CN117217630A - Equipment reliability maintenance method and system for intelligent power plant - Google Patents
Equipment reliability maintenance method and system for intelligent power plant Download PDFInfo
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Abstract
The application discloses a device reliability maintenance method for an intelligent power plant, which relates to the technical field of device fault prediction diagnosis and comprises the following steps: acquiring real-time operation data of equipment, cleaning the data, and then performing data quality analysis; performing equipment digital modeling by combining the cleaned data and equipment information; and the fault early warning and diagnosis model judges and early warns according to the equipment digital model and the historical fault data. According to the equipment reliability maintenance method for the intelligent power plant, equipment is monitored and early-warned, a scientific decision basis is provided for key equipment overhaul, the passive maintenance is changed into predictive maintenance, and the probability of human error action is reduced; through implementation of equipment fault early warning and diagnosis and equipment reliability evaluation, the equipment reliability is remarkably improved, the maintenance cost is reduced, the power generation efficiency is improved, and the application has better effects in reducing the unplanned power failure time, reducing the maintenance cost and improving the power generation efficiency.
Description
Technical Field
The application relates to the technical field of equipment fault prediction diagnosis, in particular to an equipment reliability maintenance method and system for an intelligent power plant.
Background
The electric power enterprises face the development environment that the electric power demand is slowly increased and the installation proportion of new energy is continuously improved, the rough management mode needs to be changed in an accelerating way, the element growth is changed to the innovation driving, the system innovation, the management innovation and the technological innovation are advanced, and new growth advantages are cultivated and formed in the innovation.
The method is characterized in that the pearl sea electric power is based on a production cloud of a group company, other systems such as a power plant DCS industrial control system, an SIS real-time monitoring system, various special sensors and the like are utilized to automatically control and collect data of the production system, a comprehensive operation technology platform based on big data is constructed, the data is uniformly analyzed and managed, four large application constructions of intelligent power plant cluster uniform supervision, performance analysis and optimization, equipment reliability maintenance and safe production management are developed, and the intelligent construction capacity of the power plant is improved from the aspects of data service, management, visual identification, fusion linkage, unit performance analysis, expert guidance interaction, early warning and decision model construction and the like.
The number of unscheduled shutdown hours which can be realized by the existing method for maintaining the reliability of equipment in a power plant does not meet the requirements of the power plant, the reliability management pressure of the equipment is high, the events such as scaling of a condenser titanium tube, faults of a condensate pump frequency converter, short circuit of a water-cooled unit power switch, abnormal water level protection tripping of a boiler high-pressure steam drum and the like occur in the power plant, the equipment is increased along with the increase of the service time, the maintenance cost is increased, the fault frequency is increased, the field operation mainly depends on the experience of operating personnel, the technology of the free inheritance of a engineer is mainly faced with the actual situation that the personnel flow is large, the possibility of manual misoperation is increased, and the safe and stable operation of the unit is influenced.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems.
Therefore, the technical problems solved by the application are as follows: the existing equipment reliability maintenance method has the problems of long unscheduled power failure time, high artificial error action probability, high maintenance cost and reduced power generation efficiency.
In order to solve the technical problems, the application provides the following technical scheme: a device reliability maintenance method for an intelligent power plant, comprising:
acquiring real-time operation data of equipment, cleaning the data, and then performing data quality analysis;
performing equipment digital modeling by combining the cleaned data and equipment information;
and the fault early warning and diagnosis model judges and early warns according to the equipment digital model and the historical fault data.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the data quality analysis includes dead value diagnosis, fluctuation diagnosis, threshold diagnosis, and gradient diagnosis.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the equipment digital modeling comprises equipment type modeling, production process structure modeling, twin modeling, factory modeling, unit modeling, professional modeling, system modeling and equipment modeling.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the fault early warning and diagnosing model comprises a parameter early warning model, an equipment evaluation model and a rule engine.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the parameter early-warning model comprises a thrust tile temperature exceeding early-warning model, a compressor surge early-warning model, a reheat steam temperature difference overlarge early-warning model and a high-pressure steam drum continuous exhaust electric regulating valve jamming early-warning model.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the pre-warning includes the steps of,
if the temperature difference between the inner wall and the outer wall of the high-pressure regulating valve is more than 80 ℃ or the temperature difference between the upper half inner wall and the lower half inner wall of the middle-pressure steam inlet is more than 80 ℃ or the left temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the right temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the temperature difference rising rate is more than or equal to 30 ℃/h, the thrust tile temperature exceeding early warning condition is achieved;
if the cover vibration of the steam turbine is more than 50 mu m or the cover vibration is between 40 and 50 mu m, and the rising speed is more than or equal to 30 mu m/h or the condensing pump vibration value of the condensing machine is more than 4.5mm/s, the surge early warning condition of the air compressor is achieved;
if the two-stage steam extraction hydraulic valve is opened and the opening is more than 20%, the valve of the temperature-reducing water regulating valve is closed, and the temperature difference of steam before and after the temperature reducer is more than 100 ℃, the early warning condition of overlarge reheat steam temperature difference is achieved;
if the opening of the condenser side of the TCA cooler is less than 5%, the deviation between the fitting value of the opening of the boiler side of the TCA cooler and the actual value is more than 10%, or the flow rate after the recirculation valve is more than 0, one pump is running, and the flow rate of the outlet of the water supply pump is more than 40t/h, the clamping early warning condition of the high-pressure steam drum continuous-discharge electric regulating valve is achieved.
As a preferred embodiment of the device reliability maintenance method for an intelligent power plant according to the present application, the device reliability maintenance method comprises: the diagnosis may include the step of,
if the temperature of the thrust tile exceeds the standard, early warning is carried out, the temperature difference is not regulated and controlled, the degradation level of the equipment is calculated, the residual available time of the equipment and parts is calculated, the situation that the inside of a cylinder of a body of the steam turbine is hydrophobic and not hydrophobic, the load change is too fast, the temperature difference of steam entering the cylinder is too large with the temperature of the cylinder body is diagnosed, the situation that the temperature difference is not reduced to below 80 ℃ is diagnosed, the thermocouple fault is detected, the safety of the thermocouple fault is checked, the replacement of fault parts is carried out, and the residual available time of the residual parts is calculated;
if the surge early warning of the gas compressor occurs, the control is not carried out, the degradation level of the equipment is calculated, the residual available time of the equipment and parts is calculated, the turning time of the gas turbine is diagnosed to be insufficient, the cover vibration is not reduced to be less than 50 mu m or the vibration value of a condensing pump of the gas condenser is greater than 4.5mm/s, the fastening screw of the end cover of the gas turbine is diagnosed to be loose, the bearing bush is heated unevenly or the condensing pump is resonated under the natural frequency, the part maintenance and the replacement of the bearing bush are carried out on the gas turbine, the condensing pump bearing is replaced, and the residual available time of the residual parts is calculated;
if the reheat steam temperature difference is too large, diagnosing that the valve core in the valve of the low-pressure heat supply attemperator of the steam turbine is worn, the sealing of the inner side of the valve is aged and is not tight, checking the safety of the valve and replacing fault parts, and calculating the residual available time of the residual parts;
if the high-pressure steam drum continuous-discharge electric regulating valve is in a clamping early warning, the opening is changed below 5%, the deviation between the fitting value of the opening at the boiler side of the TCA cooler and the actual value can be reduced to 10%, the valve clamping or the inconsistent opening setting and feedback of the control valve is diagnosed, the lubrication treatment is carried out, calculating the remaining usable time of a valve, overhauling a valve opening control system, and if the normal operation cannot be recovered, diagnosing that the valve core of the valve of the TCA cooler of the fuel engine or the valve core in the valve of the water supply pump is worn, and sealing the inner side of the valve is aged and is not tight by the TCA cooler condenser and the water supply pump;
and after fault reasons are diagnosed, reminding power plant personnel in time in a real-time alarm mode, and displaying unprocessed and still-alarming information on a proxy alarm interface, wherein the information is divided into rule alarms, model alarms, DCS, TCS, sedimentation, fire protection, temperature and humidity, and operation and maintenance personnel process the proxy alarms according to actual alarm conditions.
The application also aims to provide a device reliability maintenance system for an intelligent power plant, which can monitor and early warn devices through real-time data change and solve the technical problems of long unplanned power failure time, high probability of human error action and high maintenance cost of the conventional device reliability maintenance system.
In order to solve the technical problems, the application provides the following technical scheme: a device reliability maintenance system for an intelligent power plant, comprising: a data cleaning module, a device digitizing module, a fault early warning and diagnosing module and a warning module,
the data processing module is a device for filtering access data and is used for performing dead value diagnosis, fluctuation diagnosis, threshold diagnosis and gradient diagnosis, and providing reliable real-time equipment operation data and historical equipment fault data for the equipment digitizing module;
the equipment digitizing module is a device for digitizing equipment information and running states into virtual equipment and is used for constructing equipment type modeling, production process structure modeling, twin modeling, factory modeling, unit modeling, professional modeling, system modeling and equipment modeling;
the fault early warning and diagnosing module is a device for predicting whether equipment fails and analyzing the failure cause and the processing method, and analyzes the equipment degradation level and the equipment failure cause according to the real-time state data, the historical failure data and the rule engine of the equipment;
the warning module is a warning device and is used for informing operation and maintenance personnel of fault equipment information and fault reasons, and displaying unprocessed and still-warning information on a proxy warning interface.
As a preferable scheme of the small current grounding line selection method based on the network signaling system, the application comprises the following steps: a computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements a device reliability maintenance method for an intelligent power plant when executing the computer program.
As a preferable scheme of the small current grounding line selection method based on the network signaling system, the application comprises the following steps: a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a device reliability maintenance method for an intelligent power plant.
The application has the beneficial effects that: according to the equipment reliability maintenance method for the intelligent power plant, equipment is monitored and early-warned, a scientific decision basis is provided for key equipment overhaul, the passive maintenance is changed into predictive maintenance, and the probability of human error action is reduced; through implementation of equipment fault early warning and diagnosis and equipment reliability evaluation, the equipment reliability is remarkably improved, the maintenance cost is reduced, the power generation efficiency is improved, and the application has better effects in reducing the unplanned power failure time, reducing the maintenance cost and improving the power generation efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is an overall flow chart of a device reliability maintenance method for an intelligent power plant according to one embodiment of the present application.
FIG. 2 is an overall flow chart of a device reliability maintenance system for an intelligent power plant according to a second embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to FIG. 1, for one embodiment of the present application, a device reliability maintenance method for an intelligent power plant is provided, comprising:
s1: and acquiring real-time operation data of the equipment, and performing data quality analysis after cleaning the data.
Further, the data quality analysis includes: dead value diagnosis, fluctuation diagnosis, threshold diagnosis, gradient diagnosis.
It should be noted that the data cleaning realizes the effective filtration of the system access data, improves the data quality, and ensures the validity and accuracy of the fault alarm.
It should be noted that dead value diagnosis is to judge that the dead point is abnormal when the parameter has not changed within the designated time or the collection times; the threshold diagnosis is data in which the parameter exceeds the effective range and is judged to be an ineffective range; the fluctuation diagnosis is that the fluctuation range of the parameter exceeds a set value in the appointed time or the collection times, and the fluctuation is judged to be overlarge; the gradient judgment is that the change speed of the parameter exceeds a set value in the designated time or the collection times, and the fluctuation is judged to be too fast.
S2: and combining the cleaned data and equipment information to perform equipment digital modeling.
Further, digitally modeling a device includes device type modeling, production process structure modeling, twin modeling, plant modeling, unit modeling, specialty modeling, system modeling, and device modeling.
It should be noted that, the device type modeling is defined for a certain device functional structure type, and specifically includes device attribute, structure diagram, component, monitoring parameter, vibration parameter, diagnostic analysis model, and evaluation index configuration. The main function of equipment type modeling is model standardized management and model multiplexing, for example, a platform can be rapidly instantiated into a #2 steam turbine high-pressure feed pump and a #4 steam turbine high-pressure feed pump on the basis of defining an equipment type model of the high-pressure feed pump.
It should also be noted that the modeling of the production process structure is used for constructing the hierarchical relationships of units, professions, systems and equipment of different power generation process types, such as a gas turbine generator unit model and a waste heat generator unit model, as templates of specific power plant entity units, and in this embodiment, the modeling is used for completing the rapid construction of the #1 gas turbine generator unit, the #2 gas turbine generator unit, the #3 gas turbine generator unit and the #5 gas turbine generator unit of the pearly power plant.
Furthermore, the twin modeling is responsible for constructing a structured digital model comprising characteristic parameters, operation conditions, mechanism diagnosis, parameter early warning, equipment state evaluation and the like of unit equipment on the basis of a basic template constructed by an equipment type model and a production process structure model.
It should be noted that, the plant modeling is under the system gas-electricity group node, the model of the power plant is created, the equipment position can be displayed during the alarm, the connection equipment is displayed in the node, the early warning is carried out on other equipment, and the occurrence of the cascading failure is prevented.
It should also be noted that, the unit modeling is based on main information such as the unit level, the maintenance working mode, the fault diagnosis model, the comprehensive evaluation model, etc., and the fault level is displayed, and if a plurality of equipment faults occur, the high-level unit is preferentially maintained or overhauled.
Further, the professional modeling is based on a professional level, and maintains main models such as a fault diagnosis model, a comprehensive evaluation model and the like.
It should be noted that the system modeling is a digital modeling for process systems such as a gas turbine system, a waste heat boiler system, a steam turbine system, an electric system and the like, and specifically includes configuration of a system operation condition mode, a system fault diagnosis model, a system comprehensive evaluation model and an operation report.
It should also be noted that the device modeling is based on device hierarchy, maintaining main information such as device basic configuration, diagnostic analysis, vibration analysis, index analysis, diagnostic report, operation report, etc., and performing refinement analysis on individual devices.
S3: and the fault early warning and diagnosis model judges and early warns according to the equipment digital model and the historical fault data.
Further, the fault early warning and diagnosis model comprises: parameter early warning model, equipment evaluation model and rule engine.
It should be noted that the parameter early warning model includes: the device comprises a thrust tile temperature exceeding early warning model, a compressor surge early warning model, a reheat steam temperature difference overlarge early warning model and a high-pressure steam drum continuous exhaust electric regulating valve jamming early warning model.
It should also be noted that the early warning includes:
if the temperature difference between the inner wall and the outer wall of the high-pressure regulating valve is more than 80 ℃ or the temperature difference between the upper half inner wall and the lower half inner wall of the middle-pressure steam inlet is more than 80 ℃ or the left temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the right temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the temperature difference rising rate is more than or equal to 30 ℃/h, the thrust tile temperature exceeding early warning condition is achieved; if the cover vibration of the steam turbine is more than 50 mu m or the cover vibration is between 40 and 50 mu m, and the rising speed is more than or equal to 30 mu m/h or the condensing pump vibration value of the condensing machine is more than 4.5mm/s, the surge early warning condition of the air compressor is achieved; if the two-stage steam extraction hydraulic valve is opened and the opening is more than 20%, the valve of the temperature-reducing water regulating valve is closed, and the temperature difference of steam before and after the temperature reducer is more than 100 ℃, the early warning condition of overlarge reheat steam temperature difference is achieved; if the opening of the condenser side of the TCA cooler is less than 5%, the deviation between the fitting value of the opening of the boiler side of the TCA cooler and the actual value is more than 10%, or the flow rate after the recirculation valve is more than 0, one pump is running, and the flow rate of the outlet of the water supply pump is more than 40t/h, the clamping early warning condition of the high-pressure steam drum continuous-discharge electric regulating valve is achieved.
Furthermore, the temperature difference between the inner wall and the outer wall of the high-pressure regulating valve, the temperature difference between the upper half inner wall and the lower half inner wall of the middle-pressure steam inlet part and the temperature difference between the inner wall and the outer wall of the upper half flange are all lower than 80 ℃, and the temperature difference of a conventional steam turbine is generally lower than 50 ℃, because the radial gap at the lower part of the regulating stage is reduced by about 0.1mm when the temperature difference is increased by 10 ℃. The radial clearance of the high-pressure turbine baffle gland seal is smaller, generally 0.4-0.7 mm, so that the temperature difference of the cylinder wall is usually not more than 50 ℃, but three parts selected and calculated by the application are higher in thickness and 3-4 times of the cylinder wall, the temperature of the three parts can bear higher temperature difference, the temperature is easier to measure, and the thermometer is easier to be dangerous when placed in the cylinder wall, so that when the temperature difference exceeds 80 ℃, the three parts are selected, the inner thermal expansion amount is large, the outer thermal expansion amount is small, bending deformation is generated along each cross section of the axial direction of the cylinder, the two ends are outwards bent, and the middle is inwards bent. Because the rigidity of the cylinder in the vertical direction is small, under the action of the deformation force, the horizontal parts at the two ends of the cylinder are pulled to be large, the horizontal parts are in a transverse oval shape, the flanges are provided with internal opening, and after the cylinder is in the extrusion state for a long time, the internal opening can appear on the cylinder joint surface when the cylinder body is restored to the cold state, so that equipment is damaged. The equipment heats up too fast, and more than 30 ℃/h can cause that flange padding green-house paper can not be completely attached to the equipment, and the insulation effect is affected.
It should be noted that the cover vibration of the steam turbine generally occurs in the condition of insufficient warm-up time, too fast rise or loading, and the heated air cylinder is not uniform, if the cover vibration exceeds 50 μm or is close to 50 μm but the rise speed is more than or equal to 30 μm/h, the problem of deformation or inaccurate center of the cylinder body may exist; the vibration value of the coagulation pump of the gas coagulation machine is larger than 4.5mm/s, resonance exists, the running frequency of equipment can be changed, and if the coagulation pump support is not reinforced at this time, the displacement of the coagulation pump can be caused to cause danger.
It should also be noted that the opening of the two-stage steam-extraction hydraulic valve is greater than 20% to represent a heating input state, and when the desuperheater is normally not input with desuperheater, the temperature difference between the front and the rear of the desuperheater is below 100, and when the temperature difference exceeds 100 and the temperature difference is maintained for a long time, the internal leakage is represented.
Furthermore, the side opening is less than 5% and is the total closing of the condenser side of the TCA cooler, the history data of the flow of TCA to the boiler side predicts the opening of the regulating valve, and the deviation between the actual value of the opening of the regulating valve and the fitting value is not more than 10% in the normal running state.
It should be noted that, the outlet flow rate of the water feeding pump is more than 40t/h, which indicates that the water feeding pump does not have internal leakage phenomenon during normal operation, but the recirculation valve has flow rate, which indicates that the recirculation valve is not screwed or falls off, when the phenomenon occurs, equipment maintenance is required to be directly carried out, the flow rate of the recirculation valve is not large, the water quantity in the pump is small or no water exists, the heat generated by sliding friction caused by the centrifugal impeller cannot be completely taken away by the water, the temperature in the pump is increased, the water is vaporized, cavitation is formed, and serious damage is caused to equipment and the impeller.
Still further, the diagnosing includes:
if the temperature of the thrust tile exceeds the standard, early warning is carried out, the temperature difference is reduced to below 80 ℃, the degradation level of the equipment, the residual available time of the equipment and the parts are calculated, the situation that the inside of the cylinder of the engine body is hydrophobic and not hydrophobic, the load fluctuation is too fast, the temperature difference of steam entering the cylinder is too large with the temperature of the cylinder body is diagnosed, the situation that the temperature difference is not reduced to below 80 ℃ is diagnosed as a thermocouple fault, the safety of the thermocouple fault is checked, the replacement of fault parts is carried out, and the residual available time of the residual parts is calculated.
If the surge early warning of the gas compressor occurs, the control is not carried out, the degradation level of the equipment is calculated, the residual available time of the equipment and parts is calculated, the turning time of the gas turbine is diagnosed to be insufficient, the cover vibration is not reduced to be less than 50 mu m or the vibration value of a condensing pump of the gas condenser is greater than 4.5mm/s, the fastening screw of the end cover of the gas turbine is diagnosed to be loose, the bearing bush is heated unevenly or the condensing pump is resonated under the natural frequency, the part maintenance and the replacement of the bearing bush are needed to be carried out on the gas turbine, the condensing pump bearing is replaced, and the residual available time of the residual parts is calculated.
If the reheat steam temperature difference is too large, diagnosing that the valve core in the valve of the low-pressure heat supply attemperator of the steam turbine is worn, the sealing of the inner side of the valve is aged and is not tight, checking the safety of the valve and replacing fault parts, and calculating the residual available time of the residual parts.
If the high-pressure steam drum continuous-discharge electric regulating valve is in a clamping early warning, the opening is changed below 5%, the deviation between the fitting value of the opening at the boiler side of the TCA cooler and the actual value can be reduced to 10%, the valve clamping or the inconsistent opening setting and feedback of the control valve is diagnosed, the lubrication treatment is carried out, and calculating the remaining usable time of the valve, overhauling the valve opening control system, and if the normal operation cannot be recovered, diagnosing the TCA cooler condenser and the water supply pump as the valve core of the valve of the TCA cooler of the fuel engine is fallen or the valve core in the valve of the water supply pump is worn, and the inner side of the valve is aged and sealed incompletely.
It should be noted that, after fault cause diagnosis, power plant personnel are timely reminded through a real-time alarm mode, unprocessed and still alarmed information is displayed on a proxy alarm interface, and the method is divided into rule alarm, model alarm, DCS, TCS, settlement, fire protection, temperature and humidity, and operation and maintenance personnel process the proxy alarm according to actual alarm conditions.
Example 2
In order to verify the beneficial effects of the application, scientific demonstration is carried out through economic benefit calculation and practical application.
The test results of the application in the pearl sea electric power plant are compared with the table, and the application is tested on line at 10/12/2021, and the data is up to 31/5/the next year.
By implementing equipment reliability maintenance, fault early warning and diagnosis, system operation parameters are optimized, start-up and stop time is shortened, and comprehensive gas consumption is reduced. The comprehensive gas consumption rate is 0.1966m3/KWh after the period of 31 months, which is lower than the gas consumption level of 0.1977m3/KWh in the last year; calculated according to the same generated energy, the method is equivalent to 68.4 square meters in gas consumption reduction, and the power generation efficiency is improved by 0.14 percent.
The equipment reliability maintenance is carried out, scientific decision basis is provided for key equipment maintenance aiming at early warning equipment combined with historical fault information, and the equipment is changed into predictive maintenance passively; on-line intelligent and off-line knowledge base diagnosis is carried out on 5 kinds of pump equipment, so that equipment fault expansion is effectively prevented. The daily maintenance cost of inspection and repair is estimated to be reduced by 424.2 ten thousand yuan all year round (wherein the direct maintenance cost is 20.6 ten thousand yuan and the indirect benefit is 403.6 ten thousand yuan). The daily maintenance expenditure of 2021 is 4084.3 ten thousand yuan throughout the year, and compared with 2021, the daily maintenance expenditure of 2022 is expected to be reduced by 10.4 percent.
Through implementation of equipment fault early warning and diagnosis and equipment reliability evaluation, the equipment reliability is obviously improved, and the number of non-stop hours of two sets of units is obviously reduced. The two sets of units in 2021 of the pearl sea power plant are accumulated for non-stop 130.667 hours all year round, and the accumulation for non-stop 3.1 hours is 5 months and 31 days after the year. The last year is in the same period and is accumulated for 41.47 hours, and the same ratio is reduced by 92.5 percent.
TABLE 1 comparison Table of test results for pearl sea electric power plant
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Example 3
Referring to FIG. 2, for one embodiment of the present application, there is provided a device reliability maintenance system for an intelligent power plant, comprising: the system comprises a data cleaning module, a device digitizing module, a fault early warning and diagnosing module and a warning module.
The data processing module is a device for filtering access data and is used for performing dead value diagnosis, fluctuation diagnosis, threshold diagnosis and gradient diagnosis, and providing reliable real-time equipment operation data and historical equipment fault data for the equipment digitizing module; the equipment digitizing module is a device for digitizing equipment information and running states into virtual equipment and is used for constructing equipment type modeling, production process structure modeling, twin modeling, factory modeling, unit modeling, professional modeling, system modeling and equipment modeling; the fault early warning and diagnosing module is a device for predicting whether the equipment is faulty or not and analyzing the fault cause and the processing method, and analyzes the equipment degradation level and the equipment fault cause according to the real-time state data, the historical fault data and the rule engine of the equipment; the warning module is a warning device and is used for informing operation and maintenance personnel of fault equipment information and fault reasons, and displaying unprocessed and still-warning information on a proxy warning interface.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Claims (10)
1. A device reliability maintenance method for an intelligent power plant, comprising:
acquiring real-time operation data of equipment, cleaning the data, and then performing data quality analysis;
performing equipment digital modeling by combining the cleaned data and equipment information;
and the fault early warning and diagnosis model judges and early warns according to the equipment digital model and the historical fault data.
2. The device reliability maintenance method for an intelligent power plant of claim 1, wherein: the data quality analysis includes dead value diagnosis, fluctuation diagnosis, threshold diagnosis, and gradient diagnosis.
3. The device reliability maintenance method for an intelligent power plant of claim 1, wherein: the equipment digital modeling comprises equipment type modeling, production process structure modeling, twin modeling, factory modeling, unit modeling, professional modeling, system modeling and equipment modeling.
4. The device reliability maintenance method for an intelligent power plant of claim 3, wherein: the fault early warning and diagnosing model comprises a parameter early warning model, an equipment evaluation model and a rule engine.
5. The device reliability maintenance method for an intelligent power plant of claim 4, wherein: the parameter early-warning model comprises a thrust tile temperature exceeding early-warning model, a compressor surge early-warning model, a reheat steam temperature difference overlarge early-warning model and a high-pressure steam drum continuous exhaust electric regulating valve jamming early-warning model.
6. The device reliability maintenance method for an intelligent power plant according to claim 1 or 5, wherein: the pre-warning includes the steps of,
if the temperature difference between the inner wall and the outer wall of the high-pressure regulating valve is more than 80 ℃ or the temperature difference between the upper half inner wall and the lower half inner wall of the middle-pressure steam inlet is more than 80 ℃ or the left temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the right temperature difference between the inner wall and the outer wall of the upper half flange is more than 80 ℃ or the temperature difference rising rate is more than or equal to 30 ℃/h, the thrust tile temperature exceeding early warning condition is achieved;
if the cover vibration of the steam turbine is more than 50 mu m or the cover vibration is between 40 and 50 mu m, and the rising speed is more than or equal to 30 mu m/h or the condensing pump vibration value of the condensing machine is more than 4.5mm/s, the surge early warning condition of the air compressor is achieved;
if the two-stage steam extraction hydraulic valve is opened and the opening is more than 20%, the valve of the temperature-reducing water regulating valve is closed, and the temperature difference of steam before and after the temperature reducer is more than 100 ℃, the early warning condition of overlarge reheat steam temperature difference is achieved;
if the opening of the condenser side of the TCA cooler is less than 5%, the deviation between the fitting value of the opening of the boiler side of the TCA cooler and the actual value is more than 10%, or the flow rate after the recirculation valve is more than 0, one pump is running, and the flow rate of the outlet of the water supply pump is more than 40t/h, the clamping early warning condition of the high-pressure steam drum continuous-discharge electric regulating valve is achieved.
7. The device reliability maintenance method for an intelligent power plant according to claim 1 or 4, wherein: the diagnosis may include the step of,
if the temperature of the thrust tile exceeds the standard, early warning is carried out, the temperature difference is not regulated and controlled, the degradation level of the equipment is calculated, the residual available time of the equipment and parts is calculated, the situation that the inside of a cylinder of a body of the steam turbine is hydrophobic and not hydrophobic, the load change is too fast, the temperature difference of steam entering the cylinder is too large with the temperature of the cylinder body is diagnosed, the situation that the temperature difference is not reduced to below 80 ℃ is diagnosed, the thermocouple fault is detected, the safety of the thermocouple fault is checked, the replacement of fault parts is carried out, and the residual available time of the residual parts is calculated;
if the surge early warning of the gas compressor occurs, the control is not carried out, the degradation level of the equipment is calculated, the residual available time of the equipment and parts is calculated, the turning time of the gas turbine is diagnosed to be insufficient, the cover vibration is not reduced to be less than 50 mu m or the vibration value of a condensing pump of the gas condenser is greater than 4.5mm/s, the fastening screw of the end cover of the gas turbine is diagnosed to be loose, the bearing bush is heated unevenly or the condensing pump is resonated under the natural frequency, the part maintenance and the replacement of the bearing bush are carried out on the gas turbine, the condensing pump bearing is replaced, and the residual available time of the residual parts is calculated;
if the reheat steam temperature difference is too large, diagnosing that the valve core in the valve of the low-pressure heat supply attemperator of the steam turbine is worn, the sealing of the inner side of the valve is aged and is not tight, checking the safety of the valve and replacing fault parts, and calculating the residual available time of the residual parts;
if the high-pressure steam drum continuous-discharge electric regulating valve is in a clamping early warning, the opening is changed below 5%, the deviation between the fitting value of the opening at the boiler side of the TCA cooler and the actual value can be reduced to 10%, the valve clamping or the inconsistent opening setting and feedback of the control valve is diagnosed, the lubrication treatment is carried out, calculating the remaining usable time of a valve, overhauling a valve opening control system, and if the normal operation cannot be recovered, diagnosing that the valve core of the valve of the TCA cooler of the fuel engine or the valve core in the valve of the water supply pump is worn, and sealing the inner side of the valve is aged and is not tight by the TCA cooler condenser and the water supply pump;
after fault reasons are diagnosed, a system reminds power plant personnel in time through a real-time alarm mode, and information which is not processed and still is in alarm is displayed on a proxy alarm interface and is divided into rule alarm, model alarm, DCS, TCS, settlement, fire protection and temperature and humidity, and operation and maintenance personnel process the proxy alarm according to actual alarm conditions.
8. A plant reliability maintenance system for an intelligent power plant employing any one of claims 1-7, characterized in that: comprises a data cleaning module, a device digitizing module, a fault early warning and diagnosing module and a warning module,
the data processing module is a device for filtering access data and is used for performing dead value diagnosis, fluctuation diagnosis, threshold diagnosis and gradient diagnosis, and providing reliable real-time equipment operation data and historical equipment fault data for the equipment digitizing module;
the equipment digitizing module is a device for digitizing equipment information and running states into virtual equipment and is used for constructing equipment type modeling, production process structure modeling, twin modeling, factory modeling, unit modeling, professional modeling, system modeling and equipment modeling;
the fault early warning and diagnosing module is a device for predicting whether equipment fails and analyzing the failure cause and the processing method, and analyzes the equipment degradation level and the equipment failure cause according to the real-time state data, the historical failure data and the rule engine of the equipment;
the warning module is a warning device and is used for informing operation and maintenance personnel of fault equipment information and fault reasons, and displaying unprocessed and still-warning information on a proxy warning interface.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the device reliability maintenance method for an intelligent power plant of any one of claims 1 to 7.
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