Thermal power plant boiler fault monitoring and early warning method and system

Abstract

The invention discloses a thermal power plant boiler fault monitoring and early warning method and system, and the method comprises the steps: obtaining and analyzing the operation monitoring data of a thermal power plant boiler, and determining an abnormal operation parameter; performing data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and performing analysis to determine abnormal features; evaluating the abnormal state of the thermal power plant boiler based on each abnormal feature to obtain an abnormal state evaluation value, and judging whether the thermal power plant boiler has a fault according to the abnormal state evaluation value; after the thermal power plant boiler is judged to have the fault, determining the fault type based on the abnormal operation parameters; and determining a fault level based on the fault type, and determining an early warning coping strategy according to the fault level. According to the invention, prediction is carried out by analyzing the operation monitoring data, the future change condition of the boiler is determined, and the fault is determined by evaluation based on the change condition, so that early warning is carried out, the fault risk can be effectively reduced, the safety, reliability and operation efficiency of the boiler are improved, and the production benefit is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler warning in thermal power plants, and particularly to a method and system for monitoring and warning of boiler faults in thermal power plants. Background Art

[0002] In today's society, thermal power plants play a crucial role, providing a large amount of power supply for people's lives and industrial production. However, as one of the core equipment in thermal power plants, boilers may face various faults and problems during long-term operation. Once a fault occurs, it may lead to production interruption, safety accidents, and even economic losses.

[0003] However, traditional methods usually rely on manual inspections, which are subjective and limited, unable to achieve comprehensive and real-time monitoring and warning. Moreover, the manual inspection method has a slow response speed, unable to detect and handle sudden faults in a timely manner, easily leading to production interruption and safety accidents. In addition, traditional methods have limited processing capabilities for a large amount of data, unable to effectively analyze and mine potential information in the data, and thus unable to quickly and accurately determine the cause and location of faults, reducing the accuracy of fault prediction and affecting the efficiency of fault handling. The warning strategies of traditional methods are relatively single, lacking personalization and flexibility, unable to achieve automatic data collection, analysis, and warning, and unable to be adjusted and optimized in a timely manner according to the actual situation, limiting the efficiency and accuracy of the monitoring and warning system. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for monitoring and warning of boiler faults in thermal power plants, including: Obtaining the operation monitoring data of the boiler in the thermal power plant, and analyzing the operation monitoring data to determine abnormal operation parameters; Performing data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyzing the abnormal prediction data to determine abnormal characteristics; Evaluating the abnormal state of the boiler in the thermal power plant based on each abnormal characteristic to obtain an abnormal state evaluation value, and judging whether there is a fault in the boiler in the thermal power plant according to the abnormal state evaluation value; After judging that there is a fault in the boiler in the thermal power plant, determining the fault type of the boiler in the thermal power plant based on the abnormal operation parameters; Determining the fault level of the boiler in the thermal power plant based on the fault type, and determining the warning response strategy of the boiler in the thermal power plant according to the fault level.

[0005] Further, the obtaining the operation monitoring data of the boiler in the thermal power plant, and analyzing the operation monitoring data to determine abnormal operation parameters includes: Obtaining the operation monitoring data of the boiler in the thermal power plant, and dividing the operation monitoring data into several groups of operation parameter data according to the parameter type; Determine the preset normal data range corresponding to each set of operating parameter data groups, and determine the number of data in each set of operating parameter data groups that exceed the corresponding preset normal data range as the first data volume; Determine the parameters corresponding to the operating parameter data groups whose first data volume exceeds the corresponding preset quantity as abnormal operating parameters.

[0006] Furthermore, perform data prediction on each abnormal operating parameter according to the preset prediction model to obtain the corresponding abnormal prediction data, and analyze the abnormal prediction data to determine the abnormal characteristics, including: Obtain the preset prediction models set for extracting each operating parameter, and input the operating parameter data groups corresponding to each abnormal operating parameter into the corresponding preset prediction models, and output the abnormal prediction data corresponding to each abnormal operating parameter by each preset prediction model; Determine the amount of data in the abnormal prediction data that exceeds the corresponding preset normal data range as the second data volume, and calculate the average value of the abnormal prediction data as the second average value; Construct a curve of the time progress based on the abnormal prediction data to obtain the abnormal prediction data curve, and determine the change amplitude of the abnormal prediction data curve; Determine the second data volume, the second average value, and the change amplitude as the abnormal characteristics of the abnormal prediction data corresponding to each abnormal operating parameter.

[0007] Furthermore, evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, including: Determine the second data volume and the first data volume in the abnormal characteristics, calculate the ratio of the second data volume to the first data volume, and evaluate the ratio to obtain a quantity ratio evaluation value; Determine the operating parameter data groups corresponding to each abnormal operating parameter, and calculate the average value of the operating parameter data groups as the first average value; Determine the second average value in the abnormal characteristics, calculate the ratio of the second average value to the first average value, and evaluate the ratio to obtain an average ratio evaluation value; Determine the change amplitude in the abnormal characteristics, and evaluate the change amplitude to obtain a change amplitude evaluation value; Add up the quantity ratio evaluation value, the average ratio evaluation value, and the change amplitude evaluation value corresponding to each abnormal operating parameter to obtain the abnormal evaluation value of each abnormal operating parameter; Determine the preset weight of each abnormal operating parameter, and perform weighted addition calculation on the preset weight of each abnormal operating parameter and the corresponding abnormal evaluation value to obtain the abnormal state evaluation value of the thermal power plant boiler.

[0008] Furthermore, determine whether there is a fault in the thermal power plant boiler according to the abnormal state evaluation value, including: Determine a preset evaluation threshold, and judge whether there is a fault in the thermal power plant boiler based on the relationship between the evaluation threshold and the abnormal state evaluation value; If the abnormal state evaluation value is greater than or equal to the evaluation threshold, it is judged that there is a fault in the thermal power plant boiler; If the abnormal state evaluation value is less than the evaluation threshold, it is judged that there is no fault in the thermal power plant boiler.

[0009] Further, after judging that there is a fault in the thermal power plant boiler, determining the fault type of the thermal power plant boiler based on the abnormal operation parameters includes: After judging that there is a fault in the thermal power plant boiler, determine each abnormal operation parameter and the type of each abnormal operation parameter; Match the fault type that is consistent with the types of all abnormal operation parameters from the fault database according to the types of each abnormal operation parameter, and determine this fault type as the fault type of the thermal power plant boiler.

[0010] Further, determining the fault level of the thermal power plant boiler based on the fault type includes: Preset the fault level - fault type correspondence. For each fault type in the fault level - fault type correspondence, a corresponding fault level is associated; Obtain the fault type of the thermal power plant boiler, and select the corresponding fault level based on the mapping relationship of this fault type in the fault level - fault type correspondence, and determine it as the fault level of the thermal power plant boiler.

[0011] Further, determining the early warning response strategy of the thermal power plant boiler according to the fault level includes: Judge the level of the fault level of the thermal power plant boiler, and determine the early warning response strategy of the thermal power plant boiler according to the judgment result; If the level of the fault level is greater than the first preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent green warning prompt in the monitoring system and recording the fault in the maintenance plan; If the level of the fault level is greater than the second preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent orange warning prompt in the monitoring system and triggering the automatic maintenance plan; If the level of the fault level is greater than the third preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent red warning prompt in the monitoring system and automatically starting the emergency shutdown procedure; Among them, the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

[0012] The present invention also provides a fault monitoring and early warning system for a thermal power plant boiler, including: An acquisition module, configured to acquire the operation monitoring data of a thermal power plant boiler, analyze the operation monitoring data, and determine abnormal operation parameters; A prediction module, configured to perform data prediction on each abnormal operation parameter according to a preset prediction model, obtain corresponding abnormal prediction data, and analyze the abnormal prediction data to determine abnormal characteristics; A judgment module, configured to evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic, obtain an abnormal state evaluation value, and determine whether there is a fault in the thermal power plant boiler according to the abnormal state evaluation value; A determination module, configured to determine the fault type of the thermal power plant boiler based on the abnormal operation parameters after determining that there is a fault in the thermal power plant boiler; An early warning module, configured to determine the fault level of the thermal power plant boiler based on the fault type, and determine the early warning response strategy of the thermal power plant boiler according to the fault level.

[0013] Compared with the prior art, the beneficial effects of the method and system for monitoring and early warning of thermal power plant boiler faults according to the embodiments of the present invention are as follows: Through real-time data acquisition and predictive analysis, the present invention can monitor the operation status of the thermal power plant boiler in real time, identify potential fault risks in advance, thereby formulating preventive maintenance plans and avoiding accidents; The present invention determines the fault type according to the abnormal operation parameters, which helps to accurately locate the problem, speed up the fault troubleshooting speed, and reduce the impact of the fault on production; The present invention formulates corresponding early warning response strategies according to the fault level, which helps to optimize the emergency handling process, improve the fault handling efficiency, and ensure the stability and reliability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a schematic flow structure diagram of the method for monitoring and early warning of thermal power plant boiler faults according to the embodiments of the present invention; Figure 2 is a schematic composition diagram of the system for monitoring and early warning of thermal power plant boiler faults according to the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0016] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0017] The terms "", "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "", "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0018] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "install", "connect", "couple" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0019] As Figure 1 shown, in the embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided, including: S100: Obtain the operation monitoring data of the thermal power plant boiler, and analyze the operation monitoring data to determine abnormal operation parameters; S200: Perform data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyze the abnormal prediction data to determine abnormal characteristics; S300: Evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, and determine whether there is a fault in the thermal power plant boiler according to the abnormal state evaluation value; S400: After determining that there is a fault in the thermal power plant boiler, determine the fault type of the thermal power plant boiler based on the abnormal operation parameters; S500: Determine the fault level of the thermal power plant boiler based on the fault type, and determine the warning response strategy for the thermal power plant boiler according to the fault level.

[0020] Furthermore, through real-time data collection and predictive analysis, the present invention can monitor the operating status of the boilers in thermal power plants in real time, identify potential fault risks in advance, and thus formulate preventive maintenance plans to avoid accidents; the present invention determines the fault type based on abnormal operating parameters, which helps to accurately locate problems, speed up the fault troubleshooting process, and reduce the impact of faults on production; the present invention formulates corresponding early warning response strategies according to the fault level, which helps to optimize the emergency handling process, improve the fault handling efficiency, and ensure the stability and reliability of equipment operation.

[0021] In an embodiment of the present application, a method for monitoring and early warning of boiler faults in a thermal power plant is provided. The method includes obtaining the operation monitoring data of the boilers in the thermal power plant and analyzing the operation monitoring data to determine abnormal operating parameters, including: obtaining the operation monitoring data of the boilers in the thermal power plant, and dividing the operation monitoring data into several groups of operation parameter data groups according to the parameter type; determining the corresponding preset normal data range for each group of operation parameter data groups, and determining the number of data that exceed the corresponding preset normal data range in each group of operation parameter data groups as the first data volume; determining the parameters corresponding to the operation parameter data groups whose first data volume exceeds the corresponding preset number as abnormal operating parameters.

[0022] Specifically, collect various operation monitoring data of the boilers in the thermal power plant, which may include various parameters such as temperature, pressure, flow rate, fuel consumption, etc.; classify the collected operation monitoring data according to the parameter type to form several groups of operation parameter data groups, and each group contains parameter data of the same type; for each group of operation parameter data groups, it is necessary to determine its corresponding preset normal data range, that is, the typical value range of the parameter under normal operation; for each group of operation parameter data groups, check the number of data points that exceed the preset normal data range, and use the number of these data points that exceed the range as the first data volume; determine the parameters in the operation parameter data groups whose first data volume exceeds the corresponding preset number as abnormal operating parameters, indicating that the values of these parameters deviate from the normal range. This step realizes the real-time monitoring and abnormal detection of the operating status of the boilers in the thermal power plant by grouping the operation parameter data of the boilers in the thermal power plant, setting the normal range, and detecting abnormal data; by promptly discovering abnormal operating parameters, it can help the operation and maintenance personnel quickly locate problems, reduce the possibility of faults occurring, improve the reliability and safety of equipment; it provides an important basis for the preventive maintenance of the boilers in the thermal power plant, helps to avoid potential faults and improve the service life of equipment; the data-based abnormal detection method can improve the accuracy and efficiency of monitoring, reduce the subjectivity of human judgment, and provide a scientific basis for the management and maintenance of the boiler equipment in the thermal power plant.

[0023] In an embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided. Data prediction is performed on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and the abnormal prediction data is analyzed to determine abnormal characteristics, including: obtaining the preset prediction models extracted and set for each operation parameter, inputting the operation parameter data groups corresponding to each abnormal operation parameter into the corresponding preset prediction models, and outputting the abnormal prediction data corresponding to each abnormal operation parameter by each preset prediction model; determining the data volume exceeding the corresponding preset normal data range in the abnormal prediction data as the second data volume, and calculating the average value of the abnormal prediction data as the second average value; constructing a curve of the time progress based on the abnormal prediction data to obtain an abnormal prediction data curve, and determining the change amplitude of the abnormal prediction data curve; determining the second data volume, the second average value, and the change amplitude as the abnormal characteristics of the abnormal prediction data corresponding to each abnormal operation parameter.

[0024] Specifically, for each abnormal operation parameter, the corresponding preset prediction models are extracted and set in advance. These models can be trained based on historical data and are used to predict the change range of the parameter. The operation parameter data groups corresponding to each abnormal operation parameter are input into the corresponding preset prediction models, and the abnormal prediction data is obtained through the models. For each abnormal prediction data, the data volume exceeding the preset normal data range is determined as the second data volume, and the average value of the abnormal prediction data is calculated as the second average value. Based on the abnormal prediction data, a curve of the time progress can be constructed to visually display the change trend of the abnormal prediction data over time, obtaining an abnormal prediction data curve. The number of data points exceeding the preset normal data range in the abnormal prediction data curve is determined as the second data volume in the abnormal characteristics, the average value of the abnormal prediction data curve is calculated as the second average value in the abnormal characteristics, and at the same time, the change amplitude of the abnormal prediction data curve is used to describe the abnormal characteristics. This step can more accurately identify abnormal situations by using the preset prediction models to predict abnormal operation parameters, helping the operation and maintenance personnel to discover problems in time and take corresponding measures. By calculating the second data volume, the second average value, and the change amplitude of the abnormal prediction data, the degree and characteristics of the abnormal situation can be quantitatively described, which is helpful for quickly judging the severity of the abnormal situation. Constructing the abnormal prediction data curve and analyzing its change trend can intuitively display the development process of the abnormal situation, providing an important reference for further fault diagnosis and treatment. The abnormal prediction method based on the preset prediction model combines the advantages of data analysis and model prediction, improves the monitoring and prediction ability of the abnormal situation of the thermal power plant boiler, and helps to improve the reliability and safety of the equipment.

[0025] In an embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided. The abnormal state of the thermal power plant boiler is evaluated based on each abnormal feature to obtain an abnormal state evaluation value, including: determining the second data volume and the first data volume in the abnormal features, calculating the ratio of the second data volume to the first data volume, and evaluating this ratio to obtain a quantity ratio evaluation value; determining the operation parameter data groups corresponding to each abnormal operation parameter, and calculating the average value of the operation parameter data groups as the first average value; determining the second average value in the abnormal features, calculating the ratio of the second average value to the first average value, and evaluating this ratio to obtain an average ratio evaluation value; determining the change amplitude in the abnormal features, and evaluating the change amplitude to obtain a change amplitude evaluation value; adding and calculating the quantity ratio evaluation value, the average ratio evaluation value, and the change amplitude evaluation value corresponding to each abnormal operation parameter to obtain the abnormal evaluation value of each abnormal operation parameter; determining the preset weight of each abnormal operation parameter, and performing weighted addition calculation on the preset weight of each abnormal operation parameter and the corresponding abnormal evaluation value to obtain the abnormal state evaluation value of the thermal power plant boiler.

[0026] Specifically, determine the second data volume and the first data volume in the abnormal features, and then calculate the ratio of the second data volume to the first data volume. This ratio reflects the proportion of the abnormal data volume in the total data volume and is used to evaluate the quantity degree of the abnormal data; determine the second average value and the first average value in the abnormal features, calculate the ratio of the second average value to the first average value, and this ratio is used to evaluate the change degree of the average value of the abnormal data relative to the normal situation; evaluate the change amplitude in the abnormal features to obtain a change amplitude evaluation value, which is used to describe the change degree of the abnormal data; add the quantity ratio evaluation value, the average ratio evaluation value, and the change amplitude evaluation value to obtain the abnormal evaluation value of each abnormal operation parameter. This value comprehensively considers the abnormal data volume, the average value change, and the change amplitude and is used to quantitatively describe the severity of the abnormal situation; determine the preset weight of each abnormal operation parameter, multiply the abnormal evaluation value of each abnormal operation parameter by its corresponding weight and add them up to obtain the abnormal state evaluation value of the thermal power plant boiler. This value is a comprehensive evaluation of the abnormal conditions of each abnormal parameter and helps to judge the severity of the overall abnormal situation. This step can more comprehensively evaluate the severity of the abnormal situation by quantitatively evaluating the abnormal features and comprehensively considering factors such as quantity, average value, and change amplitude, helping the operation and maintenance personnel quickly locate problems and take corresponding measures; introducing the preset weight can perform weighted evaluation of the abnormal situation according to the importance of different abnormal parameters, improving the accuracy and practicality of the evaluation results; by calculating the abnormal state evaluation value of the thermal power plant boiler, intuitive abnormal state information can be provided for the management personnel to guide decision-making and optimize the operation and maintenance strategy, further improving the reliability and safety of the equipment.

[0027] In an embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided. Judging whether there is a fault in the thermal power plant boiler according to the abnormal state evaluation value includes: determining a preset evaluation threshold, and judging whether there is a fault in the thermal power plant boiler based on the relationship between the evaluation threshold and the abnormal state evaluation value; if the abnormal state evaluation value is greater than or equal to the evaluation threshold, it is judged that there is a fault in the thermal power plant boiler; if the abnormal state evaluation value is less than the evaluation threshold, it is judged that there is no fault in the thermal power plant boiler.

[0028] Specifically, a evaluation threshold is preset in the system. This threshold can be determined according to experience, historical data or expert knowledge, and is used as a standard for judging whether there is a fault in the thermal power plant boiler; based on the relationship between the abnormal state evaluation value and the preset evaluation threshold, a judgment is made. If the abnormal state evaluation value is greater than or equal to the evaluation threshold, it is judged that there is a fault in the thermal power plant boiler. If the abnormal state evaluation value is less than the evaluation threshold, it is judged that there is no fault in the thermal power plant boiler. By setting the relationship between the evaluation threshold and the abnormal state evaluation value, this step can establish a simple and effective fault judgment mechanism to help operation and maintenance personnel quickly and accurately judge the state of the thermal power plant boiler; different evaluation thresholds can be set according to specific situations to adapt to different operating environments and equipment states, improving the accuracy and flexibility of judgment; by automatically comparing the abnormal state evaluation value with the evaluation threshold, the influence of human subjective factors on fault judgment is reduced, and the objectivity and consistency of judgment are improved.

[0029] In an embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided. After judging that there is a fault in the thermal power plant boiler, determining the fault type of the thermal power plant boiler based on abnormal operating parameters includes: after judging that there is a fault in the thermal power plant boiler, determining each abnormal operating parameter and the type of each abnormal operating parameter; matching from the fault database a fault type that is consistent with the types of all abnormal operating parameters according to the types of each abnormal operating parameter, and determining this fault type as the fault type of the thermal power plant boiler.

[0030] Specifically, to determine that there is a fault in a thermal power plant boiler, it is necessary to identify each abnormal operating parameter that causes the fault; for each abnormal operating parameter, determine its specific abnormal type, such as too high, too low, excessive fluctuation, etc., which helps to more accurately locate the specific cause of the fault; match the type of each abnormal operating parameter with the records in the fault database to find the fault types that are consistent with the types of all abnormal operating parameters. These fault types are the previously recorded fault cases corresponding to the abnormal types; determine the fault type of the thermal power plant boiler as the matched fault type, which helps to quickly and accurately identify the problem, so as to take corresponding maintenance measures. This step can quickly and accurately determine the fault type of the thermal power plant boiler by matching the type of abnormal operating parameters with the records in the fault database, saving the time and effort of fault diagnosis; the matching method based on historical data and experience can improve the accuracy and reliability of fault diagnosis, ensure targeted problem-solving, and reduce the risk of misdiagnosis; it helps to establish a knowledge base and experience accumulation for fault diagnosis, provide reference for future similar faults, and improve the efficiency and quality of fault handling; by automatically matching the fault type, it reduces the influence of human subjective factors on fault diagnosis and improves the objectivity and consistency of judgment.

[0031] In an embodiment of the present application, a method for monitoring and warning of faults in a thermal power plant boiler is provided. Determining the fault level of the thermal power plant boiler based on the fault type includes: presetting a fault level - fault type correspondence relationship, where for each fault type, a corresponding fault level is associated; obtaining the fault type of the thermal power plant boiler, and based on the mapping relationship of the fault type in the fault level - fault type correspondence relationship, selecting the corresponding fault level and determining it as the fault level of the thermal power plant boiler.

[0032] Specifically, first establish a mapping relationship table of fault level - fault type, where each fault type corresponds to one or more fault levels; determine the fault type of the thermal power plant boiler, and based on the fault type of the thermal power plant boiler, find the corresponding mapping relationship in the fault level - fault type correspondence relationship, select the corresponding fault level, and determine it as the fault level of the thermal power plant boiler. This step can help the operation and maintenance personnel to more clearly understand and evaluate the fault situation of the thermal power plant boiler by mapping the fault type to the corresponding fault level, which is beneficial to formulating corresponding treatment plans and priorities; the preset fault level - fault type correspondence relationship can be adjusted and optimized according to the actual situation, making the determination of the fault level more accurate and reasonable; it helps to quickly and systematically classify and grade faults, improving the efficiency and quality of fault handling; by automatically mapping the fault type to the fault level, it reduces the influence of human subjective factors on the judgment of the fault level and improves the objectivity and consistency of judgment.

[0033] In an embodiment of the present application, a method for monitoring and early warning of boiler faults in a thermal power plant is provided, wherein the early warning response strategy of the boiler in the thermal power plant is determined according to the fault level, including: judging the level of the fault level of the boiler in the thermal power plant, and determining the early warning response strategy of the boiler in the thermal power plant according to the judgment result; if the level of the fault level is greater than a first preset value, the early warning response strategy of the boiler in the thermal power plant is determined to be a prominent green warning prompt popping up in the monitoring system, and recording the fault in the maintenance plan; if the level of the fault level is greater than a second preset value, the early warning response strategy of the boiler in the thermal power plant is determined to be a prominent orange warning prompt popping up in the monitoring system, and triggering an automated maintenance plan; if the level of the fault level is greater than a third preset value, the early warning response strategy of the boiler in the thermal power plant is determined to be a prominent red warning prompt popping up in the monitoring system, and automatically starting the emergency shutdown program; wherein the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

[0034] Specifically, according to the fault level determined in the previous steps, the fault level of the thermal power plant boiler is divided into levels; according to the level of the fault level, the corresponding early warning response strategy is determined. If the level of the fault level is greater than the first preset value, the early warning response strategy is to pop up a prominent green warning prompt in the monitoring system and record the fault in the maintenance plan; if the level of the fault level is greater than the second preset value, the early warning response strategy is to pop up a prominent orange warning prompt in the monitoring system and trigger an automated maintenance plan; if the level of the fault level is greater than the third preset value, the early warning response strategy is to pop up a prominent red warning prompt in the monitoring system and automatically start the emergency shutdown procedure. It should be noted that the first preset value is less than the second preset value, and the second preset value is less than the third preset value, so as to ensure the increasing order of the warning levels. This step determines the early warning response strategy according to the level of the fault grade, and can take corresponding response measures according to the severity of the fault, and handle it in a targeted manner, thereby improving the efficiency and timeliness of fault handling; setting early warning response strategies at different levels can flexibly respond to various fault conditions according to actual conditions, thereby improving the robustness and resilience of the system; by automatically triggering the early warning response strategy, the possibility of human delays is reduced, and the speed and accuracy of responding to faults are improved; recording faults and triggering maintenance plans, automated maintenance plans, and emergency shutdown procedures and other measures help maintenance personnel to understand and handle faults in a timely manner, and ensure the safe operation and production efficiency of thermal power plant boilers.

[0035] like Figure 2As shown in the figure, in the embodiment of the present application, a fault monitoring and warning system for a thermal power plant boiler is provided, including: an acquisition module, configured to acquire the operation monitoring data of the thermal power plant boiler, analyze the operation monitoring data, and determine abnormal operation parameters; a prediction module, configured to perform data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyze the abnormal prediction data to determine abnormal characteristics; a judgment module, configured to evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, and determine whether the thermal power plant boiler has a fault according to the abnormal state evaluation value; a determination module, configured to determine the fault type of the thermal power plant boiler based on the abnormal operation parameters after determining that the thermal power plant boiler has a fault; and a warning module, configured to determine the fault level of the thermal power plant boiler based on the fault type, and determine the warning response strategy for the thermal power plant boiler according to the fault level.

[0036] In summary, the embodiment of the present invention provides a method and system for monitoring and warning of faults in a thermal power plant boiler, which includes: acquiring and analyzing the operation monitoring data of the thermal power plant boiler to determine abnormal operation parameters; performing data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyzing to determine abnormal characteristics; evaluating the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, and determining whether the thermal power plant boiler has a fault according to it; after determining that the thermal power plant boiler has a fault, determining the fault type based on the abnormal operation parameters; determining the fault level based on the fault type, and determining the warning response strategy according to the fault level. The present invention predicts by analyzing the operation monitoring data, determines the future changes of the boiler, and evaluates and determines the fault based on the changes, so as to give a warning, which can effectively reduce the fault risk, improve the safety, reliability and operation efficiency of the boiler, and increase the production benefit.

[0037] Finally, it should be noted that: Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

[0038] The above is only one embodiment of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the protection scope of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related descriptions of the above-described platform can refer to the corresponding process in the foregoing platform embodiment, and will not be repeated here.

[0039] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, platform, article, or apparatus / platform that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to those processes, platforms, articles, or apparatus / platforms.

[0040] So far, the technical solution of the present invention has been described in connection with the further embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0041] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for monitoring and warning of boiler failures in a thermal power plant, characterized in that, Including: Obtain the operation monitoring data of the thermal power plant boiler, analyze the operation monitoring data, and determine the abnormal operation parameters; Perform data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyze the abnormal prediction data to determine the abnormal characteristics; Evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, and determine whether there is a fault in the thermal power plant boiler according to the abnormal state evaluation value; After determining that there is a fault in the thermal power plant boiler, determine the fault type of the thermal power plant boiler based on the abnormal operation parameters; Determine the fault level of the thermal power plant boiler based on the fault type, and determine the early warning response strategy of the thermal power plant boiler according to the fault level.

2. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 1, characterized in that, The obtaining the operation monitoring data of the thermal power plant boiler, analyzing the operation monitoring data, and determining the abnormal operation parameters includes: Obtain the operation monitoring data of the thermal power plant boiler, and divide the operation monitoring data into several groups of operation parameter data groups according to the parameter type; Determine the preset normal data range corresponding to each group of operation parameter data groups, and determine the number of data that exceed the corresponding preset normal data range in each group of operation parameter data groups as the first data volume; Determine the parameters corresponding to the operation parameter data groups with the first data volume exceeding the corresponding preset quantity as the abnormal operation parameters.

3. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 2, characterized in that, The performing data prediction on each abnormal operation parameter according to a preset prediction model to obtain corresponding abnormal prediction data, and analyzing the abnormal prediction data to determine the abnormal characteristics includes: Obtain the preset prediction models set for extracting each operation parameter, and input the operation parameter data groups corresponding to each abnormal operation parameter into the corresponding preset prediction models, and output the abnormal prediction data corresponding to each abnormal operation parameter by each preset prediction model; Determine the data volume that exceeds the corresponding preset normal data range in the abnormal prediction data as the second data volume, and calculate the average value of the abnormal prediction data as the second average value; Construct a curve of the time progress based on the abnormal prediction data to obtain an abnormal prediction data curve, and determine the change amplitude of the abnormal prediction data curve; Determine the second data volume, the second average value, and the change amplitude as the abnormal characteristics of the abnormal prediction data corresponding to each abnormal operation parameter.

4. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 3, characterized in that, The evaluating the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value includes: Determine the second data volume and the first data volume in the abnormal characteristics, calculate the ratio of the second data volume to the first data volume, and evaluate the ratio to obtain a quantity ratio evaluation value; Determine the operation parameter data groups corresponding to each abnormal operation parameter, and calculate the average value of the operation parameter data groups as the first average value; Determine the second average value in the abnormal characteristics, calculate the ratio of the second average value to the first average value, and evaluate the ratio to obtain an average ratio evaluation value; Determine the change amplitude in the abnormal characteristics, and evaluate the change amplitude to obtain a change amplitude evaluation value; Add and calculate the quantity ratio evaluation value, the average ratio evaluation value, and the change amplitude evaluation value corresponding to each abnormal operation parameter to obtain the abnormal evaluation value of each abnormal operation parameter; Determine the preset weights of each abnormal operating parameter, and perform weighted summation calculation on the preset weights of each abnormal operating parameter and the corresponding abnormal evaluation value to obtain the abnormal state evaluation value of the thermal power plant boiler.

5. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 4, characterized in that, The judging whether the thermal power plant boiler has a fault according to the abnormal state evaluation value includes: Determine a preset evaluation threshold, and judge whether the thermal power plant boiler has a fault based on the relationship between the evaluation threshold and the abnormal state evaluation value; If the abnormal state evaluation value is greater than or equal to the evaluation threshold, it is judged that the thermal power plant boiler has a fault; If the abnormal state evaluation value is less than the evaluation threshold, it is judged that the thermal power plant boiler has no fault.

6. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 5, characterized in that, The determining the fault type of the thermal power plant boiler based on the abnormal operating parameter after judging that the thermal power plant boiler has a fault includes: After judging that the thermal power plant boiler has a fault, determine each abnormal operating parameter and the type of each abnormal operating parameter; Match the fault type consistent with the types of all abnormal operating parameters from the fault database according to the types of each abnormal operating parameter, and determine this fault type as the fault type of the thermal power plant boiler.

7. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 6, characterized in that, The determining the fault level of the thermal power plant boiler based on the fault type includes: Preset the fault level - fault type correspondence relationship. For each fault type in the fault level - fault type correspondence relationship, a corresponding fault level is associated; Obtain the fault type of the thermal power plant boiler, and select the corresponding fault level as the fault level of the thermal power plant boiler based on the mapping relationship of this fault type in the fault level - fault type correspondence relationship.

8. A method for monitoring and warning of boiler faults in a thermal power plant according to claim 7, characterized in that, The determining the early warning response strategy of the thermal power plant boiler according to the fault level includes: Judge the level of the fault level of the thermal power plant boiler, and determine the early warning response strategy of the thermal power plant boiler according to the judgment result; If the level of the fault level is greater than the first preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent green warning prompt in the monitoring system and recording the fault in the maintenance plan; If the level of the fault level is greater than the second preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent orange warning prompt in the monitoring system and triggering the automatic maintenance plan; If the level of the fault level is greater than the third preset value, determine the early warning response strategy of the thermal power plant boiler as popping up a prominent red warning prompt in the monitoring system and automatically starting the emergency shutdown procedure; Wherein, the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

9. A boiler fault monitoring and early warning system for a thermal power plant, characterized in that, It includes: An acquisition module, configured to acquire the operation monitoring data of the thermal power plant boiler, analyze the operation monitoring data, and determine the abnormal operating parameters; A prediction module, configured to perform data prediction on each abnormal operating parameter according to a preset prediction model to obtain the corresponding abnormal prediction data, and analyze the abnormal prediction data to determine the abnormal characteristics; A judgment module, configured to evaluate the abnormal state of the thermal power plant boiler based on each abnormal characteristic to obtain an abnormal state evaluation value, and judge whether the thermal power plant boiler has a fault according to the abnormal state evaluation value; A determination module, configured to determine the fault type of the thermal power plant boiler based on the abnormal operating parameter after judging that the thermal power plant boiler has a fault; An early warning module, which is used to determine the fault level of a thermal power plant boiler based on the fault type and determine the early warning response strategy for the thermal power plant boiler according to the fault level.

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