table 1.
Constraint one:
TABLE 1 control ranges of main parameters and control ranges in the operation of steam turbines of a certain thermal power plant
Constraint two: all control parameters vary within the allowable operating range of the process.
Constraint condition three: all control parameter adjustments were within +/-2% of the current operating level to ensure continuity and stability of production adjustments.
Deployment implementation is performed using the new method based on the above conditions.
Step S100: analyzing and cleaning industrial data of the steam turbine process;
based on the thermal mechanism of the steam engine, factors influencing the heat rate are determined. The recording was performed as sampled every 1 minute. Thus, corresponding samples of 1440 sets of production data and one set of experimental data were generated each day during continuous production at the factory. Data samples collected every 1 minute were averaged over 15 minutes to obtain 96 data sets per day. After the abnormal sample is removed, the obtained healthy sample belongs to training of a steam turbine heat consumption rate prediction model.
Step S200: a turbine heat rate prediction model is determined for 12 parameter historical data affecting heat rate. The high dimension of the heat rate y approximates the model structure.
As shown in fig. 2, the parameters of the high-dimensional approximation model of the main steam flow y in step S200 are solved by the linear optimization method established in steps S210-S270. The training results of the approximation model are shown in table 2, and the prediction results are shown in fig. 3.
TABLE 2 prediction results of the approximation model
Step S300: establishing an optimized control model based on a high-dimensional approximate model;
the heat rate optimization aims at: max (y);
finally, the heat consumption rate prediction equation and the operation objective function form a steam turbine heat consumption rate optimization control mathematical programming model.
Step S400: based on the nonlinear mathematical programming problem established in step S300, the nonlinear mathematical programming problem can be efficiently solved by using a mathematical programming technique and a classical Successive Convex Approximation (Successive Convex Approximation) algorithm.
And (4) optimizing the result: compared with historical data of 2 months in a factory, the optimized heat consumption rate is improved from the original 8350kJ/kW.h to 8136kJ/kW.h, and the reduction of 2.25% can be realized.
Claims (6)
1. The method for optimizing the heat rate of the steam turbine based on industrial data and a process mechanism is characterized by comprising the following steps of:
step S100: preprocessing historical production data based on a thermal mechanism of a steam engine, removing abnormal samples, and using health data for model training;
step S200: training a steam turbine heat consumption rate model based on process mechanism analysis;
step S300: establishing an optimized control model based on a high-dimensional approximate model;
step S400: solving the heat rate nonlinear optimization problem.
2. The method for optimizing the heat rate of a steam turbine based on industrial data and process mechanisms according to claim 1, wherein in step S100, the preprocessing of the data comprises:
the data collected when the sensor fails or the data collecting device fails in an accident condition is eliminated;
manually determining the normalized maximum and minimum values of each parameter by combining the possible maximum variation range of each parameter in the unit load lifting process, and performing normalization processing on all data;
a plurality of measuring points are often installed at key control points of the steam turbine, and the average value of the measuring points is obtained;
and (4) preprocessing historical production data and then using the preprocessed historical production data for training a steam turbine heat consumption rate model.
3. The method for optimizing the heat rate of the steam turbine based on the industrial data and the process mechanism according to claim 1, wherein in step S200, a high-dimensional model representation polynomial modeling training method is adopted, and based on the production data samples cleaned in step S100, high-dimensional approximate models of the heat rate y, the power generation load x1, the main steam pressure x2, the main steam temperature x3, the reheater outlet steam pressure x4, the reheater outlet steam temperature x5, the reheater inlet steam temperature x6, the reheater inlet steam pressure x7, the reheated attemperation water flow x8, the superheated attemperation water flow x9, the turbine back pressure x10, the circulating water inlet temperature x11 and the water flow x12 are trained;
the high-dimensional approximation model of the output variable y is:
in formula (1), K is the maximum order of the input variable x, i and i 'represent each specific variable x, K and K' represent the order of each variable x, and the model parameters include: C. a. thei，kAnd Bi，i’，k，k’Where C represents the zeroth order response to the output variable y; a. thei，kFinger input variable xiThe effect on the output variable y when acting alone; b isi，i’，k，k’Is an input variable xiAnd xi’The effect of the coupling on the output variable y.
4. The method of optimizing heat rate in a steam turbine based on industrial data and process mechanics according to claim 3 further comprising:
step S210: establishing a calculation relation between an output variable predicted value y and an input variable x based on a high-dimensional approximation model of an expression (1), wherein a subscript M represents each group of data, M is the group number of the data, and other symbols are the same as the expression (1):
step S220: constraining the error range sigma of the high-dimensional approximate model, and introducing two variables ya not less than 0mAnd ybmBuilding type (3) - (6), y* mIs a sample value of the output variable;
0≤yam≤σ，m∈M (5)
0≤ybm≤σ，m∈M (6)
step S230: establishing a target value r for linear optimization, and making the error ya between the predicted value and the data sample valuem+ybmMinimum, as in formula (7);
step S240: setting an error range sigma, and setting an initial order K of an input variable x to be 1;
step S250: and solving a linear optimization problem. Aiming at the linear optimization problem established in the steps S210-S240, the mathematical programming technology is utilized, and a classical dual simplex algorithm is adopted to efficiently solve the problem;
step S260: judging whether the linear optimization problem has a solution; if the solution exists, outputting the result, and stopping the algorithm; if no solution exists, the step S270 is executed;
step S270: increasing the order of an input variable x, wherein K is K + 1; returning to the step S250, solving the linear optimization problem after the variable x order is updated; repeatedly executing the steps S250-S270 by continuously increasing the order K of the variable x to obtain all the parameters C, A of the high-dimensional approximate model within the error range sigmai，kAnd Bi，i’，k，k’。
5. The method for optimizing the heat rate of a steam turbine based on industrial data and process mechanisms according to claim 3, wherein in the step S300, the control target of the heat rate is Max (y) (8);
a heat rate optimization mathematical programming model is formed by a heat rate formula (1) training equation and a heat rate formula (8) objective function.
6. The steam turbine heat rate optimization method based on industrial data and process mechanisms of claim 5, wherein in step S400, the nonlinear mathematical programming problem established in step S300 is solved efficiently by a classical successive convex approximation algorithm using a mathematical programming technique.
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