CN114757380B - Fault early warning system and method for thermal power plant, electronic equipment and storage medium - Google Patents
Fault early warning system and method for thermal power plant, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a thermal power plant fault early warning system, a thermal power plant fault early warning method, electronic equipment and a storage medium, wherein the system comprises: the system comprises a data acquisition module, a data management module and a model construction module; the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit; the data management module is used for analyzing the operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data and determining operation characteristic information of each power equipment; the model construction module is used for constructing fault early warning models corresponding to the power equipment according to the model data and the operation characteristic information of the power equipment so as to perform fault early warning on the current thermal power plant based on the fault early warning models. By combining the operation characteristic information of each power device, a fault early warning model of each power device in the current thermal power plant unit is constructed, faults of the power devices are found in advance, fault management is conducted timely, and the stability of unit operation is guaranteed.
Description
Technical Field
The application relates to the technical field of thermal power plant safety management, in particular to a thermal power plant fault early warning system, a thermal power plant fault early warning method, electronic equipment and a storage medium.
Background
At present, a distributed control system (Distributed Control System, abbreviated as DCS) has wide application in various industries such as electric power, metallurgy, petrochemical industry and the like by the characteristics of universality, multilayer grading and reliability. In a traditional thermal power plant, the control of each electric device is mainly DCS distributed control.
In the prior art, when an abnormal measuring point reaches an alarm position or a protection position, a DCS system usually performs key display or interlocking protection action of an upper computer, and the safety alarm of a thermal power plant unit is realized, but the running stability of the unit cannot be ensured.
Disclosure of Invention
The application provides a thermal power plant fault early warning system, a thermal power plant fault early warning method, electronic equipment and a storage medium, so as to solve the defect that the running stability of a thermal power plant unit cannot be guaranteed in the prior art.
A first aspect of the present application provides a thermal power plant fault early warning system, comprising: the system comprises a data acquisition module, a data management module and a model construction module;
the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module;
the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment, and sending the model data and the operation characteristic information of each power equipment to the model construction module;
the model construction module is used for constructing a fault early warning model corresponding to each power device according to the model data and the operation characteristic information of each power device so as to perform fault early warning on the current thermal power plant based on each fault early warning model.
Optionally, the data acquisition module is specifically configured to:
acquiring measurement data of each electric power device from a measurement instrument of each electric power device in the current thermal power plant unit;
acquiring monitoring data of each piece of electric equipment from the DCS system of the current thermal power plant;
and summarizing the measurement data and the monitoring data to obtain the operation data of each power device.
Optionally, the data management module is specifically configured to:
carrying out working condition analysis on the received operation data to distinguish the operation data of each electric power equipment under different working conditions;
and determining the operation data under the target working condition as model data required for constructing the power equipment fault early warning model aiming at any power equipment.
Optionally, the data management module is specifically configured to:
comparing the same points among the operation characteristic information of a plurality of units with the same type as the current thermal power plant unit to obtain basic operation characteristic information of the current thermal power plant unit;
determining operation characteristic information of each power device according to the operation data of each power device in the current thermal power plant unit and the basic operation characteristic information;
wherein the operation data includes a capital-time operation data and operation-time operation data of the power equipment.
Optionally, the model building module is specifically configured to:
aiming at any one of the power equipment, constructing an initial fault early warning model corresponding to the power equipment according to the interaction relation between different operation indexes represented by the operation characteristic information of the power equipment;
and training the initial fault early-warning model by using model data required by the fault early-warning model of the power equipment to obtain the fault early-warning model of the power equipment.
Optionally, the method further comprises:
the optimization module is used for acquiring abnormal data generated in the application process of the fault early-warning model and updating the operation data and the operation characteristic information in the data management module according to an application blind area of the fault early-warning model represented by the abnormal data; and optimizing the fault early warning model according to the updating conditions of the operation data and the operation characteristic information in the data management module.
Optionally, the fault early warning model at least comprises a reverse osmosis membrane pollution blocking model of a water dissolving specialty, a steam feed pump output deficiency model of a steam turbine specialty, a flue resistance abnormality model of a boiler specialty and a unit index abnormality early warning model.
The second aspect of the application provides a thermal power plant fault early warning method, which comprises the following steps:
acquiring operation data of each power device in a current thermal power plant unit;
analyzing the operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, and determining operation characteristic information of each power equipment;
and constructing a fault early warning model corresponding to each power device according to the model data and the operation characteristic information of each power device so as to perform fault early warning on the current thermal power plant based on each fault early warning model.
A third aspect of the present application provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored by the memory such that the at least one processor performs the method as described above for the second aspect and the various possible designs for the second aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the method as described above in the second aspect and the various possible designs of the second aspect.
The technical scheme of the application has the following advantages:
the application provides a thermal power plant fault early warning system, a thermal power plant fault early warning method, electronic equipment and a storage medium, wherein the system comprises: the system comprises a data acquisition module, a data management module and a model construction module; the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module; the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment and sending the model data and the operation characteristic information of each power equipment to the model construction module; the model construction module is used for constructing fault early warning models corresponding to the power equipment according to the model data and the operation characteristic information of the power equipment so as to perform fault early warning on the current thermal power plant based on the fault early warning models. According to the system provided by the scheme, the fault early-warning model of each power device in the current thermal power plant unit is constructed by combining the operation characteristic information of each power device, the fault existing in the power device is found in advance before the DCS alarms by utilizing the fault early-warning model, the fault management is carried out timely, and the running stability of the unit is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic structural diagram of a thermal power plant fault early warning system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a thermal power plant fault early warning method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but to illustrate the concepts of the present application to those skilled in the art with reference to the specific embodiments.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. In the following description of the embodiments, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the prior art, when the operation of some power systems or equipment of a thermal power plant unit is in an abnormal state in the operation process, a professional personnel must find out in time and accurately judge the cause of the fault by combining with professional knowledge, and further, the operating personnel adjust the working state of the abnormal power systems or equipment in time to enable the power systems or equipment to recover to a normal working condition, otherwise, when the abnormal measuring point reaches an alarm position or a protection position, the DCS system respectively performs the key display or the interlocking protection action of an upper computer. In actual operation on site, because the thermal power generating set contains more professional theoretical knowledge and operation faults of the power system or the equipment, the complexity of each power system or equipment is improved, and meanwhile, the nonlinearity, the high coupling and the large inertia among the operation working conditions of the power system or the equipment are high, so that the requirements of professional personnel on all aspects of fault hidden danger pre-judgment cannot be basically realized. When early symptoms of the power system or equipment faults cannot be found in time, under the diffusion influence of abnormal working conditions, the safe, economical and stable operation of the unit can be influenced, and even irreversible damage of the power system or equipment can be caused when the problems are serious, the unit is stopped abnormally.
Aiming at the problems, the embodiment of the application provides a thermal power plant fault early warning system, a thermal power plant fault early warning method, electronic equipment and a storage medium, wherein the system comprises: the system comprises a data acquisition module, a data management module and a model construction module; the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module; the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment and sending the model data and the operation characteristic information of each power equipment to the model construction module; the model construction module is used for constructing fault early warning models corresponding to the power equipment according to the model data and the operation characteristic information of the power equipment so as to perform fault early warning on the current thermal power plant based on the fault early warning models. According to the system provided by the scheme, the fault early-warning model of each power device in the current thermal power plant unit is constructed by combining the operation characteristic information of each power device, the fault existing in the power device is found in advance before the DCS alarms by utilizing the fault early-warning model, the fault management is carried out timely, and the running stability of the unit is ensured.
The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
The embodiment of the application provides a fault early warning system of a thermal power plant, which is used for carrying out fault early warning on power equipment in a thermal power plant unit.
As shown in fig. 1, a schematic structural diagram of a thermal power plant fault early warning system according to an embodiment of the present application is provided, and the system 10 includes: a data acquisition module 101, a data management module 102 and a model building module 103.
The data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module; the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment and sending the model data and the operation characteristic information of each power equipment to the model construction module; the model construction module is used for constructing fault early warning models corresponding to the power equipment according to the model data and the operation characteristic information of the power equipment so as to perform fault early warning on the current thermal power plant based on the fault early warning models.
The power equipment mainly comprises a steam turbine, a boiler and the like, and the operation data of the power equipment comprise a plurality of operation indexes of the power equipment at the moment, such as main water supply flow, total coal amount, total air amount and the like.
Specifically, the operation data of each power equipment in the current thermal power plant unit can be obtained as comprehensively as possible based on the data acquisition module, and the data acquisition module sends the operation data to the data management module because the acquired operation data are various and are not easy to directly analyze and process. The data management module analyzes and processes the obtained operation data according to the construction requirements of the fault early-warning models of different power equipment, and finally, model data required by constructing the fault early-warning models of different power equipment are obtained. Meanwhile, as the coupling exists between the operation data of the electric equipment, such as the total air quantity and the total coal quantity, the combustion effect in the furnace and the like can be influenced, the data management module can determine the operation characteristic information of each electric equipment according to the operation data of each electric equipment by combining the related knowledge of thermal power generation. Further, the data management module sends the model data and the operation characteristic information of each power device to the model construction module so as to construct a fault early warning model of each power device.
Further, after the fault early warning is carried out on the current thermal power plant based on the fault early warning model and the fault early warning signal of the current thermal power plant is generated, the fault early warning signal is pushed to the PC end or the mobile end of the professional engineer and the operation operator so as to remind the professional engineer and the operation operator of carrying out corresponding fault treatment work in time.
Further, in order to further improve the fault treatment efficiency of professional engineers and operation operators, the fault early-warning model can push corresponding fault treatment schemes according to the fault occurrence reasons while pushing the fault early-warning signals. After the power equipment with early warning is recovered from the fault early warning state to the normal operation condition, new unit operation data is formed and reflected into the data stream in real time to be used as a new round of fault early warning cycle.
Because the thermal power plant is divided into five major professions of steam turbines, boilers, electricity, thermal control and power plant chemistry, the fault early warning model provided by the embodiment of the application at least comprises a reverse osmosis membrane pollution blocking model of a chemical water profession, a steam feed pump output deficiency model of the steam turbines profession, a flue resistance abnormity model of the boilers profession and a unit index abnormity early warning model.
For the reverse osmosis membrane fouling model, as the fouling condition of the reverse osmosis membrane is represented by the inter-section pressure difference, the water inlet flow, the water inlet temperature, the water inlet conductance, the high-pressure pump frequency and the like can be selected as parameters, the fouling trend process is identified through the reverse osmosis membrane fouling model, and corresponding early warning is carried out when the reverse osmosis membrane is seriously polluted along with the running process. Aiming at a model of insufficient output of a steam-driven water feed pump, as the insufficient output of the steam-driven water feed pump is represented by main water supply flow of a unit, industrial steam extraction pressure, industrial steam extraction temperature, deaerator water level, deaerator pressure, steam pump inlet flow, steam pump rotating speed, condenser vacuum and the like are selected as parameters, the insufficient output of the steam-driven water feed pump under the influence of multiple parameter variables is predicted and determined through the model of insufficient output of the steam-driven water feed pump, the real-time output of the steam-driven water feed pump is compared, and corresponding early warning is carried out when the output of the steam-driven water feed pump is abnormal. Aiming at the abnormal flue resistance model, the abnormal flue resistance takes the inlet pressure and the outlet pressure of a flue at a certain point as the characterization, uncorrected total coal quantity, the oxygen content of flue gas, total air quantity, flue gas temperature and the like are selected as parameters, the abnormal flue resistance model predicts the flue resistance of the boiler under the influence of multi-parameter variables, the real-time flue resistance is compared, and corresponding early warning is carried out when the abnormal resistance occurs. Aiming at a unit index abnormality early warning model, the unit index abnormality takes a unit design parameter as a representation, a unit actual parameter as a comparison parameter, the current unit operation state is determined in real time through the unit index abnormality early warning model, the current unit operation state is evaluated through the unit operation index with the optimal current operation condition, and corresponding early warning is carried out when the abnormality of the current unit operation state is determined.
It should be noted that, the fault early-warning model provided by the embodiment of the application is not limited to the reverse osmosis membrane fouling model, the pneumatic water supply pump output deficiency model, the flue resistance abnormality model and the unit index abnormality early-warning model, and the corresponding fault early-warning model can be specifically constructed according to actual early-warning requirements.
On the basis of the above embodiment, as an implementation manner, in an embodiment, the data acquisition module is specifically configured to acquire measurement data of each electrical device from a measurement instrument of each electrical device in a current thermal power plant unit; acquiring monitoring data of each power device from a DCS (distributed control system) of a current thermal power plant; and summarizing the measurement data and the monitoring data to obtain the operation data of each power device.
It should be noted that, if the measurement data of the electrical equipment is obtained only at the measurement instrument of the electrical equipment, and the measurement data is used as the operation data of the electrical equipment, the integrity and the comprehensiveness of the operation data may not be ensured, so the data acquisition module also obtains the monitoring data of the electrical equipment from the monitoring data storage platform of the DCS system of the current thermal power plant.
The obtained measurement data and monitoring data have some repeated data, so that the measurement data and the monitoring data can be processed in a union mode to obtain the operation data of the power equipment.
Specifically, the data acquisition module can acquire monitoring data of each power device from the DCS system of the current thermal power plant by utilizing the big data traceability platform of the current thermal power plant.
On the basis of the above embodiment, as an implementation manner, in an embodiment, the data management module is specifically configured to perform working condition analysis on the received operation data, so as to distinguish the operation data of each electrical device under different working conditions; and determining the operation data under the target working condition as model data required for constructing the power equipment fault early warning model aiming at any power equipment.
It should be noted that, the operation data acquired by the data acquisition module is all operation data of the power equipment within years, and covers various working conditions, and if the fault early-warning model of the power equipment is directly constructed based on the obtained operation data, the accuracy of the fault early-warning model cannot be ensured.
Specifically, the data management module analyzes the operation data of the currently received power equipment according to a preset time interval, such as 1 minute or 5 minutes, and the like, determines a specific operation condition of the power equipment in the time interval according to a plurality of operation indexes included in the operation data in the current time interval, and the like, divides the operation data of the power equipment into operation data under each operation condition, and then selects the operation data corresponding to at least one target operation condition as model data of the power equipment.
By taking a reverse osmosis membrane as an example, the operation data of the reverse osmosis membrane under the conditions of flushing, variable flow, small flow, large flow, maintenance, chemical cleaning and shutdown are distinguished by analyzing the operation data of the reverse osmosis membrane. And if the high-flow working condition is defined as the target working condition by combining with the early warning target requirement of the reverse osmosis membrane fouling model, extracting operation data corresponding to the high-flow working condition as model data required for constructing the reverse osmosis membrane fouling model. And if the large-flow working condition and the small-flow working condition are defined as target working conditions, extracting operation data corresponding to the large-flow working condition as model data required for constructing a first reverse osmosis membrane fouling model, and extracting operation data corresponding to the small-flow working condition as model data required for constructing a second reverse osmosis membrane fouling model, wherein the first reverse osmosis membrane fouling model is applied to the large-flow working condition, the second reverse osmosis membrane fouling model is applied to the small-flow working condition, and the first reverse osmosis membrane fouling model and the second reverse osmosis membrane fouling model can be coupled into one reverse osmosis membrane fouling model.
Because the thermal power plant unit relates to a large number of power equipment, the embodiment of the application does not explain other power equipment one by one, and particularly can be adjusted according to the adaptability of the practical application object.
Specifically, in an embodiment, the data management module is specifically configured to compare the same points between the operation feature information of a plurality of units with the same type as the current thermal power plant unit to obtain basic operation feature information of the current thermal power plant unit; and determining the operation characteristic information of each power device according to the operation data and the basic operation characteristic information of each power device in the current thermal power plant unit.
It should be noted that, since the data quality of the operation data received by the data management module cannot be guaranteed, after the operation data is obtained by the data management module, the operation data is first subjected to preprocessing operations, such as null processing, noise reduction processing, normalization processing, and the like. The noise reduction treatment can adopt modes such as differential transformation, logarithmic transformation, fourier transformation, wavelet transformation, median threshold noise reduction, manual expert analysis, regression fitting, clustering analysis and the like, and the normalization treatment can adopt modes such as maximum and minimum value normalization, standard normalization, batch normalization, layer-by-layer normalization and the like.
Wherein the differential variation may be based on a function: Δf (x) k )＝f(x k+1 )-f(x k ) Proceeding; the logarithmic change may be based on a function: s=log a N is carried out; the fourier transform may be based on a function: is carried out.
Wherein the normalization process includes, but is not limited to, linear function normalization, which converts the method of linearizing the raw data to [0,1 ]]The linear function normalization formula is shown as the formula Standard normalization, which normalizes the original data into a data set with mean value of 0 and variance of 1, wherein the normalization formula is shown as formula +.>Robust normalization, which converts the raw data distribution into a distribution with a median of 0 and an IQR of 1, and a robust normalization formula is shown as formula d' i ＝(d i -median)/(quantile 75 -quantile 25 ) And (5) performing equal normalization.
Specifically, the commonality principle and the operation characteristics of the current thermal power plant unit can be determined by comparing the same points among the operation characteristic information of a plurality of units of other thermal power plants with the same type of the current thermal power plant unit, so as to obtain the basic operation characteristic information of the current thermal power plant unit. Further, the independent operation characteristic information of each power device in the current thermal power plant unit is determined, and the operation characteristic information of each power device in the current thermal power plant unit is determined by combining the basic operation characteristic information and the independent operation characteristic information of the current thermal power plant unit.
The operation data used for determining the independent operation characteristic information of the current thermal power plant unit comprises the basic construction period operation data and the operation period operation data of each power equipment in the unit, so that the integrity of the operation data is ensured.
On the basis of the above embodiment, as an implementation manner, in an embodiment, the model building module is specifically configured to build, for any one power device, an initial fault early warning model corresponding to the power device according to an interaction relationship between different operation indexes represented by operation characteristic information of the power device; and training an initial fault early-warning model by using model data required by the fault early-warning model of the power equipment to obtain the fault early-warning model of the power equipment.
The fault early warning model can be constructed based on a k-nearest neighbor algorithm, a naive Bayesian algorithm, a support vector machine, a decision tree, a k-mean value, various neural networks and the like, and also can be constructed based on a generation model algorithm, a transfer learning model algorithm, a combined training model algorithm, a semi-supervised support vector machine, a graph theory-based algorithm, a sequence structure algorithm, various neural networks and the like, and the specific construction mode can be selected according to actual conditions.
Specifically, after the initial fault early-warning model is trained, a test sample can be utilized to test the initial fault early-warning model, if the accuracy rate of the test result representation is more than 95%, the model training is determined to be completed, and then the fault early-warning model of the power equipment is obtained, otherwise, the training is continued.
Further, in an embodiment, the system further includes an optimization module, configured to obtain abnormal data generated in the application process by the fault early-warning model, and update the operation data and the operation feature information in the data management module according to an application blind area of the fault early-warning model represented by the abnormal data; and optimizing a fault early warning model according to the updating conditions of the operation data and the operation characteristic information in the data management module.
It should be noted that, because each power device in the current thermal power plant unit is changed to a certain extent in the operation process, for example, after a certain power device is manually maintained, the interaction relationship between different operation indexes changes, or a new operation index is generated, so that the currently applied fault early warning model has deviation or application blind areas.
Specifically, the model building module and the data management module can interact based on the optimization module, wherein the data management module can serve as a judging device of the model building module to comprehensively guide the model building process of the model building module so as to obtain a corresponding fault early warning model. After the fault early warning model is put into use, the fault early warning model can react to the data management module according to the specific application condition.
Specifically, the optimization module may send the operation index obtained by the fault early-warning model to the data management module according to an application blind area of the fault early-warning model represented by abnormal data generated by the fault early-warning model in the application process, where the operation index includes a new operation index. The data management module updates the operation data and the operation characteristic information according to the currently received operation index, redetermines the operation characteristic information of the power equipment, and further optimizes the fault early warning model correspondingly.
The fault early warning system of thermal power plant that this application embodiment provided includes: the system comprises a data acquisition module, a data management module and a model construction module; the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module; the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment and sending the model data and the operation characteristic information of each power equipment to the model construction module; the model construction module is used for constructing fault early warning models corresponding to the power equipment according to the model data and the operation characteristic information of the power equipment so as to perform fault early warning on the current thermal power plant based on the fault early warning models. According to the system provided by the scheme, the fault early warning model of each power device in the current thermal power plant unit is constructed by combining the operation characteristic information of each power device, the fault existing in the power device is found in advance before the DCS system alarms by utilizing the fault early warning model, the fault is treated in time, the stability of the operation of the unit is ensured, and the aims of reducing the diffusion of the abnormal working state of the power device, maintaining the safe, economical and stable operation affecting the unit, avoiding the irreversible damage of the power device and avoiding the abnormal shutdown of the unit are fulfilled.
The embodiment of the application provides a thermal power plant fault early warning method which is used for carrying out fault early warning on power equipment in a thermal power plant unit. The execution main body of the embodiment of the application is electronic equipment, such as a server, a desktop computer, a notebook computer, a tablet computer and other electronic equipment which can be used as an upper computer of a thermal power plant to perform fault early warning on power equipment in a thermal power plant unit.
As shown in fig. 2, a flow chart of a thermal power plant fault early warning method provided in an embodiment of the present application is shown, where the method includes:
step 201, acquiring operation data of each power device in a current thermal power plant unit;
step 202, analyzing the operation data to screen model data required for constructing fault early warning models of different power equipment from the operation data, and determining operation characteristic information of each power equipment;
and 203, constructing a fault early warning model corresponding to each power device according to the model data and the operation characteristic information of each power device so as to perform fault early warning on the current thermal power plant based on each fault early warning model.
The specific implementation of each step in the thermal power plant fault early warning method in this embodiment has been described in detail in the embodiment related to the system, and will not be described in detail here.
The thermal power plant fault early warning method provided by the embodiment of the application is the application method of the thermal power plant fault early warning system provided by the embodiment, and the implementation mode and the principle are the same and are not repeated.
The embodiment of the application provides electronic equipment for executing the thermal power plant fault early warning method provided by the embodiment.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 30 includes: at least one processor 31 and a memory 32.
The memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the thermal power plant fault early warning method provided in the above embodiment.
The electronic device provided in the embodiment of the present application is configured to execute the thermal power plant fault early warning method provided in the foregoing embodiment, and its implementation manner and principle are the same and are not repeated.
The embodiment of the application provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when a processor executes the computer execution instructions, the fault early warning method of the thermal power plant provided by any embodiment is realized.
The storage medium containing the computer executable instructions in the embodiments of the present application may be used to store the computer executable instructions of the thermal power plant fault early warning method provided in the foregoing embodiments, and the implementation manner and the principle are the same, and are not repeated.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform part of the steps of the methods described in 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 (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the system is divided into different functional modules to perform all or part of the functions described above. The specific working process of the system described above may refer to the corresponding process in the foregoing method embodiment, and will not be described herein.
Finally, 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; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. A thermal power plant fault early warning system, comprising: the system comprises a data acquisition module, a data management module and a model construction module;
the data acquisition module is used for acquiring the operation data of each power device in the current thermal power plant unit and writing the operation data into the data management module;
the data management module is used for analyzing the received operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, determining operation characteristic information of each power equipment, and sending the model data and the operation characteristic information of each power equipment to the model construction module;
the model construction module is used for constructing a fault early warning model corresponding to each piece of electric equipment according to the model data and the operation characteristic information of each piece of electric equipment so as to perform fault early warning on the current thermal power plant based on each fault early warning model;
the data acquisition module is specifically configured to:
acquiring measurement data of each electric power device from a measurement instrument of each electric power device in the current thermal power plant unit;
acquiring monitoring data of each piece of electric equipment from the DCS system of the current thermal power plant;
summarizing the measurement data and the monitoring data to obtain the operation data of each power device;
the system further comprises:
the optimization module is used for acquiring abnormal data generated in the application process of the fault early-warning model and updating the operation data and the operation characteristic information in the data management module according to an application blind area of the fault early-warning model represented by the abnormal data; optimizing the fault early warning model according to the updating conditions of the operation data and the operation characteristic information in the data management module;
the operation data used for determining the independent operation characteristic information of the current thermal power plant unit comprises the basic construction period operation data and the operation period operation data of each electric power device in the unit;
combining basic operation characteristic information and independent operation characteristic information of the current thermal power plant unit to determine operation characteristic information of each power device in the current thermal power plant unit;
determining the universality principle and the operation characteristic of the current thermal power plant unit by comparing the same points among the operation characteristic information of a plurality of units of other thermal power plants with the same type as the current thermal power plant unit so as to obtain the basic operation characteristic information of the current thermal power plant unit;
according to the operation data of each power equipment in the current thermal power plant unit, determining independent operation characteristic information of the current thermal power plant unit;
the fault early warning model at least comprises a reverse osmosis membrane pollution blocking model of a water dissolving specialty, a steam feed pump output deficiency model of a steam turbine specialty, a flue resistance abnormality model of a boiler specialty and a unit index abnormality early warning model;
the method comprises the steps of selecting water inflow, water inflow temperature, water inflow conductivity and high-pressure pump frequency as parameters, identifying a fouling trend process through a reverse osmosis membrane fouling model, and carrying out corresponding early warning when a reverse osmosis membrane is seriously clogged along with the running process;
selecting industrial steam extraction pressure, industrial steam extraction temperature, deaerator water level, deaerator pressure, steam pump inlet flow, steam pump rotating speed and condenser vacuum as parameters, predicting and determining steam feed pump output under the influence of multi-parameter variables through a steam feed pump output deficiency model, comparing real-time steam feed pump output, and carrying out corresponding early warning when the steam feed pump output is abnormal;
the uncorrected total coal quantity, the oxygen content of the flue gas, the total air quantity and the flue gas temperature are selected as parameters, the flue resistance of the boiler under the influence of multi-parameter variables is predicted through a flue resistance abnormal model, the real-time flue resistance is compared, and corresponding early warning is carried out when the resistance is abnormal;
and the actual parameters of the unit are selected as comparison parameters, the current unit operation state is determined in real time through a unit index abnormality early warning model, the current unit operation state is evaluated through the unit operation index with the optimal current operation condition, and corresponding early warning is carried out when the current unit operation state is determined to be abnormal.
2. The system according to claim 1, wherein the data management module is specifically configured to:
carrying out working condition analysis on the received operation data to distinguish the operation data of each electric power equipment under different working conditions;
and determining the operation data under the target working condition as model data required for constructing the power equipment fault early warning model aiming at any power equipment.
3. The system according to claim 1, wherein the data management module is specifically configured to:
comparing the same points among the operation characteristic information of a plurality of units with the same type as the current thermal power plant unit to obtain basic operation characteristic information of the current thermal power plant unit;
determining operation characteristic information of each power device according to the operation data of each power device in the current thermal power plant unit and the basic operation characteristic information;
wherein the operation data includes a capital-time operation data and operation-time operation data of the power equipment.
4. The system according to claim 1, wherein the model building module is specifically configured to:
aiming at any one of the power equipment, constructing an initial fault early warning model corresponding to the power equipment according to the interaction relation between different operation indexes represented by the operation characteristic information of the power equipment;
and training the initial fault early-warning model by using model data required by the fault early-warning model of the power equipment to obtain the fault early-warning model of the power equipment.
5. The fault early warning method for the thermal power plant is characterized by comprising the following steps of:
acquiring operation data of each power device in a current thermal power plant unit;
analyzing the operation data to screen model data required by constructing fault early warning models of different power equipment from the operation data, and determining operation characteristic information of each power equipment;
constructing a fault early warning model corresponding to each piece of power equipment according to the model data and the operation characteristic information of each piece of power equipment so as to perform fault early warning on the current thermal power plant based on each fault early warning model;
the method for acquiring the operation data of each power device in the current thermal power plant unit comprises the following steps:
acquiring measurement data of each electric power device from a measurement instrument of each electric power device in the current thermal power plant unit;
acquiring monitoring data of each piece of electric equipment from the DCS system of the current thermal power plant;
summarizing the measurement data and the monitoring data to obtain the operation data of each power device;
the method further comprises the steps of:
acquiring abnormal data generated in the application process of the fault early-warning model, and updating the operation data and the operation characteristic information according to an application blind area of the fault early-warning model represented by the abnormal data; optimizing the fault early warning model according to the updating conditions of the operation data and the operation characteristic information;
the operation data used for determining the independent operation characteristic information of the current thermal power plant unit comprises the basic construction period operation data and the operation period operation data of each electric power device in the unit;
combining basic operation characteristic information and independent operation characteristic information of the current thermal power plant unit to determine operation characteristic information of each power device in the current thermal power plant unit;
determining the universality principle and the operation characteristic of the current thermal power plant unit by comparing the same points among the operation characteristic information of a plurality of units of other thermal power plants with the same type as the current thermal power plant unit so as to obtain the basic operation characteristic information of the current thermal power plant unit;
according to the operation data of each power equipment in the current thermal power plant unit, determining independent operation characteristic information of the current thermal power plant unit;
the fault early warning model at least comprises a reverse osmosis membrane pollution blocking model of a water dissolving specialty, a steam feed pump output deficiency model of a steam turbine specialty, a flue resistance abnormality model of a boiler specialty and a unit index abnormality early warning model;
the method comprises the steps of selecting water inflow, water inflow temperature, water inflow conductivity and high-pressure pump frequency as parameters, identifying a fouling trend process through a reverse osmosis membrane fouling model, and carrying out corresponding early warning when a reverse osmosis membrane is seriously clogged along with the running process;
selecting industrial steam extraction pressure, industrial steam extraction temperature, deaerator water level, deaerator pressure, steam pump inlet flow, steam pump rotating speed and condenser vacuum as parameters, predicting and determining steam feed pump output under the influence of multi-parameter variables through a steam feed pump output deficiency model, comparing real-time steam feed pump output, and carrying out corresponding early warning when the steam feed pump output is abnormal;
the uncorrected total coal quantity, the oxygen content of the flue gas, the total air quantity and the flue gas temperature are selected as parameters, the flue resistance of the boiler under the influence of multi-parameter variables is predicted through a flue resistance abnormal model, the real-time flue resistance is compared, and corresponding early warning is carried out when the resistance is abnormal;
and the actual parameters of the unit are selected as comparison parameters, the current unit operation state is determined in real time through a unit index abnormality early warning model, the current unit operation state is evaluated through the unit operation index with the optimal current operation condition, and corresponding early warning is carried out when the current unit operation state is determined to be abnormal.
6. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of claim 5.
7. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor implement the method of claim 5.
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| PCT/CN2022/143096 WO2023207190A1 (en) | 2022-04-29 | 2022-12-29 | Fault early warning system and method for fossil fuel power plant, electronic device, and storage medium |
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| CN115618214A (en) * | 2022-10-14 | 2023-01-17 | 南京天洑软件有限公司 | Fault diagnosis method and device for rotating equipment |
| CN115795999B (en) * | 2022-10-26 | 2023-08-01 | 国网新源控股有限公司 | Early warning method for abnormal performance of long-term service pumped storage unit |
| CN116880395A (en) * | 2023-07-19 | 2023-10-13 | 天津市易控科技发展有限公司 | Monitoring method, device, equipment and medium based on DCS system |
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