Intelligent remote auxiliary diagnosis method and system for operation fault of thermal power generating unit

Abstract

The invention relates to the technical field of thermal power generating unit fault monitoring, in particular to a thermal power generating unit operation fault intelligent remote auxiliary diagnosis method and system, and the method comprises the steps: obtaining liquid level collection data of a condenser in a thermal power generating unit in response to a remote diagnosis starting instruction, and generating a liquid level fluctuation index according to the liquid level collection data; judging whether the liquid level fluctuation index is greater than or equal to a calibration fluctuation index; if yes, condenser operation data of the condenser are obtained, and a condensed steam stability factor is generated according to the condenser operation data; and whether the condensed steam stability factor is larger than a condensed steam stability threshold value or not is judged, and if yes, a condensed steam overhaul instruction is generated. The purpose of remotely diagnosing whether the thermal power generating unit needs to be overhauled or not based on analysis of the operation state is achieved, and then the purpose of performing pre-fault overhaul indication according to operation fluctuation to reduce the fault damage rate and improve the safety performance is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of fault monitoring of thermal power units, and particularly to an intelligent remote auxiliary diagnosis method and system for operating faults of thermal power units. Background Art

[0002] A thermal power unit is a unit that uses coal, oil, combustible gas, etc. as fuel to heat the water in the boiler to increase its temperature, and then uses steam with a certain pressure to drive a steam turbine to generate electricity.

[0003] In the prior art, there are many technologies for fault monitoring of thermal power units. For example, the Chinese invention patent with the publication number CN118393399A discloses an intelligent early warning method for equipment faults of thermal power units on July 26, 2024, including: acquiring and storing the power output characteristic parameters and equipment characteristic parameters of the thermal power unit respectively; responding to the fluctuation of the output voltage and constructing an operation evaluation value of the unit based on the equipment characteristic parameters; preliminarily determining whether the operation of the unit meets the preset standard based on the operation evaluation value of the unit; determining whether the unit has a fault based on the frequency and level ratio of fluctuation events within a preset time period and issuing a corresponding early warning.

[0004] Although the technical solution in the above patent document can improve the accuracy of intelligent early warning of equipment faults of thermal power units, there are still problems. Specifically, when performing operation evaluation, the considered factors are relatively single, only considering voltage fluctuation. And when the voltage fluctuates, the thermal power unit generally has already had a fault, and at this time, losses are likely to have occurred, so there is a problem of untimely fault prediction. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a diagnostic method for thermal power units that judges from point to surface and from components to the whole, and through the stability analysis of the liquid level gauge, realizes the analysis based on the operating state to achieve the purpose of remotely diagnosing whether the thermal power unit needs maintenance, and further realizes an intelligent remote auxiliary diagnosis method and system for operating faults of thermal power units that gives pre-maintenance instructions according to operating fluctuations to reduce the fault damage rate.

[0006] The technical solution of the present invention is as follows:

[0007] An intelligent remote auxiliary diagnosis method for operating faults of a thermal power unit, the method comprising:

[0008] Responding to a remote diagnosis start instruction, acquiring liquid level acquisition data of a condenser in the thermal power unit, and generating a liquid level fluctuation index according to the liquid level acquisition data;

[0009] Judging whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index;

[0010] If the judgment is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data;

[0011] Judge whether the condenser stability factor is greater than the condenser stability threshold. If the judgment is yes, generate a condenser maintenance instruction, and instruct the unit maintenance personnel to maintain the thermal power unit according to the condenser maintenance instruction.

[0012] Optionally, obtain the liquid level acquisition data of the condenser in the thermal power unit, and generate a liquid level fluctuation index according to the liquid level acquisition data, including:

[0013] Obtain the liquid level acquisition data of the condenser in the thermal power unit, split the liquid level acquisition data according to a preset liquid level acquisition period, and generate multiple liquid level data groups;

[0014] Generate the highest liquid level in the group, the lowest liquid level in the group and the liquid level average value according to the liquid level values in the liquid level data group, wherein one liquid level data group corresponds to one liquid level average value;

[0015] Generate a liquid level fluctuation factor based on the following formula according to the highest liquid level in the group and the lowest liquid level in the group:

[0016]

[0017] where LG is the liquid level fluctuation factor, Lmax is the highest liquid level in the group, Lmin is the lowest liquid level in the group, and s is the number of liquid level values in the liquid level data group;

[0018] Generate a liquid level fluctuation index according to each liquid level fluctuation factor.

[0019] Optionally, generate a liquid level fluctuation index based on the following formula:

[0020]

[0021] where Qa is the liquid level fluctuation index, n is the number of liquid level fluctuation factors, LGj is the liquid level fluctuation factor corresponding to the jth liquid level data group, Lvj is the liquid level average value corresponding to the jth liquid level data group, and ΔL is the average value of the j liquid level average values.

[0022] Optionally, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data, including:

[0023] Obtain the condenser operation data of the condenser, and extract the circulating water inlet temperature data, steam inlet temperature data, circulating water drainage temperature data and marker bit vibration information of the condenser from the condenser operation data;

[0024] Extract the inlet temperature data to be analyzed within the standard steam inlet temperature range from the steam inlet temperature data, and obtain the temperature acquisition time period of the inlet temperature data to be analyzed;

[0025] Extract the inlet water temperature data to be analyzed from the circulating water inlet temperature data according to the temperature acquisition time period, and extract the outlet water temperature data to be analyzed from the circulating water outlet temperature data;

[0026] Generate a condenser stability factor based on the inlet temperature data to be analyzed, the inlet water temperature data to be analyzed, the outlet water temperature data to be analyzed, and the marked position vibration information.

[0027] Optionally, the inlet temperature data to be analyzed includes the steam inlet temperature values corresponding to each data acquisition moment;

[0028] The inlet water temperature data to be analyzed is the inlet water temperature value corresponding to each data acquisition moment;

[0029] The outlet water temperature data to be analyzed is the outlet water temperature value corresponding to each data acquisition moment;

[0030] The marked position vibration information includes multiple acquired vibration amplitudes;

[0031] Generate a condenser stability factor based on the following formula, including:

[0032]

[0033] Where CSF is the condenser stability factor, Tv is the time length occupied by the temperature acquisition time period, RT(t) is the steam inlet temperature value corresponding to the acquisition moment t, Tout(t) is the inlet water temperature value corresponding to the acquisition moment t, Tin(t) is the outlet water temperature value corresponding to the acquisition moment t, N is the number of acquired vibration amplitudes, Pi is the i-th acquired vibration amplitude, and ΔP is the mean value of each acquired vibration amplitude.

[0034] Optionally, the marked position vibration information is the vibration information collected by the vibration information acquisition position pre-set on the condenser. The steps of setting the vibration information acquisition position include:

[0035] Set the side wall near the tube sheet area inside the manhole of the condenser as the vibration information acquisition position, where the vibration information acquisition position is used to install a vibration monitoring sensor, and the vibration monitoring sensor is used to collect the marked position vibration information.

[0036] Optionally, according to the condenser maintenance instruction, instruct the unit maintenance personnel to perform maintenance on the thermal power unit, including:

[0037] Obtain the pre-stored maintenance guide according to the condenser maintenance instruction;

[0038] According to the maintenance guide, the maintenance personnel of the unit are instructed to maintain the thermal power unit.

[0039] Optionally, an intelligent remote auxiliary diagnosis system for operation faults of a thermal power unit is also provided. The system includes:

[0040] A remote diagnosis startup module, configured to obtain the liquid level acquisition data of the condenser in the thermal power unit in response to a remote diagnosis startup instruction, and generate a liquid level fluctuation index according to the liquid level acquisition data;

[0041] A liquid level fluctuation judgment module, configured to judge whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index;

[0042] A condenser operation analysis module, configured to, if the judgment result is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data;

[0043] A unit maintenance instruction module, configured to judge whether the condenser stability factor is greater than a condenser stability threshold. If the judgment result is yes, generate a condenser maintenance instruction, and instruct the maintenance personnel of the unit to maintain the thermal power unit according to the condenser maintenance instruction.

[0044] Optionally, the remote diagnosis startup module is further configured to: obtain the liquid level acquisition data of the condenser in the thermal power unit, split the liquid level acquisition data according to a preset liquid level acquisition period, and generate a plurality of liquid level data groups; generate a highest liquid level within the group, a lowest liquid level within the group, and a liquid level average value according to the liquid level values in the liquid level data groups, where one liquid level data group corresponds to one liquid level average value; generate a liquid level fluctuation factor according to the highest liquid level within the group and the lowest liquid level within the group based on the following formula:

[0045]

[0046] where LG is the liquid level fluctuation factor, Lmax is the highest liquid level within the group, Lmin is the lowest liquid level within the group, and s is the number of liquid level values in the liquid level data group; generate a liquid level fluctuation index according to each liquid level fluctuation factor.

[0047] Optionally, the remote diagnosis startup module is further configured to: generate a liquid level fluctuation index based on the following formula:

[0048]

[0049] where Qa is the liquid level fluctuation index, n is the number of liquid level fluctuation factors, LGj is the liquid level fluctuation factor corresponding to the jth liquid level data group, Lvj is the liquid level average value corresponding to the jth liquid level data group, and ΔL is the average value of the j liquid level average values.

[0050] Optionally, the condenser operation analysis module is further configured to: obtain the condenser operation data of the condenser, and extract the circulating water inlet temperature data, steam inlet temperature data, circulating water drain temperature data, and marked vibration information of the condenser from the condenser operation data; extract the inlet temperature data to be analyzed within the standard steam inlet temperature range from the steam inlet temperature data, and obtain the temperature acquisition time period of the inlet temperature data to be analyzed; extract the inlet temperature data to be analyzed from the circulating water inlet temperature data according to the temperature acquisition time period, and extract the drain temperature data to be analyzed from the circulating water drain temperature data; generate a condenser stability factor according to the inlet temperature data to be analyzed, the inlet temperature data to be analyzed, the drain temperature data to be analyzed, and the marked vibration information.

[0051] Optionally, the inlet temperature data to be analyzed includes the steam inlet temperature value corresponding to each data acquisition moment; the inlet temperature data to be analyzed includes the inlet temperature value corresponding to each data acquisition moment; the drain temperature data to be analyzed includes the drain temperature value corresponding to each data acquisition moment; the marked vibration information includes multiple acquisition vibration amplitudes; the condenser operation analysis module is further configured to: generate a condenser stability factor based on the following formula, including:

[0052]

[0053] where CSF is the condenser stability factor, Tv is the time length occupied by the temperature acquisition time period, RT(t) is the steam inlet temperature value corresponding to the acquisition moment t, Tout(t) is the inlet temperature value corresponding to the acquisition moment t, Tin(t) is the drain temperature value corresponding to the acquisition moment t, N is the number of acquisition vibration amplitudes, Pi is the i-th acquisition vibration amplitude, and ΔP is the mean value of each acquisition vibration amplitude.

[0054] Optionally, the marked vibration information is the vibration information collected by the vibration information acquisition position preset on the condenser, and the condenser operation analysis module is further configured to set the vibration information acquisition position, including: setting the side wall near the tube sheet area in the manhole of the condenser as the vibration information acquisition position, where the vibration information acquisition position is used to install a vibration monitoring sensor, and the vibration monitoring sensor is used to collect the marked vibration information.

[0055] Optionally, the unit maintenance instruction module is further configured to: obtain the pre-stored maintenance guide according to the condenser maintenance instruction; instruct the unit maintenance personnel to perform maintenance on the thermal power unit according to the maintenance guide.

[0056] Optionally, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned intelligent remote auxiliary diagnosis method for operating faults of thermal power units are implemented.

[0057] Optionally, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent remote auxiliary diagnosis method for operating faults of thermal power units are implemented.

[0058] The technical effects achieved by the present invention are as follows:

[0059] The above intelligent remote auxiliary diagnosis method and system for the operation faults of thermal power units respond to a remote diagnosis start instruction, obtain the liquid level acquisition data of the condenser in the thermal power unit, and generate a liquid level fluctuation index based on the liquid level acquisition data; determine whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index; if the determination is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor based on the condenser operation data; determine whether the condenser stability factor is greater than a condenser stability threshold, and if the determination is yes, generate a condenser maintenance instruction, and instruct the unit maintenance personnel to perform maintenance on the thermal power unit according to the condenser maintenance instruction. In this application, when there is a need for remote diagnosis, a remote diagnosis start instruction initiated by the management personnel of the thermal power unit is obtained first, and based on the remote diagnosis start instruction, the liquid level acquisition data of the condenser in the thermal power unit is obtained. The liquid level acquisition data is the data collected by the liquid level gauge in the hot well of the condenser, indicating the liquid level data in the hot well. In a thermal power unit, the main function of the condenser is to condense the steam discharged from the steam turbine into liquid water, and the main function of the liquid level gauge is to monitor the condensate level inside the condenser in real time. By accurately collecting the liquid level data, the working state of the condenser can be initially estimated intuitively and efficiently. Therefore, in this application, obtaining the liquid level acquisition data and generating a liquid level fluctuation index based on the liquid level acquisition data are set as the first step to remotely diagnose whether the thermal power unit has faults. From the structure of the thermal power unit, liquid level gauge installation ports are provided in the hot wells, so that there is no need to separately set the installation position of the liquid level gauge. For the users of the thermal power unit, the cost is reduced. On the other hand, the data collection is fast and convenient, improving the efficiency of fault monitoring and diagnosis. The liquid level fluctuation index is used to represent the fluctuation state of the liquid level in the hot well. Then, a calibrated fluctuation index is preset, and the calibrated fluctuation index is used as a reference for measuring whether the liquid level fluctuation of the condenser is normal. Therefore, it is determined whether the liquid level fluctuation index is greater than or equal to the calibrated fluctuation index. When the liquid level fluctuation index is less than the calibrated fluctuation index, it means that the fluctuation of the liquid level of the condenser is within the normal range, indicating that the working state of the condenser is normal. If it is determined that the liquid level fluctuation index is greater than or equal to the calibrated fluctuation index, it means that the liquid level fluctuation is too large at this time, and the working state is abnormal. Therefore, it is necessary to further determine the operating state of the condenser. Specifically, the condenser operation data of the condenser is obtained, and a condenser stability factor is generated based on the condenser operation data. The condenser operation data is the data representing the overall working state of the condenser. The condenser stability factor is used to represent the overall working stability of the condenser. The smaller the condenser stability factor, the more stable the working state, and the larger the condenser stability factor, the more unstable the working state.Next, by setting a condenser stability threshold, it is possible to determine whether the condenser stability factor is greater than the condenser stability threshold. When the determination is negative, that is, the condenser stability factor is less than or equal to the condenser stability threshold, it indicates that the working state of the condenser is relatively stable at this time, without any faults or trends of faults, and the previous liquid level fluctuations belong to normal working fluctuations. If the determination is positive, a condenser maintenance instruction is generated, and according to the condenser maintenance instruction, the maintenance personnel of the unit are instructed to perform maintenance on the thermal power unit. Therefore, in this application, for the remote diagnosis of the operation faults of the thermal power unit, a diagnostic method from point to surface, first judging the components and then the whole is adopted. Through the stability analysis of the liquid level gauge, the purpose of remotely diagnosing whether the thermal power unit needs maintenance based on the operating state is achieved, and further, the pre-fault maintenance instruction is given according to the operating fluctuations to reduce the fault damage rate and improve the safety performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 FIG. is a schematic flowchart of an intelligent remote auxiliary diagnosis method for operation faults of a thermal power unit in an embodiment;

[0061] Figure 2 FIG. is a block diagram of the structure of an intelligent remote auxiliary diagnosis system for operation faults of a thermal power unit in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to make the objectives, technical solutions and advantages of this application clearer, the following further elaborates on this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0063] In one embodiment, a terminal is provided, and the terminal is used to: in response to a remote diagnosis start instruction, obtain liquid level acquisition data of a condenser in a thermal power unit, and generate a liquid level fluctuation index according to the liquid level acquisition data; determine whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index; if the determination is positive, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data; determine whether the condenser stability factor is greater than a condenser stability threshold, and if the determination is positive, generate a condenser maintenance instruction, and according to the condenser maintenance instruction, instruct the maintenance personnel of the unit to perform maintenance on the thermal power unit.

[0064] The terminal may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices.

[0065] In one embodiment, as Figure 1 shown, an intelligent remote auxiliary diagnosis method for operation faults of a thermal power unit is provided, and the method includes:

[0066] Step S100: In response to a remote diagnosis start instruction, obtain the liquid level acquisition data of the condenser in the thermal power unit, and generate a liquid level fluctuation index based on the liquid level acquisition data;

[0067] Step S200: Determine whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index;

[0068] Step S300: If the determination is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor based on the condenser operation data;

[0069] Step S400: Determine whether the condenser stability factor is greater than a condenser stability threshold. If the determination is yes, generate a condenser maintenance instruction, and instruct the unit maintenance personnel to perform maintenance on the thermal power unit according to the condenser maintenance instruction.

[0070] In this embodiment, when remote diagnosis is required, a remote diagnosis start instruction initiated by the management personnel of the thermal power unit is obtained, and based on the remote diagnosis start instruction, the liquid level acquisition data of the condenser in the thermal power unit is obtained. The liquid level acquisition data is the data collected by the liquid level gauge in the hot well of the condenser, representing the liquid level data in the hot well. In a thermal power unit, the main function of the condenser is to condense the steam discharged from the steam turbine into liquid water, and the main function of the liquid level gauge is to monitor the condensate liquid level inside the condenser in real time. By accurately collecting the liquid level data, the working state of the condenser can be initially estimated intuitively and efficiently. Therefore, in this application, the acquisition of the liquid level acquisition data is set, and a liquid level fluctuation index is generated based on the liquid level acquisition data as the first step in remotely diagnosing whether the thermal power unit has a fault. From the perspective of the structure of the thermal power unit, liquid level gauge installation ports are provided in the hot wells, so that there is no need to separately set the installation position of the liquid level gauge. For the user of the thermal power unit, the cost is reduced. On the other hand, data collection is fast and convenient, improving the efficiency of fault monitoring and diagnosis. The liquid level fluctuation index is used to represent the fluctuation state of the liquid level in the hot well. Then, a calibrated fluctuation index is preset, and the calibrated fluctuation index is used as a reference for measuring whether the liquid level fluctuation of the condenser is normal. Therefore, it is judged whether the liquid level fluctuation index is greater than or equal to the calibrated fluctuation index. When the liquid level fluctuation index is less than the calibrated fluctuation index, it means that the liquid level fluctuation of the condenser is within the normal range, indicating that the working state of the condenser is normal. If it is judged that the liquid level fluctuation index is greater than or equal to the calibrated fluctuation index, it means that the liquid level fluctuation is too large at this time, and the working state is abnormal. Therefore, it is necessary to further judge the operating state of the condenser. Specifically, the condenser operation data of the condenser is obtained, and a condenser stability factor is generated based on the condenser operation data. The condenser operation data is the data representing the overall working state of the condenser, and the condenser stability factor is used to represent the overall working stability of the condenser. The smaller the condenser stability factor, the more stable the working state, and the larger the condenser stability factor, the more unstable the working state. Then, a condenser stability threshold is also set, so that it can be judged whether the condenser stability factor is greater than the condenser stability threshold. When the judgment is negative, that is, the condenser stability factor is less than or equal to the condenser stability threshold, it means that the working state of the condenser is relatively stable at this time, no fault has occurred, and there is no tendency of a fault to occur. The previous liquid level fluctuation belongs to normal working fluctuations. If the judgment is positive, a condenser maintenance instruction is generated, and based on the condenser maintenance instruction, the unit maintenance personnel are instructed to perform maintenance on the thermal power unit. Therefore, in this application, the remote diagnosis of the operation fault of the thermal power unit adopts a diagnostic method from point to surface, first judging the component and then the whole, and through the stability analysis of the liquid level gauge, the purpose of remotely diagnosing whether the thermal power unit needs to be maintained based on the operating state is achieved. Furthermore, the pre-fault maintenance instruction is realized according to the operation fluctuation to reduce the fault damage rate and improve the safety performance.

[0071] In another embodiment, in step S100, triggering the remote diagnosis start instruction includes the following situations:

[0072] First, when the unit maintenance personnel are not on duty, that is, the unit maintenance personnel go out for maintenance work. At this time, there is no professional unit maintenance personnel around the thermal power unit, and remote maintenance can be carried out at this time, thereby triggering the remote diagnosis start instruction, and controlling the preset sensor to collect data through the remote diagnosis start instruction.

[0073] Second, when the unit management personnel need to verify the diagnosis function, the unit management personnel instruct the unit maintenance personnel to conduct a public display, and at this time, the unit maintenance personnel trigger the remote diagnosis start instruction.

[0074] Third, when the manufacturer conducts maintenance, regular inspections need to be carried out on the thermal power unit. At this time, it is necessary to first judge whether the condenser in the thermal power unit requires detailed maintenance work, and the method of remote diagnosis is adopted first. At this time, the remote diagnosis start instruction is triggered by the manufacturer of the thermal power unit. And based on the data obtained after the remote diagnosis is started, a judgment is made on whether further maintenance is required.

[0075] Of course, there can also be other triggering conditions to trigger the remote diagnosis start instruction, which will not be elaborated in this application.

[0076] In another embodiment, in step S100, obtaining the liquid level acquisition data of the condenser in the thermal power unit and generating a liquid level fluctuation index based on the liquid level acquisition data includes:

[0077] Step S110: Obtain the liquid level acquisition data of the condenser in the thermal power unit, split the liquid level acquisition data according to a preset liquid level acquisition period, and generate multiple liquid level data groups;

[0078] In this step, in order to conduct fine data analysis and improve the accuracy rate, a liquid level acquisition period is preset in advance. The liquid level acquisition period is set to 2 minutes or 5 minutes, for example. The shorter the event interval occupied by the liquid level acquisition period, the higher the data analysis accuracy of the thermal power unit.

[0079] Step S120: Generate the highest liquid level within the group, the lowest liquid level within the group, and the liquid level average value based on the liquid level values in the liquid level data group. Among them, one liquid level data group corresponds to one liquid level average value;

[0080] In this step, the number of the liquid level values is multiple and is preset in advance. Calculate the average value based on each liquid level value and set it as the liquid level average value. And count the maximum value and the minimum value among each liquid level value, and set them as the highest liquid level within the group and the lowest liquid level within the group respectively.

[0081] Step S130: Generate a liquid level fluctuation factor based on the following formula according to the highest liquid level in the group and the lowest liquid level in the group:

[0082]

[0083] where LG is the liquid level fluctuation factor, Lmax is the highest liquid level in the group, Lmin is the lowest liquid level in the group, and s is the number of liquid level values in the liquid level data group;

[0084] In this step, the liquid level fluctuation factor represents the magnitude of growth. The larger the liquid level fluctuation factor LG, the greater the growth, that is, it indicates that the liquid level fluctuation of a liquid level data group is greater.

[0085] Step S140: Generate a liquid level fluctuation index according to each liquid level fluctuation factor.

[0086] In this embodiment, by setting n liquid level data groups, first analyzing each liquid level data group respectively to generate a liquid level fluctuation factor for representing the fluctuation situation of each liquid level data group, and then generating a fluctuation situation for representing the overall data of the liquid level acquisition data according to each liquid level fluctuation factor, so as to realize the data processing of the overall liquid level fluctuation in the hot well based on refined liquid level analysis and improve the accuracy of diagnosis.

[0087] In another embodiment, based on the following formula, generate a liquid level fluctuation index:

[0088]

[0089] where Qa is the liquid level fluctuation index, n is the number of liquid level fluctuation factors, LGj is the liquid level fluctuation factor corresponding to the jth liquid level data group, Lvj is the average liquid level corresponding to the jth liquid level data group, and ΔL is the average of the j liquid level means.

[0090] In another embodiment, in step S300, obtain the condenser operation data of the condenser and generate a condenser stability factor according to the condenser operation data, including:

[0091] Step S310: Obtain the condenser operation data of the condenser and extract the circulating water inlet temperature data, steam inlet temperature data, circulating water drainage temperature data and marker bit vibration information of the condenser from the condenser operation data;

[0092] In this step, the circulating water inlet temperature data is the temperature data collected at the cooling water inlet of the condenser. The steam inlet temperature data is the steam temperature data collected at the steam inlet of the condenser, which is discharged from the exhaust port of the steam turbine of the thermal power unit. The circulating water outlet temperature data is the temperature data collected at the cooling water outlet of the condenser.

[0093] The marked position vibration information is the vibration information collected at the vibration information acquisition position preset on the condenser.

[0094] Step S320: Extract the inlet temperature data to be analyzed within the standard steam inlet temperature range from the steam inlet temperature data, and obtain the temperature acquisition time period of the inlet temperature data to be analyzed.

[0095] In this embodiment, the steam inlet temperature of the condenser is too high, which is not conducive to analyzing its stability. Therefore, the data corresponding to the appropriate temperature is selected for analysis, that is, the standard steam inlet temperature range is set in advance, so as to avoid the problem of inaccurate analysis of the stable state of the condenser caused by extreme situations such as too high or too low temperature.

[0096] Specifically, extract the inlet temperature data to be analyzed within the standard steam inlet temperature range from the steam inlet temperature data. Through the acquisition of the inlet temperature data to be analyzed, the subsequent accurate analysis of the working stability of the condenser is realized.

[0097] Since the circulating water inlet temperature data, the steam inlet temperature data, and the circulating water outlet temperature data are in one-to-one correspondence in time, that is, in a time period, there is a corresponding circulating water inlet temperature data, steam inlet temperature data, and circulating water outlet temperature data. Therefore, when screening one of the data, the other data should also be screened according to the time point. Specifically, obtain the temperature acquisition time period corresponding to the inlet temperature data to be analyzed, and then perform corresponding data extraction based on the temperature acquisition time period.

[0098] Step S330: Extract the inlet temperature data to be analyzed from the circulating water inlet temperature data according to the temperature acquisition time period, and extract the outlet temperature data to be analyzed from the circulating water outlet temperature data.

[0099] Step S340: Generate a condenser stability factor according to the inlet temperature data to be analyzed, the inlet temperature data to be analyzed, the outlet temperature data to be analyzed, and the marked position vibration information.

[0100] In this step, by first extracting the inlet water temperature data to be analyzed from the circulating water inlet temperature data according to the temperature acquisition time period, and extracting the outlet water temperature data to be analyzed from the circulating water outlet temperature data, corresponding data to the inlet temperature data to be analyzed is obtained, enabling analysis based on the temperature data collected at the cooling water inlet, steam inlet, and cooling water outlet of the condenser, and further realizing comprehensive analysis.

[0101] In another embodiment, the inlet temperature data to be analyzed includes the steam inlet temperature values corresponding to each data acquisition moment;

[0102] The inlet water temperature data to be analyzed includes the inlet water temperature values corresponding to each data acquisition moment;

[0103] The outlet water temperature data to be analyzed includes the outlet water temperature values corresponding to each data acquisition moment;

[0104] The marked vibration information includes multiple collected vibration amplitudes;

[0105] Generate a condenser stability factor based on the following formula, including:

[0106]

[0107] Where CSF is the condenser stability factor, Tv is the time length occupied by the temperature acquisition time period, RT(t) is the steam inlet temperature value corresponding to the acquisition moment t, Tout(t) is the inlet water temperature value corresponding to the acquisition moment t, Tin(t) is the outlet water temperature value corresponding to the acquisition moment t, N is the number of collected vibration amplitudes, Pi is the i-th collected vibration amplitude, and ΔP is the mean value of each collected vibration amplitude.

[0108] The value of t ranges from 1 to m, where m is the number of acquisition moments. If the number of acquisition moments is 10, then t ranges from 1 to 10.

[0109] In this embodiment, the smaller the condenser stability factor, the more stable the operation of the condenser. Thus, refined analysis is performed based on vibration and amplitude values to improve diagnostic accuracy.

[0110] It should be noted that different weights can also be set for temperature and vibration amplitude, that is, a proportionality coefficient can be set. This part can be set according to the actual situation and is not specifically limited in this application. Additionally, since all calculations are proportional calculations, data analysis can be performed without normalization.

[0111] In another embodiment, the marked vibration information is the vibration information collected at the vibration information acquisition positions pre-set on the condenser. The steps of setting the vibration information acquisition positions include:

[0112] Set the side wall near the tube sheet area inside the manhole of the condenser as the vibration information acquisition position, where the vibration information acquisition position is used to install a vibration monitoring sensor, and the vibration monitoring sensor is used to collect vibration information of the marked position.

[0113] In this embodiment, setting the side wall near the tube sheet area inside the manhole of the condenser as the vibration information acquisition position and installing a vibration monitoring sensor, on the one hand, the vibration detection point is set inside the condenser, making the detected vibration more accurate. On the other hand, the vibration monitoring sensor can be accessed from inside the manhole, and the position where the vibration monitoring sensor is installed can be adjusted in real time, which is convenient and fast.

[0114] In another embodiment, in step S400, according to the condenser maintenance instruction, the unit maintenance personnel are instructed to maintain the thermal power unit, including:

[0115] Step S410: Obtain the pre-stored maintenance guide according to the condenser maintenance instruction;

[0116] Step S420: Instruct the unit maintenance personnel to maintain the thermal power unit according to the maintenance guide.

[0117] In this embodiment, by pre-setting the maintenance guide, it is convenient for the maintenance personnel to perform efficient and reliable maintenance. Specifically, the pre-stored maintenance guide is obtained according to the condenser maintenance instruction, and the unit maintenance personnel are instructed to maintain the thermal power unit according to the maintenance guide.

[0118] In another embodiment, it further includes the step of adjusting the vibration information acquisition position:

[0119] Obtain the preset position adjustment time interval, and adjust the vibration information acquisition position according to the preset position adjustment time interval.

[0120] In another embodiment, as Figure 2 shown, it further provides an intelligent remote auxiliary diagnosis system for the operation faults of a thermal power unit, and the system includes:

[0121] A remote diagnosis start module, which is used to respond to a remote diagnosis start instruction, obtain the liquid level acquisition data of the condenser in the thermal power unit, and generate a liquid level fluctuation index according to the liquid level acquisition data;

[0122] A liquid level fluctuation judgment module, which is used to judge whether the liquid level fluctuation index is greater than or equal to the calibrated fluctuation index;

[0123] A condenser operation analysis module, which is used to, if the judgment is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data;

[0124] The unit maintenance instruction module is used to determine whether the condenser stability factor is greater than the condenser stability threshold. If the determination result is yes, a condenser maintenance instruction is generated, and according to the condenser maintenance instruction, the unit maintenance personnel are instructed to perform maintenance on the thermal power unit.

[0125] In one embodiment, the remote diagnosis startup module is further configured to: obtain the liquid level acquisition data of the condenser in the thermal power unit, split the liquid level acquisition data according to a preset liquid level acquisition period, and generate multiple liquid level data groups; generate the highest liquid level within the group, the lowest liquid level within the group, and the liquid level average value according to the liquid level values in the liquid level data group, where one liquid level data group corresponds to one liquid level average value; generate a liquid level fluctuation factor based on the following formula according to the highest liquid level within the group and the lowest liquid level within the group:

[0126]

[0127] where LG is the liquid level fluctuation factor, Lmax is the highest liquid level within the group, Lmin is the lowest liquid level within the group, and s is the number of liquid level values in the liquid level data group; generate a liquid level fluctuation index according to each liquid level fluctuation factor.

[0128] In one embodiment, the remote diagnosis startup module is further configured to: generate a liquid level fluctuation index based on the following formula:

[0129]

[0130] where Qa is the liquid level fluctuation index, n is the number of liquid level fluctuation factors, LGj is the liquid level fluctuation factor corresponding to the jth liquid level data group, Lvj is the liquid level average value corresponding to the jth liquid level data group, and ΔL is the average value of the liquid level average values of j.

[0131] In one embodiment, the condenser operation analysis module is further configured to: obtain the condenser operation data of the condenser, and extract the circulating water inlet temperature data, steam inlet temperature data, circulating water drainage temperature data, and marker vibration information of the condenser from the condenser operation data; extract the to-be-analyzed inlet temperature data within the standard steam inlet temperature range from the steam inlet temperature data, and obtain the temperature acquisition time period of the to-be-analyzed inlet temperature data; extract the to-be-analyzed inlet water temperature data from the circulating water inlet temperature data according to the temperature acquisition time period, and extract the to-be-analyzed drainage temperature data from the circulating water drainage temperature data; generate a condenser stability factor according to the to-be-analyzed inlet temperature data, the to-be-analyzed inlet water temperature data, the to-be-analyzed drainage temperature data, and the marker vibration information.

[0132] In one embodiment, the inlet temperature data to be analyzed includes the steam inlet temperature values corresponding to each data acquisition moment; the inlet water temperature data to be analyzed includes the inlet water temperature values corresponding to each data acquisition moment; the outlet water temperature data to be analyzed includes the outlet water temperature values corresponding to each data acquisition moment; the marked vibration information includes multiple collected vibration amplitudes; the condenser operation analysis module is further configured to: generate a condenser stability factor based on the following formula, including:

[0133]

[0134] where CSF is the condenser stability factor, Tv is the time length occupied by the temperature acquisition time period, RT(t) is the steam inlet temperature value corresponding to the acquisition moment t, Tout(t) is the inlet water temperature value corresponding to the acquisition moment t, Tin(t) is the outlet water temperature value corresponding to the acquisition moment t, N is the number of collected vibration amplitudes, Pi is the i-th collected vibration amplitude, and ΔP is the mean value of each collected vibration amplitude.

[0135] In one embodiment, the marked vibration information is the vibration information collected by the vibration information acquisition positions preset on the condenser. The condenser operation analysis module is further configured to set the vibration information acquisition positions, including: setting the side wall near the tube sheet area in the manhole of the condenser as the vibration information acquisition position, where the vibration information acquisition position is used to install a vibration monitoring sensor, and the vibration monitoring sensor is used to collect the marked vibration information.

[0136] In one embodiment, the unit maintenance instruction module is further configured to: obtain a pre-stored maintenance guide according to the condenser maintenance instruction; instruct the unit maintenance personnel to perform maintenance on the thermal power unit according to the maintenance guide.

[0137] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned intelligent remote auxiliary diagnosis method for thermal power unit operation faults are implemented.

[0138] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent remote auxiliary diagnosis method for thermal power unit operation faults are implemented.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0140] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0141] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An intelligent remote auxiliary diagnosis method for operating faults of thermal power units, characterized in that, The method includes: In response to a remote diagnosis start instruction, obtaining liquid level acquisition data of a condenser in a thermal power unit, and generating a liquid level fluctuation index according to the liquid level acquisition data; Judging whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index; If the judgment is yes, obtaining condenser operation data of the condenser, and generating a condenser stability factor according to the condenser operation data; Judging whether the condenser stability factor is greater than a condenser stability threshold. If the judgment is yes, generating a condenser maintenance instruction, and instructing unit maintenance personnel to perform maintenance on the thermal power unit according to the condenser maintenance instruction.

2. The intelligent remote auxiliary diagnosis method for operating faults of thermal power units according to claim 1, characterized in that Obtaining liquid level acquisition data of a condenser in a thermal power unit, and generating a liquid level fluctuation index according to the liquid level acquisition data, includes: Obtaining liquid level acquisition data of a condenser in a thermal power unit, splitting the liquid level acquisition data according to a preset liquid level acquisition period, and generating a plurality of liquid level data groups; Generating a highest liquid level within the group, a lowest liquid level within the group, and a liquid level average value according to the liquid level values in the liquid level data group, wherein one liquid level data group corresponds to one liquid level average value; Generating a liquid level fluctuation factor based on the following formula according to the highest liquid level within the group and the lowest liquid level within the group: where LG is the liquid level fluctuation factor, Lmax is the highest liquid level within the group, Lmin is the lowest liquid level within the group, and s is the number of liquid level values in the liquid level data group; Generating a liquid level fluctuation index according to each of the liquid level fluctuation factors.

3. The intelligent remote auxiliary diagnosis method for operating faults of thermal power units according to claim 2, characterized in that, Generating a liquid level fluctuation index based on the following formula: where Qa is the liquid level fluctuation index, n is the number of liquid level fluctuation factors, LGj is the liquid level fluctuation factor corresponding to the jth liquid level data group, Lvj is the liquid level average value corresponding to the jth liquid level data group, and ΔL is the average value of the j liquid level average values.

4. The intelligent remote auxiliary diagnosis method for operating faults of thermal power units according to claim 1, characterized in that Obtaining condenser operation data of the condenser, and generating a condenser stability factor according to the condenser operation data, includes: Obtaining condenser operation data of the condenser, and extracting circulating water inlet temperature data, steam inlet temperature data, circulating water drain temperature data, and marked position vibration information of the condenser from the condenser operation data; Extracting to-be-analyzed inlet temperature data within a standard steam inlet temperature range from the steam inlet temperature data, and obtaining a temperature acquisition time period of the to-be-analyzed inlet temperature data; Extracting to-be-analyzed inlet water temperature data from the circulating water inlet temperature data according to the temperature acquisition time period, and extracting to-be-analyzed drain water temperature data from the circulating water drain temperature data; Generating a condenser stability factor according to the to-be-analyzed inlet temperature data, the to-be-analyzed inlet water temperature data, the to-be-analyzed drain water temperature data, and the marked position vibration information.

5. The intelligent remote auxiliary diagnosis method for operating faults of thermal power units according to claim 4, wherein The to-be-analyzed inlet temperature data includes steam inlet temperature values corresponding to each data acquisition moment; The to-be-analyzed inlet water temperature data includes inlet water temperature values corresponding to each data acquisition moment; The to-be-analyzed drain water temperature data includes drain water temperature values corresponding to each data acquisition moment; The marked position vibration information includes a plurality of collected vibration amplitudes; Generating a condenser stability factor based on the following formula, includes: Among them, CSF is the condenser stability factor, Tv is the time length occupied by the temperature acquisition time period, RT(t) is the steam inlet temperature value corresponding to the acquisition time t, Tout(t) is the inlet water temperature value corresponding to the acquisition time t, Tin(t) is the drain water temperature value corresponding to the acquisition time t, N is the number of collected vibration amplitudes, Pi is the i-th collected vibration amplitude, and ΔP is the mean value of each collected vibration amplitude.

6. The intelligent remote auxiliary diagnosis method for operation faults of thermal power units according to claim 5, wherein The marked position vibration information is the vibration information collected by the vibration information acquisition position preset on the condenser. The steps of setting the vibration information acquisition position include: Setting the side wall near the tube sheet area in the manhole of the condenser as the vibration information acquisition position, where the vibration information acquisition position is used to install a vibration monitoring sensor, and the vibration monitoring sensor is used to collect the marked position vibration information.

7. The intelligent remote auxiliary diagnosis method for operating faults of thermal power units according to claim 1, characterized in that According to the condenser maintenance instruction, instructing the maintenance personnel of the unit to perform maintenance on the thermal power unit, including: Obtaining the pre-stored maintenance guide according to the condenser maintenance instruction; Instructing the maintenance personnel of the unit to perform maintenance on the thermal power unit according to the maintenance guide.

8. An intelligent remote auxiliary diagnosis system for operating faults of a thermal power unit, characterized in that, The system includes: A remote diagnosis startup module, configured to obtain the liquid level acquisition data of the condenser in the thermal power unit in response to a remote diagnosis startup instruction, and generate a liquid level fluctuation index according to the liquid level acquisition data; A liquid level fluctuation judgment module, configured to judge whether the liquid level fluctuation index is greater than or equal to a calibrated fluctuation index; A condenser operation analysis module, configured to, if the judgment is yes, obtain the condenser operation data of the condenser, and generate a condenser stability factor according to the condenser operation data; A unit maintenance instruction module, configured to judge whether the condenser stability factor is greater than a condenser stability threshold. If the judgment is yes, generate a condenser maintenance instruction, and instruct the maintenance personnel of the unit to perform maintenance on the thermal power unit according to the condenser maintenance instruction.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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