A method, system, device and storage medium for performance evaluation of a thermal power unit

Abstract

The application discloses a kind of performance evaluation method, system, equipment and storage medium of thermoelectric unit, it is related to thermoelectric unit test technical field, including steps: by the historical data of thermoelectric unit operation to establish stable working condition prediction model, when meeting the operation period of performance test automatically sends test suggestion, after confirming to carry out test, thermoelectric unit is automatically switched from operation mode to test mode;Collect operating parameter and liquid level data, and determine working medium real-time temperature using working medium real-time temperature prediction technology, complete after thermoelectric unit is restored to normal operation mode;Using working condition stability criterion to evaluate the working condition stability of the collected data under test mode, data meeting working condition stability condition are stored as effective test data;Effective test data are analyzed by system heat balance, and the thermal performance index of thermoelectric unit is obtained.The application realizes the energy consumption state evaluation of equipment and system stable working condition, and completes thermoelectric unit performance degradation analysis according to energy consumption analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat and power unit test, in particular to a performance evaluation method, system, device and storage medium of heat and power unit. BACKGROUND

[0002] The cylinder efficiency and heat rate of the heat and power unit are the main technical indexes for judging the working state of the thermal power generation system, and are also the basis for energy consumption diagnosis and operation optimization of the heat and power unit.

[0003] For fossil fuel thermal power generation, the inlet steam and exhaust steam of the high-pressure cylinder and the medium-pressure cylinder of the steam turbine are usually superheated steam, and the cylinder efficiency can be directly calculated according to the measurement results of the inlet steam pressure and temperature and the exhaust steam pressure and temperature. However, for the high-pressure cylinder and the low-pressure cylinder of the nuclear power steam turbine and the low-pressure cylinder of the fossil fuel thermal power steam turbine, the exhaust steam of the cylinder is wet steam, and the steam humidity needs to be determined in order to calculate the cylinder efficiency. However, so far, there is no steam humidity measuring instrument that can meet the engineering application requirements, so the steam humidity must be determined by other indirect methods. In addition, for the steam turbine containing wet steam working medium, the calculation of heat rate also needs to determine the steam humidity by similar methods.

[0004] The commonly used method is to directly measure the condensate flow or the main feed water flow by the flow meter installed on the condensate pipe or the main feed water pipe, to estimate the exhaust steam humidity, to determine the main steam flow, the cylinder efficiency of the steam turbine and the output power by heat balance calculation, to correct the exhaust steam humidity according to the difference between the output power and the measured power of the generator, to match the two power values by iterative calculation, and to determine the exhaust steam humidity and the cylinder efficiency of the steam turbine and the heat rate of the heat and power unit.

[0005] However, due to the existence of energy storage devices in the thermal power steam-water system, such as the condenser hot well, the deaerator water tank, etc., and various unknown leaks affecting the steam balance, the calculation of the main steam flow from the condensate flow or the main feed water flow and the heat balance are very complex, and the transient mass and heat balance of the steam-water system cannot reflect the actual performance state of the heat and power unit, and specific test conditions need to be met to scientifically evaluate the performance of the heat and power unit by the above mass and heat balance method. Therefore, at present, the heat and power unit is entrusted to professional and technical personnel of special agencies such as the Electric Power Research Institute to carry out tests and analysis according to relevant test procedures, which is not only time-consuming and laborious, but also very costly, and can only be used for important occasions such as performance evaluation of new heat and power units, performance evaluation before and after major overhaul or modification of heat and power units, and cannot be used for regular tests according to state monitoring requirements, and it is also difficult to track the change process of the performance state of the heat and power unit, which is the main obstacle to the current intelligent operation and maintenance of the heat and power unit.

[0006] With the gradual improvement of the operation monitoring system of the thermal power unit, in theory, the performance evaluation of the thermal power unit can be completed by using the daily operation data. In fact, the existing thermal power unit monitoring system is mostly configured with the function of calculating the heat rate and other energy consumption indicators of the thermal power unit according to the online operation data, and the purpose is to realize the online monitoring and evaluation of the performance state of the thermal power unit. However, since the transient mass heat balance cannot reflect the actual performance state of the thermal power unit, the related indicators obtained by the above function may be seriously deviated from the actual performance state of the thermal power unit, and have little practical value for the performance evaluation and optimal operation of the thermal power unit. SUMMARY

[0007] The present application aims at the shortcomings of the prior art, and provides a performance evaluation method, system, device and storage medium of a thermal power unit, to solve the problem that the related indicators obtained by the above function may be seriously deviated from the actual performance state of the thermal power unit, and have little practical value for the performance evaluation and optimal operation of the thermal power unit due to the fact that the transient mass heat balance cannot reflect the actual performance state of the thermal power unit.

[0008] The present application specifically provides the following technical solutions: a performance evaluation method of a thermal power unit, comprising the following steps:

[0009] Obtaining the historical data of the operation parameters of the thermal power unit, performing machine learning on the historical data based on the working condition stability criterion, constructing a stable working condition prediction model through the machine-learned historical data, and selecting a test period through the stable working condition prediction model; the operation parameters include the load of the thermal power unit, the main steam, the reheat steam parameters and the cold end parameters;

[0010] In the selected test period, the operation mode of the thermal power unit is automatically switched to the test mode, and the valve affecting the calculation accuracy of the main steam flow is closed in the test mode; the real-time operation parameters, the real-time temperature of the working medium and the liquid level of the water storage container of the thermal power unit in the test mode are collected, the test data are obtained, and the thermal power unit is automatically restored to the normal operation mode after the test data are obtained;

[0011] Using the working condition stability criterion to evaluate the working condition stability of the test data, and saving the test data meeting the working condition stability condition as valid test data;

[0012] Performing system mass heat balance analysis according to the valid test data, obtaining the thermal performance indicators of the thermal power unit, and performing energy consumption state evaluation of the stable working condition through the thermal performance indicators of the thermal power unit.

[0013] Preferably, the thermal power unit includes a thermal power unit of a thermal power plant, a thermal power unit of an atomic power plant and a thermal power unit of a solar thermal power plant.

[0014] Preferably, when the real-time operating parameters of the thermoelectric generator set, the real-time temperature of the working medium and the liquid level of the water storage container are collected in the test mode, the real-time temperature of the working medium is collected by a working medium real-time temperature prediction technology, the working medium real-time temperature prediction technology comprises: installing or replacing a temperature sensor with fast response characteristics in a thermal system where the thermoelectric generator set is located, and predicting the real-time temperature of the working medium; using a computer to perform machine learning on the measurement data of the conventional temperature sensor, and predicting the real-time temperature of the working medium through the machine learning result; using a conventional temperature sensor and edge AI coupling technology to measure the real-time temperature of the working medium.

[0015] Preferably, when the real-time operating parameters of the thermoelectric generator set, the real-time temperature of the working medium and the liquid level of the water storage container are collected in the test mode, the liquid level of the water storage container is collected by a liquid level measurement technology, wherein the liquid level measurement technology uses a remote differential pressure liquid level transmitter, a redundant arrangement of multiple liquid level meters and a data comprehensive analysis method to remotely detect the real-time liquid level of the water storage container; wherein the water storage container includes a condenser hot well, a deaerator water tank and a boiler drum.

[0016] Preferably, when the valves affecting the calculation accuracy of the main steam flow are closed in the test mode, for valves that cannot be closed for a long time due to the safety of the thermoelectric generator set, the leakage of the working medium of the valves is measured by adding a flowmeter.

[0017] Preferably, when the experimental data is obtained, the thermoelectric generator set is automatically restored to the normal operating mode.

[0018] Preferably, after the energy consumption state of the stable working condition is evaluated by the thermoelectric generator set thermal performance index, the performance degradation analysis and early warning of the thermoelectric generator set are completed according to the energy consumption analysis results of different times and operating conditions.

[0019] The present application provides a performance evaluation system of a thermoelectric generator set, comprising:

[0020] A working condition stability prediction module is configured to obtain historical data of operating parameters of the thermoelectric generator set, perform machine learning on the historical data based on a working condition stability criterion, construct a stable working condition prediction model based on the machine-learned historical data, and select a test period by using the stable working condition prediction model; the operating parameters include the load of the thermoelectric generator set, the main steam, the reheat steam parameters and the cold end parameters.

[0021] A performance test module is configured to automatically switch the operating mode of the thermoelectric generator set to the test mode in the selected test period, and close the valves affecting the calculation accuracy of the main steam flow in the test mode; collect the real-time operating parameters of the thermoelectric generator set, the real-time temperature of the working medium and the liquid level of the water storage container in the test mode, obtain test data, and automatically restore the thermoelectric generator set to the normal operating mode after the experimental data is obtained.

[0022] a data evaluation module configured to evaluate the test data using a working condition stability criterion, and save test data meeting a working condition stability condition as valid test data;

[0023] a performance evaluation module configured to perform a system heat balance analysis according to the valid test data, obtain a thermoelectric unit thermal performance index, and perform an energy consumption state evaluation of the stable working condition through the thermoelectric unit thermal performance index.

[0024] The application provides a computer device, comprising a memory and a processor, the memory storing a program, and the program being executed by the processor to make the processor perform the steps of the performance evaluation method of the thermoelectric unit.

[0025] The application provides a storage medium, comprising a stored computer program, and the computer program being executed by at least one processor to make the at least one processor perform the steps of the performance evaluation method of the thermoelectric unit.

[0026] Compared with the prior art, the application has the following advantages:

[0027] The application uses the existing instruments and data acquisition system of the thermoelectric unit, that is, by obtaining historical data of the thermoelectric unit, a stable working condition prediction model is constructed, when the test is determined, the operation mode of the thermoelectric unit is switched to the test mode, the operation parameters and liquid level data of the thermoelectric unit are collected, and the working substance real-time temperature prediction is performed, and the thermodynamic performance test of the thermoelectric unit is autonomously completed according to the established control strategy without affecting the normal operation of the thermoelectric unit, which is in line with the actual performance state of the thermoelectric unit, not only improves the success rate of the performance test using the daily operation load, but also reduces the operation risk of the thermoelectric unit in the test mode. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a schematic diagram of a thermoelectric unit performance test module provided by the application;

[0029] Figure 2 is a schematic diagram of a working condition stability prediction system provided by the application;

[0030] Figure 3 is a schematic diagram of embodiment one provided by the application;

[0031] Figure 4 is a schematic diagram of embodiment two provided by the application. DETAILED DESCRIPTION

[0032] With reference to the drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the protection scope of the present application.

[0033] The present application aims at the above problems, and provides a performance evaluation method, system, device and storage medium of a thermal power unit. The performance evaluation system of the thermal power unit includes real-time working medium temperature prediction, high-reliability liquid level measurement, automatic test mode switching and effective test data evaluation, and the main purpose is to shorten the test time, significantly improve the success rate of performance test using daily operation load, and reduce the system isolation risk in the test mode. Specifically, as shown in Figure 1

[0034] The embodiment of the present application provides a performance evaluation method of a thermal power unit, including the following steps:

[0035] Step S1: historical data of the thermal power unit is obtained, and machine learning is performed on the historical data based on a working condition stability criterion to establish a stable working condition prediction model for working condition stability prediction. The stable working condition prediction model is constructed by the historical data after machine learning, and is used to select a test period. When the thermal power unit enters the test period, a test suggestion is automatically sent to relevant personnel. The test mode is automatically switched on by manual confirmation when the test condition is met in the operation period; the historical data includes but is not limited to thermal power unit load, main steam and reheat steam parameters and cold end parameters. The specific process is shown in Figure 2

[0036] The stability criterion can refer to but is not limited to the relevant contents of GB / T 8117.3-2014 Steam Turbine Thermal Performance Acceptance Test Code and ASME PTC6-2004 Steam Turbine Performance Test Code.

[0037] In addition, the performance degradation analysis and early warning of the thermal power unit can be completed according to the energy consumption analysis results of different times and operation conditions.

[0038] ​​Step S2: Confirming the test, the (operation mode automatic switching system) sends instructions to the thermal power unit to switch the operation mode of the thermal power unit to the test mode. After receiving the test instruction, the operation mode automatic switching system automatically closes the related valves that affect the calculation accuracy of the main steam flow according to the built-in (test) instruction, and keeps the running state of each device unchanged. The running parameter monitoring and acquisition system is used to collect the running parameters of the thermal power unit in the test mode, and the real-time temperature prediction technology and (high reliability) liquid level measurement technology are used to collect the running parameters of the thermal power unit, real-time temperature of the working medium and liquid level of the water storage container, to obtain reliable test data. After the experimental data is obtained, the thermal power unit is automatically restored to the normal operation mode.

[0039] In this step, the thermal power unit includes a thermal power unit of a thermal power plant, a thermal power unit of an atomic power plant, and a thermal power unit of a solar thermal power plant.

[0040] The working medium real-time temperature prediction method includes but is not limited to installing or replacing a temperature sensor with fast response characteristics in the thermal system where the thermal power unit is located, and predicting the real-time temperature of the working medium; using a computer to machine learn the measurement data of a conventional temperature sensor, and predicting the real-time temperature of the working medium through the machine learning result; using a conventional temperature sensor and edge AI coupling technology to measure the real-time temperature of the working medium. The temperature sensor includes but is not limited to a temperature sensor and a temperature transmitter based on the thermocouple and thermal resistance temperature measurement principle. The working medium real-time temperature prediction refers to a processing method that shortens the time for the temperature sensor to completely respond to the temperature change of the working medium to within 20 seconds.

[0041] High-reliability liquid level measurement is mainly used for remote monitoring of the real-time liquid level of the condenser hot well, the deaerator water tank, and the boiler drum and other water storage containers. It uses but is not limited to a remote differential pressure liquid level transmitter, multiple sets of liquid level meter redundant arrangement, and data comprehensive analysis method for liquid level monitoring.

[0042] For valves that cannot be closed due to the safety of the thermal power unit, a flowmeter is added to measure the leakage of the working medium. After the test, the valve closed in the test mode is restored to the normal operation mode by the operation mode automatic switching system.

[0043] The operation mode automatic switching system refers to an automatic control instruction or operation system for quickly switching the normal operation mode and the test mode of the thermal power unit.

[0044] The specific operation is as follows:

[0045] After receiving the opening instruction, the operation mode automatic switching system can manually / automatically quickly close the related valves that affect the main steam flow calculation accuracy according to the performance test needs of the thermal power unit, and maintain the operation state of each device as much as possible during the test. After receiving the closing instruction, the valves and devices quickly return to the normal operation mode.

[0046] All operations of switching from the normal operation mode to the test mode are performed according to the control strategy formulated after safety evaluation. For the valves that cannot be closed due to the safety of the thermal power unit operation, a flowmeter can be added to measure the working medium leakage.

[0047] Step S3: The working condition stability criterion is used to evaluate the working condition stability of the test data, the test data meeting the working condition stability condition are saved as valid test data, and the test mode automatic switching mode is closed.

[0048] Step S4: The system heat balance analysis is performed according to the valid test data, the thermal performance indicators of the thermal power unit are obtained, and the energy consumption state evaluation of the stable working condition is performed through the thermal performance indicators of the thermal power unit. And according to the energy consumption analysis results of different times and operation conditions, the performance degradation analysis and early warning of the thermal power unit are completed.

[0049] Example 1:

[0050] The method is used for the self-determination and rapid performance test evaluation method of the thermal power unit under the non-controllable operation load.

[0051] The specific implementation method is shown in the accompanying Figure 3 The system in the self-determination and rapid performance test method includes the performance evaluation system of the thermal power unit, which includes a working condition stability prediction module, a thermal power unit performance test module, a data evaluation module and a performance evaluation module. The working condition stability prediction module is connected upstream of the thermal power unit performance test module, and the performance evaluation module is connected downstream of the thermal power unit performance test module, and the performance evaluation module is connected downstream of the data evaluation module. Based on the existing instruments and data acquisition system of the thermal power unit, the thermal performance test of the thermal power unit can be completed within 30 minutes, and the energy consumption state of the equipment and system is evaluated.

[0052] Specifically, the self-determination and rapid performance test evaluation needs to first predict the operation period suitable for the test according to the thermal power unit stable working condition prediction model established according to the historical data of the thermal power unit, and then open the test after the operation personnel agree and confirm, and complete the switching of the operation mode and the test mode, and all test measurements, data analysis and processing, etc. And according to the needs, performance test report, energy consumption diagnosis report and performance degradation analysis report, etc. can be issued.

[0053] Specifically, the thermal power unit performance autonomous rapid evaluation system mainly includes the existing thermal power generation system and the operation parameter monitoring and collecting system, and the newly added or improved working substance real-time temperature prediction, high-reliability liquid level measurement, test mode automatic switching and effective test data evaluation key technologies. The main purpose is to shorten the test time, significantly improve the success rate of performance test using daily operation load, and reduce the system isolation risk in test mode.

[0054] Specifically, the specific implementation of the embodiment can refer to the summary of the invention. The main feature is that the thermal power unit test does not need to apply to the power grid, and the test process does not need to be intervened by the operation personnel. The autonomous rapid performance test system adjusts the thermal power unit operation state to the most reasonable test parameters, and judges the safety of the thermal power unit operation in the test mode in real time. When the safety of the thermal power unit operation in the test mode cannot be guaranteed, the test mode can be quickly released, and the normal operation mode is switched.

[0055] Specifically, the embodiment can use the convenience of autonomous rapid test to carry out periodic test, establish the change rule of thermal power unit performance with time and working condition, realize reliable evaluation of thermal power unit performance degradation state, and can be widely applied to intelligent operation and maintenance and scientific management of thermal power unit.

[0056] Embodiment 2:

[0057] The method is used for autonomous rapid performance test evaluation method of thermal power unit under controllable test load.

[0058] The specific implementation method is shown in the accompanying Figure 4 The autonomous rapid performance test evaluation method includes working substance real-time temperature prediction, high-reliability liquid level measurement, operation mode manual switching and effective test data evaluation key technologies. After the operation personnel start the test according to the plan, the operation mode automatic switching system automatically switches the thermal power unit from the normal operation mode to the test mode, or the operation personnel manually isolate the related valves and operation parameter adjustment according to the prompt information, and then autonomously complete all test measurement, data analysis and processing, and can generate performance test report and energy consumption diagnosis report according to the needs.

[0059] Specifically, the specific implementation of the embodiment can refer to the summary of the invention. The main feature is that the thermal power unit test needs to apply to the power grid before the test, and the operation personnel can manually or automatically adjust the thermal power unit operation state to the most reasonable test parameters by the autonomous rapid performance test system during the test. Since the test load does not need to be adjusted according to the power grid load, the safety of the thermal power unit operation in the test mode can be guaranteed, and the normal operation mode is switched under the related instruction after the test is completed.

[0060] Specifically, the embodiment does not need to be operated by professional technicians of special agencies such as the Electric Power Research Institute in the application process, and can be independently completed by power plant operators, thereby reducing the test cost. The embodiment can be tested according to the relevant requirements of the “GB / T8117.3-2014 Steam Turbine Thermal Performance Acceptance Test Procedure” and the “ASME PTC6-2004 Steam Turbine Performance Test Procedure”, or can carry out rapid test, and is mainly applied to internal evaluation of the performance and energy consumption state of the thermal power generating unit in the thermal power plant.

[0061] Meanwhile, the embodiment only needs to be independently completed by the power plant operators, and can generate a performance test report and an energy consumption diagnosis report according to requirements. In addition, the regular test can be carried out by using the convenience of the independent rapid test, the change rule of the performance of the thermal power generating unit with time and working condition is established, the reliable evaluation of the performance degradation state of the thermal power generating unit is realized, and the embodiment can be widely applied to intelligent operation and maintenance and scientific management of the thermal power generating unit.

[0062] Based on the above method and statement, the embodiment provides a performance evaluation system of a thermal power generating unit, which comprises a working condition stability prediction module, a performance test module, a data evaluation module and a performance evaluation module.

[0063] The working condition stability prediction module is used for acquiring historical data of the operating parameters of the thermal power generating unit, performing machine learning on the historical data based on a working condition stability criterion, constructing a stable working condition prediction model through the machine-learned historical data, and selecting a test period through the stable working condition prediction model. The operating parameters include the load of the thermal power generating unit, the main steam, the reheat steam parameters and the cold end parameters. The performance test module is used for automatically switching the operating mode of the thermal power generating unit to a test mode in the selected test period, closing the valve affecting the calculation accuracy of the main steam flow in the test mode, collecting the real-time operating parameters, the real-time temperature of the working medium and the liquid level of the water storage container of the thermal power generating unit in the test mode, obtaining test data, and automatically restoring the thermal power generating unit to the normal operating mode after the test data is acquired. The data evaluation module is used for evaluating the working condition stability of the test data by using the working condition stability criterion, and saving the test data meeting the working condition stability condition as valid test data. The performance evaluation module is used for performing system heat and mass balance analysis according to the valid test data, obtaining the thermal performance index of the thermal power generating unit, and performing energy consumption state evaluation of the stable working condition through the thermal performance index of the thermal power generating unit.

[0064] The embodiment also provides a computer device comprising a memory and a processor, the memory stores a program, and the program is executed by the processor to make the processor execute the steps of the performance evaluation method of the thermal power generating unit.

[0065] According to disclosed embodiments, a computer device can communicate with one or more external devices (e.g. a keyboard, a pointing device, a Bluetooth communication, etc.), or with any devices (e.g. a router, a modem, etc.) that enable the computer device to communicate with one or more other computer devices.

[0066] The present application also provides a storage medium, which comprises a stored computer program, the computer program being executed by at least one processor to enable the at least one processor to perform the steps of the method for evaluating performance of a thermoelectric unit.

[0067] According to disclosed embodiments, the storage medium can be a non-transitory computer-readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a storage medium can be any tangible medium that contains, or stores a program for use by or in connection with an instruction execution system, apparatus, or device.

[0068] The above description is further explained in connection with the specific preferred embodiments, and those skilled in the art will readily understand that many modifications and substitutions can be made in the present application without departing from the scope of the application.

Claims

1. A method of performance evaluation of a thermal power plant, characterized in that, The method comprises the following steps: acquiring historical data of operating parameters of a thermal power unit, performing machine learning on the historical data based on a working condition stability criterion, constructing a stable working condition prediction model based on the machine-learned historical data, and selecting a test period through the stable working condition prediction model; the operating parameters include thermal power unit load, main steam, reheat steam parameters, and cold end parameters; in the selected test period, automatically switching the thermal power unit operating mode to a test mode, closing valves that affect the calculation accuracy of main steam flow in the test mode, collecting real-time operating parameters, working medium real-time temperature, and water storage container liquid level of the thermal power unit in the test mode, obtaining test data, and automatically restoring the thermal power unit to a normal operating mode after the test data is acquired; performing working condition stability evaluation on the test data using the working condition stability criterion, and saving test data that meets the working condition stability condition as valid test data; performing system heat and mass balance analysis according to the valid test data, obtaining thermal performance indicators of the thermal power unit, and performing energy consumption state evaluation of the stable working condition through the thermal performance indicators of the thermal power unit.

2. A method of performance assessment of a thermoelectric generator set as claimed in claim 1, wherein, The thermal power unit includes a thermal power unit of a thermal power plant, a nuclear power plant, and a solar thermal power plant.

3. A method of performance assessment of a thermoelectric generator set as claimed in claim 1, wherein, When collecting real-time operating parameters, working medium real-time temperature, and water storage container liquid level of the thermal power unit in the test mode, the working medium real-time temperature is collected through working medium real-time temperature prediction technology, which includes installing or replacing temperature sensors with fast response characteristics in the thermal system where the thermal power unit is located, and performing real-time temperature prediction on the working medium; using a computer to perform machine learning on the measurement data of a conventional temperature sensor, and predicting the real-time temperature of the working medium through the machine learning result; using conventional temperature sensor and edge AI coupling technology to measure the real-time temperature of the working medium.

4. A method of performance assessment of a thermoelectric generator set as defined in claim 1, characterized in that, When collecting real-time operating parameters, working medium real-time temperature, and water storage container liquid level of the thermal power unit in the test mode, the water storage container liquid level is collected through liquid level measurement technology, which uses remote differential pressure liquid level transmitter, multiple sets of liquid level meter redundant arrangement, and data comprehensive analysis method to remotely detect the real-time liquid level of the water storage container; the water storage container includes a condenser hot well, a deaerator water tank, and a boiler drum.

5. The method of claim 1, wherein When closing the valves that affect the calculation accuracy of main steam flow in the test mode, for valves that cannot be closed for a long time due to thermal power unit operating safety, the working medium leakage of the valves is measured by adding flow meters.

6. A method of performance assessment of a thermoelectric generator set as defined in claim 1, characterized in that, When the thermal power unit is automatically restored to the normal operating mode after the test data is acquired, the valves closed in the test mode are restored to the conventional operating mode.

7. A method of performance assessment of a thermoelectric generator set as defined in claim 1, characterized by After performing energy consumption state evaluation of the stable working condition through the thermal performance indicators of the thermal power unit, the performance degradation analysis and early warning of the thermal power unit are completed according to the energy consumption analysis results of different times and operating conditions.

8. A performance evaluation system for a thermal power plant, characterized by The method comprises the following steps: The working condition stability prediction module is configured to acquire historical data of operating parameters of the cogeneration unit, perform machine learning on the historical data based on a working condition stability criterion, construct a stable working condition prediction model based on the historical data after machine learning, and select a test period through the stable working condition prediction model. The operating parameters include a load of the cogeneration unit, main steam, reheat steam parameters, and cold end parameters. The performance test module is configured to automatically switch the operating mode of the cogeneration unit to a test mode in the selected test period, close valves that affect the calculation accuracy of the main steam flow in the test mode, acquire real-time operating parameters, real-time working medium temperature, and water storage container liquid level of the cogeneration unit in the test mode, obtain test data, and automatically restore the cogeneration unit to a normal operating mode after the test data is acquired. The data evaluation module is configured to perform working condition stability evaluation on the test data by using the working condition stability criterion, and save test data that meets the working condition stability condition as valid test data. The performance evaluation module is configured to perform system mass and heat balance analysis based on the valid test data, obtain thermal performance indexes of the cogeneration unit, and perform energy consumption state evaluation of the stable working condition based on the thermal performance indexes of the cogeneration unit.

9. A computer device, comprising: The storage medium includes a stored computer program, and the computer program is executed by at least one processor to enable the at least one processor to perform the steps of the performance evaluation method of the cogeneration unit according to any one of claims 1-7.

10. A storage medium, characterized by The storage medium includes a stored computer program, and the computer program is executed by at least one processor to enable the at least one processor to perform the steps of the performance evaluation method of the cogeneration unit according to any one of claims 1-7.

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