CN115220403A - Configurable intelligent monitoring early warning and fault diagnosis system for thermal power plant - Google Patents
Configurable intelligent monitoring early warning and fault diagnosis system for thermal power plant Download PDFInfo
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- G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention relates to an intelligent monitoring early warning and fault diagnosis system of an organized thermal power plant.A data acquisition module acquires real-time data and historical data of unit equipment operation from an SIS system second level; the data storage processing module comprises a data preprocessing module, an intelligent early warning module and a fault diagnosis module; the data modeling module comprises a dynamic threshold value analysis module, a regular characteristic monitoring module and a big data analysis module; the basic configuration module is used for unit configuration, monitoring system configuration, monitoring equipment configuration, monitoring measuring point configuration, monitoring index configuration and fault rule configuration; the query display module is used for real-time unit alarm, real-time index monitoring, monthly index state report, fault diagnosis implementation and fault alarm statistics; and the intelligent monitoring module is used for sequentially displaying the early warning information and the fault information in real time. The invention can reduce the workload of operators, improve the technical level of operation monitoring and equipment management, discover equipment abnormality in time and reduce the workload of field personnel operation supervision and abnormality analysis.
Description
Technical Field
The invention relates to the technical field of thermal power generation, in particular to an intelligent monitoring and early warning and fault diagnosis system for a thermal power plant.
Background
The existing DCS and SIS system has a single alarm mode and cannot meet the requirements of monitoring personnel. Mostly, the direct guardianship of prison plate personnel, this mode needs prison plate personnel to possess the professional skill that makes up, and the data of field acquisition only leans on the operation personnel to judge the analysis, and it is great to receive the influence of subjective factor, requires very high to personnel's whole quality moreover. Therefore, the problems that the burden of monitoring personnel is large, the data recording is lagged, the early warning efficiency is low, the false alarm probability is high, the existing expert knowledge cannot be solidified and the like are caused.
Disclosure of Invention
The invention aims to provide a configurable intelligent monitoring disc early warning and fault diagnosis system for a thermal power plant, which provides a flexible configuration tool for monitoring indexes, a plurality of index early warning modes and a diagnosis rule tool, has the characteristic of flexible configuration, can reduce the workload of operators, improve the technical level of operation monitoring and equipment management, discover equipment abnormality in time, reduce the workload of field personnel operation monitoring disc and abnormality analysis, solve the problem that the requirements of the field operators cannot be met due to single alarm mode, single-variable and fixed threshold alarm as the main alarm mode of the existing DCS and SIS systems, and simultaneously reduce the workload of the monitoring disc personnel.
The invention provides an intelligent thermal power plant monitoring early warning and fault diagnosis system which is an organized system, comprising a data acquisition module, a data storage processing module, a data modeling module, a basic configuration module, an inquiry display module and an intelligent monitoring module;
the data acquisition module is used for acquiring real-time data and historical data of unit equipment operation from the SIS system second level;
the data storage processing module comprises a data preprocessing module, an intelligent early warning module and a fault diagnosis module; wherein the content of the first and second substances,
the data preprocessing module is used for carrying out data cleaning and data screening on the collected historical data and carrying out preprocessing for modeling, and judging real-time data dead points and dead points;
the intelligent early warning module is used for correspondingly calculating data according to a data processing method and rules of basic configuration, giving early warning information and storing an intelligent monitoring early warning result so as to inquire the data called by the display module;
the fault diagnosis module is used for storing fault diagnosis rules and suggested processing modes to form an expert database so as to be convenient for the fault alarm module to call;
the data modeling module comprises a dynamic threshold value analysis module, a regular characteristic monitoring module and a big data analysis module; wherein the content of the first and second substances,
the dynamic threshold analysis module is used for calculating the upper limit value and the lower limit value of the parameter point under the same load by adopting a normal distribution method; obtaining a dynamic threshold value by analyzing and modeling historical data by utilizing a linear regression algorithm and a nonlinear regression algorithm;
the regular characteristic monitoring module is used for calculating the trend of data change and the change rate of the data change in different time periods;
the big data analysis module is used for constructing a corresponding index model for the complex parameters; carrying out fault parameter modeling by adopting a nonlinear modeling and neural network algorithm;
the basic configuration module is used for unit configuration, monitoring system configuration, monitoring equipment configuration, monitoring measuring point configuration, monitoring index configuration and fault rule configuration;
the query display module is used for real-time unit alarm, real-time index monitoring, monthly index state report, fault diagnosis implementation and fault alarm statistics;
the intelligent monitoring module is used for displaying the early warning information and the fault information in real time according to the time sequence, so that monitoring personnel can quickly know the abnormal information and the reason and time causing the abnormality through the monitoring screen, and manual abnormal alarm elimination operation is provided.
Further, the unit configuration comprises unit ID, unit name, rated load, load point number and stop judgment, and the background calculation program automatically judges whether to stop subsequent calculation according to the configured stop judgment information;
the monitoring system is configured for system configuration, including configuration of related systems at a unit level; monitoring equipment configuration;
the monitoring equipment configuration comprises the steps of configuring related equipment under a monitored system level, wherein the related equipment comprises equipment names, belonging systems and whether fault diagnosis is involved;
the monitoring measuring point configuration comprises a unit, a name and a point number of a single-point configuration measuring point, and a template is adopted for batch import;
the monitoring index configuration is used for configuring a standard index, a change index, a calculation combination index, a fixed limit alarm, a function limit alarm, a same-load limit alarm and a trend alarm, and the false alarm rate is reduced by configuring the combination of a trend monitoring period and a trend reaching percentage;
the fault rule configuration is used for diagnosing the configuration of the suggestion and the trigger condition and giving a fault handling suggestion.
Furthermore, the unit alarms in real time to display alarm information of each unit, including the system number of each unit and the system number of alarm lamp information;
the index real-time monitoring is to monitor the indexes configured by the basic configuration module, comprises a real-time value, upper and lower index limits, a trend and a change rate, and inquires a history curve of the index of the user-defined time period;
the index state monthly report is used for inquiring and counting abnormal conditions of indexes according to user-defined time;
the real-time fault diagnosis provides real-time fault monitoring, displays the equipment with faults, the fault types and the fault treatment suggestions, and inquires the history details of the fault indexes.
By means of the scheme, through the organized intelligent monitoring and early warning and fault diagnosis system of the thermal power plant, the workload of operators can be reduced, the technical level of operation monitoring and equipment management is improved, equipment abnormity can be found in time, the workload of operation monitoring and abnormity analysis of field personnel is reduced, and the intelligent monitoring and early warning and fault diagnosis system is suitable for the management requirements of state monitoring, early warning and fault diagnosis of power plant equipment. The method covers the equipment management of main equipment such as boilers, steam turbines, chemical conversion rings and electrical equipment and important auxiliary machines, and realizes the full-period monitoring of the running state of the equipment.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
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Fig. 1 is a block diagram of a thermal power plant intelligent monitoring early warning and fault diagnosis system.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1, the embodiment provides an intelligent thermal power plant monitoring early warning and fault diagnosis system, which includes a data acquisition module, a data storage processing module, a data modeling module, a basic configuration module, an inquiry display module, and an intelligent monitoring module;
the data acquisition module is used for acquiring real-time data and historical data of unit equipment operation from the SIS system second level;
the data storage processing module comprises a data preprocessing module, an intelligent early warning module and a fault diagnosis module; the data preprocessing module is used for carrying out data cleaning and data screening on the collected historical data, carrying out preprocessing for modeling, and judging real-time data dead points and dead points; the intelligent early warning module is used for correspondingly calculating data according to the data processing method and the rules of the basic configuration, giving early warning information and storing the early warning result of the intelligent monitoring disk so as to inquire the calling data of the display module; the fault diagnosis module is used for storing fault diagnosis rules and suggested processing modes to form an expert library for the calling of the fault alarm module;
the data modeling module comprises a dynamic threshold value analysis module, a regular characteristic monitoring module and a big data analysis module; the dynamic threshold analysis module is used for calculating the upper limit value and the lower limit value of the parameter point under the same load by adopting a normal distribution method; obtaining a dynamic threshold value by analyzing and modeling historical data by utilizing a linear regression algorithm and a nonlinear regression algorithm; the regular characteristic monitoring module is used for calculating the trend of data change and the change rate of the data change in different time periods; the big data analysis module is used for constructing a corresponding index model for the complex parameters; fault parameter modeling is carried out by adopting nonlinear modeling and neural network algorithm;
the basic configuration module is used for unit configuration, monitoring system configuration, monitoring equipment configuration, monitoring measuring point configuration, monitoring index configuration and fault rule configuration;
the query display module is used for real-time unit alarm, real-time index monitoring, monthly index state report, fault diagnosis implementation and fault alarm statistics;
the intelligent monitoring module is used for displaying the early warning information and the fault information in real time according to the time sequence, so that monitoring personnel can quickly know the abnormal information and the reason and time causing the abnormality through the monitoring screen, and manual abnormal alarm elimination operation is provided.
In this embodiment, the unit configuration includes the unit ID, the unit name, the rated load, the load point number, the stop determination, and the like, and the background calculation program automatically determines whether to stop the subsequent calculation according to the configured stop determination information; the monitoring system configuration provides a system configuration function, and related systems such as a steam water system, a wind and smoke system and the like can be configured under a unit level; monitoring equipment configuration; the monitoring equipment configuration can configure related equipment under a monitored system level, wherein the related equipment comprises the equipment name, the system to which the equipment belongs, whether to participate in fault diagnosis and the like; and (3) monitoring measuring point configuration: the contents such as the unit, the name, the point number and the like of the measuring point can be configured in a single point mode, and a template can be adopted for batch import; the monitoring index configuration is the most important basic configuration module of the intelligent monitoring disc early warning and fault diagnosis system, standard indexes, change indexes, calculation combination indexes, fixed limit value alarm (obtained according to upper and lower limits calculated by a background data modeling module), function limit value alarm (obtained according to a model provided by the background data modeling module) and load limit value alarm (obtained according to the model provided by the background data modeling module) trend alarm and the like can be configured in the module, and the false alarm rate is reduced by combining configuration of trend monitoring period and trend reaching percentage; the fault rule configuration realizes the configuration of diagnosis suggestions and trigger conditions and gives fault handling suggestions. The module combines the data modeling module and the basic configuration module to realize the combination of an artificial intelligence algorithm and expert rules to carry out early warning and diagnosis, and provides disposal suggestions for monitoring personnel when faults occur.
In this embodiment, the unit alarms in real time to display alarm information of each unit, including the number of systems of each unit and the number of systems with alarm lamp information; the index real-time monitoring is to monitor the indexes configured by the basic configuration module, comprises a real-time value, upper and lower index limits, a trend and a change rate, and inquires a history curve of the index of the user-defined time period; the index state monthly report is used for inquiring the abnormal condition of the statistical index according to the user-defined time; the real-time fault diagnosis provides real-time fault monitoring, displays the equipment with faults, the fault types and the fault treatment suggestions, and inquires the history details of the fault indexes.
The system is flexible in configuration and can be continuously updated and optimized, firstly, a flexible configuration tool for monitoring indexes is provided, more than ten common functions are preset, and operators can flexibly configure the monitoring indexes (such as terminal difference, flow difference and the like) according to actual requirements; secondly, various index early warning modes are provided, including fixed limit values, function limit values, same working condition limit values, change rates, neural network prediction and the like, and operating personnel can flexibly select and directly configure model parameters; and thirdly, a diagnosis rule tool is provided, and the configuration of the diagnosis logic can be realized without writing program codes.
The system takes real-time data from the SIS once every 30 seconds, carries out operation according to a measuring point, an index monitoring model and a monitoring rule which are configured in advance, gives an index alarm prompt when the operation result accords with the alarm rule, and stores the alarm historical data, thereby being convenient for counting and analyzing the alarm conditions of units and equipment; the system carries out fault early warning and diagnosis on the unit equipment according to a preset fault diagnosis rule and a logic combination according to an index monitoring result, and gives a diagnosis processing suggestion.
The system collects SIS system data, and performs data storage, cleaning and big data modeling; constructing equipment parameters and index models by using a big data mining and AI intelligent modeling technology, and setting an intelligent threshold according to the models to realize self-adaptive pre-alarm of the equipment parameters; the artificial intelligence single-parameter residual error alarm and expert knowledge are fused, the combined alarm of an intelligent energy algorithm and an expert rule is realized, and the intelligent alarm is carried out on the equipment abnormity; calculating the change rate by adopting a linear regression algorithm, combining the operation duration, and shielding false alarm in the process of excessively fast parameter change and variable load; establishing a fault diagnosis expert rule base, and matching a fault diagnosis module to carry out fault diagnosis and processing; a configurable configuration tool is formed, diagnosis models and rules are flexibly changed, the technical level of operation monitoring and equipment management is improved, equipment abnormity is timely discovered, and the operation monitoring and abnormity analysis workload of field personnel is reduced.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A system for intelligent monitoring early warning and fault diagnosis of a thermal power plant is characterized by comprising a data acquisition module, a data storage processing module, a data modeling module, a basic configuration module, an inquiry display module and an intelligent monitoring module;
the data acquisition module is used for acquiring real-time data and historical data of unit equipment operation from the SIS system second level;
the data storage processing module comprises a data preprocessing module, an intelligent early warning module and a fault diagnosis module; wherein the content of the first and second substances,
the data preprocessing module is used for carrying out data cleaning and data screening on the collected historical data and carrying out preprocessing for modeling, and judging real-time data dead points and dead points;
the intelligent early warning module is used for correspondingly calculating data according to a data processing method and rules of basic configuration, giving early warning information and storing an intelligent monitoring early warning result so as to inquire the data called by the display module;
the fault diagnosis module is used for storing fault diagnosis rules and suggested processing modes to form an expert library for calling the fault alarm module;
the data modeling module comprises a dynamic threshold value analysis module, a regular characteristic monitoring module and a big data analysis module; wherein the content of the first and second substances,
the dynamic threshold analysis module is used for calculating the upper limit value and the lower limit value of the parameter point under the same load by adopting a normal distribution method; obtaining a dynamic threshold value by analyzing and modeling historical data by utilizing a linear regression algorithm and a nonlinear regression algorithm;
the regular characteristic monitoring module is used for calculating the trend of data change and the change rate of the data change in different time periods;
the big data analysis module is used for constructing a corresponding index model for the complex parameters; fault parameter modeling is carried out by adopting nonlinear modeling and neural network algorithm;
the basic configuration module is used for unit configuration, monitoring system configuration, monitoring equipment configuration, monitoring measuring point configuration, monitoring index configuration and fault rule configuration;
the query display module is used for real-time unit alarm, real-time index monitoring, monthly index state report, fault diagnosis implementation and fault alarm statistics;
the intelligent monitoring module is used for displaying the early warning information and the fault information in real time according to the time sequence, so that monitoring personnel can quickly know the abnormal information and the reason and time causing the abnormality through the monitoring screen, and manual abnormal alarm elimination operation is provided.
2. The configured intelligent thermal power plant monitoring, early warning and fault diagnosis system according to claim 1, wherein the unit configuration comprises a unit ID, a unit name, a rated load, a load point number and a shutdown determination, and the background calculation program automatically determines whether to stop subsequent calculation according to the configured shutdown determination information;
the monitoring system is configured for system configuration, including configuration of related systems at a unit level; monitoring equipment configuration;
the monitoring equipment configuration comprises the steps of configuring related equipment under a monitored system level, wherein the related equipment comprises equipment names, belonging systems and whether fault diagnosis is involved;
the monitoring measuring point configuration comprises a unit, a name and a point number of a single-point configuration measuring point, and a template is adopted for batch import;
the monitoring index configuration is used for configuring a standard index, a change index, a calculation combination index, a fixed limit alarm, a function limit alarm, a same-load limit alarm and a trend alarm, and the false alarm rate is reduced by configuring the combination of a trend monitoring period and a trend reaching percentage;
the fault rule configuration is used for diagnosing suggestions and configuring triggering conditions and giving fault handling suggestions.
3. The configured intelligent thermal power plant monitoring, early warning and fault diagnosis system according to claim 1, wherein the units alarm in real time to display alarm information of each unit, including the number of systems of each unit and the number of systems with alarm lamp information;
the index real-time monitoring is to monitor the indexes configured by the basic configuration module, comprises a real-time value, upper and lower index limits, a trend and a change rate, and inquires a history curve of the index of the user-defined time period;
the index state monthly report is used for inquiring and counting abnormal index conditions according to user-defined time;
the real-time fault diagnosis provides real-time fault monitoring, displays the equipment with faults, the fault types and the fault treatment suggestions, and inquires the history details of the fault indexes.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860123A (en) * | 2023-03-02 | 2023-03-28 | 哈尔滨电机厂有限责任公司 | Fault diagnosis reasoning and checking method for water turbine |
| CN116088398A (en) * | 2023-04-10 | 2023-05-09 | 中国电力工程顾问集团西南电力设计院有限公司 | Be used for wisdom prison dish alarm system of thermal power plant |
| CN117806290A (en) * | 2024-03-01 | 2024-04-02 | 矿冶科技集团有限公司 | Industrial fault alarm system, method, computer equipment and readable storage medium |
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- 2022-06-22 CN CN202210712215.0A patent/CN115220403A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860123A (en) * | 2023-03-02 | 2023-03-28 | 哈尔滨电机厂有限责任公司 | Fault diagnosis reasoning and checking method for water turbine |
| CN116088398A (en) * | 2023-04-10 | 2023-05-09 | 中国电力工程顾问集团西南电力设计院有限公司 | Be used for wisdom prison dish alarm system of thermal power plant |
| CN117806290A (en) * | 2024-03-01 | 2024-04-02 | 矿冶科技集团有限公司 | Industrial fault alarm system, method, computer equipment and readable storage medium |
| CN117806290B (en) * | 2024-03-01 | 2024-05-31 | 矿冶科技集团有限公司 | Industrial fault alarm system, method, computer equipment and readable storage medium |
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