Gas unit consumption difference analysis method and system based on multi-dimensional data analysis

Abstract

The present invention relates to a method and system for analyzing the consumption difference of a gas unit based on multi-dimensional data analysis, which belongs to the technical field of gas unit operation optimization, and includes the following steps: Step 1, collecting real-time operating parameters; Step 2, real-time data screening and processing: screening the collected real-time data; identifying data jumps, and simulating and assigning values ​​based on data within 10 seconds; Step 3, abnormal data processing; Step 4, collecting historical operating data; Step 5, operating condition division and parameter threshold configuration; Step 6, daily report data statistics; Step 7, consumption difference analysis and economic evaluation. The advantages of the present invention are: comprehensiveness: by collecting and analyzing the real-time operating parameters, historical operating data, auxiliary equipment status and daily report data of the gas unit, the method can comprehensively evaluate the operating status of the unit and ensure the comprehensiveness and accuracy of the analysis results.

Description

Technical Field

[0001] The present invention relates to a gas unit consumption difference analysis method based on multi-dimensional data analysis and a system thereof, belonging to the technical field of gas unit operation optimization. Background Art

[0002] The consumption difference analysis system of existing gas units usually relies on a preset benchmark value, which is often determined based on design parameters or test values. This method may be relatively accurate in the initial operation stage of the unit, but as time goes by and operating conditions change, the design value may no longer reflect the actual operating status of the unit, resulting in deviations in the results of the consumption difference analysis. This deviation may mislead operation and maintenance decisions and affect the economy and efficiency of the unit.

[0003] On the other hand, although extracting benchmark values ​​from historical operating data can better reflect the actual operating conditions of the unit, this method also has its limitations. In practical applications, if the selection of historical data does not fully consider the operating conditions of the unit's main and auxiliary equipment, such as equipment wear, aging or maintenance, the obtained benchmark values ​​may not be accurate enough to truly reflect the optimal operating conditions of the unit. In addition, environmental factors, changes in fuel quality, and the technical level of operators will also affect the accuracy of consumption difference analysis.

[0004] Nowadays, thermal power units often use the consumption difference analysis method to quantitatively analyze the impact of various operating parameters on the unit's energy consumption. It converts the deviation between the real-time operating parameters and the reference value into a numerical value that affects the unit's energy consumption. However, in the actual operation of the unit, the reference value is often selected based on the design value, test value, etc. As the unit's operating time continues to increase, the reference value will also change continuously. Simply using the design value or test value as the basis for the consumption difference analysis cannot meet its accuracy. Summary of the invention

[0005] In order to overcome the defects of the prior art, the present invention provides a gas unit consumption difference analysis method and system based on multi-dimensional data analysis. The technical solution of the present invention is:

[0006] The gas unit consumption difference analysis method based on multi-dimensional data analysis includes the following steps:

[0007] S1. Collecting real-time operating parameters: collecting real-time operating parameters of the gas unit;

[0008] S2. Real-time data screening and processing: Screen the collected real-time data; identify data jumps and perform simulation assignment based on data within 10 seconds;

[0009] S3, abnormal data processing;

[0010] S4. Collect historical operation data;

[0011] S5. Working condition division and parameter threshold configuration: Use unit load data to divide working conditions; use the operating parameters of auxiliary equipment to configure the operating parameter threshold of the equipment;

[0012] S6. Daily report data statistics: statistics of daily power generation, gas consumption or converted coal consumption energy consumption parameters;

[0013] S7. Consumption difference analysis and economic evaluation: Based on the energy loss obtained from the consumption difference analysis, an economic evaluation of the unit's operating status for the day is conducted.

[0014] The step S1 is specifically as follows: collecting real-time operating parameters of the gas unit, including exhaust temperature of waste heat boiler, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load parameters; the operating parameters are transmitted to the database through the unit control system DCS, and the real-time operating parameters are obtained from the database.

[0015] The step S2 is specifically as follows: first, upper and lower limits are set for the real-time operating parameters of the gas unit, so as to perform threshold judgment on the obtained parameter value. When the parameter value is within the set threshold range, it indicates that the data is normal, and when the parameter value exceeds the set threshold, it indicates that the data is abnormal. At this time, the data is processed, and the average value of the data in the first 10 seconds before the parameter exceeds the threshold time point is taken as the current value for assignment.

[0016] The step S3 is specifically as follows: according to the upper and lower limits of the operating parameters, when the operating parameters exceed the threshold time for more than 1 minute, this indicates that the data abnormality is not caused by a jump but by damage to the measuring point, and this parameter will affect the consumption difference data. Therefore, the data affected by this parameter is marked in red, indicating that the data is abnormal, and an alarm is generated and fed back to the relevant personnel for processing.

[0017] The step S4 is specifically as follows: the historical operation data collected include waste heat boiler exhaust temperature, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load. The historical operation data is obtained from a database, and data in a certain time period is selected for subsequent processing.

[0018] The step S5 is specifically as follows: the unit consumption difference reference value will be different according to the unit load, the historical data is divided according to the unit load, each operating condition is optimized to obtain the reference value under each operating condition, and the unit load is divided into intervals of 5MW; after the unit is divided, the upper and lower limits are set for the operating parameters of the auxiliary equipment. When the auxiliary equipment operating parameters exceed the limit, it indicates that the unit operating data is unstable at this time, so this historical data is discarded in the optimization process.

[0019] The step S6 is specifically as follows: the power generation is calculated by the change value of the generator forward active power measurement point from 0:00 to 24:00 on the same day, the formula is: , where W 24 It indicates the value of the generator forward active power measurement point at 24 hours, W0 indicates the value of the generator forward active power measurement point at 0 hours, and n indicates the generator active power meter multiplier;

[0020] The gas consumption for the day is calculated by the cumulative measurement point change value of natural gas standard condition from 0:00 to 24:00 on the day. The formula is: , where G 24 G0 represents the value of the cumulative measurement point of natural gas standard condition at 24 hours, and G1 represents the value of the cumulative measurement point of natural gas standard condition at 0 hours. The difference between the real-time power generation rate and the reference value is obtained. The power generation rate under the reference value operating conditions is obtained through the real-time power generation rate and the difference. The formula is as follows: , where g fjz It indicates the power consumption rate of the current working condition under the reference value operating conditions; from this, the power consumption rate of the base value operating conditions of the day can be obtained, according to the formula: , thus obtaining the natural gas consumption under the benchmark operating conditions with the same power generation; by comparing G f and G fjz , using the formula: , and obtain the additional natural gas consumption rate under the real-time operating parameters of the day.

[0021] The step S7 is specifically as follows: after obtaining the natural gas consumption G f And the natural gas consumption G under the benchmark value fjz Finally, according to the natural gas unit price P, the actual fuel cost C = G f *P; fuel cost C under the benchmark value jz =G fjz *P; thus the actual operating conditions are obtained as the cost ΔC=CC jz , additional cost percentage , thus the economic losses can be obtained.

[0022] In step S4, the historical operation data is collected for selecting the reference value in the consumption difference analysis, with the power generation consumption rate as the target. The power generation consumption rate formula is: , where V f is the instantaneous consumption of natural gas under standard conditions; P f is the unit load; the lower the power generation rate, the lower the natural gas consumption and energy consumption under the same load, which is used as the target value for optimizing historical data.

[0023] A system for realizing a gas unit consumption difference analysis method based on multi-dimensional data analysis, comprising:

[0024] Real-time operation parameter collection module, used to collect real-time operation parameters of the gas unit;

[0025] The real-time data screening and processing module is used to screen the collected real-time data, identify data jumps, and perform simulation assignment based on data within 10 seconds;

[0026] The abnormal data processing module is used to monitor the data. If the measuring point is damaged and the data is abnormal, and the data exceeds the limit for more than 1 minute, the parameter will be marked in red and the abnormal data will be fed back to the relevant personnel for processing;

[0027] Historical operation data collection module, used for collecting historical operation data;

[0028] The operating condition division and parameter threshold configuration module is used to divide the operating conditions using the unit load data; and configure the operating parameter threshold of the equipment using the operating parameters of the auxiliary equipment;

[0029] Daily report data statistics module, used to count daily power generation, gas consumption or converted coal consumption energy consumption parameters;

[0030] The consumption difference analysis and economic evaluation module is used to combine the energy consumption loss obtained from the consumption difference analysis to conduct an economic evaluation of the unit's operating status on that day.

[0031] The advantages of the present invention are:

[0032] 1. Comprehensiveness: By collecting and analyzing the real-time operating parameters, historical operating data, auxiliary equipment status and daily report data of the gas unit, this method can comprehensively evaluate the operating status of the unit and ensure the comprehensiveness and accuracy of the analysis results.

[0033] 2. Real-time: Real-time data screening and processing ensure that the analysis system can respond to data changes in a timely manner. Through simulation assignment and abnormal data processing, it can quickly identify and handle data anomalies and reduce the impact on analysis results.

[0034] 3. Accuracy: Through simulation assignment and red marking of abnormal data, this method can effectively reduce the impact of data jumps and measuring point damage on consumption difference analysis and improve the accuracy of the benchmark value.

[0035] 4. Dynamicity: The operating condition division and parameter threshold configuration enable the system to dynamically adjust parameters according to the actual operating conditions of the unit, adapt to the operating requirements under different operating conditions, and improve the adaptability and flexibility of the system.

[0036] 5. Economic efficiency: Through daily report data statistics and consumption difference analysis, this method can evaluate the operating economy of the unit, help operators find potential energy-saving opportunities, optimize operating strategies, and reduce energy consumption costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the gas unit consumption difference analysis method based on multi-dimensional data analysis of the present invention.

[0038] Figure 2 It is a system structure block diagram of the gas unit consumption difference analysis method based on multi-dimensional data analysis implemented by the present invention.

[0039] Description of reference numerals:

[0040] 1. Real-time operation parameter collection module; 2. Real-time data screening and processing module; 3. Abnormal data processing module; 4. Historical operation data collection module; 5. Working condition division and parameter threshold configuration module; 6. Daily report data statistics module; 7. Consumption difference analysis and economic evaluation module. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0042] See also Figure 1 The present invention relates to a gas unit consumption difference analysis method based on multi-dimensional data analysis, comprising the following steps:

[0043] S1. Collecting real-time operating parameters: collecting real-time operating parameters of the gas unit;

[0044] S2. Real-time data screening and processing: Screen the collected real-time data; identify data jumps and perform simulation assignment based on data within 10 seconds;

[0045] S3, abnormal data processing;

[0046] S4. Collect historical operation data;

[0047] S5. Working condition division and parameter threshold configuration: Use unit load data to divide working conditions; use the operating parameters of auxiliary equipment to configure the operating parameter threshold of the equipment;

[0048] S6. Daily report data statistics: statistics of daily power generation, gas consumption or converted coal consumption energy consumption parameters;

[0049] S7. Consumption difference analysis and economic evaluation: Based on the energy loss obtained from the consumption difference analysis, an economic evaluation of the unit's operating status for the day is conducted.

[0050] The step S1 is specifically as follows: collecting real-time operating parameters of the gas unit, including exhaust temperature of waste heat boiler, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load parameters; the operating parameters are transmitted to the database through the unit control system DCS, and the real-time operating parameters are obtained from the database.

[0051] For example, the upper and lower limits of the main steam pressure parameter are set. When the limit is exceeded, the data is ignored and the parameter is simulated and assigned based on the data of the previous 10 seconds. If the acquired parameter exceeds the limit for more than 1 minute, the parameter will be marked in red in the consumption difference analysis system and pushed to the relevant personnel for processing to complete the closed loop.

[0052] The consumption difference of gas units adopts the formula: , where Δg is the total consumption difference caused by the gas consumption rate deviating from the index reference value, Δg i The difference caused by a single operating indicator deviating from the baseline value.

[0053] , where M is the real-time operating value of the unit parameters, M jz is the unit parameter reference value, λ is the loss difference factor, g jz It is the reference value of gas consumption rate. Therefore, the real-time operating parameters of the unit are processed and used for consumption difference analysis.

[0054] The step S2 is specifically as follows: first, upper and lower limits are set for the real-time operating parameters of the gas unit, so as to perform threshold judgment on the obtained parameter value. When the parameter value is within the set threshold range, it indicates that the data is normal, and when the parameter value exceeds the set threshold, it indicates that the data is abnormal. At this time, the data is processed, and the average value of the data in the first 10 seconds before the parameter exceeds the threshold time point is taken as the current value for assignment.

[0055] The step S3 is specifically as follows: according to the upper and lower limits of the operating parameters, when the operating parameters exceed the threshold time for more than 1 minute, this indicates that the data abnormality is not caused by a jump but by damage to the measuring point, and this parameter will affect the consumption difference data. Therefore, the data affected by this parameter is marked in red, indicating that the data is abnormal, and an alarm is generated and fed back to the relevant personnel for processing.

[0056] The step S4 is specifically as follows: the historical operation data collected include waste heat boiler exhaust temperature, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load. The historical operation data is obtained from a database, and data in a certain time period is selected for subsequent processing.

[0057] Taking the power generation rate as the target, the power generation rate formula is: , where V f is the instantaneous consumption of natural gas under standard conditions; P f is the unit load; the lower the power generation rate, the lower the natural gas consumption and energy consumption under the same load, which is used as the target value for optimizing historical data.

[0058] The step S5 is specifically as follows: the unit consumption difference reference value will be different according to the unit load, the historical data is divided according to the unit load, each operating condition is optimized to obtain the reference value under each operating condition, and the unit load is divided into intervals of 5MW; after the unit is divided, the upper and lower limits are set for the operating parameters of the auxiliary equipment. When the auxiliary equipment operating parameters exceed the limit, it indicates that the unit operating data is unstable at this time, so this historical data is discarded in the optimization process.

[0059] In the process of obtaining the benchmark value, the power generation consumption rate is used as the optimization target, and whether the main and auxiliary equipment are in an over-limit operation state and whether the environmental protection indicators are exceeded is ignored. The over-limit and over-standard alarm is performed by configuring the main and auxiliary equipment parameters, so that this section of historical data is discarded in the process of benchmark value selection, and then the benchmark value under this working condition is re-searched, thereby completing the optimization of the benchmark value under each working condition.

[0060] The historical data is divided into 5MW intervals. There are multiple data under each operating condition. Based on the power consumption rate index as the target value, the optimal value of each operating parameter under each operating condition is obtained as the benchmark value, thereby obtaining the benchmark value of the unit's full operating parameters. At the same time, historical data is used to query the operating status of main and auxiliary equipment and daily report data statistics, thereby optimizing the consumption difference analysis system. Historical data is the real-time data obtained by the power plant DCS system after processing and stored in the consumption difference analysis system database.

[0061] The step S6 is specifically as follows: the power generation is calculated by the change value of the generator forward active power measurement point from 0:00 to 24:00 on the same day, the formula is: , where W 24 It indicates the value of the generator forward active power measurement point at 24 hours, W0 indicates the value of the generator forward active power measurement point at 0 hours, and n indicates the generator active power meter multiplier;

[0062] The gas consumption for the day is calculated by the cumulative measurement point change value of natural gas standard condition from 0:00 to 24:00 on the day. The formula is: , where G 24 G0 represents the value of the cumulative measurement point of natural gas standard condition at 24 hours, and G1 represents the value of the cumulative measurement point of natural gas standard condition at 0 hours. The difference between the real-time power generation rate and the reference value is obtained. The power generation rate under the reference value operating conditions is obtained through the real-time power generation rate and the difference. The formula is as follows: , where g fjz It indicates the power consumption rate of the current working condition under the reference value operating conditions; from this, the power consumption rate of the base value operating conditions of the day can be obtained, according to the formula: , thus obtaining the natural gas consumption under the benchmark operating conditions with the same power generation; by comparing G f and G fjz , using the formula: , and obtain the additional natural gas consumption rate under the real-time operating parameters of the day.

[0063] The step S7 is specifically as follows: after obtaining the natural gas consumption G f And the natural gas consumption G under the benchmark value fjz Finally, according to the natural gas unit price P, the actual fuel cost C = G f *P; fuel cost C under the benchmark value jz =G fjz *P; thus the actual operating conditions are obtained as the cost ΔC=CC jz , additional cost percentage , thus the economic losses can be obtained.

[0064] In step S4, the historical operation data is collected for selecting the reference value in the consumption difference analysis, with the power generation consumption rate as the target. The power generation consumption rate formula is: , where V f is the instantaneous consumption of natural gas under standard conditions; P f is the unit load; the lower the power consumption rate, the lower the natural gas consumption and energy consumption under the same load, which is used as the target value for optimizing historical data.

[0065] like Figure 2 As shown, the present invention also relates to a system for realizing a gas unit consumption difference analysis method based on multi-dimensional data analysis, comprising:

[0066] A real-time operation parameter collection module 1 is used to collect the real-time operation parameters of the gas unit;

[0067] The real-time data screening and processing module 2 is used to screen the collected real-time data, identify data jumps, and perform simulation assignment based on data within 10 seconds;

[0068] Abnormal data processing module 3 is used to monitor the data. If the measuring point is damaged and the data is abnormal, and the data exceeds the limit for more than 1 minute, the parameter will be marked in red and the abnormal data will be fed back to the relevant personnel for processing;

[0069] A historical operation data collection module 4 is used for collecting historical operation data;

[0070] The operating condition division and parameter threshold configuration module 5 is used to divide the operating conditions using the unit load data; and configure the operating parameter threshold of the equipment using the operating parameters of the auxiliary equipment;

[0071] Daily report data statistics module 6, used to count daily power generation, gas consumption or converted coal consumption energy consumption parameters;

[0072] The consumption difference analysis and economic evaluation module 7 is used to evaluate the economic efficiency of the unit operation status on that day in combination with the energy consumption loss obtained from the consumption difference analysis.

[0073] In the present invention, firstly, the real-time operating parameters of the gas unit are collected, and these parameters include but are not limited to key operating indicators such as boiler exhaust temperature, main steam pressure, and ambient temperature.

[0074] The collected data will be screened to identify and process data jumps, and simulated assignments will be used to ensure data continuity and accuracy.

[0075] For monitoring data, once data anomalies caused by damage to the measuring point are detected, and the data exceeds the limit for a preset time (such as 1 minute), the system will mark it in red and notify relevant personnel to handle it.

[0076] Collect and utilize historical operating data, which are used to select benchmark values ​​in consumption difference analysis, with the power generation rate of consumption as the optimization target.

[0077] Use unit load data to divide operating conditions, and configure equipment operating parameter thresholds based on auxiliary equipment operating parameters.

[0078] Statistics on daily power generation, gas consumption and other energy consumption parameters provide basic data for consumption difference analysis.

[0079] Combine real-time data with historical data to conduct consumption difference analysis, evaluate energy consumption losses, and conduct economic evaluation of the unit operating status based on the analysis results.

[0080] Based on the setting of this application method, the following is achieved:

[0081] Improve accuracy: Through the simulation assignment of real-time data and abnormal data processing, the impact of inaccurate data on the consumption difference analysis results is reduced.

[0082] Enhanced real-time performance: The system can quickly respond to data changes, update analysis results in a timely manner, and provide real-time support for operational decisions.

[0083] Optimize the selection of benchmark values: Use historical operating data to select benchmark values, which is more in line with the actual operating conditions of the unit and improves the accuracy of consumption difference analysis.

[0084] Dynamic parameter configuration: Dynamically configure parameter thresholds according to different working conditions so that the system can adapt to different operating conditions.

[0085] Comprehensive analysis: Comprehensive consideration of real-time operating parameters, historical data, equipment status and daily report data provides a comprehensive energy consumption analysis.

[0086] Economic evaluation: Through consumption difference analysis, the operating economy of the unit can be evaluated to help operators find energy-saving opportunities.

[0087] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A gas unit consumption difference analysis method based on multi-dimensional data analysis, characterized in that: The following steps are involved: S1. Collecting real-time operating parameters: collecting real-time operating parameters of the gas unit; S2. Real-time data screening and processing: Screen the collected real-time data; identify data jumps and perform simulation assignment based on data within 10 seconds; S3, abnormal data processing; S4. Collect historical operation data; S5. Working condition division and parameter threshold configuration: Use unit load data to divide working conditions; use the operating parameters of auxiliary equipment to configure the operating parameter threshold of the equipment; S6. Daily report data statistics: statistics of daily power generation, gas consumption or converted coal consumption energy consumption parameters; S7. Consumption difference analysis and economic evaluation: Combined with the energy loss obtained from the consumption difference analysis, the economic evaluation of the unit operation status on that day is conducted; The step S1 is specifically as follows: collecting real-time operating parameters of the gas unit, including exhaust temperature of waste heat boiler, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load parameters, the operating parameters are transmitted to the database through the unit control system DCS, and the real-time operating parameters are obtained from the database; The step S2 is specifically as follows: first, upper and lower limits are set for the real-time operating parameters of the gas unit, so as to perform threshold judgment on the obtained parameter value. When the parameter value is within the set threshold range, it indicates that the data is normal, and when the parameter value exceeds the set threshold, it indicates that the data is abnormal. At this time, the data is processed, and the average value of the data in the first 10 seconds before the time point when the parameter exceeds the threshold is taken as the current value for assignment; The step S3 is specifically as follows: according to the upper and lower limits of the operating parameters, when the operating parameters exceed the threshold value for more than 1 minute, it indicates that the data abnormality is not caused by a jump but by damage to the measuring point, and this parameter will affect the consumption difference data, so the data affected by this parameter is marked in red, indicating that the data is abnormal, and an alarm is generated and fed back to the corresponding personnel for processing; The step S4 is specifically as follows: the collected historical operation data include waste heat boiler exhaust temperature, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load. The historical operation data is obtained from the database, and data in a certain time period is selected for subsequent processing. In the step S4, the historical operation data is collected for the selection of the benchmark value in the consumption difference analysis, with the power generation power consumption rate as the target. The power generation power consumption rate formula is: , where Vf is the instantaneous consumption of natural gas under standard conditions; Pf is the unit load; the lower the power consumption rate of power generation, the lower the natural gas consumption and energy consumption under the same load, which is used as the target value for historical data optimization; The step S5 is specifically as follows: the unit consumption difference reference value will be different according to the unit load, the historical data is divided according to the unit load, each working condition is optimized to obtain the reference value under each working condition, and the unit load is divided at intervals of 5MW; after the unit is divided, the upper and lower limits are set for the auxiliary equipment operating parameters. When the auxiliary equipment operating parameters exceed the limit, it indicates that the unit operating data is unstable at this time, so this historical data is discarded in the optimization process; The step S6 is specifically as follows: the power generation is calculated by the change value of the generator forward active power measurement point from 0:00 to 24:00 on the same day, the formula is: , where W24 represents the value of the generator forward active power measurement point at 24 o'clock, W0 represents the value of the generator forward active power measurement point at 0 o'clock, and n represents the generator active power meter multiplier; The gas consumption for the day is calculated by the cumulative measurement point change value of natural gas standard condition from 0:00 to 24:00 on the day. The formula is: , where G24 represents the value of the cumulative measurement point of natural gas at standard conditions at 24 o'clock, and G0 represents the value of the cumulative measurement point of natural gas at standard conditions at 0 o'clock; The difference between the real-time power generation rate and the reference value is obtained. The power generation rate under the reference value operating conditions is obtained through the real-time power generation rate and the difference. The formula is as follows: , where gfjz represents the power generation rate under the benchmark operating conditions of the current operating condition; the power generation rate under the benchmark operating conditions of the day is obtained according to the formula: , thus obtaining the natural gas consumption under the benchmark operating conditions with the same power generation; by comparing Gf and Gfjz, using the formula: , and obtain the additional consumption rate of natural gas under the real-time operating parameters of the day; The step S7 is specifically as follows: after obtaining the natural gas consumption Gf and the natural gas consumption under the reference value Gfjz, according to the natural gas unit price P, the actual fuel cost C=Gf*P; the fuel cost under the reference value Cjz=Gfjz*P; thus, the actual operating condition is obtained as the cost ΔC=C-Cjz, and the additional cost ratio is , thus obtaining the economic losses.

2. A system for realizing a gas unit consumption difference analysis method based on multi-dimensional data analysis, characterized in that: include: The real-time operation parameter collection module is used to collect the real-time operation parameters of the gas unit, including the exhaust temperature of the waste heat boiler, the water replenishment rate, the main steam temperature, the main steam pressure, the reheat steam temperature, the steam turbine back pressure, the high-pressure cylinder efficiency, the medium-pressure cylinder efficiency, the compressor inlet pressure loss, the ambient temperature, the ambient pressure and the unit load parameters. The operation parameters are transmitted to the database through the unit control system DCS, and the real-time operation parameters are obtained from the database; The real-time data screening and processing module is used to screen the collected real-time data. First, the upper and lower limits of the real-time operating parameters of the gas unit are set, so as to make a threshold judgment on the obtained parameter value. When the parameter value is within the set threshold range, it indicates that the data is normal, and when the parameter value exceeds the set threshold, it indicates that the data is abnormal. At this time, the data is processed, and the average value of the data in the first 10 seconds before the time point when the parameter exceeds the threshold is taken as the current value for assignment; The abnormal data processing module is used to monitor the data. According to the upper and lower limits of the operating parameters, when the operating parameters exceed the threshold for more than 1 minute, it indicates that the data abnormality is not caused by a jump but by damage to the measuring point. This parameter will affect the consumption difference data. Therefore, the data affected by this parameter will be marked in red to indicate that the data is abnormal, and an alarm will be generated and fed back to the relevant personnel for processing; The historical operation data collection module is used to collect historical operation data. The collected historical operation data include waste heat boiler exhaust temperature, water replenishment rate, main steam temperature, main steam pressure, reheat steam temperature, turbine back pressure, high pressure cylinder efficiency, medium pressure cylinder efficiency, compressor inlet pressure loss, ambient temperature, ambient pressure and unit load. The historical operation data is obtained from the database, and the data in a certain time period is selected for subsequent processing; The operating condition division and parameter threshold configuration module is used to divide the operating conditions using the unit load data; and configure the operating parameter threshold of the equipment using the operating parameters of the auxiliary equipment; The unit consumption difference benchmark value will vary according to the unit load. The historical data is divided according to the unit load, and each operating condition is optimized to obtain the benchmark value under each operating condition. The unit load is divided into intervals of 5MW. After the unit is divided, the upper and lower limits of the auxiliary equipment operating parameters are set. When the auxiliary equipment operating parameters exceed the limit, it indicates that the unit operating data is unstable at this time, so this historical data is discarded in the optimization process; The daily report data statistics module is used to count the daily power generation, gas consumption or converted coal consumption energy consumption parameters; the power generation is calculated by the change value of the generator's positive active power measurement point from 0:00 to 24:00 on the same day, the formula is: , where W24 represents the value of the generator forward active power measurement point at 24 o'clock, W0 represents the value of the generator forward active power measurement point at 0 o'clock, and n represents the generator active power meter multiplier; The gas consumption for the day is calculated by the cumulative measurement point change value of natural gas standard condition from 0:00 to 24:00 on the day. The formula is: , where G24 represents the value of the cumulative measurement point of natural gas at standard conditions at 24 o'clock, and G0 represents the value of the cumulative measurement point of natural gas at standard conditions at 0 o'clock; The difference between the real-time power generation rate and the reference value is obtained. The power generation rate under the reference value operating conditions is obtained through the real-time power generation rate and the difference. The formula is as follows: , where gfjz represents the power generation rate under the benchmark operating conditions of the current operating condition; the power generation rate under the benchmark operating conditions of the day is obtained according to the formula: , thus obtaining the natural gas consumption under the benchmark operating conditions with the same power generation; by comparing Gf and Gfjz, using the formula: , and obtain the additional consumption rate of natural gas under the real-time operating parameters of the day; The consumption difference analysis and economic evaluation module is used to combine the energy loss obtained from the consumption difference analysis to evaluate the economic performance of the unit operation status on that day; after obtaining the natural gas consumption Gf and the natural gas consumption Gfjz under the benchmark value, the actual fuel cost C=Gf*P is obtained according to the natural gas unit price P; the fuel cost Cjz under the benchmark value is Gfjz*P; thus, the additional cost under the actual operating conditions is ΔC=C-Cjz, and the additional cost ratio is , thus obtaining the economic losses.

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