CN113095591B - Consumption difference analysis method for self-optimization of operation parameters of thermal power generating unit - Google Patents
Consumption difference analysis method for self-optimization of operation parameters of thermal power generating unit Download PDFInfo
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Abstract
The invention relates to a consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit, which has the technical scheme that the consumption difference of energy consumption indexes and main parameters of the unit can be calculated on line in real time, real-time display and prompt are carried out on an operator station through DCS, the consumption difference state of each parameter is intelligently judged, if the consumption difference state is abnormal, professional and accurate reasons and guidance suggestions are pushed to the operator in real time, and the operation operator is guided to carry out the operation of eliminating the consumption difference, so that the energy consumption of the unit is reduced, and the safe and stable operation of the unit is ensured; the method has the advantages that the dynamic benchmark value library is established, automatic optimization of the benchmark values of the main operation parameters of the unit is realized, the optimal parameter values are automatically found and are put into the dynamic benchmark library to be provided for consumption difference calculation in real time, the optimal benchmark values of the operation parameters are guaranteed constantly, the method is convenient to use and good in effect, is an innovation in a consumption difference analysis method of the thermal power plant, and has good social and economic benefits.
Description
Technical Field
The invention relates to a consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit, and belongs to the field of optimization of operating parameters of thermal power generating units.
Background
When the thermal power generating unit operates, the thermal economy of the thermal power generating unit needs to be known in time, and factors influencing the normal operation of the thermal power generating unit are analyzed and adjusted, so that the thermal power generating unit is in a high-level low-energy-consumption operation state as much as possible. When the unit is operated at a lower level, a reference is needed for adjusting the operation parameters of the unit. The consideration that the single slave operation parameter deviates from the optimal value can play a certain role, but the method is not perfect enough, and when two or more operation parameters deviate from the normal operation value at the same time, the adjustment of which parameter is taken as the main point, and the consideration of the single slave operation parameter is not solved. The consumption difference analysis can reflect the influence of the deviation of the operation parameters from the optimized target value on the unit economy on the change of the coal consumption, and when the unit heat economy is reduced, the influence of the deviation of each parameter from the target value on the coal consumption can be obtained through the consumption difference analysis, so that the operation parameters with large influence on the coal consumption are adjusted in a targeted manner, the reasons and the parts which cause the reduction of the unit heat economy are determined, the unit operation is optimized, and the energy-saving potential is developed.
Through research on calculation and analysis software of the current steam turbine energy consumption indexes in the industry, the following defects generally exist in the current consumption difference calculation and analysis software used by each thermal power plant:
(1) The working environment of the steam turbine, the boiler and the auxiliary thermodynamic system is severe, and the accuracy of a field measuring point is low due to the influence of the precision of the transmitter and the thermocouple, so that the accuracy of the calculated energy consumption index is poor, the deviation from an actual value is more, and the reference significance is not large.
(2) The traditional consumption difference calculation and analysis system is basically built in an SIS system at present, and due to the limitation of structure and function positioning, only one-way data transmission can be carried out between the SIS system and a DCS of a power plant, so that the calculation and diagnosis optimization results of the performance parameters of the unit at present cannot be transmitted to the DCS in real time, and the consumption difference calculation and analysis system has no guiding significance to operating personnel.
(3) The current traditional consumption difference analysis system only provides a consumption difference value for a guide object, and the problems that how the consumption difference is influenced by parameters related to the consumption difference value, where the related data originates from and the like cannot be solved well, the visual guidance is poor, and corresponding reasons can be analyzed by a special worker or an operator by self analysis, calculation or by means of on-site measuring point comparison, adjustment test and the like.
(4) And (4) obtaining the energy efficiency evaluation of the steam turbine without opening the operation reference state, and obtaining the operation state of the current body only by comparing the actual value with the benchmark value. However, the benchmarking value acquisition method adopted in the industry at present only uses a design value or a thermal test value as a reference value of the benchmarking value, which is inaccurate and cannot be used for a long time. With the increase of the operation time of the steam turbine and the accessory system thereof, the state of the equipment is necessarily degraded or the power plant carries out technical transformation on the equipment, and the change of the benchmark value is obvious. The above-mentioned economic evaluation methods for steam turbines, which cannot be updated in time with changes in the plant state, obviously cannot be used on site for a long time.
(5) The existing energy efficiency evaluation system only calculates the energy-saving potential when the parameters deviate from the reference, does not analyze the true reason causing the index deviation, and provides a real and effective guidance scheme for the operation of the unit after the coal consumption rises.
Therefore, improvement and innovation thereof are imperative.
Disclosure of Invention
In view of the above situation, and in order to overcome the defects of the prior art, the present invention aims to provide a consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit, which can effectively solve the problems of real-time online calculation of consumption difference, establishment of a dynamic benchmarking value library, realization of automatic optimization of benchmarking values of main operating parameters of the unit, and guarantee of optimal benchmarking values of the operating parameters at all times.
The technical scheme for solving the problem is as follows:
a consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit comprises the following steps:
step 1: establishing an intelligent control platform, wherein the intelligent control platform comprises a controller and a server, establishing bidirectional data transmission between the intelligent control platform and the DCS of the original unit, and transmitting real-time operation data in the DCS to the server of the intelligent control platform through an agreed interface protocol for storage;
step 2: determining raw data parameters for a consumption difference analysis method, comprising: the system comprises a main steam pressure, a main steam temperature, a high exhaust pressure, a high exhaust temperature, a reheat pressure, a reheating temperature, an intermediate exhaust pressure, an intermediate exhaust temperature, a low-pressure cylinder exhaust pressure, pressure and temperature of each steam extraction section, temperature of inlet and outlet water of each heater, flow rate of condensate of an oxygen inlet and outlet converter, water supply temperature, raw coal calorific value, raw coal ash, flue fly ash combustible substances, flue gas oxygen content of an air preheater outlet, flue gas temperature of an air preheater inlet, primary air temperature of the air preheater inlet, secondary air temperature of the air preheater inlet, flue gas temperature of an economizer inlet, flue gas temperature of a hearth outlet, raw coal ash and slag combustible substances;
and step 3: based on the intelligent control platform, preprocessing the original data parameters in the step 2 on the intelligent control platform, wherein the specific preprocessing method comprises the following steps:
(1) Screening out mutation data in the original data so as to ensure the validity and accuracy of the data;
(2) If the real-time data is a dead pixel, interpolation under different loads is carried out by adopting design values provided by a manufacturer to be used as default values to replace the real-time data for calculation, so that the on-line calculation program can be ensured to carry out normal calculation;
and 4, step 4: carrying out arithmetic mean processing on the preprocessed original data at fixed time intervals, taking the processed data as input parameters of the consumption difference calculation, finishing data preprocessing and arithmetic mean processing every 5 minutes at each time interval of 5 minutes, and writing the processed data into a JSON format data packet;
X PJ ＝(x 1 +x 2 +....+x n )/n
in the formula, X PJ Representing the mean value of a certain parameter, x i Representing DCS data collected every second, wherein n represents the number of original parameters subjected to pretreatment and screening within 5 minutes;
and 5: based on the intelligent control platform, carrying out format analysis on the data processed in the step 4, and then calculating a real-time value of the thermal index, wherein the calculated real-time value comprises: the system comprises a heat consumption rate, a heater upper end difference, a heater lower end difference, a high-pressure cylinder efficiency, a medium-pressure cylinder efficiency, a low-pressure cylinder efficiency, a condenser end difference, a condensate supercooling degree, a power plant power consumption rate, a boiler efficiency and a power supply coal consumption rate;
step 6: based on an intelligent control platform, calculating the consumption difference (the consumption difference refers to the coal consumption deviation amount generated when a certain parameter deviates from a benchmark value), wherein the calculated consumption difference parameters comprise: the method comprises the following steps of (1) main steam pressure loss difference, main steam temperature loss difference, reheat steam temperature loss difference, steam consumption difference for a steam turbine of a water supply pump, low-pressure cylinder steam exhaust pressure loss difference, final water supply temperature loss difference, upper end difference consumption difference of a heater, lower end difference consumption difference of the heater, reheater desuperheating water flow rate difference, reheater pressure loss difference, high-pressure cylinder efficiency loss difference, intermediate pressure cylinder efficiency loss difference, low-pressure cylinder efficiency loss difference, condensate water supercooling degree loss difference, steam extraction pressure loss difference, fly ash carbon content loss difference, slag carbon content difference, exhaust smoke oxygen content difference, exhaust smoke temperature loss difference, heating value loss difference and ash content difference;
and 7: processing the calculated thermal index and the loss difference result, subtracting the flagpole value from the real-time value to obtain a deviation value, and taking the real-time value, the flagpole value, the deviation value and the loss difference value as basic data of loss difference analysis;
and 8: establishing a reason analysis and guidance suggestion library of key indexes on an intelligent control platform, wherein the key indexes mainly comprise main steam temperature, reheat steam temperature, steam consumption of a steam turbine of a water supply pump, low-pressure cylinder exhaust steam pressure, water supply temperature, upper end difference of a heater, lower end difference of the heater, reheater pressure loss, high-pressure cylinder efficiency, intermediate pressure cylinder efficiency, condensate water supercooling degree, condenser end difference, ash carbon content, exhaust oxygen content and exhaust temperature;
when the content of the reason analysis and guidance suggestion library contains the actual value of a certain parameter deviating from the benchmark value, analyzing the reason of the consumption difference and the phenomenon generated by the index, and providing corresponding operation guidance suggestion;
and step 9: the intelligent control platform finds out a direct reason causing the parameter loss difference from the reason analysis and guidance suggestion library established in the step 8 according to the result of the loss difference calculation, and transmits the reason analysis and guidance suggestion back to a DCS operator station through a transmission protocol to guide an operator to carry out the operation of eliminating the loss difference;
step 10: repeating the operation of the step 3 to the step 7, judging the working condition of the working condition within the time interval of every 5 minutes, if the working condition is judged to be in a stable state, learning the running states of all boilers and steam turbine equipment and the operation behaviors of operators under the working condition by adopting a decision tree algorithm model for autonomous learning of a machine by the intelligent control platform, calculating an optimal benchmark value forming the consumption difference index under the working condition, and finally forming an optimal benchmark value library, wherein the optimal benchmark value library is obtained by writing the parameter values generating the lowest coal consumption into a database for storage under the same working condition in the historical actual operation process of operators, and the parameters of the self-searching optimal benchmark value are kept consistent with the index calculating the consumption difference;
iterative calculation and verification are carried out through continuous working conditions, the parameter value with the lowest coal consumption is extracted and put into a constructed optimal benchmark value library, the optimal benchmark value library is continuously perfected, and the benchmark value in the library is ensured to be optimal all the time, so that the consumption difference value based on the optimal benchmark value is calculated;
step 11: the operation personnel adjusts the operation in real time according to the consumption difference value of each parameter seen in the DCS and the reason analysis and guidance suggestion of real-time pushing, so that the actual operation value is continuously close to the optimal benchmark value, the consumption difference value is continuously reduced, and the purposes of saving energy and reducing consumption are achieved.
The invention provides a consumption difference analysis system and method applied to self-optimization of operating parameters of a thermal power generating unit, which can perform real-time online calculation on energy consumption indexes and consumption differences of main parameters of the unit in real time, display and prompt the operator station in real time through a DCS (distributed control system), intelligently judge the consumption difference state of each parameter, if the consumption difference state is abnormal, push professional and accurate reasons and guide suggestions to the operator in real time, guide the operating operator to perform the operation of eliminating the consumption difference, reduce the energy consumption of the unit and ensure the safe and stable operation of the unit; the method comprises the steps of establishing a dynamic benchmarking value library, realizing automatic optimization of benchmarking values of main operation parameters of a unit, automatically searching for optimal parameter values, putting the optimal parameter values into the dynamic benchmarking library, providing consumption difference calculation in real time, guaranteeing the benchmarking values of the operation parameters to be optimal constantly, conducting index assessment on operators by using the calculated consumption difference values, combining rules of small index competition of a power plant, guiding the operation behaviors of the operators and reducing the energy consumption of the unit by a novel assessment method, realizing quantitative assessment of the operation operations, judging whether the team performs corresponding operations according to the benchmarking database, realizing guidance and scientific assessment on the operation behaviors, and achieving convenience in use, good effect, innovation in a method for analyzing the consumption difference of the thermal power plant and good social and economic benefits.
Drawings
FIG. 1 is a block diagram of the calculated thermodynamic indicator and consumption difference results processed in step 7 of the present invention.
FIG. 2 is a schematic diagram of a step 10 decision tree algorithm model according to the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
As shown in fig. 1-2, the consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit of the present invention includes the following steps:
firstly, by checking and verifying actual field measuring points of the thermal power generating unit, newly adding and replacing a large number of original equipment measuring points in a plant, and ensuring full coverage or large-area coverage of high-precision measuring points, a set of high-precision data acquisition module is constructed, the integrity and accuracy of each equipment measuring point of the thermal power generating unit are ensured, and comprehensive and accurate data guarantee is provided for calculation and analysis of the whole system.
The newly added and replaced measuring points are shown in the following table 1, the measuring point list shown in the following table is an important measuring point for calculating consumption difference analysis, is important for the accuracy of a calculation result, and needs to be replaced by a high-precision transmitter, but is not limited to the following measuring points when replaced on site.
TABLE 1 Add and replace station List
Step 1: establishing an intelligent control platform, wherein the intelligent control platform comprises a controller and a server, establishing bidirectional data transmission between the intelligent control platform and the DCS of the original unit, and transmitting real-time operation data in the DCS to the server of the intelligent control platform through an agreed interface protocol for storage;
the period of transmitting real-time operation data in the DCS to a server of the intelligent control platform is 1s;
in actual application, on the basis of the original set DCS system, 4 controllers, 3 application servers and 2 real-time historical database servers are deployed to establish closed-loop relation between a conventional basic control layer and an intelligent control layer of the existing DCS system, the boundary between the conventional DCS control and the intelligent control is opened longitudinally, bidirectional data intercommunication with the DCS system is realized, and real-time operation data in the DCS system is transmitted to the real-time historical database server of the intelligent control platform through an agreed interface protocol;
step 2: determining raw data parameters for a consumption difference analysis method, comprising: the system comprises a main steam pressure, a main steam temperature, a high exhaust pressure, a high exhaust temperature, a reheating pressure, a reheating temperature, a middle exhaust pressure, a middle exhaust temperature, a low-pressure cylinder exhaust pressure, pressure and temperature of each steam extraction section, temperature of inlet and outlet water of each heater, flow of condensed water of an oxygen inlet and outlet deaerator, water supply temperature, raw coal calorific value, raw coal ash, flue fly ash combustible, air preheater outlet flue gas oxygen content, air preheater inlet flue gas temperature, air preheater inlet primary air temperature, air preheater inlet secondary air temperature, economizer inlet flue gas temperature, furnace outlet flue gas temperature, raw coal ash and slag combustible;
and step 3: based on the intelligent control platform, preprocessing the original data parameters in the step 2 on the intelligent control platform, wherein the specific preprocessing method comprises the following steps:
(1) Screening out mutation data in the original data so as to ensure the validity and accuracy of the data;
(2) If the real-time data is a dead pixel, interpolation under different loads is carried out by adopting design values provided by a manufacturer to be used as default values to replace the real-time data for calculation, so that the on-line calculation program can be ensured to carry out normal calculation;
and 4, step 4: carrying out arithmetic mean processing on the preprocessed original data at fixed time intervals, taking the processed data as input parameters of the loss calculation, finishing data preprocessing and arithmetic mean processing every 5 minutes at each time interval of 5 minutes, and writing the data into a JSON format data packet;
X PJ ＝(x 1 +x 2 +....+x n )/n
in the formula, X PJ Representing the mean value of a certain parameter, x i Representing DCS data collected every second, wherein n represents the number of original parameters subjected to pretreatment and screening within 5 minutes;
and 5: based on the intelligent control platform, carrying out format analysis on the data processed in the step 4, and then calculating a real-time value of the thermal index, wherein the calculated real-time value comprises: the system comprises a heat consumption rate, a heater upper end difference, a heater lower end difference, a high-pressure cylinder efficiency, an intermediate pressure cylinder efficiency, a low-pressure cylinder efficiency, a condenser end difference, a condensate supercooling degree, a power plant power consumption rate, a boiler efficiency and a power supply coal consumption rate;
the calculation method of each real-time value comprises the following steps:
a. rate of heat loss
HR-Heat Rate, kJ/kWh
W ms Main steam flow, t/h
h ms -main steamingEnthalpy of vaporization, kJ/kg
W hr -flow of hot reheat steam, t/h
h hr -enthalpy of hot reheat steam, kJ/kg
W cr Flow of Cold reheat steam, t/h
h cr Enthalpy of cold reheat steam, kJ/kg
W fw -final feed water flow, t/h
h fw -final feed water enthalpy, kJ/kg
W rh Reheat steam attemperation Water flow, t/h
h rh -reheat steam to reduce enthalpy of water, kJ/kg
P g Generator outlet power, MW
P ex -generator excitation power, MW
b. High, medium and low pressure cylinder efficiency
h o -enthalpy of admission of high, medium and low pressure cylinders of the steam turbine kJ/kg
h c -exhaust enthalpy of high, medium and low pressure cylinders of the steam turbine kJ/kg
hcl-isentropic expansion end point enthalpy of high, medium and low pressure cylinders of steam turbine, kJ/kg
c. Upper end difference of heater
δ＝t s -t o
Delta-poor upper end of heater, [ deg. ] C
t s -saturation temperature at the working pressure of the heater, ° c
t o -outlet water temperature of heater, ° c
d. Lower end difference of heater
γ＝t d -t i
Gamma-poor lower end of heater, ° c
t d -heatingHydrophobic temperature of the vessel, DEG C
t i -inlet water temperature of heater, ° c
e. End difference of condenser
Δt k ＝t bbh -t xhi
Δt k Condenser end-to-end, deg.C
t bbh Saturation temperature of steam turbine exhaust pressure, DEG C
t xhi Inlet temperature of circulating water, deg.C
f. Degree of supercooling of condensed water
Δt gl ＝t bbh -t rj
Δt gl Degree of supercooling of condensate, DEG C
t bbh Saturation temperature of steam turbine exhaust pressure, DEG C
t rj -temperature of condensate in condenser hot well, C
g. Power rate of power plant
W d ＝W cy -W r
L fcy -power plant power consumption,%
W cy -plant power consumption in statistical period
W d Plant power consumption for power generation, kWh
W r Plant power consumption for heat supply, kWh
h. Rate of coal consumption of power supply
b gd The coal consumption rate of power generation, g/(kWh).
Step 6: based on an intelligent control platform, calculating the consumption difference (the consumption difference refers to the coal consumption deviation amount generated when a certain parameter deviates from a benchmark value), wherein the calculated consumption difference parameters comprise: the method comprises the following steps of (1) main steam pressure loss difference, main steam temperature loss difference, reheat steam temperature loss difference, steam consumption difference for a steam turbine of a water supply pump, low-pressure cylinder steam exhaust pressure loss difference, final water supply temperature loss difference, upper end difference consumption difference of a heater, lower end difference consumption difference of the heater, reheater desuperheating water flow rate difference, reheater pressure loss difference, high-pressure cylinder efficiency loss difference, intermediate pressure cylinder efficiency loss difference, low-pressure cylinder efficiency loss difference, condensate water supercooling degree loss difference, steam extraction pressure loss difference, fly ash carbon content loss difference, slag carbon content difference, exhaust smoke oxygen content difference, exhaust smoke temperature loss difference, heating value loss difference and ash content difference;
the consumption difference calculation method comprises the following steps:
assuming that a plurality of factors deviate from the reference value to change the boiler efficiency, the value of the boiler efficiency target is eta' b Efficiency η of operating boiler b Then the relative change in boiler efficiency is:
in the above formula: δ η b Representing the relative variation of the boiler efficiency;
q 2 、q 3 、q 4 、q 5 、q 6 respectively representing the heat loss of smoke exhaust, the heat loss of incomplete combustion of gas, the heat loss of incomplete combustion of solid, heat dissipation loss and the physical heat loss of ash residue,%;
q′ 2 、q′ 3 、q′ 4 、q′ 5 、q′ 6 respectively representing the exhaust heat loss benchmark value, the incomplete combustion gas heat loss benchmark value, the incomplete combustion solid heat loss benchmark value, the heat dissipation loss benchmark value and the ash physical heat loss benchmark value,%;
Δq 2 、Δq 3 、Δq 4 、Δq 5 、Δq 6 respectively showing the deviation of heat loss of exhaust smoke, the deviation of heat loss of incomplete combustion of gas, the deviation of heat loss of incomplete combustion of solid, the deviation of heat dissipation loss and the deviation of physical heat loss of ash slagAmount,%.
Δ q (i) represents the ith deviation, i =1,2,3,4,5;
the coal consumption deviation amount of the unit is as follows:
in the above formula, delta b represents the coal consumption deviation amount, namely the consumption difference, g/kWh;
b represents the coal consumption rate, g/kWh;
the change of the parameters of the exhaust gas temperature, the exhaust gas oxygen content, the fly ash carbon content, the slag carbon content, the coal ash content and the coal calorific value can cause the change of the boiler efficiency, and the consumption difference formula of a single factor is as follows:
in the above formula,. DELTA.b (i) Representing the coal consumption deviation amount caused by the ith variable, g/kWh;
suppose that the thermodynamic system on the steam side has n variation factors (X) 1 ,X 2 ,Λ,X n ) The marker post values of the n variable factors are (X' 1 ，X′ 2 ，...，X′ n ) Which changes the new steam work into (Δ H) X1 ，ΔH X2 ，...，ΔG Xn ) The cyclic heat absorption changes are respectively (Δ W) X1 ，ΔW X2 ，...，ΔW Xn ) The following conclusions can be drawn:
ΔH Xj ＝f(X j ，X′ j )，j＝1，...，n
ΔW Xj ＝f(X j ，X′ j )，j＝1，...，n
in the above formula, H represents the enthalpy of the fresh steam, kJ/kg; delta b represents the coal consumption deviation amount, namely the consumption difference; eta j To representChange in cycle efficiency,%, due to the change in the jth parameter.
The above formula is decomposed to obtain the loss equation of the jth factor as:
the coal consumption deviation of each factor of the boiler side and the steam turbine side can be calculated by using the formulas (1) and (2), the method calculates the consumption difference of multiple factors, not only considers the mutual influence among the multiple factors, but also effectively decomposes the influence of the multiple factors on the consumption difference into the influence of each single factor, and sums the consumption differences of each factor to obtain the change of the whole consumption difference of the unit;
the consumption difference calculation takes an equivalent heat drop method as a basic framework of quantitative analysis, and simultaneously introduces the concepts of a heating unit and a unit water inlet coefficient by combining the characteristics of a circulation function method, thereby establishing a thermal economical quantitative analysis model; the model is characterized in that the electric load characteristic, the environmental characteristic, the thermodynamic system structural characteristic and the mutual influence among multiple factors and the like of the influence degree of various economic factors on the coal consumption rate are considered, the consumption difference value after the parameters of the computer set are influenced mutually can be accurately calculated, and the actual situation on site is more met;
and 7: processing the calculated thermodynamic index and the consumption difference result, subtracting the benchmark value from the real-time value to obtain a deviation value, and taking the real-time value, the benchmark value, the deviation value and the consumption difference value as basic data of consumption difference analysis; displaying the real-time value, the benchmark value, the deviation value and the consumption difference value on an intelligent control platform, and generating a trend graph by establishing a WEB interface, wherein the trend graph is displayed in a list form; meanwhile, the calculation result is returned to the DCS through the intelligent control platform;
and 8: establishing a reason analysis and guidance suggestion library of key indexes on an intelligent control platform, wherein the key indexes mainly comprise main steam temperature, reheat steam temperature, steam consumption of a steam turbine of a water supply pump, low-pressure cylinder exhaust steam pressure, water supply temperature, upper end difference of a heater, lower end difference of the heater, reheater pressure loss, high-pressure cylinder efficiency, intermediate pressure cylinder efficiency, condensate water supercooling degree, condenser end difference, ash carbon content, exhaust oxygen content and exhaust temperature;
when the content of the reason analysis and guidance suggestion library contains the actual value of a certain parameter deviating from the benchmark value, the reason of the consumption difference and the phenomenon of the index is analyzed, and corresponding operation guidance suggestions are given;
for example: the difference consumption of the upper end of the heater exceeds the standard, and the established reason analysis library is as follows:
1. the heater DCS liquid level meter is not corresponding to the field liquid level meter;
2. the water level of the heater is too low, and the drained water cannot be sufficiently cooled;
3. the hydrophobic inlet tube plate of the heater deforms to destroy the water seal, steam leaks into the hydrophobic cooling section, and the hydrophobic temperature rises.
4. Scaling on the heating surface of the hydrophobic cooling section, which results in poor heat exchange effect;
5. the heat exchange amount of the water inlet pipe bundle of the heater partially blocked the hydrophobic cooling section is reduced.
The guidance suggestion library is established as follows:
1. carefully checking whether the on-site liquid level meter is matched with the DCS liquid level meter, and if not, timely adjusting.
2. And adjusting the water level of the heater to a reasonable water level interval.
3. And (4) checking whether the drain inlet tube plate of the heater deforms or not by using the machine set maintenance opportunity, and if so, timely repairing the drain inlet tube plate of the heater.
4. And (4) checking whether the hydrophobic cooling section of the heater has scaling pollution conditions by using a unit maintenance opportunity, and if so, timely processing.
5. The machine set maintenance machine is used for checking whether the tube bundle is blocked or not, and if the tube bundle is blocked, the tube bundle is required to be dredged in time.
The reason analysis and operation suggestions can be obtained by summarizing the existing test reports and researching the related documents, the content is the prior art, and other parameters are not listed.
And step 9: the intelligent control platform finds out a direct reason causing the parameter loss difference from the reason analysis and guidance suggestion library established in the step 8 according to the result of the loss difference calculation, transmits the reason analysis and guidance suggestion back to a DCS operator station through a transmission protocol, pops up a window of the reason analysis and guidance suggestion in a pop-up window mode, and guides an operator to carry out the operation of eliminating the loss difference;
finishing the consumption difference analysis calculation and the display of the consumption difference result and the guidance suggestion for reducing the consumption difference, and then continuing to realize the benchmark value self-optimization process required by the consumption difference calculation;
step 10: repeating the operation of the step 3 to the step 7, performing working condition judgment on the working condition within each 5-minute time interval, if the working condition is judged to be in a stable state, learning the running states of all boilers and steam turbine equipment and the operation behaviors of operators under the working condition by adopting a decision tree algorithm model (a schematic diagram of the decision tree algorithm model is shown in fig. 2) for machine autonomous learning by the intelligent control platform, calculating to form an optimal benchmark value of the consumption difference index under the working condition, and finally forming an optimal benchmark value library, wherein the optimal benchmark value library is obtained by writing the parameter values which generate the lowest coal consumption into a database for storage under the same working condition in the historical actual operation process of the operators, and the parameters of the self-searching benchmark value are consistent with the index of the calculated consumption difference;
iterative calculation and verification are carried out through continuous working conditions, the parameter value with the lowest coal consumption is extracted and put into a constructed optimal benchmark value library, the optimal benchmark value library is continuously perfected, and the benchmark value in the library is ensured to be optimal all the time, so that the consumption difference value based on the optimal benchmark value is calculated;
the basis of the stable judgment of the working condition is as follows:
and standard variance calculation is carried out on all the 7 parameters of the main steam temperature, the main steam pressure, the reheating temperature, the reheating pressure, the unit load, the exhaust steam pressure and the condensate flow entering the deaerator in the fixed time period, and the formula is as follows:
in the formula: delta 2 Representing the standard deviation of a certain parameter.
The smaller the standard deviation is, the more stable the parameter is in the calculated time period, when the standard deviation of the main steam temperature and the reheat temperature is below 5, the standard deviation of the main steam pressure and the reheat pressure is below 0.3, the standard deviation of the unit load is below 10, the standard deviation of the exhaust steam pressure is below 0.5, and the standard deviation of the condensate flow is below 10, the condition can be judged to be in a stable state in the time period.
Step 11: the operation personnel adjusts the operation in real time according to the consumption difference value of each parameter seen in the DCS and the reason analysis and guidance suggestion of real-time pushing, so that the actual operation value is continuously close to the optimal benchmark value, the consumption difference value is continuously reduced, and the purposes of saving energy and reducing consumption are achieved.
The invention also discloses an implementation process of an assessment method combining small indexes and loss difference indexes of a thermal power generating unit, based on the self-optimization consumption difference analysis method of the operation parameters of the thermal power generating unit, which comprises the following steps:
step 1: performing background program arrangement on the consumption difference values of all parameters during the operation of the team, and grading according to the consumption difference values to obtain a total score of the consumption difference index of the value and a subentry score of the consumption difference of all parameters;
step 2: carrying out background program arrangement on the small index parameters in the operating period of the team, and scoring according to the parameter deviation value to obtain the total score of the small index of the value and the subentry score of each index;
and step 3: the small index assessment score and the loss difference index assessment score are added to obtain a total score of assessment results, the assessment total scores obtained by each team of the unit are ranked in real time, the real-time ranking is calculated, the ranking of each team in a selected time period can be inquired, meanwhile, the average value of each index of a certain team in a query time period, the actual score, the maximum score, the score and the ranking can be inquired;
and 4, step 4: according to the consumption difference score and the ranking condition of each parameter, the operation behavior of the team operation operator is judged, according to the fact that the deviation flagpole value of a certain consumption difference index of the team in the query time period is too large, the operator can be judged not to adjust the parameter according to the flagpole value, the operation level of the value is objectively evaluated, and the evaluation system can be divided into the following steps: excellent, good, qualified and failing.
The invention obtains the same or similar effect through practical application, and concretely comprises the following steps:
case 1:
taking a certain 1000MW unit of the marine steam turbine plant as an example, the unit is put into production in 2016 and 10 months, the monitoring of the unit operation indexes after the unit is put into production is only limited to the condition that the parameters reach the design values, the benchmark values of the parameter indexes under different loads are fixed, and the operators only adjust the parameters according to the design parameters under different loads, and the real-time values and the design values of part of the parameters are displayed in the following table:
after the system is put into operation in 3 months in 2020, the system automatically establishes a dynamic optimal benchmark value library, the benchmark values of the operation parameters are optimal in real time under each load of real-time operation, and personnel can adjust the parameters according to the optimal benchmark values in real time. For example, the upper end difference benchmarking value for the high plus No. 2 is shown in the following table at 4 months and 1 days:
| load (MW) | Real-time value (. Degree. C.) | Post value (. Degree. C.) | Deviation value (. Degree. C.) | Loss difference value (g/kWh) |
| 1000 | 2.1 | 0 | 2.1 | 0.42 |
| 990 | 2.1 | 0 | 2.1 | 0.42 |
| 980 | 2 | -0.03 | 2.03 | 0.406 |
| 970 | 2.1 | -0.03 | 2.13 | 0.426 |
| 960 | 2.2 | -0.05 | 2.25 | 0.45 |
| 950 | 2.1 | -0.05 | 2.15 | 0.43 |
| 940 | 1.9 | -0.05 | 1.95 | 0.39 |
| 930 | 1.9 | -0.06 | 1.96 | 0.392 |
| 920 | 1.8 | -0.05 | 1.85 | 0.37 |
| 910 | 1.9 | -0.05 | 1.95 | 0.39 |
| 900 | 1.9 | -0.07 | 1.97 | 0.394 |
| ... | ... | ... | ... | ... |
| 550 | 1.3 | -1.11 | 2.41 | 0.482 |
| 540 | 1.2 | -1.11 | 2.31 | 0.462 |
| 530 | 1.3 | -1.14 | 2.44 | 0.488 |
| 520 | 1.1 | -1.16 | 2.26 | 0.452 |
| 510 | 1.2 | -1.17 | 2.37 | 0.474 |
| 500 | 1.3 | -1.17 | 2.47 | 0.494 |
| ... | ... | ... | ... | ... |
When the consumption difference exceeds 0.4g/kWh, the DCS picture judges that the consumption difference of the parameter is abnormal, and the reason for popping up the consumption difference through the calculation of the intelligent control platform is as follows: the water level of the heater is too high, which affects the heat exchange of the heater tube bundle. The guidance suggestion is as follows: carefully checking whether the on-site liquid level meter is matched with the DCS liquid level meter, and if not, timely adjusting; and adjusting the water level of the heater to a reasonable water level interval.
And operating by an operator according to the reason analysis and guidance suggestion, checking the No. 2 high-pressure heater liquid level meter on site, finding that the liquid level meter is inconsistent with the upper liquid level of the DCS, and after the liquid level meter is processed by a maintainer, displaying the same with the DCS on site by the liquid level meter, so that the difference of the upper end difference of the No. 2 high-pressure heater is reduced.
After the problem is processed, the No. 2 high-plus upper end difference benchmark value is automatically optimized and self-learned, as shown in the following table, the automatic optimization updating of the benchmark value is realized, and the consumption difference value is updated in real time.
| Load (MW) | Real-time value (. Degree. C.) | Post value (. Degree. C.) | Deviation value (. Degree. C.) | Loss difference value (g/kWh) |
| 1000 | 0.2 | -0.05 | 0.25 | 0.05 |
| 990 | 0.2 | -0.05 | 0.25 | 0.05 |
| 980 | 0.21 | -0.06 | 0.27 | 0.054 |
| 970 | 0.22 | -0.05 | 0.27 | 0.054 |
| 960 | 0.2 | -0.05 | 0.25 | 0.05 |
| 950 | 0.21 | -0.07 | 0.28 | 0.056 |
| 940 | 0.19 | -0.08 | 0.27 | 0.054 |
| 930 | 0.19 | -0.09 | 0.28 | 0.056 |
| 920 | 0.21 | -0.08 | 0.29 | 0.058 |
| 910 | 0.19 | -0.09 | 0.28 | 0.056 |
| 900 | 0.2 | -0.1 | 0.3 | 0.06 |
| ... | ... | ... | ... | ... |
| 550 | -0.5 | -1.3 | 0.8 | 0.16 |
| 540 | -0.55 | -1.33 | 0.78 | 0.156 |
| 530 | -0.51 | -1.35 | 0.84 | 0.168 |
| 520 | -0.53 | -1.4 | 0.87 | 0.174 |
| 510 | -0.51 | -1.44 | 0.93 | 0.186 |
| 500 | -0.5 | -1.43 | 0.93 | 0.186 |
| ... | ... | ... | .... | ... |
After the operation of half a year, the thermal power plant reduces the coal consumption of the unit by 1.3g/kWh by utilizing the operation parameter searching self-optimizing system, and the energy-saving effect is obvious.
Case 2:
taking a certain 660MW unit of an oriental steam turbine plant as an example, the unit is put into production in 2018 and 1 month, the monitoring of the unit operation indexes after the unit is put into production is only limited to the condition that the parameters reach the design values, only whether the important parameters of small indexes reach the standard or not is concerned, the examination of a special worker on the operation personnel is only aimed at whether the small running indexes reach the standard or not, and the energy-saving effect of the small index examination is not obvious.
The vacuum is taken as an example, the benchmark value used after the power plant is put into operation is shown in the following table, the vacuum benchmark value is only divided into 3 intervals according to the difference of the environmental temperature, and the operation personnel adjust the operation mode of the circulating water pump according to the difference of the benchmark value so as to adjust the vacuum value of the unit, so that the operation personnel can only operate according to experience, the part load causes the over-high power consumption of the plant, and the coal consumption of the unit is increased.
After the system is put into operation in 5 months in 2020, the system automatically establishes a dynamic optimal benchmark value library, the benchmark values of the operation parameters are optimal in real time under each real-time operation load, and personnel can adjust the parameters according to the real-time optimal benchmark values. The benchmarking values for unit vacuum at 6 months 23 days are shown in the following table:
the system provides real-time values, target values, deviation values and consumption difference values of vacuum values under different loads and different circulating water inlet temperatures. According to different real-time loads, real-time loads and real-time vacuum consumption difference values at circulating water inlet temperatures can be displayed at a DCS operator station. When the vacuum consumption difference value exceeds 2g/kWh, the consumption difference of the parameter is pushed to be abnormal in a DCS picture, and the reason for the consumption difference is popped up through the calculation of the intelligent control platform as follows: the output of the vacuum pump A is abnormal, and the cooling capacity of the cooling tower is reduced. The guidance suggestion is as follows: please check whether the operating environment of the vacuum pump A is abnormal on site; please check whether the cooling tower has uneven water distribution or damaged filler on site, if so, please deal with the problem in time.
The operators carry out field inspection according to the analysis and guidance suggestions of the reasons, and find that the temperature of the bearing is increased and the vibration is large when the vacuum pump A operates on site, and the operators switch the standby vacuum pump to operate in time; inside the inspection cooling tower, it is more to discover the inside mud in distributing groove, has blockked up partly distributing pipe, leads to the inside water distribution of cooling tower uneven, and cooling capacity descends, arranges maintainer promptly and carries out the mud clearance, and the water distribution of guarantee cooling tower is even.
And 6, 29 months, after the defects of the vacuum pump and the cooling tower are processed, the automatic optimization and self-learning of the vacuum benchmark value of the unit are realized, and the automatic optimization updating and the real-time updating of the consumption difference value of the benchmark value are realized as shown in the following table.
By combining the evaluation method which is newly created by the system and comprises the loss index, the operation binary of the unit is timely due to the fact that the vacuum loss abnormity problem is processed, the monthly index and the loss index are evaluated as the first name, and the operation evaluation is excellent.
From the operation of the system to the end of 2020, the power plant reduces the coal consumption of the unit by 2.2g/kWh by using the searching operation parameter self-optimizing system, and the energy-saving effect is obvious.
Claims (4)
1. A consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit is characterized by comprising the following steps:
step 1: establishing an intelligent control platform, wherein the intelligent control platform comprises a controller and a server, establishing bidirectional data transmission between the intelligent control platform and the DCS of the original unit, and transmitting real-time operation data in the DCS to the server of the intelligent control platform through an agreed interface protocol for storage;
step 2: determining raw data parameters for a consumption difference analysis method, comprising: the system comprises a main steam pressure, a main steam temperature, a high exhaust pressure, a high exhaust temperature, a reheat pressure, a reheating temperature, an intermediate exhaust pressure, an intermediate exhaust temperature, a low-pressure cylinder exhaust pressure, pressure and temperature of each steam extraction section, temperature of inlet and outlet water of each heater, flow rate of condensate of an oxygen inlet and outlet converter, water supply temperature, raw coal calorific value, raw coal ash, flue fly ash combustible substances, flue gas oxygen content of an air preheater outlet, flue gas temperature of an air preheater inlet, primary air temperature of the air preheater inlet, secondary air temperature of the air preheater inlet, flue gas temperature of an economizer inlet, flue gas temperature of a hearth outlet, raw coal ash and slag combustible substances;
and step 3: and (3) based on an intelligent control platform, carrying out data preprocessing on the original data parameters in the step (2) on the intelligent control platform, wherein the specific preprocessing method comprises the following steps:
(1) Screening out mutation data in the original data so as to ensure the validity and accuracy of the data;
(2) If the real-time data is a dead pixel, interpolation under different loads is carried out by adopting design values provided by a manufacturer to be used as default values to replace the real-time data for calculation, so that the on-line calculation program can be ensured to carry out normal calculation;
and 4, step 4: carrying out arithmetic mean processing on the preprocessed original data at fixed time intervals, taking the processed data as input parameters of the consumption difference calculation, finishing data preprocessing and arithmetic mean processing every 5 minutes at each time interval of 5 minutes, and writing the processed data into a JSON format data packet;
X PJ ＝(x 1 +x 2 +....+x n )/n
in the formula, X PJ Representing the mean value of a certain parameter, x i Representing DCS data collected every second, wherein n represents the number of original parameters subjected to pretreatment and screening within 5 minutes;
and 5: based on the intelligent control platform, carrying out format analysis on the data processed in the step 4, and then calculating a real-time value of the thermal index, wherein the calculated real-time value comprises: the system comprises a heat consumption rate, a heater upper end difference, a heater lower end difference, a high-pressure cylinder efficiency, an intermediate pressure cylinder efficiency, a low-pressure cylinder efficiency, a condenser end difference, a condensate supercooling degree, a power plant power consumption rate, a boiler efficiency and a power supply coal consumption rate;
step 6: based on the intelligent control platform, the consumption difference is calculated, and the calculated consumption difference parameters comprise: the method comprises the following steps of (1) main steam pressure loss difference, main steam temperature loss difference, reheat steam temperature loss difference, steam consumption difference for a steam turbine of a water supply pump, low-pressure cylinder steam exhaust pressure loss difference, final water supply temperature loss difference, upper end difference consumption difference of a heater, lower end difference consumption difference of the heater, reheater desuperheating water flow rate difference, reheater pressure loss difference, high-pressure cylinder efficiency loss difference, intermediate pressure cylinder efficiency loss difference, low-pressure cylinder efficiency loss difference, condensate water supercooling degree loss difference, steam extraction pressure loss difference, fly ash carbon content loss difference, slag carbon content difference, exhaust smoke oxygen content difference, exhaust smoke temperature loss difference, heating value loss difference and ash content difference;
and 7: processing the calculated thermal index and the loss difference result, subtracting the flagpole value from the real-time value to obtain a deviation value, and taking the real-time value, the flagpole value, the deviation value and the loss difference value as basic data of loss difference analysis;
and 8: establishing a reason analysis and guidance suggestion library of key indexes on an intelligent control platform, wherein the key indexes mainly comprise main steam temperature, reheat steam temperature, steam consumption of a steam turbine of a water supply pump, low-pressure cylinder exhaust steam pressure, water supply temperature, upper end difference of a heater, lower end difference of the heater, reheater pressure loss, high-pressure cylinder efficiency, intermediate pressure cylinder efficiency, condensate water supercooling degree, condenser end difference, ash carbon content, exhaust oxygen content and exhaust temperature;
when the content of the reason analysis and guidance suggestion library contains the actual value of a certain parameter deviating from the benchmark value, the reason of the consumption difference and the phenomenon of the index is analyzed, and corresponding operation guidance suggestions are given;
and step 9: the intelligent control platform finds out a direct reason causing the loss difference from the reason analysis and guidance suggestion library established in the step 8 according to the result of the loss difference calculation, and transmits the reason analysis and guidance suggestion back to a DCS operator station through a transmission protocol to guide an operator to carry out the operation of eliminating the loss difference;
step 10: repeating the operation of the step 3 to the step 7, judging the working condition of the working condition within the time interval of every 5 minutes, if the working condition is judged to be in a stable state, learning the running states of all boilers and steam turbine equipment and the operation behaviors of operators under the working condition by adopting a decision tree algorithm model for autonomous learning of a machine by the intelligent control platform, calculating an optimal benchmark value forming the consumption difference index under the working condition, and finally forming an optimal benchmark value library, wherein the optimal benchmark value library is obtained by writing the parameter values generating the lowest coal consumption into a database for storage under the same working condition in the historical actual operation process of operators, and the parameters of the self-searching optimal benchmark value are kept consistent with the index calculating the consumption difference;
iterative calculation and verification are carried out through continuous working conditions, the parameter value with the lowest coal consumption is extracted and put into a constructed optimal benchmark value library, the optimal benchmark value library is continuously perfected, and the benchmark value in the library is ensured to be optimal all the time, so that the consumption difference value based on the optimal benchmark value is calculated;
step 11: the operation personnel adjusts the operation in real time according to the consumption difference value of each parameter seen in the DCS and the reason analysis and guidance suggestion pushed in real time, so that the actual operation value is continuously close to the optimal benchmark value, the consumption difference value is continuously reduced, and the purposes of saving energy and reducing consumption are achieved;
the method for calculating the loss difference in the step 6 comprises the following steps:
assuming that a plurality of factors deviate from the reference value to change the boiler efficiency, the value of the boiler efficiency target is eta' b Efficiency η of operating boiler b Then the relative change in boiler efficiency is:
in the above formula: δ η b Representing the relative variation of the boiler efficiency;
q 2 、q 3 、q 4 、q 5 、q 6 respectively representing the heat loss of smoke exhaust, the heat loss of incomplete combustion of gas, the heat loss of incomplete combustion of solid, heat dissipation loss and the physical heat loss of ash residue,%;
q′ 2 、q′ 3 、q′ 4 、q′ 5 、q′ 6 respectively representing the exhaust heat loss benchmark value, the incomplete combustion gas heat loss benchmark value, the incomplete combustion solid heat loss benchmark value, the heat dissipation loss benchmark value and the ash physical heat loss benchmark value,%;
Δq 2 、Δq 3 、Δq 4 、Δq 5 、Δq 6 respectively showing the deviation of heat loss of exhaust smoke, the deviation of heat loss of incomplete combustion of gas, the deviation of heat loss of incomplete combustion of solid, the deviation of heat dissipation loss and the deviation of physical heat loss of ash residue,%;
Δq (i) represents the ith deviation, i =1,2,3,4,5;
the coal consumption deviation amount of the unit is as follows:
in the formula, delta b represents the coal consumption deviation amount, namely the consumption difference, g/kWh;
b represents the coal consumption rate, g/kWh;
the change of the parameters of the exhaust gas temperature, the exhaust gas oxygen content, the fly ash carbon content, the slag carbon content, the coal ash content and the coal calorific value can cause the change of the boiler efficiency, and the consumption difference formula of a single factor is as follows:
in the above formula,. DELTA.b (i) Representing the coal consumption deviation amount caused by the ith variable, g/kWh;
suppose that the thermodynamic system on the steam side has n variation factors (X) 1 ,X 2 ,Λ,X n ) The marker post values of the n variable factors are (X' 1 ,X′ 2 ，…，X′ n ) Which changes the new steam work into (Δ H) X1 ,ΔH X2 ,…,ΔH Xn ) The cyclic heat absorption changes are respectively (Δ W) X1 ,ΔW X2 ,…,ΔW Xn ) The following conclusions can be drawn:
ΔH Xj ＝f(X j ,X′ j )，j＝1，…，n
ΔW Xj ＝f(X j ,X′ j )，j＝1，…，n
in the above formula, H represents the enthalpy of the fresh steam, kJ/kg; delta b represents the coal consumption deviation amount, namely the consumption difference; eta j Represents the change of the cycle efficiency,%, caused by the change of the jth parameter;
the above formula is decomposed to obtain the loss equation of the jth factor as:
the coal consumption deviation of each factor at the boiler side and the steam turbine side can be calculated by using the formulas (1) and (2), the method calculates the consumption difference of multiple factors, not only considers the mutual influence among the multiple factors, but also effectively decomposes the influence of the multiple factors on the consumption difference into the influence of each single factor, and sums the consumption difference of each factor to obtain the change of the whole consumption difference of the unit.
2. The consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit according to claim 1, wherein the period for transmitting the real-time operating data in the DCS system of step 1 to the server of the intelligent control platform is 1s.
3. The consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit according to claim 1, wherein the calculation method of each real-time value in the step 5 is as follows:
a. rate of heat loss
HR-Heat Rate, kJ/kWh
W ms Main steam flow, t/h
h ms Main steam enthalpy, kJ/kg
W hr Flow of Hot reheat steam, t/h
h hr -enthalpy of hot reheat steam, kJ/kg
W cr Flow of Cold reheat steam, t/h
h cr Enthalpy of cold reheat steam, kJ/kg
W fw Final feed water flow, t/h
h fw -final feed water enthalpy, kJ/kg
W rh Reheat steam attemperation Water flow, t/h
h rh -reheat steam desuperheating Water enthalpy, kJ/kg
P g -generator outlet power, MW
P ex -excitation power of the generator, MW
b. High, medium and low pressure cylinder efficiency
h o -inlet enthalpy of high, medium and low pressure cylinders of the steam turbine kJ/kg
h c -exhaust enthalpy of high, medium and low pressure cylinders of the steam turbine kJ/kg
hcl-isentropic expansion end point enthalpy of high, medium and low pressure cylinders of steam turbine, kJ/kg
c. Upper end difference of heater
δ＝t s -t o
Delta-poor upper end of heater, [ deg. ] C
t s -saturation temperature at the working pressure of the heater, ° c
t o -outlet water temperature of heater, ° c
d. Lower end difference of heater
γ＝t d -t i
Gamma-poor lower end of heater, ° c
t d -hydrophobic temperature of heater, ° c
t i -inlet water temperature of heater, ° c
e. End difference of condenser
Δt k ＝t bbh -t xhi
Δt k Condenser end-to-end, deg.C
t bbh Saturation temperature of steam turbine exhaust pressure, DEG C
t xhi Inlet temperature of circulating water, deg.C
f. Degree of supercooling of condensed water
Δt gl ＝t bbh -t rj
Δt gl Degree of supercooling of condensate, DEG C
t bbh Saturation temperature of steam turbine exhaust pressure, DEG C
t rj -temperature of condensate in condenser hot well, C
g. Power rate of power plant
W d ＝W cy -W r
L fcy -power plant power consumption,%
W cy -the amount of service power consumption in the statistical period
W d Plant power consumption for power generation, kWh
W r Plant power consumption for heat supply, kWh
h. Rate of coal consumption of power supply
b gd The coal consumption rate of power generation, g/(kWh).
4. The consumption difference analysis method for self-optimization of operating parameters of a thermal power generating unit according to claim 1, wherein the criterion of the working condition in the step 10 is that:
and standard variance calculation is carried out on all the 7 parameters of the main steam temperature, the main steam pressure, the reheating temperature, the reheating pressure, the unit load, the exhaust steam pressure and the condensate flow entering the deaerator in the fixed time period, and the formula is as follows:
in the formula: delta 2 Represents the standard deviation of a certain parameter;
the smaller the standard deviation is, the more stable the parameter is in the calculated time period, when the standard deviation of the main steam temperature and the reheat temperature is below 5, the standard deviation of the main steam pressure and the reheat pressure is below 0.3, the standard deviation of the unit load is below 10, the standard deviation of the exhaust steam pressure is below 0.5, and the standard deviation of the condensate flow is below 10, the condition in the time interval can be judged to be in a stable state.
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