A method, system, device and storage medium for optimizing working conditions based on historical data analysis

Abstract

The present invention discloses a method, system, device and storage medium for optimizing working conditions based on historical data analysis, including: obtaining real-time and historical data of unit operating parameters, taking the first benchmark data of the historical data as the working condition judgment condition, dividing the historical data into working conditions to obtain working condition intervals; sorting the historical data based on the first-level index of the second benchmark data in the working condition interval, and selecting the data at the first ratio position as the optimization value of the second benchmark data in the working condition interval; matching the real-time value of the first benchmark data with the divided working condition interval; assisting the operator to adjust the parameters based on the influencing factors. The present invention can accurately determine the operating parameter benchmark values ​​of thermal power units under different working conditions and quantitatively analyze the relevant parameters affecting the power supply coal consumption, and improve the unit's operating economy and environmental performance through operation adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power unit operation optimization, and in particular to an operating condition optimization method, system, equipment and storage medium based on historical data analysis. Background Art

[0002] In recent years, under the development trend of new power systems, new energy will gradually replace traditional thermal power and become the main energy supplier, and the role of traditional thermal power in the power system will gradually change from a power provider to a frequency regulation, standby, and capacity service provider. The daily peak regulation range of thermal power units continues to expand, and power generation auxiliary services such as deep peak regulation, start-stop peak regulation, and frequency regulation auxiliary services have become the norm; at the same time, the requirements for energy saving and consumption reduction, carbon emission reduction, etc. of the units are getting higher and higher. Power supply coal consumption is a key indicator reflecting the energy efficiency of the unit. Reducing the power supply coal consumption of the unit has a significant effect on reducing carbon emissions in the power industry.

[0003] In order to optimize the unit operation control strategy and reduce the coal consumption of power supply under wide load operation conditions, it is necessary to determine the benchmark values ​​of each operating parameter of the unit under different operating conditions. The traditional method of determining the benchmark values ​​of operating parameters mainly comes from the equipment design manual or the unit thermal performance test, but the design manual often only gives the benchmark values ​​of key parameters under the design conditions, and the thermal performance test only gives the benchmark values ​​of parameters under several typical conditions, which does not cover the full operating conditions of the unit; at the same time, as the unit's operating life increases, the parameter benchmark values ​​will also change. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method, system, device and storage medium for optimizing operating conditions based on historical data analysis to solve the problems that existing methods cannot accurately determine the baseline values ​​of operating parameters of thermal power units under different operating conditions and cannot accurately quantitatively analyze the parameters related to power supply coal consumption.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing operating conditions based on historical data analysis, comprising: acquiring real-time and historical data of unit operating parameters, taking first reference data of the historical data as an operating condition judgment condition, and dividing the historical data into operating conditions to obtain operating condition intervals;

[0009] The operating condition interval sorts the historical data based on the first-level index of the second benchmark data, and selects the data at the first ratio position as the optimal value of the second benchmark data in the operating condition interval;

[0010] Match the real-time value of the first benchmark data with the divided working condition interval. If the deviation between the real-time value of the first-level indicator of the current second benchmark data and the optimal value is greater than the preset threshold, the key parameters affecting the first-level indicator of the second benchmark data are analyzed by calculating the difference between the real-time value and the optimal value of the second-level indicator and the third-level indicator of the second benchmark data respectively;

[0011] Based on the influencing factors, assist the operator to adjust the parameters.

[0012] As a preferred solution of the method for optimizing the working condition based on historical data analysis described in the present invention, wherein: the first benchmark data of the historical data is the working condition judgment condition, and the working condition intervals obtained by dividing the historical data into working conditions include: the first benchmark data includes unit load, heating ratio and ambient temperature;

[0013] When the operating conditions are divided by unit load, the interval from zero to 30% of the installed capacity is taken as the first operating condition interval; the interval where the unit load is higher than 90% of the installed capacity is taken as the second operating condition interval; the interval where the unit load is between 30% and 90% of the installed capacity is divided into other operating condition intervals with the first step as the interval;

[0014] When the working condition is divided by the heating ratio, the interval from zero to the maximum heating ratio that can be achieved during operation is used as the interval, and each working condition interval is obtained by dividing it with the second step length as the interval;

[0015] When the working condition is divided according to the ambient temperature, the interval from the lowest ambient temperature to the highest ambient temperature in the historical data is divided into various working condition intervals at intervals of the third step length.

[0016] As a preferred solution of the method for optimizing the working condition based on historical data analysis described in the present invention, wherein: the working condition interval sorts the historical data based on the second benchmark data, and selects the data at the first ratio position as the optimal value of the parameter in the working condition interval, including: the second benchmark data includes the first-level indicator power supply coal consumption;

[0017] The secondary indicators that affect power supply coal consumption include boiler efficiency, steam turbine efficiency and plant power consumption rate; each secondary indicator corresponds to a corresponding third-level sub-indicator;

[0018] When optimizing the unit operating conditions, the secondary indicators that affect the power supply coal consumption are identified through the deviation of the operating parameters, and the tertiary indicators that affect the parameter changes are found based on the secondary indicators.

[0019] As a preferred solution of the working condition optimization method based on historical data analysis described in the present invention, it also includes: dividing the historical data into working conditions to obtain Three-dimensional operating condition intervals are arranged in ascending order based on the power supply coal consumption in each three-dimensional operating condition interval. Row data, [0.1* ] The data in the row are the optimal values ​​of all parameters under this working condition.

[0020] As a preferred solution of the operating condition optimization method based on historical data analysis described in the present invention, matching the real-time value of the operating parameter with the divided operating condition interval includes:

[0021] The current real-time unit load, heating ratio and ambient temperature are matched to obtain the first The working condition interval is the matching interval, where is a positive integer, and ;

[0022] Obtain the difference between the current real-time value of power supply coal consumption and the optimal value of power supply coal consumption that matches the operating condition interval. If the difference is greater than the preset threshold, perform operating condition optimization.

[0023] As a preferred solution of the method for optimizing the operating condition based on historical data analysis described in the present invention, wherein: by calculating the difference between the real-time value and the optimized value of the operating parameter, the influencing factors of the second benchmark data are analyzed, including: when optimizing the unit operating condition, respectively calculating the difference between the real-time value of the current boiler efficiency, the steam turbine efficiency, and the plant power rate and the optimized value of the boiler efficiency, the steam turbine efficiency, and the plant power rate in the matching operating condition interval;

[0024] The difference in boiler efficiency, turbine efficiency and plant power consumption rate is expressed as:

[0025] ,

[0026] in, , , are the consumption difference factors of boiler efficiency, turbine efficiency and plant power consumption rate, is the difference between the real-time value and the optimal value of boiler efficiency, is the difference between the real-time value and the optimal value of the turbine efficiency, It is the difference between the real-time value and the optimal value of the plant power consumption rate.

[0027] As a preferred solution of the working condition optimization method based on historical data analysis described in the present invention, it also includes:

[0028] If the boiler efficiency consumption difference is greater than the first threshold, the relevant parameters affecting the boiler efficiency are optimized, and the consumption differences of the boiler efficiency sub-indicators are calculated respectively. According to the consumption difference values ​​of each sub-indicator parameter, the key parameters that reduce the boiler efficiency are determined; similarly,

[0029] If the consumption difference of the steam turbine efficiency is greater than the first threshold, the consumption difference of the steam turbine efficiency sub-indicators is calculated respectively to determine the influencing factors of the steam turbine efficiency; if the consumption difference of the plant power rate is greater than the first threshold, the consumption difference of the plant power rate sub-indicators is calculated respectively to determine the influencing factors of the plant power rate.

[0030] In a second aspect, the present invention provides a working condition optimization system based on historical data analysis, comprising:

[0031] A data acquisition and division module is used to acquire real-time and historical data of the unit operation parameters, and divide the historical data into operating conditions to obtain operating condition intervals using the first reference data of the historical data as the operating condition judgment condition;

[0032] An optimal value determination module, used for sorting the historical data in the operating interval based on the first-level index of the second benchmark data, and selecting the data at the first ratio position as the optimal value of the second benchmark data in the operating interval;

[0033] A monitoring and matching module is used to match the real-time value of the first benchmark data with the divided working condition interval. If the deviation between the real-time value of the first-level indicator of the current second benchmark data and the optimal value is greater than a preset threshold, the difference between the real-time value of the second-level indicator and the third-level indicator of the second benchmark data and the optimal value is calculated respectively to analyze and obtain the key parameters affecting the first-level indicator of the second benchmark data;

[0034] The parameter adjustment module is used to assist operators in adjusting parameters based on influencing factors.

[0035] In a third aspect, the present invention provides a computing device, comprising:

[0036] Memory and processor;

[0037] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the operating condition optimization method based on historical data analysis are implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the operating condition optimization method based on historical data analysis.

[0039] Compared with the prior art, the present invention has the following beneficial effects: the present invention adds ambient temperature and heat supply ratio as working condition judgment conditions, considers the influence of unit heating and environmental factors on unit economic indicators, and improves the accuracy of parameter optimization value; the present invention not only optimizes macro indicators such as power supply coal consumption, boiler efficiency, steam turbine efficiency, plant power consumption rate, etc., but also expands the optimization scope to specific sub-indicators, and classifies the sub-indicators to achieve step-by-step expansion of optimization, thereby expanding the optimization scope; the working condition optimization is combined with indicator consumption difference analysis, and the parameter consumption difference is calculated in real time based on the parameter optimization value. The influence of each parameter on the power supply coal consumption is obtained through consumption difference analysis, and through quantitative analysis, the operating personnel are guided to adjust the parameters to approach the optimization value, reduce the power supply coal consumption, and improve the operating economy and environmental protection performance of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 A method flow chart of a method for optimizing working conditions based on historical data analysis according to an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of system modules of a working condition optimization system based on historical data analysis according to an embodiment of the present invention;

[0043] Figure 3 A distribution diagram of power supply coal consumption after data cleaning of a method for optimizing working conditions based on historical data analysis according to an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of parameter difference analysis of a working condition optimization method based on historical data analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0048] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0049] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Example 1

[0052] Reference Figure 1~Figure 2 , is an embodiment of the present invention, which provides a method for optimizing working conditions based on historical data analysis, including:

[0053] S1: Acquire real-time and historical data of the unit operating parameters, use the first reference data of the historical data as the working condition judgment condition, and divide the historical data into working conditions to obtain working condition intervals;

[0054] It should be noted that in the embodiment of the present application, the historical data of the unit including the first benchmark data and the second benchmark data can be obtained through the plant-level monitoring information system (SIS system) or the DCS control system data interface system; the first benchmark data includes the unit load, heat supply ratio and ambient temperature; the second benchmark data includes the first-level index, the second-level index and the third-level sub-indicator; among which, the first-level index is the power supply coal consumption; the second-level index is the boiler efficiency, the steam turbine efficiency and the plant power consumption rate; the sub-indicators of the boiler efficiency include the exhaust gas temperature, the exhaust gas oxygen content, the fly ash carbon content, the low calorific value of the coal entering the furnace, the moisture content of the coal entering the furnace, and the ash content of the coal entering the furnace. , combustible gas content; the sub-indicators of steam turbine efficiency include main steam pressure, main steam temperature, reheat steam temperature, unit back pressure, feed water temperature, superheater desuperheating water flow rate, reheater desuperheating water flow rate, and terminal difference of each heater; the sub-indicators of plant power rate include coal mill plant power rate, induced draft fan plant power rate, forced draft fan plant power rate, primary fan plant power rate, electric feed water pump plant power rate, condensate water system plant power rate, circulating water system plant power rate, desulfurization system plant power rate, dust removal and ash removal system plant power rate, denitrification system plant power rate, coal transportation system plant power rate, chemical water system plant power rate, and other plant power rates.

[0055] The historical data in the embodiment of the present application needs to be processed before the operating condition division, including value range cleaning, rate cleaning, discrete data cleaning and other cleaning methods, to remove the unit shutdown data and abnormal jump data, and retain the historical data of normal operation.

[0056] In the embodiment of the present application, the first reference data of the historical data is the working condition judgment condition, and the working condition intervals obtained by dividing the historical data into working conditions include:

[0057] When the operating conditions are divided by unit load, the interval from zero to 30% of the installed capacity is taken as the first operating condition interval; the interval where the unit load is higher than 90% of the installed capacity is taken as the second operating condition interval; the interval where the unit load is between 30% and 90% of the installed capacity is divided into other operating condition intervals with the first step as the interval;

[0058] When the working condition is divided by the heating ratio, the interval from zero to the maximum heating ratio that can be achieved during operation is used as the interval, and each working condition interval is obtained by dividing it with the second step length as the interval;

[0059] When the working condition is divided according to the ambient temperature, the interval from the lowest ambient temperature to the highest ambient temperature in the historical data is divided into various working condition intervals at intervals of the third step length.

[0060] It should be noted that in the embodiment of the present application, the first step length may be 10 MW, the second step length may be 0.02, and the third step length may be 5 degrees Celsius.

[0061] In an optional embodiment, the operating condition is divided based on the unit load, heating ratio, and ambient temperature as the operating condition judgment conditions. The unit load is divided into 10MW intervals in the interval [unit installed capacity*0.3, unit installed capacity*0.9], (0, unit installed capacity*0.3) as a working condition interval, and (unit installed capacity*0.9, +∞) as a working condition interval; the heating ratio is divided into 0.02 intervals in the interval (0, maximum heating ratio); the ambient temperature is divided into 5°C intervals in the interval (minimum ambient temperature, maximum ambient temperature).

[0062] S2: The operating condition interval sorts the historical data based on the second benchmark data, and selects the data at the first ratio position as the optimal parameter value of the operating condition;

[0063] The operating condition interval sorts the historical data based on the second benchmark data, and selects the data at the first ratio position as the optimal value of the parameter in the operating condition interval, including: the second benchmark data includes the first-level indicator power supply coal consumption;

[0064] The secondary indicators that affect power supply coal consumption include boiler efficiency, steam turbine efficiency and plant power consumption rate; each secondary indicator corresponds to a corresponding third-level sub-indicator;

[0065] When optimizing the unit operating conditions, the secondary indicators that affect the power supply coal consumption are identified through the deviation of the operating parameters, and the tertiary indicators that affect the parameter changes are found based on the secondary indicators.

[0066] It should be noted that the first-level indicator of power supply coal consumption and the second-level indicators of boiler efficiency, steam turbine efficiency, and plant power consumption rate are optimization target parameters, and the third-level sub-indicators are general optimization parameters.

[0067] It should also be noted that the heat supply ratio and ambient temperature have a relatively large impact on the power supply coal consumption, especially the heat supply factor (heat supply ratio). For example, in the non-heating period, the power supply coal consumption of a 600MW unit at 80% load is 300g / kWh, while in the heating period, the power supply coal consumption at 80% load will drop a lot, possibly to 260g / kWh. If the heat supply ratio is not considered and the optimal value is simply found by dividing the operating conditions according to the unit load, the power supply coal consumption in the heating period will be used as the optimal value. In the non-heating period, no matter how the operating parameters of the unit are optimized, it is impossible to reduce the power supply coal consumption to 260g / kWh, and the purpose of optimizing the operating conditions cannot be achieved.

[0068] In the embodiment of the present application, it also includes: dividing the historical data into working conditions to obtain Three-dimensional operating condition intervals are arranged in ascending order based on the power supply coal consumption in each three-dimensional operating condition interval. Row data, [0.1* ] The data in the row are the optimal values ​​of all parameters under this working condition.

[0069] In an optional embodiment, the historical data is divided into Three-dimensional operating intervals; in the first operating interval, based on the coal consumption of power supply, the results are arranged in ascending order. row data, find the [0.1* ] (rounding function) row data is used as the optimal value of all parameters under this working condition; in the second working condition interval, based on the power supply coal consumption as the benchmark, it is arranged in ascending order to obtain row data, find the [0.1* ] row data is used as the optimal value of all parameters under this condition; and so on.

[0070] S3: Matching the real-time operating parameters with the divided operating condition intervals, if the deviation of the current second benchmark data does not meet the preset threshold, then analyzing the influencing factors of the second benchmark data by calculating the difference between the real-time operating parameters and the optimal parameter value;

[0071] In the embodiment of the present application, matching the real-time value of the operating parameter with the divided operating condition interval includes:

[0072] The current real-time unit load, heating ratio and ambient temperature are matched to obtain the first The working condition interval is the matching interval, where is a positive integer, and ;

[0073] Obtain the difference between the current real-time value of power supply coal consumption and the optimal value of power supply coal consumption that matches the operating condition interval. If the difference is greater than the preset threshold, perform operating condition optimization.

[0074] It should be noted that in the embodiment of the present application, the preset threshold can be set to 2g / kWh. For power supply coal consumption, when it is found that the real-time power supply coal consumption is 2g / kWh higher than the historical optimal power supply coal consumption, the optimization search is started to find the influencing factors and further decompose them; if the real-time power supply coal consumption is not 2g / kWh higher than the historical optimal power supply coal consumption, the optimization search is not performed and the status quo is maintained.

[0075] In the embodiment of the present application, by calculating the difference between the real-time value and the optimal value of the operating parameter, the influencing factors of the second benchmark data are analyzed and obtained, including: when optimizing the unit operating conditions, respectively calculating the difference between the real-time value of the current boiler efficiency, steam turbine efficiency, and plant power rate and the optimal value of the boiler efficiency, steam turbine efficiency, and plant power rate in the matching operating condition interval;

[0076] The difference in boiler efficiency, turbine efficiency and plant power consumption rate is expressed as:

[0077] ,

[0078] in, , , are the consumption difference factors of boiler efficiency, turbine efficiency and plant power consumption rate, is the difference between the real-time value and the optimal value of boiler efficiency, is the difference between the real-time value and the optimal value of the turbine efficiency, It is the difference between the real-time value and the optimal value of the plant power consumption rate.

[0079] It should be noted that the various consumption difference factors in the embodiments of the present application are obtained based on the "DLT 2170-2020 Coal-fired Unit Consumption Difference Calculation Method".

[0080] In the embodiment of the present application, it also includes:

[0081] If the boiler efficiency consumption difference is greater than the first threshold, the relevant parameters affecting the boiler efficiency are optimized, and the consumption differences of the boiler efficiency sub-indicators are calculated respectively. According to the consumption difference values ​​of each sub-indicator parameter, the key parameters affecting the boiler efficiency are determined; similarly,

[0082] If the steam turbine efficiency consumption difference is greater than the first threshold, the consumption difference of the steam turbine efficiency sub-indicators is calculated respectively to determine the key parameters affecting the steam turbine efficiency; if the plant power rate consumption difference is greater than the first threshold, the consumption difference of the plant power rate sub-indicators is calculated respectively to determine the key parameters affecting the plant power rate.

[0083] It should be noted that the first threshold in the embodiment of the present application may be 0.5.

[0084] In an optional embodiment, if , optimize the relevant parameters that affect boiler efficiency, and calculate the consumption differences of flue gas temperature, flue gas oxygen content, fly ash carbon content, low calorific value of coal entering the furnace, moisture content of coal entering the furnace, ash content of coal entering the furnace, and combustible gas content. Compare the values ​​of consumption differences and find the largest three. The corresponding parameters are the three major parameters that affect the reduction of boiler efficiency.

[0085] like , then calculate the main steam pressure, main steam temperature, reheat steam temperature, unit back pressure, feed water temperature, superheater desuperheating water flow, reheater desuperheating water flow, and the consumption difference of each heater end difference respectively, and find out the three major parameters that affect the reduction of steam turbine efficiency;

[0086] like , then calculate the power consumption rate of coal mill, induced draft fan, forced draft fan, primary fan, electric feed water pump, condensate water system, circulating water system, desulfurization system, dust and ash removal system, denitrification system, coal transportation system, chemical water system and other power consumption rates respectively, and find out the three major parameters that affect the increase of power consumption rate.

[0087] S4: Based on the above analysis results, assist the operator to adjust parameters.

[0088] In an optional embodiment, if the analysis shows that the relevant influencing factors are low feed water temperature, high reheater desuperheating water flow, high flue gas oxygen content, etc., the relevant processing measures for parameter adjustment by the operating personnel include adjusting the opening of the continuous exhaust air valve, strictly controlling the water level during the high-pressure water level adjustment process, and strictly controlling the feed water temperature rise and temperature drop rate as required when the high-pressure water heater is put into operation and stopped; optimizing the boiler operation mode, adjusting the boiler air volume and coal volume, and reducing the reheater overheating and desuperheating water demand; ensuring that the ratio of primary and secondary air meets the boiler load requirements, and adjusting the air volume in time when the load changes.

[0089] The above is a schematic scheme of a method for optimizing working conditions based on historical data analysis in this embodiment. It should be noted that the technical scheme of the system for optimizing working conditions based on historical data analysis and the technical scheme of the method for optimizing working conditions based on historical data analysis mentioned above belong to the same concept. For the details not described in detail in the technical scheme of the system for optimizing working conditions based on historical data analysis in this embodiment, please refer to the description of the technical scheme of the method for optimizing working conditions based on historical data analysis mentioned above.

[0090] In this embodiment, a working condition optimization system based on historical data analysis includes:

[0091] The data acquisition and division module 100 is used to obtain the real-time and historical data of the unit operation parameters, and divide the historical data into operating conditions to obtain the operating condition intervals by taking the first reference data of the historical data as the operating condition judgment condition;

[0092] The optimal value determination module 200 is used to sort the historical data based on the first-level index of the second benchmark data in the working condition interval, and select the data at the first ratio position as the optimal value of the second benchmark data in the working condition interval;

[0093] The monitoring and matching module 300 is used to match the real-time value of the first benchmark data with the divided working condition interval. If the deviation between the real-time value of the first-level indicator of the second benchmark data and the optimal value is greater than a preset threshold, the difference between the real-time value of the second-level indicator and the third-level indicator of the second benchmark data and the optimal value is calculated to analyze and obtain the key parameters affecting the first-level indicator of the second benchmark data;

[0094] The parameter adjustment module 400 is used to assist the operator in adjusting the parameters based on the influencing factors.

[0095] This embodiment further provides a computing device, which is applicable to the case of a working condition optimization method based on historical data analysis, including:

[0096] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the working condition optimization method based on historical data analysis as proposed in the above embodiment.

[0097] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the working condition based on historical data analysis as proposed in the above embodiment is implemented.

[0098] The storage medium proposed in this embodiment and the method for optimizing the working condition based on historical data analysis proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0099] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0100] Example 2

[0101] Reference Figure 3~Figure 4 , which is an embodiment of the present invention. This embodiment is different from the first embodiment in that this embodiment takes a 315MW subcritical heating unit as an example to verify the beneficial effects of the present invention.

[0102] In this embodiment, the historical data of the unit load, heat supply ratio, ambient temperature, power supply coal consumption and other parameters in the past year are obtained through the SIS system. The unit shutdown data and abnormal jump data are removed through cleaning methods such as value range cleaning, rate cleaning, and discrete data cleaning to obtain the historical data under the normal operation of the unit. Figure 3 As shown, Figure 3 The vertical axis showing frequency density is the ratio of frequency to total number of samples, which reflects the relative frequency of data in each interval. The sum of frequencies of all intervals is 1. For example, if the total number of samples is 50 and the frequency in a certain interval is 10, then the frequency of the interval is 10÷50=0.2. Figure 3 The height corresponding to the vertical coordinate is 0.2.

[0103] Import historical data into the working condition optimization system as the optimization history library.

[0104] The operating condition judgment conditions are as follows: the unit load below 94.5MW is divided into an interval of 10MW between 94.5MW and 284.5MW, and above 284.5MW is divided into an interval; the heating ratio between 0 and 0.58 is divided into intervals of 0.02; the ambient temperature between -20℃ and 40℃ is divided into intervals of 5℃. After division, a total of 7308 operating condition intervals are obtained. The operating condition optimization system matches the optimization values ​​of each parameter in each operating condition interval from historical data according to the operating condition judgment conditions and the optimization value acquisition method, and records them in the operating condition optimization benchmark library, as shown in Table 1:

[0105] Table 1 Power supply coal consumption benchmark database

[0106] ,

[0107] ,

[0108] For example, in this embodiment, the real-time values ​​of the operating condition judgment parameters (unit load, ambient temperature, and heat supply ratio) are 131.95MW, 31.89℃, and 0, respectively, and the real-time value of the power supply coal consumption is 333.77 g / kWh; according to the matching results of the operating condition optimization system, the optimal value of the power supply coal consumption under similar operating conditions is 331.39 g / kWh (as the optimization value for this operating condition), and the corresponding operating condition judgment parameters are: the unit load is 133.81MW, the ambient temperature is 32.1℃, and the heat supply ratio is 0.

[0109] At this time, the operating condition optimization system detected that the deviation between the actual value of power supply coal consumption and the optimal value was greater than 2g / kWh, and further optimization decomposition was required to calculate the deviation between the actual value of boiler efficiency, steam turbine efficiency and plant power consumption rate and their optimal values, and at the same time, combined with the consumption difference factor, calculate the consumption difference of the three parameters. The consumption difference results are shown in Table 2:

[0110] Table 2 Consumption difference calculation results

[0111] ,

[0112] As can be seen from Table 2, the consumption difference of steam turbine efficiency is large. We continue to optimize and decompose the relevant parameters affecting steam turbine efficiency, and calculate the consumption difference of main steam pressure, main steam temperature, reheat steam temperature, unit back pressure, feed water temperature, superheater desuperheating water flow, reheater desuperheating water flow, and each heater end difference. We sort the consumption difference values ​​and find out that the top three consumption difference values ​​are feed water temperature 0.47 g / kWh, unit back pressure 0.15 g / kWh, and main steam pressure 0.11 g / kWh. Figure 4 In this embodiment, the feed water temperature with the largest consumption difference of the unit is taken as an example. The actual value monitored is 272.58°C, the optimal value is 278.79°C, the deviation value is -6.06°C, and the consumption difference is 0.47 g / kWh.

[0113] A consumption difference analysis was conducted on the feed water temperature with the largest consumption difference, and the relevant influencing factors were found to be unstable heater water level, insufficient heater steam, internal leakage on the water side of the heater, poor heat exchange effect of the heater, etc. The relevant treatment measures for parameter adjustment by the operating personnel can be to ensure the normal opening of the relevant valves on the heater extraction pipe; ensure that the air valves of each heater are switched correctly to ensure the heating effect and reduce the heater end difference; maintain the normal water level of the heater to prevent erosion of the tube bundle and cause heater leakage; each heater should be started and stopped randomly as much as possible, and other controls should be within the qualified range to prevent damage due to excessive thermal stress of the tube bundle, thereby reducing the unit's power supply coal consumption.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing working conditions based on historical data analysis, characterized in that: include: Acquire real-time and historical data of the unit operating parameters, use the first reference data of the historical data as the working condition judgment condition, and divide the historical data into working conditions to obtain working condition intervals; The first reference data of the historical data is the working condition judgment condition, and the working condition intervals obtained by dividing the historical data into working conditions include: the first reference data includes unit load, heating ratio and ambient temperature; When the operating conditions are divided by unit load, the interval from zero to 30% of the installed capacity is taken as the first operating condition interval; the interval where the unit load is higher than 90% of the installed capacity is taken as the second operating condition interval; the interval where the unit load is between 30% and 90% of the installed capacity is divided into other operating condition intervals with the first step as the interval; When the working condition is divided by the heating ratio, the interval from zero to the maximum heating ratio that can be achieved during operation is used as the interval, and each working condition interval is obtained by dividing it with the second step length as the interval; When the working condition is divided by ambient temperature, the lowest ambient temperature to the highest ambient temperature interval in the historical data is divided into various working condition intervals with the third step length as the interval; The operating condition interval sorts the historical data based on the first-level indicator of the second benchmark data, and selects the data at the first ratio position as the optimal value of the second benchmark data in the operating condition interval; the operating condition interval sorts the historical data based on the second benchmark data, and selects the data at the first ratio position as the optimal value of the parameter in the operating condition interval includes: the second benchmark data includes the first-level indicator power supply coal consumption; The secondary indicators that affect power supply coal consumption include boiler efficiency, steam turbine efficiency and plant power consumption rate; each secondary indicator corresponds to a corresponding third-level sub-indicator; When optimizing the unit operating conditions, the secondary indicators that affect the power supply coal consumption are identified through the deviation of the operating parameters, and the tertiary indicators that affect the parameter changes are found based on the secondary indicators; The method also includes: dividing the historical data into m three-dimensional operating condition intervals, arranging them in ascending order based on the power supply coal consumption in each three-dimensional operating condition interval, and obtaining n m Row data, [0.1*n m ] The data in the row are the optimal values ​​of all parameters under this working condition; Matching the real-time value of the first benchmark data with the divided operating condition interval, if the deviation between the real-time value of the first-level indicator of the current second benchmark data and the optimal value is greater than the preset threshold, then by calculating the difference between the real-time value and the optimal value of the second-level indicator and the third-level indicator of the second benchmark data, the key parameters affecting the first-level indicator of the second benchmark data are analyzed; matching the real-time value of the operating parameter with the divided operating condition interval includes: The current real-time unit load, heating ratio and ambient temperature are matched to obtain the xth operating condition interval as the matching interval, where x is a positive integer and x≤m; Obtain the difference between the current real-time value of power supply coal consumption and the optimal value of power supply coal consumption matching the working condition interval. If the difference is greater than the preset threshold, perform working condition optimization; Based on the influencing factors, assist the operator to adjust the parameters.

2. The method for optimizing working conditions based on historical data analysis according to claim 1, characterized in that: By calculating the difference between the real-time value and the optimal value of the operating parameters, the influencing factors of the second benchmark data are analyzed, including: when optimizing the unit operating conditions, the difference between the real-time values ​​of the current boiler efficiency, steam turbine efficiency, and plant power rate and the optimal values ​​of the boiler efficiency, steam turbine efficiency, and plant power rate in the matching operating condition interval are calculated respectively; The difference in boiler efficiency, turbine efficiency and plant power consumption rate is expressed as: b η =D*F η 、b γ =Δγ*F γ 、b c =Δc*F c Among them, F η 、F γ 、F c are the consumption difference factors of boiler efficiency, steam turbine efficiency and plant power consumption rate respectively, Δη is the difference between the real-time value and the optimal value of boiler efficiency, Δγ is the difference between the real-time value and the optimal value of steam turbine efficiency, and Δc is the difference between the real-time value and the optimal value of plant power consumption rate.

3. The method for optimizing working conditions based on historical data analysis according to claim 2, characterized in that: Also includes: If the boiler efficiency consumption difference is greater than the first threshold, the relevant parameters affecting the boiler efficiency are optimized, and the consumption differences of the boiler efficiency sub-indicators are calculated respectively. According to the consumption difference values ​​of each sub-indicator parameter, the key parameters affecting the boiler efficiency are determined; similarly, If the steam turbine efficiency consumption difference is greater than the first threshold, the consumption difference of the steam turbine efficiency sub-indicators is calculated respectively to determine the key parameters affecting the steam turbine efficiency; if the plant power rate consumption difference is greater than the first threshold, the consumption difference of the plant power rate sub-indicators is calculated respectively to determine the key parameters affecting the plant power rate.

4. A working condition optimization system based on historical data analysis, characterized in that: include: A data acquisition and division module (100) is used to acquire real-time and historical data of unit operating parameters, and to divide the historical data into operating conditions to obtain operating condition intervals using first reference data of the historical data as operating condition judgment conditions; The first reference data of the historical data is the working condition judgment condition, and the working condition intervals obtained by dividing the historical data into working conditions include: the first reference data includes unit load, heating ratio and ambient temperature; When the operating conditions are divided by unit load, the interval from zero to 30% of the installed capacity is taken as the first operating condition interval; the interval where the unit load is higher than 90% of the installed capacity is taken as the second operating condition interval; the interval where the unit load is between 30% and 90% of the installed capacity is divided into other operating condition intervals with the first step as the interval; When the working condition is divided by the heating ratio, the interval from zero to the maximum heating ratio that can be achieved during operation is used as the interval, and each working condition interval is obtained by dividing it with the second step length as the interval; When the working condition is divided by ambient temperature, the lowest ambient temperature to the highest ambient temperature interval in the historical data is divided into various working condition intervals with the third step length as the interval; An optimal value determination module (200) is used for sorting historical data in the operating condition interval based on the first-level index of the second benchmark data, and selecting data at the first ratio position as the optimal value of the second benchmark data in the operating condition interval; the operating condition interval sorts historical data based on the second benchmark data, and selects data at the first ratio position as the optimal value of the parameter in the operating condition interval, including: the second benchmark data includes the first-level index power supply coal consumption; The secondary indicators that affect power supply coal consumption include boiler efficiency, steam turbine efficiency and plant power consumption rate; each secondary indicator corresponds to a corresponding third-level sub-indicator; When optimizing the unit operating conditions, the secondary indicators that affect the power supply coal consumption are identified through the deviation of the operating parameters, and the tertiary indicators that affect the parameter changes are found based on the secondary indicators; The method also includes: dividing the historical data into m three-dimensional operating condition intervals, arranging them in ascending order based on the power supply coal consumption in each three-dimensional operating condition interval, and obtaining n m Row data, [0.1*n m ] The data in the row are the optimal values ​​of all parameters under this working condition; The monitoring matching module (300) is used to match the real-time value of the first benchmark data with the divided working condition interval. If the deviation between the real-time value of the first-level indicator of the current second benchmark data and the optimal value is greater than a preset threshold, the difference between the real-time value of the second-level indicator and the third-level indicator of the second benchmark data and the optimal value is calculated to analyze and obtain the key parameters affecting the first-level indicator of the second benchmark data; matching the real-time value of the operating parameter with the divided working condition interval includes: The current real-time unit load, heating ratio and ambient temperature are matched to obtain the xth operating condition interval as the matching interval, where x is a positive integer and x≤m; Obtain the difference between the current real-time value of power supply coal consumption and the optimal value of power supply coal consumption matching the working condition interval. If the difference is greater than the preset threshold, perform working condition optimization; The parameter adjustment module (400) is used to assist the operator in adjusting the parameters based on the influencing factors.

5. A computing device, characterized in that: include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the operating condition optimization method based on historical data analysis described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the working condition optimization method based on historical data analysis as described in any one of claims 1 to 3.

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