CN109299582B - Turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation - Google Patents
Turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation Download PDFInfo
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Abstract
A turbine sliding pressure curve optimization method based on multi-dimensional sequencing of unit operation big data relates to the field of turbine control of thermal power plants. The problem of how to utilize big data of the unit in actual operation to optimize the sliding pressure curve is solved. According to the invention, historical data of the unit under N working conditions are used for sorting for multiple times and averaging, the data are screened, and the optimal points can be obtained through comparison, so that a sliding pressure curve is finally obtained. The invention mainly optimizes the sliding pressure curve of the steam turbine.
Description
Technical Field
The invention relates to the field of control of steam turbines of thermal power plants.
Background
At present, the sliding pressure optimization is the most effective energy-saving mode adopted by the existing typical coal-fired unit, and therefore, a great deal of theoretical research and experimental exploration work is carried out by a plurality of researchers. A learner puts forward a method for establishing an optimization model, establishes a series of functions to construct a system model by using a modern computer technology, simplifies and solves the system model, and determines an optimal pressure point; and the operation debugging personnel develop a special optimization test, and pressure optimization is carried out on a specific load point based on test data heat consumption correction calculation, so that control logic parameters in the DCS are adjusted. However, most of the above studies have given a slip pressure optimization curve with unit load as an argument when determining the optimal slip pressure point. However, in the actual variable load operation process of the steam turbine, a steam extraction working condition and a high back pressure working condition often exist, so that the corresponding loads of the unit under the same main steam flow are different. Therefore, the unit operation control pressure point obtained by inquiring the sliding pressure curve with the load as an independent variable can deviate from the actual optimal pressure of the steam turbine, and the thermal economy of the unit is greatly affected.
Therefore, a sliding pressure optimization strategy with main steam flow as an independent variable is proposed to reduce the influence of back pressure change and steam extraction working conditions on a sliding pressure curve; however, such an optimization strategy can only be obtained through a proprietary test, and in practice, many units cannot develop the proprietary test due to various reasons; in this case, how to use big data of unit operation to perform optimum design adjustment of the sliding pressure curve has not been described in detail in the related authoritative publications. Meanwhile, because most of the units are in the deep rapid load-changing operation process, large data of actual operation contain a large amount of thermal inertia noise information; if the set is directly utilized without effective processing, accurate calculation results cannot be obtained, so that how to utilize big data of the set in actual operation to optimize the sliding pressure curve needs to be solved.
Disclosure of Invention
The invention provides a turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation, which aims to solve the problem of optimizing the sliding pressure curve by using the big data of the unit in actual operation.
A turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation comprises the following steps:
step one, collecting historical data and unit design parameters of an operation unit under N working conditions, and obtaining a unit heat consumption rate under each working condition according to the historical data under each working condition, wherein N is a positive integer greater than 10;
step two, obtaining M heat supply steam extraction flow intervals according to N heat supply steam extraction flows in the historical data under N working conditions; m is a positive integer less than N;
step three, obtaining a stable main steam flow interval in each heat supply steam extraction flow interval according to the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval;
step four, obtaining a stable main steam pressure interval in each main steam flow interval according to the main steam pressure in each corresponding working condition in each main steam flow interval;
fifthly, obtaining main steam pressure intervals with stable back pressure and main steam temperature of all the units according to the back pressure and the main steam temperature of the units under the corresponding working conditions in each main steam pressure interval;
step six, obtaining the average main steam pressure and the average unit heat rate in the main steam pressure interval with stable back pressure and main steam temperature of each unit according to the main steam pressure in the main steam pressure interval with stable back pressure and main steam temperature of each unit and the corresponding unit heat rate;
drawing a main steam pressure-unit heat rate relation diagram in each main steam flow interval according to all the average main steam pressures and the average unit heat rates in each main steam flow interval, and obtaining the main steam pressure P corresponding to the lowest unit heat rate from the relation diagram min Main steam pressure P corresponding to the lowest heat rate of the unit min An optimal main steam pressure P' within the main steam flow interval in which it is located;
step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by utilizing a least square method according to the optimal main steam pressure P 'in all main steam flow intervals under each heat supply steam extraction flow, and obtaining a sliding pressure curve L' under each heat supply steam extraction flow by utilizing the minimum stable combustion pressure of the unit and the rated pressure of the unit in the unit design parameters.
Preferably, in the second step, the specific process of obtaining M heat supply and steam extraction flow intervals according to N heat supply and steam extraction flows in the history data under N working conditions is as follows:
and sorting N heat supply steam extraction flows under N working conditions according to ascending order, and removing excessive state points in the ascending order of the heat supply steam extraction flows, so as to obtain M heat supply steam extraction flow intervals.
Preferably, in the third step, the specific process of obtaining the stable main steam flow interval in each heat supply steam extraction flow interval according to the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval is as follows:
and (3) carrying out ascending sorting on the main steam flow of the unit under the corresponding working conditions in each heating steam extraction flow interval, and removing excessive state points in the ascending sorting of the main steam flow, so as to obtain a stable main steam flow interval in each heating steam extraction flow interval.
Preferably, in the fourth step, according to the main steam pressure in each working condition corresponding to each main steam flow interval, the specific process of obtaining the stable main steam pressure interval in each main steam flow interval is as follows:
and carrying out ascending order sequencing on the main steam pressure under the corresponding working conditions in each main steam flow interval, and removing excessive state points in the ascending order sequencing of the main steam pressure, so as to obtain stable main steam pressure intervals in each main steam flow interval.
Preferably, in the fifth step, the specific process of obtaining the main steam pressure interval in which all the unit back pressures and the main steam temperatures are stable according to the unit back pressures and the main steam temperatures under the corresponding working conditions in each main steam pressure interval is as follows:
and carrying out ascending sort on the unit back pressure under each working condition corresponding to each main steam pressure interval, removing excessive state points in the ascending sort of the unit back pressure, so as to obtain stable back pressure intervals in each main steam pressure interval, carrying out ascending sort on the main steam temperature under each working condition corresponding to each back pressure interval, and removing excessive state points in the ascending sort of the main steam temperature, so as to obtain the main steam pressure interval with stable unit back pressure and main steam temperature.
Preferably, in the sixth step, the specific process of obtaining the average main steam pressure and the average unit heat rate in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable according to the main steam pressure in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable and the corresponding unit heat rate is as follows:
and carrying out average treatment on the main steam pressure in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable and the corresponding unit heat rate to obtain the average main steam pressure and the average unit heat rate in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable.
Preferably, in the first step, the unit heat rate under each working condition is obtained according to the historical data under each working condition, and the method is specifically realized by adopting the following formula:
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
F ms representing the main steam flow; h ms Representing the enthalpy of the main steam;
F fw representing the main feed water flow; h fw Representing the main feed enthalpy;
F hrh representing reheat steam flow; h hrh Indicating reheat steam enthalpy;
F crh representing reheat cold leg steam flow; h crh Representing reheat cold section steam enthalpy;
F shsp representation ofThe flow of the overheated and de-warmed water; h shsp Indicating the enthalpy of the superheated desuperheated water;
F rhsp indicating the reheat attemperation water flow; h rhsp Indicating the reheat desuperheating enthalpy.
Preferably, reheat steam flow F hrh The specific mode realized by the following formula II is as follows:
F hrh ＝F ms -F 1 -F 2 (equation two),
wherein ,
F 1 and F2 All represent intermediate variables;
h fo1 representing the enthalpy of the outlet water of a first high-pressure heater of the turbine unit;
h fi1 representing the first high pressure heater inlet enthalpy of the turbine train;
h 1 representing the extraction enthalpy of a first high-pressure heater of the turbine unit;
h d1 representing the normal drainage enthalpy of a first high-pressure heater of the turbine unit;
h fo2 representing the second high pressure heater outlet enthalpy of the turbine train;
h fi2 representing the second high pressure heater inlet enthalpy of the turbine train;
h 2 representing the extraction enthalpy of a second high-pressure heater of the turbine unit;
h d2 indicating the normal hydrophobic enthalpy of the second high pressure heater of the turbine group.
Preferably F crh And F is equal to 2 Is equal in value.
The method has the advantages that the method for optimizing the sliding pressure curve of the steam turbine based on the multi-dimensional sequencing of the big data of the unit operation can screen the data according to the method of sequencing and averaging for multiple times, and the optimized points can be obtained through comparison, so that the method has stronger practical application value:
(1) The dynamic effect of the unit can be effectively eliminated by sequencing the actual running big data of the unit, and a stable working condition interval can be accurately obtained;
(2) By sequencing and screening the data for multiple times, the parameters such as back pressure, extraction quantity and the like can be ensured to be in a stable interval, the problem that the original sliding pressure curve is not suitable for an actual unit is solved, and the result is more scientific and accurate;
(3) Compared with taking average in time sequence, the method can more effectively weaken system noise and measurement noise and also can eliminate unit disturbance by carrying out average operation on the data after sequencing;
(4) The method can design the sliding pressure optimization curve under the condition that a special test cannot be performed, and has scientific and reasonable result and simple operation.
Drawings
FIG. 1 is a flowchart of a turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation according to the embodiment;
FIG. 2 is a result diagram of an operation of ascending sort of unit main steam flow; wherein, the abscissa represents the serial number, and the ordinate represents the flow;
FIG. 3 is a distribution diagram of the divide-by-transition state points of FIG. 2;
FIG. 4 is a graph of the main steam flow interval formed in FIG. 3 after the transition point is removed;
fig. 5 is a sliding pressure curve L' for each heating extraction flow obtained in step eight of the embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
Referring to fig. 1 to 5, a method for optimizing a sliding pressure curve of a steam turbine based on multi-dimensional sequencing of big data of unit operation according to the present embodiment is described, including the following steps:
step one, collecting historical data and unit design parameters of an operation unit under N working conditions, and obtaining a unit heat consumption rate under each working condition according to the historical data under each working condition, wherein N is an integer greater than 10;
step two, obtaining M heat supply steam extraction flow intervals according to N heat supply steam extraction flows in the historical data under N working conditions; m is a positive integer less than N;
step three, obtaining a stable main steam flow interval in each heat supply steam extraction flow interval according to the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval;
step four, obtaining a stable main steam pressure interval in each main steam flow interval according to the main steam pressure in each corresponding working condition in each main steam flow interval;
fifthly, obtaining main steam pressure intervals with stable back pressure and main steam temperature of all the units according to the back pressure and the main steam temperature of the units under the corresponding working conditions in each main steam pressure interval;
step six, obtaining the average main steam pressure and the average unit heat rate in the main steam pressure interval with stable back pressure and main steam temperature of each unit according to the main steam pressure in the main steam pressure interval with stable back pressure and main steam temperature of each unit and the corresponding unit heat rate;
drawing a main steam pressure-unit heat rate relation diagram in each main steam flow interval according to all the average main steam pressures and the average unit heat rates in each main steam flow interval, and obtaining the main steam pressure P corresponding to the lowest unit heat rate from the relation diagram min Main steam pressure P corresponding to the lowest heat rate of the unit min An optimal main steam pressure P' within the main steam flow interval in which it is located;
step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by utilizing a least square method according to the optimal main steam pressure P 'in all main steam flow intervals under each heat supply steam extraction flow, and obtaining a sliding pressure curve L' under each heat supply steam extraction flow by utilizing the minimum stable combustion pressure of the unit and the rated pressure of the unit in the unit design parameters.
In this embodiment, the history data for each condition includes the main vapor pressure P ms (Mpa) Main steam temperature T ms (DEGC), main steam flow F ms (t/h), reheat steam pressure F hrh (Mpa) reheat steam temperature T hrh (DEGC), main feed water flow F fw (T/h), main feedwater temperature T fw (DEGC), unit back pressure P b (kpa), unit heat supply and steam extraction flow F g (t/h), the extraction pressure P of the first high-pressure heater 1 (Mpa); a section of steam extraction temperature T 1 (DEGC), first high pressure heater inlet water temperature T fi1 (DEGC), the normal hydrophobic temperature T of the first high-pressure heater d1 (DEGC), first high-pressure heater outlet water temperature T fo1 (DEGC), the extraction pressure P of the second high-pressure heater 2 (Mpa) two-stage steam extraction temperature T 2 (DEGC), second high pressure heater inlet water temperature T fi2 (DEGC), the normal hydrophobic temperature T of the second high-pressure heater d2 (DEGC), second high-pressure heater outlet water temperature T fo2 Temperature T of overheat desuperheating water shsp (deg.C), superheated and de-ionized water flow F shsp (T/h), reheat attemperation water temperature T rhsp (DEGC) and reheat attemperation water flow F rhsp (t/h), unit load P (MW).
Because the turbine unit contains 3 high-pressure heaters, any two high-pressure heaters are used in the method, and the pressure and the temperature in the historical data are mainly used for obtaining the enthalpy value.
According to the turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the big data of the unit operation, the data can be screened according to the method of sequencing and averaging for multiple times, the optimization points can be obtained through comparison, and finally the sliding pressure curve is obtained. The present embodiment has the following effects:
(1) The dynamic effect of the unit can be effectively eliminated by sequencing the actual running big data of the unit, and a stable working condition interval can be accurately obtained;
(2) By sequencing and screening the data for multiple times, the parameters such as back pressure, extraction quantity and the like can be ensured to be in a stable interval, the problem that the original sliding pressure curve is not suitable for an actual unit is solved, and the result is more scientific and accurate;
(3) Compared with taking average in time sequence, the method can more effectively weaken system noise and measurement noise and also can eliminate unit disturbance by carrying out average operation on the data after sequencing;
(4) The method can design the sliding pressure optimization curve under the condition that a special test cannot be performed, and has scientific and reasonable result and simple operation.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the first step, the unit heat consumption rate under each working condition is obtained according to the historical data under each working condition, and the method is realized by adopting the following formula I:
reheat steam flow F hrh The specific mode realized by the following formula II is as follows:
F hrh ＝F ms -F 1 -F 2 (equation two),
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
F ms representing the main steam flow;
H ms representing the enthalpy of the main steam; h ms The value of which can be according to IAThe PWS-IF97 software utilizes the unit operation data P ms 、T ms Obtaining;
F fw representing the main feed water flow;
H fw representing the main feed enthalpy; h fw The value of (2) can utilize the unit operation data T according to IAPWS-IF97 software fw Obtaining;
F hrh representing reheat steam flow;
H hrh indicating reheat steam enthalpy; h hrh The value of (2) can utilize the unit operation data P according to IAPWS-IF97 software hrh 、T hrh Obtaining;
F crh representing reheat cold leg steam flow;
H crh representing reheat cold section steam enthalpy; h crh The value of (2) can utilize the unit operation data P according to IAPWS-IF97 software 2 、T 2 Obtaining;
F shsp indicating the flow of the overheated and desuperheated water;
H shsp indicating the enthalpy of the superheated desuperheated water; h shsp The value of (2) can utilize the unit operation data T according to IAPWS-IF97 software shsp Obtaining;
F rhsp indicating the reheat attemperation water flow;
H rhsp indicating the enthalpy of reheat desuperheating water; h rhsp The value of (2) can utilize the unit operation data T according to IAPWS-IF97 software rhsp Obtaining;
F 1 and F2 All represent intermediate variables;
h fo1 representing the enthalpy of the outlet water of a first high-pressure heater of the turbine unit; h is a fo1 Can be based on the first high-pressure heater outlet water temperature T in the unit operation data f o 1 Obtained by IAPWS-IF97 software;
h fi1 representing the first high pressure heater inlet enthalpy of the turbine train; h is a fi1 Can be based on the first high-pressure heater inlet water temperature T in the unit operation data fi1 Obtained by IAPWS-IF97 software;
h 1 representing the extraction of steam from the first high-pressure heater of a turbine setEnthalpy of; h is a 1 Can be based on the extraction temperature T of a section in the unit operation data 1 Obtained by IAPWS-IF97 software;
h d1 representing the normal drainage enthalpy of a first high-pressure heater of the turbine unit; h is a d1 Can be based on the normal water-drainage temperature T of the first high-pressure heater in the unit operation data d1 Obtained by IAPWS-IF97 software;
h fo2 representing the second high pressure heater outlet enthalpy of the turbine train; h is a fo2 Can be based on the second high-pressure heater outlet water temperature T in the unit operation data fo2 Obtained by looking up a table;
h fi2 representing the second high pressure heater inlet enthalpy of the turbine train; h is a fi2 Can be based on the second high-pressure heater inlet water temperature T in the unit operation data fi2 Obtained by IAPWS-IF97 software;
h 2 representing the extraction enthalpy of a second high-pressure heater of the turbine unit; h is a 2 Can be based on the two-stage steam extraction temperature T in the unit operation data 2 Obtained by IAPWS-IF97 software;
h d2 representing the normal drainage enthalpy of a second high-pressure heater of the turbine unit; h is a d2 Can be based on the normal water-drainage temperature T of the second high-pressure heater in the unit operation data d2 Obtained by IAPWS-IF97 software.
The preferred embodiment provides a method for obtaining the heat rate of the unit under each working condition, and the obtaining process is simple.
The preferred embodiments are: f (F) crh And F is equal to 2 Is equal to the value of T fi1 And T is fo2 Is equal in value.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the second step, according to N heat supply steam extraction flow rates in the historical data under N working conditions, the specific process of obtaining M heat supply steam extraction flow rate intervals is as follows:
and sorting N heat supply steam extraction flows under N working conditions according to ascending order, and removing excessive state points in the ascending order of the heat supply steam extraction flows, so as to obtain M heat supply steam extraction flow intervals.
In the preferred embodiment, each working condition corresponds to one heat supply steam extraction flow, the N heat supply steam extraction flows are sorted according to ascending order, excessive state points in ascending order of the heat supply steam extraction flows are removed, so that the heat supply steam extraction flows after the excessive state points are removed are divided into intervals, unstable heat supply steam extraction flows are removed, stable heat supply steam extraction flow intervals are obtained, and an accurate data basis is obtained for subsequent calculation.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the third step, according to the main steam flow of the unit under each working condition corresponding to each heating steam extraction flow interval, the specific process of obtaining the stable main steam flow interval in each heating steam extraction flow interval is as follows:
and (3) carrying out ascending sorting on the main steam flow of the unit under the corresponding working conditions in each heating steam extraction flow interval, and removing excessive state points in the ascending sorting of the main steam flow, so as to obtain a stable main steam flow interval in each heating steam extraction flow interval.
In the preferred embodiment, the main steam flow of the unit in the heat supply steam extraction flow interval is arranged in an ascending order, excessive state points in the ascending order of the main steam flow are removed, so that the main steam flow after the excessive state points are removed is divided into intervals, unstable main steam flow intervals are removed, intervals in which the heat supply steam extraction flow and the main steam flow are stable are obtained, and an accurate data basis is obtained for subsequent calculation.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the fourth step, according to the main steam pressure in each working condition corresponding to each main steam flow interval, the specific process of obtaining the stable main steam pressure interval in each main steam flow interval is as follows:
and carrying out ascending order sequencing on the main steam pressure under the corresponding working conditions in each main steam flow interval, and removing excessive state points in the ascending order sequencing of the main steam pressure, so as to obtain stable main steam pressure intervals in each main steam flow interval.
In the preferred embodiment, the main steam pressure in each main steam flow interval is sorted in ascending order, and excessive state points in the main steam pressure ascending order are removed, so that the main steam pressure after the excessive state points are removed is divided into intervals, unstable main steam pressures are removed, stable main steam pressure intervals are obtained, and an accurate data basis is obtained for subsequent calculation.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the fifth step, according to the unit back pressure and the main steam temperature under the corresponding working conditions in each main steam pressure interval, the specific process of obtaining the main steam pressure interval with stable unit back pressure and main steam temperature is as follows:
and carrying out ascending sort on the unit back pressure under each working condition corresponding to each main steam pressure interval, removing excessive state points in the ascending sort of the unit back pressure, so as to obtain stable back pressure intervals in each main steam pressure interval, carrying out ascending sort on the main steam temperature under each working condition corresponding to each back pressure interval, and removing excessive state points in the ascending sort of the main steam temperature, so as to obtain the main steam pressure interval with stable unit back pressure and main steam temperature.
In the preferred embodiment, the unit back pressure in the stable main steam pressure interval is sequenced in an ascending order, excessive state points are removed, a stable back pressure interval is obtained, the main steam temperature in the stable back pressure interval is sequenced in an ascending order, excessive state points in the main steam temperature sequencing in the ascending order are removed, and therefore the main steam pressure interval with stable unit back pressure and stable main steam temperature is obtained.
The present preferred embodiment will be described with reference to fig. 1 to 5, in which: in the sixth step, according to the main steam pressure in the main steam pressure interval and the corresponding heat rate of the unit, the specific process of obtaining the average main steam pressure and the average heat rate of the unit in the main steam pressure interval, in which the back pressure and the main steam temperature of each unit are stable, is as follows:
and carrying out average treatment on the main steam pressure in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable and the corresponding unit heat rate to obtain the average main steam pressure and the average unit heat rate in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable.
In the preferred embodiment, the process of obtaining the average main steam pressure and the average unit heat rate in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable is simple, the process of the method is simplified, and an accurate optimization curve is obtained by utilizing the big data of the actual operation of the back pressure of the unit.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways other than as described in the original claims. It is also to be understood that features described in connection with separate embodiments may be used in other described embodiments.
Claims (9)
1. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the big data of the unit operation is characterized by comprising the following steps:
step one, collecting historical data and unit design parameters of an operation unit under N working conditions, and obtaining a unit heat consumption rate under each working condition according to the historical data under each working condition, wherein N is a positive integer greater than 10; the historical data comprises main steam pressure, main steam temperature, main steam flow, reheat steam pressure, reheat steam temperature, main water supply flow, main water supply temperature, unit back pressure, unit heat supply steam extraction flow, steam extraction pressure of a first high-pressure heater, one-stage steam extraction temperature, inlet water temperature of the first high-pressure heater, normal drainage temperature of the first high-pressure heater, outlet water temperature of the first high-pressure heater, steam extraction pressure of a second high-pressure heater, two-stage steam extraction temperature, inlet water temperature of the second high-pressure heater, normal drainage temperature of the second high-pressure heater, outlet water temperature of the second high-pressure heater, superheated desuperheating water temperature, superheated desuperheating water flow, reheat desuperheating water temperature, reheat desuperheating water flow and unit load;
step two, obtaining M heat supply steam extraction flow intervals according to N heat supply steam extraction flows in the historical data under N working conditions; m is a positive integer less than N;
step three, obtaining a stable main steam flow interval in each heat supply steam extraction flow interval according to the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval;
step four, obtaining a stable main steam pressure interval in each main steam flow interval according to the main steam pressure in each corresponding working condition in each main steam flow interval;
fifthly, obtaining main steam pressure intervals with stable back pressure and main steam temperature of all the units according to the back pressure and the main steam temperature of the units under the corresponding working conditions in each main steam pressure interval;
step six, obtaining the average main steam pressure and the average unit heat rate in the main steam pressure interval with stable back pressure and main steam temperature of each unit according to the main steam pressure in the main steam pressure interval with stable back pressure and main steam temperature of each unit and the corresponding unit heat rate;
drawing a main steam pressure-unit heat rate relation diagram in each main steam flow interval according to all the average main steam pressures and the average unit heat rates in each main steam flow interval, and obtaining the main steam pressure P corresponding to the lowest unit heat rate from the relation diagram min Main steam pressure P corresponding to the lowest heat rate of the unit min An optimal main steam pressure P' within the main steam flow interval in which it is located;
step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by utilizing a least square method according to the optimal main steam pressure P 'in all main steam flow intervals under each heat supply steam extraction flow, and obtaining a sliding pressure curve L' under each heat supply steam extraction flow by utilizing the minimum stable combustion pressure of the unit and the rated pressure of the unit in the unit design parameters.
2. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the big data of the unit operation according to claim 1, wherein in the second step, the specific process of obtaining the M heat supply and steam extraction flow intervals according to the N heat supply and steam extraction flows in the historical data under the N working conditions is as follows:
and sorting N heat supply steam extraction flows under N working conditions according to ascending order, and removing excessive state points in the ascending order of the heat supply steam extraction flows, so as to obtain M heat supply steam extraction flow intervals.
3. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the unit operation big data according to claim 1, wherein in the third step, the specific process of obtaining the stable main steam flow interval in each heat supply steam extraction flow interval according to the unit main steam flow in each working condition corresponding to each heat supply steam extraction flow interval is as follows:
and (3) carrying out ascending sorting on the main steam flow of the unit under the corresponding working conditions in each heating steam extraction flow interval, and removing excessive state points in the ascending sorting of the main steam flow, so as to obtain a stable main steam flow interval in each heating steam extraction flow interval.
4. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the unit operation big data according to claim 1, wherein the specific process of obtaining the stable main steam pressure interval in each main steam flow interval according to the main steam pressure in each working condition corresponding to each main steam flow interval in the fourth step is as follows:
and carrying out ascending order sequencing on the main steam pressure under the corresponding working conditions in each main steam flow interval, and removing excessive state points in the ascending order sequencing of the main steam pressure, so as to obtain stable main steam pressure intervals in each main steam flow interval.
5. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the unit operation big data according to claim 1, wherein in the fifth step, the specific process of obtaining the main steam pressure interval with stable unit back pressure and main steam temperature according to the unit back pressure and main steam temperature under the corresponding working conditions in each main steam pressure interval is as follows:
and carrying out ascending sort on the unit back pressure under each working condition corresponding to each main steam pressure interval, removing excessive state points in the ascending sort of the unit back pressure, so as to obtain stable back pressure intervals in each main steam pressure interval, carrying out ascending sort on the main steam temperature under each working condition corresponding to each back pressure interval, and removing excessive state points in the ascending sort of the main steam temperature, so as to obtain the main steam pressure interval with stable unit back pressure and main steam temperature.
6. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the unit operation big data according to claim 1, wherein in the step six, according to the main steam pressure in the main steam pressure interval in which each unit back pressure and main steam temperature are stable and the corresponding unit heat rate, the specific process of obtaining the average main steam pressure and average unit heat rate in the main steam pressure interval in which each unit back pressure and main steam temperature are stable is as follows:
and carrying out average treatment on the main steam pressure in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable and the corresponding unit heat rate to obtain the average main steam pressure and the average unit heat rate in the main steam pressure interval in which the back pressure and the main steam temperature of each unit are stable.
7. The turbine sliding pressure curve optimization method based on the multi-dimensional sequencing of the big data of the unit operation according to claim 1, wherein in the first step, the unit heat rate under each working condition is obtained according to the historical data under each working condition, and the method is specifically realized by adopting the following formula I:
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
F ms representing the main steam flow; h ms Representing the enthalpy of the main steam;
F fw representing the main feed water flow; h fw Representing the main feed enthalpy;
F hrh representing reheat steam flow; h hrh Indicating reheat steam enthalpy;
F crh representing reheat cold leg steam flow; h crh Representing reheat cold section steam enthalpy;
F shsp indicating the flow of the overheated and desuperheated water; h shsp Indicating the enthalpy of the superheated desuperheated water;
F rhsp indicating the reheat attemperation water flow; h rhsp Indicating the reheat desuperheating enthalpy.
8. The turbine sliding pressure curve optimization method based on multi-dimensional sequencing of unit operation big data according to claim 7, wherein reheat steam flow F hrh The specific mode realized by the following formula II is as follows:
F hrh ＝F ms -F 1 -F 2 in the second formula, the first formula is,
wherein ,
/>
F 1 and F2 All represent intermediate variables;
h fo1 representing the enthalpy of the outlet water of a first high-pressure heater of the turbine unit;
h fi1 representing the first high pressure heater inlet enthalpy of the turbine train;
h 1 representing the extraction enthalpy of a first high-pressure heater of the turbine unit;
h d1 representing the normal drainage enthalpy of a first high-pressure heater of the turbine unit;
h fo2 representing the second high pressure heater outlet enthalpy of the turbine train;
h fi2 representing the second high pressure heater inlet enthalpy of the turbine train;
h 2 representing the extraction enthalpy of a second high-pressure heater of the turbine unit;
h d2 indicating the normal hydrophobic enthalpy of the second high pressure heater of the turbine group.
9. The turbine sliding pressure curve optimization method based on multi-dimensional sequencing of big data of unit operation according to claim 8, wherein F crh And F is equal to 2 Is equal in value.
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