CN109299582A - Steam turbine sliding pressure optimization of profile method based on unit operation big data multidimensional ordering - Google Patents
Steam turbine sliding pressure optimization of profile method based on unit operation big data multidimensional ordering Download PDFInfo
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Abstract
Based on the steam turbine sliding pressure optimization of profile method of unit operation big data multidimensional ordering, it is related to steam turbine of thermal power plant control field.Solves the optimization problem that sliding pressure curve how is carried out using the big data of unit in actual operation.The present invention screens data using the method that the historical data under N number of operating condition of unit is repeatedly sorted and is averaged, and optimization point can be obtained by comparison, final to obtain sliding pressure curve.The present invention mainly optimizes steam turbine sliding pressure curve.
Description
Technical Field
The invention relates to the field of steam turbine control of a thermal power plant.
Background
Currently, the sliding pressure optimization is the most effective energy-saving mode adopted by the existing typical coal-fired unit, so that a great deal of theoretical research and experimental exploration work is carried out by a plurality of researchers. The scholars provide a method for establishing an optimization model, a series of functions are established by using the modern computer technology to construct a system model, and the system model is simplified and solved to determine an optimal pressure point; and operating debugging personnel to carry out a proprietary optimization test, carrying out pressure optimization on a specific load point based on test data heat consumption correction calculation, and further adjusting control logic parameters in the DCS. However, most of the above studies have given a sliding pressure optimization curve using the unit load as an independent variable when determining the optimal sliding pressure point. However, in the variable load operation process of the actual steam turbine, an extraction working condition and a high back pressure working condition often exist, which will cause the corresponding loads of the unit under the same main steam flow to be different. Therefore, the unit operation control pressure point obtained by inquiring the sliding pressure curve with the load as the independent variable will deviate from the actual optimal pressure of the steam turbine, and the thermal economy of the unit is greatly influenced.
Therefore, a sliding pressure optimization strategy taking the main steam flow as an independent variable is proposed to reduce the influence of backpressure 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 for various reasons; under the condition, how to utilize big data of unit operation to carry out optimum design adjustment of the sliding pressure curve is not elaborated in detail by relevant authoritative published documents. Meanwhile, most of the units are in the process of deep and rapid variable load operation, so that large data in actual operation contains a large amount of thermal inertia noise information; if the data is directly utilized without effective processing, an accurate calculation result cannot be obtained, and therefore, how to optimize the sliding pressure curve by utilizing the big data of the unit in actual operation needs to be solved urgently.
Disclosure of Invention
The invention provides a turbine sliding pressure curve optimization method based on large data multidimensional sequencing of unit operation, aiming at solving the problem of optimizing a sliding pressure curve by using the large data of a unit in actual operation.
The turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation comprises the following steps:
step one, acquiring historical data and unit design parameters of an operating unit under N working conditions, and obtaining the heat consumption rate of the unit 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 under each working condition corresponding to each main steam flow interval;
step five, obtaining main steam pressure intervals with stable unit back pressure and main steam temperature according to the unit back pressure and the main steam temperature under each working condition corresponding to each main steam pressure interval;
step six, obtaining average main steam pressure and average unit heat consumption rate in a main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit according to the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit;
step seven, drawing a main steam pressure-unit heat consumption rate relation graph in each main steam flow interval according to all average main steam pressures and average unit heat consumption rates in each main steam flow interval, and obtaining the corresponding main steam pressure P when the unit heat consumption rate is the lowest from the relation graphminMain steam pressure P corresponding to the lowest heat rate of the unitminThe optimal main steam pressure P' in the main steam flow interval is obtained;
and step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by using 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 using the minimum stable combustion pressure of the unit and the rated pressure of the unit in unit design parameters.
Preferably, in the second step, the specific process of obtaining M heat supply extraction flow intervals according to N heat supply extraction flow rates in the historical data under N working conditions is as follows:
and sequencing the N heat supply steam extraction flows under the N working conditions in an ascending order, and removing transition state points in the ascending sequencing 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 performing ascending sequencing on the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval, removing transition state points in the ascending sequencing of the main steam flow, and further obtaining a stable main steam flow interval in each heat supply steam extraction flow interval.
Preferably, the specific process of obtaining the stable main steam pressure interval in each main steam flow interval according to the main steam pressure under each working condition corresponding to each main steam flow interval in the fourth step is as follows:
and performing ascending sequencing on the main steam pressure under each working condition corresponding to each main steam flow interval, and removing transition state points in the ascending sequencing of the main steam pressure to obtain a stable main steam pressure interval in each main steam flow interval.
Preferably, 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 the main steam temperature under each working condition corresponding to each main steam pressure interval is as follows:
and performing ascending sequencing on the unit back pressures under the working conditions corresponding to each main steam pressure interval, removing transition state points in the ascending sequencing of the unit back pressures, so as to obtain a stable back pressure interval in each main steam pressure interval, performing ascending sequencing on the main steam temperatures under the working conditions corresponding to each back pressure interval, and removing the transition state points in the ascending sequencing of the main steam temperatures, so as to obtain a main steam pressure interval with stable unit back pressure and main steam temperatures.
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 with stable back pressure and stable main steam temperature of each unit according to the main steam pressure and the corresponding unit heat rate in the main steam pressure interval with stable back pressure and stable main steam temperature of each unit is as follows:
and averaging the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit to obtain the average main steam pressure and the average unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit.
Preferably, in the step one, the heat rate of the unit under each working condition is obtained according to the historical data under each working condition, and the heat rate is specifically realized by adopting the following formula one:
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
Fmsrepresents the main steam flow; hmsRepresents the main steam enthalpy;
Ffwrepresenting a main feedwater flow; hfwRepresents the main feedwater enthalpy;
Fhrhrepresents reheat steam flow; hhrhRepresents reheat steam enthalpy;
Fcrhrepresenting the reheat cooling section steam flow; hcrhRepresents the reheat cold section steam enthalpy;
Fshsprepresenting the superheat attemperation water flow; hshspRepresents the enthalpy of the superheated desuperheated water;
Frhsprepresenting reheat desuperheating water flow; hrhspIndicating the reheat desuperheated water enthalpy.
Preferably, the reheat steam flow rate FhrhThe specific implementation mode realized by the following formula two is as follows:
Fhrh＝Fms-F1-F2(formula two) is shown in the figure,
wherein ,
F1 and F2All represent intermediate variables;
hfo1representing the first high pressure heater outlet water enthalpy of the steam turbine set;
hfi1representing the first high pressure heater inlet water enthalpy of the steam turbine set;
h1representing the extraction enthalpy of a first high-pressure heater of the turboset;
hd1indicating the normal drainage enthalpy of a first high-pressure heater of the steam turbine set;
hfo2representing the second high pressure heater outlet water enthalpy of the steam turbine set;
hfi2representing the second high pressure heater inlet water enthalpy of the steam turbine set;
h2representing the extraction enthalpy of a second high-pressure heater of the steam turbine set;
hd2indicating the normal hydrophobic enthalpy of the second high pressure heater of the steam turbine set.
Preferably, FcrhAnd F2Are equal in value.
The invention has the beneficial effects that the turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation can screen data according to a method of sequencing for many times and averaging, and an optimization point can be obtained by comparison, so that the method has strong practical application value:
(1) the dynamic effect of the unit can be effectively eliminated by sequencing the big data of the actual operation of the unit, and the stable working condition interval can be accurately obtained;
(2) by sequencing and screening data for multiple times, parameters such as back pressure, air extraction amount and the like can be ensured to be in a stable interval, the problem that an original sliding pressure curve is not suitable for an actual unit is solved, and the result is more scientific and accurate;
(3) compared with the averaging on the time sequence, the averaging operation is carried out on the sequenced data, so that the system noise and the measurement noise can be more effectively weakened, and the unit disturbance can be eliminated;
(4) the method can design the sliding pressure optimization curve under the condition that special tests cannot be carried out, and has scientific and reasonable result and simple and convenient operation.
Drawings
Fig. 1 is a flowchart of a turbine sliding pressure curve optimization method based on large data multidimensional sequencing of unit operation according to the embodiment;
FIG. 2 is a diagram of the results of an ascending sequencing operation on the main steam flow of the unit; wherein, the abscissa represents the serial number and the ordinate represents the flow rate;
FIG. 3 is a distribution diagram of the points of FIG. 2 except for the transition state;
FIG. 4 is a graph of the main steam flow interval formed in FIG. 3 with the transition state points removed;
fig. 5 is a sliding pressure curve L' at each heating steam extraction flow rate obtained in step eight of the embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
Referring to fig. 1 to fig. 5, the method for optimizing a turbine sliding pressure curve based on large data multidimensional sequencing of unit operation according to the embodiment includes the following steps:
step one, acquiring historical data and unit design parameters of an operating unit under N working conditions, and obtaining the heat consumption rate of the unit 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 under each working condition corresponding to each main steam flow interval;
step five, obtaining main steam pressure intervals with stable unit back pressure and main steam temperature according to the unit back pressure and the main steam temperature under each working condition corresponding to each main steam pressure interval;
step six, obtaining average main steam pressure and average unit heat consumption rate in a main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit according to the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit;
step seven, drawing a main steam pressure-unit heat consumption rate relation graph in each main steam flow interval according to all average main steam pressures and average unit heat consumption rates in each main steam flow interval, and obtaining the corresponding main steam pressure P when the unit heat consumption rate is the lowest from the relation graphminMain steam pressure P corresponding to the lowest heat rate of the unitminThe optimal main steam pressure P' in the main steam flow interval is obtained;
and step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by using 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 using the minimum stable combustion pressure of the unit and the rated pressure of the unit in unit design parameters.
In the present embodiment, the historical data under each operating condition includes the main steam pressure Pms(Mpa), main steam temperature Tms(° c), main steam flow Fms(t/h), reheat steam pressure Fhrh(Mpa), reheat steam temperature Thrh(DEG C), main feed water flow Ffw(T/h) principal feedwater temperature Tfw(° c), unit back pressure Pb(kpa) unit heat supply steam extraction flow Fg(t/h) extraction pressure P of the first high-pressure heater1(Mpa); first stage extraction temperature T1(DEG C), the inlet water temperature T of the first high-pressure heaterfi1(° c), normal drain temperature T of the first high pressure heaterd1(DEG C), the outlet water temperature T of the first high-pressure heaterfo1(DEG C), the extraction pressure P of the second high-pressure heater2(Mpa) two-stage steam extraction temperature T2(DEG C), inlet water temperature T of the second high-pressure heaterfi2(° c), normal drainage temperature T of the second high pressure heaterd2(DEG C), outlet water temperature T of the second high-pressure heaterfo2Overheat and desuperheat water temperatureDegree Tshsp(DEG C), overheat desuperheating water flow Fshsp(T/h) reheat desuperheating Water temperature Trhsp(° c) and reheat attemperation water flow Frhsp(t/h), unit load P (MW).
Because the steam turbine set contains 3 high pressure heaters, the application uses any two high pressure heaters, and the pressure and the temperature in the historical data are mainly used for solving the enthalpy value.
According to the turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation, provided by the embodiment, data can be screened according to a method of sequencing for many times and averaging, optimization points can be obtained through comparison, and finally a 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 big data of the actual operation of the unit, and the stable working condition interval can be accurately obtained;
(2) by sequencing and screening data for multiple times, parameters such as back pressure, air extraction amount and the like can be ensured to be in a stable interval, the problem that an original sliding pressure curve is not suitable for an actual unit is solved, and the result is more scientific and accurate;
(3) compared with the averaging on the time sequence, the averaging operation is carried out on the sequenced data, so that the system noise and the measurement noise can be more effectively weakened, and the unit disturbance can be eliminated;
(4) the method can design the sliding pressure optimization curve under the condition that special tests cannot be carried out, and has scientific and reasonable result and simple and convenient operation.
The preferred embodiment is described with reference to fig. 1 to 5, and is: in the first step, the heat consumption rate of the unit under each working condition is obtained according to the historical data under each working condition, and the first step is realized by adopting the following formula:
reheat steam flow FhrhThe specific implementation mode realized by the following formula two is as follows:
Fhrh＝Fms-F1-F2(formula two) is shown in the figure,
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
Fmsrepresents the main steam flow;
Hmsrepresents the main steam enthalpy; hmsThe value of (A) can utilize the unit operation data P according to IAPWS-IF97 softwarems、TmsObtaining;
Ffwrepresenting a main feedwater flow;
Hfwrepresents the main feedwater enthalpy; hfwThe value of (A) can utilize the unit operation data T according to IAPWS-IF97 softwarefwObtaining;
Fhrhrepresents reheat steam flow;
Hhrhrepresents reheat steam enthalpy; hhrhThe value of (A) can utilize the unit operation data P according to IAPWS-IF97 softwarehrh、ThrhObtaining;
Fcrhrepresenting the reheat cooling section steam flow;
Hcrhrepresents the reheat cold section steam enthalpy; hcrhThe value of (A) can utilize the unit operation data P according to IAPWS-IF97 software2、T2Obtaining;
Fshsprepresenting the superheat attemperation water flow;
Hshsprepresents the enthalpy of the superheated desuperheated water; hshspCan be utilized according to IAPWS-IF97 softwareGroup operation data TshspObtaining;
Frhsprepresenting reheat desuperheating water flow;
Hrhsprepresents the reheat desuperheating water enthalpy; hrhspThe value of (A) can utilize the unit operation data T according to IAPWS-IF97 softwarerhspObtaining;
F1 and F2All represent intermediate variables;
hfo1representing the first high pressure heater outlet water enthalpy of the steam turbine set; h isfo1According to the outlet water temperature T of the first high-pressure heater in the unit operation datafo1Obtained through IAPWS-IF97 software;
hfi1representing the first high pressure heater inlet water enthalpy of the steam turbine set; h isfi1According to the inlet water temperature T of the first high-pressure heater in the unit operation datafi1Obtained through IAPWS-IF97 software;
h1representing the extraction enthalpy of a first high-pressure heater of the turboset; h is1Can be based on the one-section steam extraction temperature T in the unit operation data1Obtained through IAPWS-IF97 software;
hd1indicating the normal drainage enthalpy of a first high-pressure heater of the steam turbine set; h isd1The normal drainage temperature T of the first high-pressure heater in the unit operation data can be determinedd1Obtained through IAPWS-IF97 software;
hfo2representing the second high pressure heater outlet water enthalpy of the steam turbine set; h isfo2According to the outlet water temperature T of the second high-pressure heater in the unit operation datafo2Obtaining through table look-up;
hfi2representing the second high pressure heater inlet water enthalpy of the steam turbine set; h isfi2According to the inlet water temperature T of the second high-pressure heater in the unit operation datafi2Obtained through IAPWS-IF97 software;
h2representing the extraction enthalpy of a second high-pressure heater of the steam turbine set; h is2Can be based on the two-stage extraction temperature T in the unit operation data2Obtained through IAPWS-IF97 software;
hd2indicating the normal drainage enthalpy of a second high-pressure heater of the steam turbine set; h isd2The normal drainage temperature T of the second high-pressure heater in the unit operation data can be determinedd2Obtained by IAPWS-IF97 software.
The preferred embodiment provides a method for obtaining the heat consumption rate of the unit under each working condition, and the obtaining process is simple.
The preferred embodiment is: fcrhAnd F2Are equal in value of (A), Tfi1And Tfo2Are equal in value.
The preferred embodiment is described with reference to fig. 1 to 5, and is: in the second step, the specific process of obtaining M heat supply steam extraction flow intervals according to N heat supply steam extraction flows in the historical data under N working conditions is as follows:
and sequencing the N heat supply steam extraction flows under the N working conditions in an ascending order, and removing transition state points in the ascending sequencing 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 a heat supply steam extraction flow, the N heat supply steam extraction flows are sorted in an ascending order, and transition state points in the ascending order of the heat supply steam extraction flows are removed, so that the heat supply steam extraction flows without the transition state points are divided into sections, some non-stable heat supply steam extraction flows are removed, a stable heat supply steam extraction flow section is obtained, and an accurate data base is obtained for subsequent calculation.
The preferred embodiment is described with reference to fig. 1 to 5, and is: 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 performing ascending sequencing on the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval, removing transition state points in the ascending sequencing of the main steam flow, and further obtaining a stable main steam flow interval in each heat supply 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, and the transition state point in the ascending order of the main steam flow is removed, so that the main steam flow after the transition state point is removed is divided into intervals, some non-stable main steam flow intervals are removed, the interval in which the heat supply steam extraction flow and the main steam flow are stable is obtained, and an accurate data base is obtained for subsequent calculation.
The preferred embodiment is described with reference to fig. 1 to 5, and is: in the fourth step, the specific process of obtaining the stable main steam pressure interval in each main steam flow interval according to the main steam pressure under each working condition corresponding to each main steam flow interval comprises the following steps:
and performing ascending sequencing on the main steam pressure under each working condition corresponding to each main steam flow interval, and removing transition state points in the ascending sequencing of the main steam pressure to obtain a stable main steam pressure interval in each main steam flow interval.
In the preferred embodiment, the main steam pressures in each main steam flow interval are sorted in an ascending order, and transition state points in the ascending sorting of the main steam pressures are removed, so that the main steam pressures with the transition state points removed are divided into intervals, some unstable main steam pressures are removed, stable main steam pressure intervals are obtained, and an accurate data basis is obtained for subsequent calculation.
The preferred embodiment is described with reference to fig. 1 to 5, and is: step five, obtaining the main steam pressure interval with stable unit back pressure and main steam temperature according to the unit back pressure and the main steam temperature under each working condition corresponding to each main steam pressure interval:
and performing ascending sequencing on the unit back pressures under the working conditions corresponding to each main steam pressure interval, removing transition state points in the ascending sequencing of the unit back pressures, so as to obtain a stable back pressure interval in each main steam pressure interval, performing ascending sequencing on the main steam temperatures under the working conditions corresponding to each back pressure interval, and removing the transition state points in the ascending sequencing of the main steam temperatures, so as to obtain a main steam pressure interval with stable unit back pressure and main steam temperatures.
In the preferred embodiment, the backpressure of the unit in the stable main steam pressure interval is sorted in an ascending order, the transition state points are removed, the stable backpressure interval is obtained, the temperature of the main steam in the stable backpressure interval is sorted in an ascending order, the transition state points in the ascending order of the temperature of the main steam are removed, the main steam pressure interval with stable backpressure and main steam temperature of the unit is obtained, and in the whole process, some unstable transition state points are gradually removed, so that an accurate data base is obtained for the subsequent data optimization.
The preferred embodiment is described with reference to fig. 1 to 5, and is: 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 with stable backpressure and main steam temperature of each unit according to the main steam pressure and the corresponding unit heat rate in the main steam pressure interval with stable backpressure and main steam temperature of each unit is as follows:
and averaging the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit to obtain the average main steam pressure and the average unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit.
In the preferred embodiment, the process of obtaining the average main steam pressure and the average unit heat consumption rate in the main steam pressure interval with stable main steam pressure and main steam temperature of each unit is simple, the method flow is simplified, and an accurate optimization curve is obtained by utilizing big data of the actual operation of the unit back pressure.
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 features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (9)
1. The turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation is characterized by comprising the following steps:
step one, acquiring historical data and unit design parameters of an operating unit under N working conditions, and obtaining the heat consumption rate of the unit 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 under each working condition corresponding to each main steam flow interval;
step five, obtaining main steam pressure intervals with stable unit back pressure and main steam temperature according to the unit back pressure and the main steam temperature under each working condition corresponding to each main steam pressure interval;
step six, obtaining average main steam pressure and average unit heat consumption rate in a main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit according to the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit;
step seven, drawing a main steam pressure-unit heat consumption rate relation graph in each main steam flow interval according to all average main steam pressures and average unit heat consumption rates in each main steam flow interval, and obtaining the corresponding main steam pressure P when the unit heat consumption rate is the lowest from the relation graphminMain steam pressure P corresponding to the lowest heat rate of the unitminThe optimal main steam pressure P' in the main steam flow interval is obtained;
and step eight, obtaining a sliding pressure optimization straight line L under each heat supply steam extraction flow by using 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 using the minimum stable combustion pressure of the unit and the rated pressure of the unit in unit design parameters.
2. The turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation according to claim 1, wherein in the second step, the specific process of obtaining M heat supply steam extraction flow intervals according to N heat supply steam extraction flows in the historical data under N working conditions comprises:
and sequencing the N heat supply steam extraction flows under the N working conditions in an ascending order, and removing transition state points in the ascending sequencing 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 large data multidimensional sequencing of unit operation 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 main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval is as follows:
and performing ascending sequencing on the main steam flow of the unit under each working condition corresponding to each heat supply steam extraction flow interval, removing transition state points in the ascending sequencing of the main steam flow, and further obtaining a stable main steam flow interval in each heat supply steam extraction flow interval.
4. The turbine sliding pressure curve optimization method based on the unit operation big data multidimensional sequencing is characterized in that the concrete process of obtaining the stable main steam pressure interval in each main steam flow interval according to the main steam pressure under each working condition corresponding to each main steam flow interval in the fourth step is as follows:
and performing ascending sequencing on the main steam pressure under each working condition corresponding to each main steam flow interval, and removing transition state points in the ascending sequencing of the main steam pressure to obtain a stable main steam pressure interval in each main steam flow interval.
5. The turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation according to claim 1, wherein in the fifth step, according to the unit back pressure and the main steam temperature under each working condition corresponding to each main steam pressure interval, a specific process for obtaining the main steam pressure interval with stable unit back pressure and main steam temperature is as follows:
and performing ascending sequencing on the unit back pressures under the working conditions corresponding to each main steam pressure interval, removing transition state points in the ascending sequencing of the unit back pressures, so as to obtain a stable back pressure interval in each main steam pressure interval, performing ascending sequencing on the main steam temperatures under the working conditions corresponding to each back pressure interval, and removing the transition state points in the ascending sequencing of the main steam temperatures, so as to obtain a main steam pressure interval with stable unit back pressure and main steam temperatures.
6. The turbine sliding pressure curve optimization method based on the unit operation big data multidimensional sequencing is characterized in that in the sixth step, according to the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and main steam temperature of each unit, the specific process of obtaining the average main steam pressure and the average unit heat consumption rate in the main steam pressure interval with stable main steam pressure and main steam temperature of each unit is as follows:
and averaging the main steam pressure and the corresponding unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit to obtain the average main steam pressure and the average unit heat consumption rate in the main steam pressure interval with stable main steam pressure and stable main steam temperature of each unit.
7. The turbine sliding pressure curve optimization method based on the large data multidimensional sequencing of unit operation according to claim 1, characterized in that in the step one, the heat rate of the unit under each working condition is obtained according to historical data under each working condition, and the method is realized by adopting the following formula one:
wherein ,
HR represents the heat rate of the unit; p represents the unit load;
Fmsrepresents the main steam flow; hmsRepresents the main steam enthalpy;
Ffwrepresenting a main feedwater flow; hfwRepresents the main feedwater enthalpy;
Fhrhrepresents reheat steam flow; hhrhRepresents reheat steam enthalpy;
Fcrhrepresenting the reheat cooling section steam flow; hcrhRepresents the reheat cold section steam enthalpy;
Fshsprepresenting the superheat attemperation water flow; hshspRepresents the enthalpy of the superheated desuperheated water;
Frhsprepresenting reheat desuperheating water flow; hrhspIndicating the reheat desuperheated water enthalpy.
8. The turbine sliding pressure curve optimization method based on unit operation big data multidimensional sequencing as claimed in claim 7, wherein the reheat steam flow FhrhThe specific implementation mode realized by the following formula two is as follows:
Fhrh＝Fms-F1-F2(formula two) is shown in the figure,
wherein ,
F1 and F2All represent intermediate variables;
hfo1representing the first high pressure heater outlet water enthalpy of the steam turbine set;
hfi1representing the first high pressure heater inlet water enthalpy of the steam turbine set;
h1representing the extraction enthalpy of a first high-pressure heater of the turboset;
hd1indicating the normal drainage enthalpy of a first high-pressure heater of the steam turbine set;
hfo2representing the second high pressure heater outlet water enthalpy of the steam turbine set;
hfi2representing the second high pressure heater inlet water enthalpy of the steam turbine set;
h2representing the extraction enthalpy of a second high-pressure heater of the steam turbine set;
hd2indicating steamThe second high pressure heater of the turbine set is normally hydrophobic in enthalpy.
9. The turbine sliding pressure curve optimization method based on unit operation big data multidimensional sequencing according to claim 8, wherein FcrhAnd F2Are equal in value.
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| CN112444396B (en) * | 2020-11-11 | 2022-08-23 | 西安热工研究院有限公司 | Turbine sliding pressure optimization method combining performance test and comprehensive variable working condition calculation |
| CN112855289A (en) * | 2021-01-13 | 2021-05-28 | 中山嘉明电力有限公司 | Automatic control method for steam turbine bypass |
| CN114004146A (en) * | 2021-10-28 | 2022-02-01 | 北方联合电力有限责任公司乌拉持发电厂 | Method for optimizing and determining flexible operation sliding pressure curve of heat supply unit based on all working conditions |
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