CN116123042A - Intelligent monitoring and early warning method and system for wind generating set - Google Patents
Intelligent monitoring and early warning method and system for wind generating set Download PDFInfo
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- CN116123042A CN116123042A CN202310213917.9A CN202310213917A CN116123042A CN 116123042 A CN116123042 A CN 116123042A CN 202310213917 A CN202310213917 A CN 202310213917A CN 116123042 A CN116123042 A CN 116123042A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention discloses an intelligent monitoring and early warning method and system of a wind generating set, and relates to the field of data processing, wherein the method comprises the following steps: obtaining a unit monitoring scheme; obtaining unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data; performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; based on the unit environment monitoring data, acquiring a multi-dimensional unit environment influence coefficient, and correcting the multi-dimensional risk assessment data according to the multi-dimensional unit environment influence coefficient to acquire corrected risk assessment data; and if the corrected risk assessment data meets the pre-set unit pre-warning constraint condition, obtaining a pre-warning signal. The technical problems of poor monitoring and early warning effects of the wind generating set caused by insufficient monitoring and early warning accuracy of the wind generating set in the prior art are solved.
Description
Technical Field
The invention relates to the field of data processing, in particular to an intelligent monitoring and early warning method and system for a wind generating set.
Background
With the gradual development of renewable resources, wind energy is receiving a great deal of attention as a renewable green resource. The application market of the wind generating set is greatly expanded, and the monitoring and early warning requirements of the wind generating set are also greatly changed. The traditional monitoring and early warning mode can not meet the monitoring and early warning requirements of the modern wind generating set. The method for optimizing, monitoring and early warning of the wind generating set is designed and has very important practical significance.
In the prior art, the technical problems of poor monitoring and early warning effects of the wind generating set caused by insufficient monitoring and early warning accuracy of the wind generating set exist.
Disclosure of Invention
The application provides an intelligent monitoring and early warning method and system for a wind generating set. The technical problems of poor monitoring and early warning effects of the wind generating set caused by insufficient monitoring and early warning accuracy of the wind generating set in the prior art are solved. The method has the advantages that the accuracy of monitoring and early warning on the wind generating set is improved, the monitoring and early warning quality of the wind generating set is improved, and the technical effect of powerful guarantee is provided for safe operation of the wind generating set.
In view of the above problems, the application provides an intelligent monitoring and early warning method and system for a wind generating set.
In a first aspect, the present application provides an intelligent monitoring and early warning method for a wind generating set, where the method is applied to an intelligent monitoring and early warning system for a wind generating set, and the method includes: information acquisition is carried out on the target fan set, and basic information of the set is obtained; obtaining multi-stage unit monitoring indexes, wherein the multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes; based on the unit basic information and the multi-stage unit monitoring indexes, monitoring planning analysis is carried out on the target fan unit, and a unit monitoring scheme is obtained; based on the unit monitoring scheme, the intelligent monitoring module monitors the target fan unit in real time to obtain a multi-dimensional unit monitoring data set, wherein the multi-dimensional unit monitoring data set comprises unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data; performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; performing unit environment influence analysis based on the unit environment monitoring data to obtain a multi-dimensional unit environment influence coefficient, and correcting the multi-dimensional risk assessment data based on the multi-dimensional unit environment influence coefficient to obtain corrected risk assessment data; obtaining pre-set unit pre-warning constraint conditions; judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions or not, if so, obtaining an early warning signal, and carrying out early warning on the target fan unit according to the early warning signal.
In a second aspect, the present application further provides an intelligent monitoring and early warning system of a wind generating set, where the system includes: the unit information acquisition module is used for acquiring information of the target fan unit to obtain unit basic information; the unit monitoring index obtaining module is used for obtaining multi-stage unit monitoring indexes, wherein the multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes; the monitoring planning analysis module is used for carrying out monitoring planning analysis on the target fan set based on the set basic information and the multi-level set monitoring indexes to obtain a set monitoring scheme; the real-time monitoring module is used for carrying out real-time monitoring on the target fan set through the intelligent monitoring module based on the set monitoring scheme to obtain a multi-dimensional set monitoring data set, wherein the multi-dimensional set monitoring data set comprises set environment monitoring data, set vibration monitoring data, set noise monitoring data and set temperature monitoring data; the risk assessment module is used for carrying out risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; the data correction module is used for carrying out unit environment influence analysis based on the unit environment monitoring data to obtain a multi-dimensional unit environment influence coefficient, correcting the multi-dimensional risk assessment data based on the multi-dimensional unit environment influence coefficient to obtain corrected risk assessment data; the early warning condition acquisition module is used for acquiring early warning constraint conditions of a preset unit; the early warning module is used for judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, if so, acquiring an early warning signal, and carrying out early warning on the target fan unit according to the early warning signal.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the intelligent monitoring and early warning method of the wind generating set when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, where the program when executed by a processor implements an intelligent monitoring and early warning method for a wind turbine generator system provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
acquiring basic information of a target fan unit by acquiring information of the target fan unit; performing monitoring planning analysis on the target fan unit through unit basic information and multi-level unit monitoring indexes to obtain a unit monitoring scheme; based on a unit monitoring scheme, real-time monitoring is carried out on a target fan unit through an intelligent monitoring module, and unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data are obtained; performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; the method comprises the steps of analyzing unit environmental impact of unit environmental monitoring data to obtain a multi-dimensional unit environmental impact coefficient, and correcting multi-dimensional risk assessment data according to the multi-dimensional unit environmental impact coefficient to obtain corrected risk assessment data; judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, if so, obtaining early warning signals, and carrying out early warning on the target fan unit according to the early warning signals. The method has the advantages that the accuracy of monitoring and early warning on the wind generating set is improved, the monitoring and early warning quality of the wind generating set is improved, and the technical effect of powerful guarantee is provided for safe operation of the wind generating set.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a schematic flow chart of an intelligent monitoring and early warning method of a wind generating set;
FIG. 2 is a schematic flow chart of a unit monitoring scheme obtained in the intelligent monitoring and early warning method of a wind turbine generator system;
FIG. 3 is a schematic structural diagram of an intelligent monitoring and early warning system of a wind turbine generator system;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a unit information acquisition module 11, a unit monitoring index acquisition module 12, a monitoring planning analysis module 13, a real-time monitoring module 14, a risk assessment module 15, a data correction module 16, an early warning condition acquisition module 17, an early warning module 18, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides an intelligent monitoring and early warning method and system for a wind generating set. The technical problems of poor monitoring and early warning effects of the wind generating set caused by insufficient monitoring and early warning accuracy of the wind generating set in the prior art are solved. The method has the advantages that the accuracy of monitoring and early warning on the wind generating set is improved, the monitoring and early warning quality of the wind generating set is improved, and the technical effect of powerful guarantee is provided for safe operation of the wind generating set.
Example 1
Referring to fig. 1, the application provides an intelligent monitoring and early warning method for a wind generating set, wherein the method is applied to an intelligent monitoring and early warning system of the wind generating set, the system comprises an intelligent monitoring module, and the method specifically comprises the following steps:
step S100: information acquisition is carried out on the target fan set, and basic information of the set is obtained;
step S200: obtaining multi-stage unit monitoring indexes, wherein the multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes;
specifically, information acquisition is carried out on the target fan unit to obtain unit basic information, and multi-level unit monitoring indexes are determined. The target wind generating set is any wind generating set which uses the intelligent monitoring and early warning system of the wind generating set to conduct intelligent monitoring and early warning. The unit basic information comprises data information such as component composition information, component structure information, component size information, component material information and the like of the target wind unit. The multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes. The method achieves the technical effects of determining unit basic information and multi-level unit monitoring indexes and laying a foundation for monitoring and early warning of the target fan unit in the follow-up process.
Step S300: based on the unit basic information and the multi-stage unit monitoring indexes, monitoring planning analysis is carried out on the target fan unit, and a unit monitoring scheme is obtained;
further, as shown in fig. 2, step S300 of the present application further includes:
step S310: based on the unit basic information and the multi-stage unit monitoring indexes, monitoring feature analysis is carried out on the target fan unit, and unit monitoring feature analysis results are obtained;
further, step S310 of the present application further includes:
step S311: extracting component composition information of a target fan unit from the unit basic information;
step S312: based on the component composition information, acquiring historical early warning information of the target fan set to obtain a set component early warning database;
step S313: performing early warning factor analysis based on the unit component early warning database to obtain a component early warning factor data set;
step S314: performing relevance analysis based on the multi-stage unit monitoring index and the component early warning factor data set to obtain an index factor relevance analysis result;
step S315: based on the multi-stage unit monitoring index, setting early warning data classification constraint conditions;
Step S316: classifying the unit component early-warning database based on the early-warning data classification constraint condition and the index factor relevance analysis result to obtain a plurality of groups of index component early-warning data sets;
step S317: performing index frequency statistics based on the plurality of groups of index component early warning data sets to obtain a plurality of groups of index frequency parameters;
step S318: and marking the component composition information based on the multiple groups of index frequency parameters to obtain the unit monitoring characteristic analysis result.
Specifically, component composition information of the target wind turbine is extracted from the turbine base information. The component composition information includes a plurality of components of a target wind turbine group, such as blades, a gearbox, a generator, and the like. And further, based on the component composition information, acquiring historical early warning information of the target fan set to obtain a component early warning database of the unit, and analyzing early warning factors of the component early warning database to obtain a component early warning factor data set. The unit component early warning database comprises a plurality of unit component early warning data sets corresponding to a plurality of components of the target fan unit. Each of the crew member early warning data sets includes a plurality of historical early warning information corresponding to each of the members of the target wind turbine. The component early warning factor dataset includes a plurality of component early warning factors. The plurality of component early warning factors comprise a plurality of early warning reason information corresponding to the plurality of historical early warning information in the component early warning database.
Further, correlation analysis is carried out on the multi-stage unit monitoring index and the component early warning factor data set respectively, and an index factor correlation analysis result is obtained. The index factor relevance analysis result comprises a plurality of index factor relevance parameters. The plurality of index factor correlation parameters include correlation parameters between each of the multi-level unit monitoring index and a plurality of component early warning factors in the component early warning factor dataset. And classifying the unit component early-warning database according to the early-warning data classification constraint condition and the index factor relevance analysis result to obtain a plurality of groups of index component early-warning data sets. The early warning data classification constraint conditions comprise preset and determined relevance parameter ranges of the multi-level unit monitoring indexes corresponding to a plurality of index factors. The multi-group index component early warning data set comprises an environment early warning data set, a vibration early warning data set, a noise early warning data set and a temperature early warning data set which correspond to the plurality of components. And then, carrying out index frequency statistics on the multi-group index component early warning data set to obtain a plurality of groups of index frequency parameters, and marking component composition information according to the plurality of groups of index frequency parameters to obtain a unit monitoring characteristic analysis result. The multiple groups of index frequency parameters comprise environment early warning frequency coefficients, vibration early warning frequency coefficients, noise early warning frequency coefficients and temperature early warning frequency coefficients corresponding to the multiple components. For example, the data size of the environmental pre-warning data set corresponding to the blades of the target wind turbine is a, and the data size of the pre-warning data set corresponding to the blades of the target wind turbine in the component pre-warning database is a. And then, the environmental early warning frequency coefficient corresponding to the blade of the target fan set is the ratio of a to A. The unit monitoring characteristic analysis result comprises a plurality of groups of index frequency parameters and component composition information. The technical effect of obtaining the unit monitoring characteristic analysis result by carrying out monitoring characteristic analysis on the target fan unit is achieved, so that the accuracy of monitoring planning analysis on the target fan unit is improved.
Step S320: constructing a monitoring planning analysis model;
step S330: and inputting the unit basic information, the multi-stage unit monitoring indexes and the unit monitoring characteristic analysis result into the monitoring planning analysis model to obtain the unit monitoring scheme.
Specifically, historical data query is performed based on unit basic information, multi-level unit monitoring indexes and unit monitoring feature analysis results, and a plurality of groups of construction data sets are obtained. Each group of construction data sets comprises historical unit basic information, multi-level unit monitoring indexes, historical unit monitoring characteristic analysis results and historical unit monitoring schemes. And carrying out random data division on the plurality of groups of constructed data sets to obtain a data training set and a data testing set. Illustratively, 70% of the data information in the plurality of sets of build data is divided into data training sets and 30% of the data information in the plurality of sets of build data is divided into data testing sets. And (3) continuously self-training and learning the data training set to a convergence state to obtain a monitoring planning analysis model. And taking the data test set as input information, inputting the input information into a monitoring planning analysis model, and updating parameters of the monitoring planning analysis model through the data test set. The monitoring planning analysis model comprises an input layer, an implicit layer and an output layer. And inputting unit basic information, multi-stage unit monitoring indexes and unit monitoring characteristic analysis results into a monitoring planning analysis model to obtain a unit monitoring scheme. The unit monitoring scheme comprises position information and quantity information of environment monitoring points, vibration monitoring points, noise monitoring points and temperature monitoring points corresponding to the target fan unit. The technical effects of accurately and reliably monitoring, planning and analyzing the target fan set through the monitoring, planning and analyzing model and improving the reliability of monitoring the target fan set are achieved.
Step S400: based on the unit monitoring scheme, the intelligent monitoring module monitors the target fan unit in real time to obtain a multi-dimensional unit monitoring data set, wherein the multi-dimensional unit monitoring data set comprises unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data;
specifically, according to the unit monitoring scheme, a wind speed sensor, a wind direction sensor, a vibration sensor, a noise sensor and a temperature sensor are arranged on the target fan unit, and the wind speed sensor, the wind direction sensor, the vibration sensor, the noise sensor and the temperature sensor which are arranged are in communication connection with the intelligent monitoring module. And monitoring the target fan set in real time by utilizing the intelligent monitoring module to obtain a multi-dimensional set monitoring data set. The multi-dimensional unit monitoring data set comprises unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data of a target fan unit. The unit environment monitoring data comprise real-time wind speed information, real-time wind direction information and real-time environment temperature information corresponding to the target wind unit. The unit vibration monitoring data comprise a plurality of real-time vibration frequency parameters and a plurality of real-time amplitude parameters corresponding to a plurality of vibration monitoring points of the target fan unit. The unit noise monitoring data comprise a plurality of real-time noise decibel parameters corresponding to a plurality of noise monitoring points of the target fan unit. The unit temperature monitoring data comprise a plurality of temperature parameter information corresponding to a plurality of temperature monitoring points of the target fan unit. The intelligent monitoring module is used for monitoring the target fan set in real time, and a reliable multi-dimensional set monitoring data set is obtained, so that the technical effect of monitoring and early warning of the target fan set is improved.
Step S500: performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data;
further, step S500 of the present application further includes:
step S510: the unit monitoring and evaluating model comprises an input layer, an abnormality detection layer, a risk evaluation layer and an output layer;
step S520: inputting the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data into the anomaly detection layer to obtain a multidimensional anomaly detection data set;
further, step S520 of the present application further includes:
step S521: acquiring data of a same type of fan set of the target fan set based on the set basic information to obtain a set normal operation database, wherein the set normal operation database comprises a set normal vibration data set, a set normal noise data set and a set normal temperature data set;
step S522: based on the normal running database of the unit, a multi-stage unit normal interval is obtained, and the multi-stage unit normal interval is embedded into the abnormality detection layer, wherein the multi-stage unit normal interval comprises a unit normal vibration interval, a unit normal noise interval and a unit normal temperature interval;
Step S523: respectively comparing the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the multi-stage unit normal interval to obtain a unit abnormal data set;
step S524: performing abnormal duty ratio calculation based on the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the unit abnormal data set to obtain a multidimensional unit abnormal coefficient;
step S525: and obtaining the multidimensional anomaly detection data set based on the unit anomaly data set and the multidimensional unit anomaly coefficients.
Specifically, based on unit basic information, data acquisition of the same type of the target fan unit is performed to obtain a normal operation database of the unit. And setting a normal interval of the multi-stage unit based on the normal operation database of the unit, and embedding the normal interval of the multi-stage unit into an abnormal detection layer. The normal running database of the unit comprises a normal vibration data set of the unit, a normal noise data set of the unit and a normal temperature data set of the unit. The normal vibration data set of the unit comprises a plurality of historical vibration frequency parameters and a plurality of historical amplitude parameters of a plurality of wind generating units of the same type of the target wind generating unit in normal operation. And, a plurality of wind generating sets of the same model have the same basic information of the unit with the goal wind generating set. The normal noise data set of the unit comprises a plurality of historical noise decibel parameters of a plurality of wind generating units with the same model of the target wind generating unit during normal operation. The normal temperature data set of the unit comprises a plurality of historical temperature parameter information of a plurality of wind generating units with the same model of the target wind generating unit in normal operation. The multi-stage unit normal interval comprises a unit normal vibration interval, a unit normal noise interval and a unit normal temperature interval. The normal vibration interval of the unit comprises vibration frequency parameter range information and amplitude parameter range information corresponding to a normal vibration data set of the unit. The normal noise interval of the unit comprises noise decibel parameter range information corresponding to a normal noise data set of the unit. The normal temperature interval of the unit comprises temperature parameter range information corresponding to a normal temperature data set of the unit.
Further, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data are respectively compared with a corresponding unit normal vibration interval, a unit normal noise interval and a unit normal temperature interval to obtain a unit abnormal data set. The unit abnormal data set comprises a unit vibration abnormal data set, a unit noise abnormal data set and a unit temperature abnormal data set. The unit vibration abnormal data set comprises unit vibration monitoring data which does not meet a unit normal vibration interval. The unit noise abnormal data set comprises unit noise monitoring data which does not meet a unit normal noise interval. The unit temperature monitoring data comprise unit temperature monitoring data which do not meet a unit normal temperature interval.
And further, carrying out data volume statistics on unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data to obtain vibration monitoring data volume, noise monitoring data volume and temperature monitoring data volume. And carrying out data volume statistics on the unit vibration abnormal data set, the unit noise abnormal data set and the unit temperature abnormal data set to obtain vibration abnormal data volume, noise abnormal data volume and temperature abnormal data volume. And then, respectively calculating the ratio of the vibration abnormal data quantity, the noise abnormal data quantity and the temperature abnormal data quantity to the corresponding vibration monitoring data quantity, the noise monitoring data quantity and the temperature monitoring data quantity to obtain a vibration abnormal coefficient, a noise abnormal coefficient and a temperature abnormal coefficient. And adding the vibration abnormal coefficient, the noise abnormal coefficient and the temperature abnormal coefficient to the multidimensional unit abnormal coefficient, and combining the unit abnormal data set to obtain a multidimensional abnormal detection data set. The multidimensional unit abnormal coefficients comprise a vibration abnormal coefficient, a noise abnormal coefficient and a temperature abnormal coefficient. The multidimensional anomaly detection data set comprises a unit anomaly data set and multidimensional unit anomaly coefficients. The technical effects of performing anomaly detection on unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data through a multi-level unit normal interval to obtain a reliable multi-dimensional anomaly detection data set, and further improving the accuracy of running risk assessment on a target fan unit are achieved.
Step S530: the multi-dimensional abnormality detection data set is input into the risk assessment layer, the risk assessment layer assesses the multi-dimensional abnormality detection data set according to multi-dimensional risk assessment indexes to obtain multi-dimensional risk assessment data, wherein the multi-dimensional risk assessment indexes comprise unit vibration risk assessment indexes, unit noise risk assessment indexes and unit temperature risk assessment indexes, and the multi-dimensional risk assessment data comprise unit vibration risk assessment coefficients, unit noise risk assessment coefficients and unit temperature risk assessment coefficients.
Specifically, the multidimensional abnormality detection data set is used as input information, and is input into a risk assessment layer to obtain multidimensional risk assessment data. The risk assessment layer comprises a multi-dimensional risk assessment index which is preset and determined. The multidimensional risk assessment indexes comprise a unit vibration risk assessment index, a unit noise risk assessment index and a unit temperature risk assessment index. The multidimensional risk assessment data comprises a unit vibration risk assessment coefficient, a unit noise risk assessment coefficient and a unit temperature risk assessment coefficient. The higher the unit vibration risk assessment coefficient is, the higher the degree of vibration abnormality of the target wind turbine unit is. Illustratively, when constructing the risk assessment layer, a historical data query is performed based on the multi-dimensional anomaly detection data set to obtain a plurality of historical anomaly detection data sets and a plurality of historical risk assessment data. And continuously self-training and learning the plurality of historical abnormality detection data sets and the plurality of historical risk assessment data to a convergence state, so that a risk assessment layer can be obtained. The risk assessment layer has the function of intelligently analyzing the input multidimensional abnormal detection data set and matching risk assessment coefficients. The method achieves the technical effects that the risk assessment layer of the unit monitoring assessment model reliably carries out risk assessment on the multidimensional abnormal detection data set to obtain accurate multidimensional risk assessment data, and therefore the accuracy of early warning on the target fan unit is improved.
Step S600: performing unit environment influence analysis based on the unit environment monitoring data to obtain a multi-dimensional unit environment influence coefficient, and correcting the multi-dimensional risk assessment data based on the multi-dimensional unit environment influence coefficient to obtain corrected risk assessment data;
further, step S600 of the present application further includes:
step S610: performing unit vibration influence evaluation based on the unit environment monitoring data to obtain an environment vibration influence coefficient;
further, step S610 of the present application further includes:
step S611: acquiring vibration abnormal record information based on the target fan unit to obtain a unit vibration abnormal record data set;
step S612: performing environment correlation analysis based on the unit vibration anomaly record data set to obtain a vibration anomaly-environment correlation analysis result;
step S613: based on the vibration anomaly-environment correlation analysis result, performing environment influence evaluation on the unit vibration anomaly record data set to obtain a vibration environment influence evaluation set;
step S614: and carrying out unit vibration influence identification on the unit environment monitoring data based on the vibration environment influence evaluation set to obtain the environment vibration influence coefficient.
Specifically, vibration anomaly record information acquisition is carried out on the target fan unit, and a unit vibration anomaly record data set is obtained. The set of vibration anomaly record data includes a plurality of vibration anomaly events for a target set of fans. And further, carrying out environment correlation analysis on the unit vibration anomaly record data set to obtain a vibration anomaly-environment correlation analysis result. The vibration anomaly-environment correlation analysis result includes a plurality of vibration anomaly-environment correlation information. The plurality of vibration anomaly-environment correlation information includes correlations between a plurality of vibration anomaly events and an ambient wind speed, an ambient wind direction, an ambient temperature of the target wind turbine group. And then, according to the analysis result of the vibration anomaly-environment correlation, carrying out environmental impact assessment on the unit vibration anomaly record data set to obtain a vibration environmental impact assessment set, and carrying out unit vibration impact identification on unit environment monitoring data according to the vibration environmental impact assessment set to obtain an environmental vibration impact coefficient.
The vibration environment influence evaluation set comprises a plurality of vibration environment influence evaluation indexes and a plurality of vibration environment influence evaluation index characteristic values. The plurality of vibration environment influence evaluation indexes include a plurality of vibration anomaly-environment correlation information. The plurality of vibration environment influence evaluation index characteristic values comprise a plurality of influence degree parameter information of the environment wind speed, the environment wind direction and the environment temperature of the target wind turbine unit on a plurality of vibration abnormal events. The higher the influence degree of the ambient wind speed, the ambient wind direction and the ambient temperature of the target wind turbine group on the vibration abnormal event is, the higher the corresponding vibration environment influence evaluation index characteristic value is. The environmental vibration influence coefficient is parameter information used for representing influence degree of unit environmental monitoring data on unit vibration. The greater the environmental vibration influence coefficient is, the higher the influence degree of the unit environmental monitoring data on the unit vibration is. The technical effects of performing unit vibration influence assessment through unit environment monitoring data, obtaining accurate environment vibration influence coefficients and correcting and tamping the multidimensional risk assessment data are achieved.
Step S620: performing unit noise influence evaluation based on the unit environment monitoring data to obtain an environment noise influence coefficient;
step S630: performing unit temperature influence evaluation based on the unit environment monitoring data to obtain an environment temperature influence coefficient;
step S640: and obtaining the multi-dimensional unit environment influence coefficient based on the environment vibration influence coefficient, the environment noise influence coefficient and the environment temperature influence coefficient.
Specifically, unit noise influence evaluation and unit temperature influence evaluation are respectively carried out on unit environment monitoring data, an environment noise influence coefficient and an environment temperature influence coefficient are obtained, and a multi-dimensional unit environment influence coefficient is obtained by combining the environment vibration influence coefficient. And correcting the multidimensional risk assessment data according to the multidimensional unit environmental influence coefficient to obtain corrected risk assessment data. The environmental noise influence coefficient, the environmental temperature influence coefficient and the environmental vibration influence coefficient are obtained in the same manner, and are not described herein for brevity of description. The multi-dimensional unit environmental influence coefficients comprise an environmental vibration influence coefficient, an environmental noise influence coefficient and an environmental temperature influence coefficient. The corrected risk assessment data comprises a corrected vibration risk assessment coefficient, a corrected noise risk assessment coefficient and a corrected temperature risk assessment coefficient. The modified vibration risk assessment coefficient includes a product between the environmental vibration influence coefficient and the unit vibration risk assessment coefficient. The modified noise risk assessment coefficient includes a product between the ambient noise impact coefficient and the unit noise risk assessment coefficient. The corrected temperature risk assessment coefficient comprises a product between the ambient temperature influence coefficient and the unit temperature risk assessment coefficient. The technical effects of adaptively correcting the multidimensional risk assessment data through the unit environment monitoring data, reducing the environment interference and obtaining reliable corrected risk assessment data are achieved, and therefore the accuracy of early warning of the target fan unit is improved.
Step S700: obtaining pre-set unit pre-warning constraint conditions;
step S800: judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions or not, if so, obtaining an early warning signal, and carrying out early warning on the target fan unit according to the early warning signal.
And judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions or not, if so, acquiring an early warning signal, and early warning the target fan unit according to the early warning signal. The pre-set unit pre-warning constraint conditions comprise a pre-set vibration risk assessment coefficient threshold value, a noise risk assessment coefficient threshold value and a temperature risk assessment coefficient threshold value. Illustratively, when the early warning signal is obtained, whether the corrected vibration risk assessment coefficient, the corrected noise risk assessment coefficient and the corrected temperature risk assessment coefficient in the corrected risk assessment data meet the corresponding vibration risk assessment coefficient threshold, noise risk assessment coefficient threshold and temperature risk assessment coefficient threshold are respectively judged. When the corrected vibration risk assessment coefficient does not meet the vibration risk assessment coefficient threshold value, a vibration early warning signal is obtained, and vibration early warning is carried out on the target fan set through the vibration early warning signal. The technical effects of judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, adaptively carrying out vibration early warning, noise early warning and temperature early warning on the target fan unit, and improving the early warning comprehensiveness and early warning quality of the target fan unit are achieved.
In summary, the intelligent monitoring and early warning method for the wind generating set provided by the application has the following technical effects:
1. acquiring basic information of a target fan unit by acquiring information of the target fan unit; performing monitoring planning analysis on the target fan unit through unit basic information and multi-level unit monitoring indexes to obtain a unit monitoring scheme; based on a unit monitoring scheme, real-time monitoring is carried out on a target fan unit through an intelligent monitoring module, and unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data are obtained; performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; the method comprises the steps of analyzing unit environmental impact of unit environmental monitoring data to obtain a multi-dimensional unit environmental impact coefficient, and correcting multi-dimensional risk assessment data according to the multi-dimensional unit environmental impact coefficient to obtain corrected risk assessment data; judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, if so, obtaining early warning signals, and carrying out early warning on the target fan unit according to the early warning signals. The method has the advantages that the accuracy of monitoring and early warning on the wind generating set is improved, the monitoring and early warning quality of the wind generating set is improved, and the technical effect of powerful guarantee is provided for safe operation of the wind generating set.
2. And the monitoring characteristic analysis result of the unit is obtained by carrying out monitoring characteristic analysis on the target fan unit, so that the accuracy of monitoring planning analysis on the target fan unit is improved.
3. And reliably performing risk assessment on the multidimensional abnormal detection data set through a risk assessment layer of the unit monitoring assessment model to obtain accurate multidimensional risk assessment data, thereby improving the accuracy of early warning on the target fan unit.
4. The multi-dimensional risk assessment data is adaptively corrected through the unit environment monitoring data, the environment interference is reduced, reliable corrected risk assessment data is obtained, and therefore the accuracy of early warning of the target fan unit is improved.
Example two
Based on the same inventive concept as the intelligent monitoring and early warning method of a wind generating set in the foregoing embodiment, the invention also provides an intelligent monitoring and early warning system of a wind generating set, wherein the system comprises an intelligent monitoring module, please refer to fig. 3, and the system comprises:
the unit information acquisition module 11 is used for acquiring information of a target fan unit to obtain unit basic information;
the unit monitoring index obtaining module 12 is configured to obtain a multi-stage unit monitoring index, where the multi-stage unit monitoring index includes a unit environment monitoring index, a unit vibration monitoring index, a unit noise monitoring index, and a unit temperature monitoring index;
The monitoring planning analysis module 13 is used for carrying out monitoring planning analysis on the target wind turbine unit based on the unit basic information and the multi-level unit monitoring indexes to obtain a unit monitoring scheme;
the real-time monitoring module 14 is configured to monitor the target fan set in real time by the intelligent monitoring module based on the set monitoring scheme, so as to obtain a multi-dimensional set monitoring dataset, where the multi-dimensional set monitoring dataset includes set environment monitoring data, set vibration monitoring data, set noise monitoring data and set temperature monitoring data;
the risk assessment module 15 is configured to perform risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model, so as to obtain multidimensional risk assessment data;
the data correction module 16 is configured to perform unit environmental impact analysis based on the unit environmental monitoring data, obtain a multi-dimensional unit environmental impact coefficient, and correct the multi-dimensional risk assessment data based on the multi-dimensional unit environmental impact coefficient to obtain corrected risk assessment data;
The early warning condition acquisition module 17, wherein the early warning condition acquisition module 17 is used for acquiring early warning constraint conditions of a preset unit;
the early warning module 18 is configured to determine whether the corrected risk assessment data meets the preset unit early warning constraint condition, and if the corrected risk assessment data meets the preset unit early warning constraint condition, obtain an early warning signal, and early warn the target fan unit according to the early warning signal.
Further, the system further comprises:
the monitoring characteristic analysis module is used for carrying out monitoring characteristic analysis on the target fan unit based on the unit basic information and the multi-stage unit monitoring indexes to obtain unit monitoring characteristic analysis results;
the construction module is used for constructing a monitoring planning analysis model;
the monitoring scheme obtaining module is used for inputting the unit basic information, the multi-stage unit monitoring indexes and the unit monitoring characteristic analysis result into the monitoring planning analysis model to obtain the unit monitoring scheme.
Further, the system further comprises:
the extraction module is used for extracting component composition information of a target wind turbine unit from the unit basic information;
The early warning information acquisition module is used for acquiring historical early warning information of the target fan set based on the component composition information to obtain a set component early warning database;
the early warning factor analysis module is used for carrying out early warning factor analysis based on the unit component early warning database to obtain a component early warning factor data set;
the index factor relevance analysis module is used for carrying out relevance analysis based on the multi-stage unit monitoring index and the component early warning factor data set to obtain index factor relevance analysis results;
the classification constraint condition setting module is used for setting early warning data classification constraint conditions based on the multi-stage unit monitoring indexes;
the data classification module is used for classifying the unit component early-warning database based on the early-warning data classification constraint condition and the index factor relevance analysis result to obtain a plurality of groups of index component early-warning data sets;
the index frequency statistics module is used for carrying out index frequency statistics based on the plurality of groups of index component early warning data sets to obtain a plurality of groups of index frequency parameters;
And the marking module is used for marking the component composition information based on the plurality of groups of index frequency parameters to obtain the unit monitoring characteristic analysis result.
Further, the system further comprises:
the first execution module is used for the unit monitoring and evaluating model and comprises an input layer, an abnormality detection layer, a risk evaluating layer and an output layer;
the abnormality detection module is used for inputting the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data into the abnormality detection layer to obtain a multidimensional abnormality detection data set;
the second execution module is used for inputting the multi-dimensional abnormality detection data set into the risk assessment layer, and the risk assessment layer assesses the multi-dimensional abnormality detection data set according to multi-dimensional risk assessment indexes to obtain multi-dimensional risk assessment data, wherein the multi-dimensional risk assessment indexes comprise unit vibration risk assessment indexes, unit noise risk assessment indexes and unit temperature risk assessment indexes, and the multi-dimensional risk assessment data comprise unit vibration risk assessment coefficients, unit noise risk assessment coefficients and unit temperature risk assessment coefficients.
Further, the system further comprises:
the third execution module is used for acquiring data of the same type of the target fan unit based on the unit basic information to obtain a unit normal operation database, wherein the unit normal operation database comprises a unit normal vibration data set, a unit normal noise data set and a unit normal temperature data set;
the normal interval obtaining module is used for obtaining a multi-stage unit normal interval based on the unit normal operation database and embedding the multi-stage unit normal interval into the abnormality detection layer, wherein the multi-stage unit normal interval comprises a unit normal vibration interval, a unit normal noise interval and a unit normal temperature interval;
the comparison module is used for respectively comparing the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the multi-stage unit normal interval to obtain a unit abnormal data set;
the abnormal duty ratio calculation module is used for calculating the abnormal duty ratio based on the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the unit abnormal data set to obtain a multidimensional unit abnormal coefficient;
And the fourth execution module is used for obtaining the multi-dimensional abnormality detection data set based on the unit abnormality data set and the multi-dimensional unit abnormality coefficient.
Further, the system further comprises:
the unit vibration influence evaluation module is used for performing unit vibration influence evaluation based on the unit environment monitoring data to obtain an environment vibration influence coefficient;
the unit noise influence evaluation module is used for performing unit noise influence evaluation based on the unit environment monitoring data to obtain an environment noise influence coefficient;
the unit temperature influence evaluation module is used for performing unit temperature influence evaluation based on the unit environment monitoring data to obtain an environment temperature influence coefficient;
and the fifth execution module is used for obtaining the environment influence coefficient of the multi-dimensional unit based on the environment vibration influence coefficient, the environment noise influence coefficient and the environment temperature influence coefficient.
Further, the system further comprises:
the vibration anomaly record information acquisition module is used for acquiring vibration anomaly record information based on the target wind turbine unit to obtain a set vibration anomaly record data set;
The environment relevance analysis module is used for carrying out environment relevance analysis based on the unit vibration anomaly record data set to obtain a vibration anomaly-environment relevance analysis result;
the environmental impact evaluation module is used for carrying out environmental impact evaluation on the unit vibration anomaly record data set based on the vibration anomaly-environment correlation analysis result to obtain a vibration environmental impact evaluation set;
and the unit vibration influence identification module is used for carrying out unit vibration influence identification on the unit environment monitoring data based on the vibration environment influence evaluation set to obtain the environment vibration influence coefficient.
The intelligent monitoring and early warning system of the wind generating set provided by the embodiment of the invention can execute the intelligent monitoring and early warning method of the wind generating set provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example III
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing a software program, a computer executable program and a module, such as a program instruction/module corresponding to an intelligent monitoring and early warning method of a wind turbine generator system in an embodiment of the present invention. The processor 31 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 32, namely, the intelligent monitoring and early warning method of the wind generating set is realized.
The application provides an intelligent monitoring and early warning method of a wind generating set, wherein the method is applied to an intelligent monitoring and early warning system of the wind generating set, and the method comprises the following steps: acquiring basic information of a target fan unit by acquiring information of the target fan unit; performing monitoring planning analysis on the target fan unit through unit basic information and multi-level unit monitoring indexes to obtain a unit monitoring scheme; based on a unit monitoring scheme, real-time monitoring is carried out on a target fan unit through an intelligent monitoring module, and unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data are obtained; performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data; the method comprises the steps of analyzing unit environmental impact of unit environmental monitoring data to obtain a multi-dimensional unit environmental impact coefficient, and correcting multi-dimensional risk assessment data according to the multi-dimensional unit environmental impact coefficient to obtain corrected risk assessment data; judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, if so, obtaining early warning signals, and carrying out early warning on the target fan unit according to the early warning signals. The technical problems of poor monitoring and early warning effects of the wind generating set caused by insufficient monitoring and early warning accuracy of the wind generating set in the prior art are solved. The method has the advantages that the accuracy of monitoring and early warning on the wind generating set is improved, the monitoring and early warning quality of the wind generating set is improved, and the technical effect of powerful guarantee is provided for safe operation of the wind generating set.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. The intelligent monitoring and early warning method for the wind generating set is characterized by being applied to an intelligent monitoring and early warning system of the wind generating set, wherein the system comprises an intelligent monitoring module, and the method comprises the following steps:
information acquisition is carried out on the target fan set, and basic information of the set is obtained;
obtaining multi-stage unit monitoring indexes, wherein the multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes;
Based on the unit basic information and the multi-stage unit monitoring indexes, monitoring planning analysis is carried out on the target fan unit, and a unit monitoring scheme is obtained;
based on the unit monitoring scheme, the intelligent monitoring module monitors the target fan unit in real time to obtain a multi-dimensional unit monitoring data set, wherein the multi-dimensional unit monitoring data set comprises unit environment monitoring data, unit vibration monitoring data, unit noise monitoring data and unit temperature monitoring data;
performing risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data;
performing unit environment influence analysis based on the unit environment monitoring data to obtain a multi-dimensional unit environment influence coefficient, and correcting the multi-dimensional risk assessment data based on the multi-dimensional unit environment influence coefficient to obtain corrected risk assessment data;
obtaining pre-set unit pre-warning constraint conditions;
judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions or not, if so, obtaining an early warning signal, and carrying out early warning on the target fan unit according to the early warning signal.
2. The method of claim 1, wherein the target wind turbine is subjected to monitoring planning analysis based on the turbine base information and the multi-level turbine monitoring index to obtain a turbine monitoring scheme, the method further comprising:
based on the unit basic information and the multi-stage unit monitoring indexes, monitoring feature analysis is carried out on the target fan unit, and unit monitoring feature analysis results are obtained;
constructing a monitoring planning analysis model;
and inputting the unit basic information, the multi-stage unit monitoring indexes and the unit monitoring characteristic analysis result into the monitoring planning analysis model to obtain the unit monitoring scheme.
3. The method of claim 2, wherein the target wind turbine is subjected to monitoring feature analysis based on the turbine base information and the multi-level turbine monitoring index to obtain a turbine monitoring feature analysis result, the method further comprising:
extracting component composition information of a target fan unit from the unit basic information;
based on the component composition information, acquiring historical early warning information of the target fan set to obtain a set component early warning database;
performing early warning factor analysis based on the unit component early warning database to obtain a component early warning factor data set;
Performing relevance analysis based on the multi-stage unit monitoring index and the component early warning factor data set to obtain an index factor relevance analysis result;
based on the multi-stage unit monitoring index, setting early warning data classification constraint conditions;
classifying the unit component early-warning database based on the early-warning data classification constraint condition and the index factor relevance analysis result to obtain a plurality of groups of index component early-warning data sets;
performing index frequency statistics based on the plurality of groups of index component early warning data sets to obtain a plurality of groups of index frequency parameters;
and marking the component composition information based on the multiple groups of index frequency parameters to obtain the unit monitoring characteristic analysis result.
4. The method of claim 1, wherein the obtaining multidimensional risk assessment data, the method further comprising:
the unit monitoring and evaluating model comprises an input layer, an abnormality detection layer, a risk evaluation layer and an output layer;
inputting the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data into the anomaly detection layer to obtain a multidimensional anomaly detection data set;
the multi-dimensional abnormality detection data set is input into the risk assessment layer, the risk assessment layer assesses the multi-dimensional abnormality detection data set according to multi-dimensional risk assessment indexes to obtain multi-dimensional risk assessment data, wherein the multi-dimensional risk assessment indexes comprise unit vibration risk assessment indexes, unit noise risk assessment indexes and unit temperature risk assessment indexes, and the multi-dimensional risk assessment data comprise unit vibration risk assessment coefficients, unit noise risk assessment coefficients and unit temperature risk assessment coefficients.
5. The method of claim 4, wherein the obtaining a multi-dimensional anomaly detection dataset, the method further comprising:
acquiring data of a same type of fan set of the target fan set based on the set basic information to obtain a set normal operation database, wherein the set normal operation database comprises a set normal vibration data set, a set normal noise data set and a set normal temperature data set;
based on the normal running database of the unit, a multi-stage unit normal interval is obtained, and the multi-stage unit normal interval is embedded into the abnormality detection layer, wherein the multi-stage unit normal interval comprises a unit normal vibration interval, a unit normal noise interval and a unit normal temperature interval;
respectively comparing the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the multi-stage unit normal interval to obtain a unit abnormal data set;
performing abnormal duty ratio calculation based on the unit vibration monitoring data, the unit noise monitoring data, the unit temperature monitoring data and the unit abnormal data set to obtain a multidimensional unit abnormal coefficient;
And obtaining the multidimensional anomaly detection data set based on the unit anomaly data set and the multidimensional unit anomaly coefficients.
6. The method of claim 1, wherein the obtaining the multi-dimensional set environmental impact coefficients further comprises:
performing unit vibration influence evaluation based on the unit environment monitoring data to obtain an environment vibration influence coefficient;
performing unit noise influence evaluation based on the unit environment monitoring data to obtain an environment noise influence coefficient;
performing unit temperature influence evaluation based on the unit environment monitoring data to obtain an environment temperature influence coefficient;
and obtaining the multi-dimensional unit environment influence coefficient based on the environment vibration influence coefficient, the environment noise influence coefficient and the environment temperature influence coefficient.
7. The method of claim 6, wherein the obtaining an environmental vibration influence coefficient, the method further comprising:
acquiring vibration abnormal record information based on the target fan unit to obtain a unit vibration abnormal record data set;
performing environment correlation analysis based on the unit vibration anomaly record data set to obtain a vibration anomaly-environment correlation analysis result;
Based on the vibration anomaly-environment correlation analysis result, performing environment influence evaluation on the unit vibration anomaly record data set to obtain a vibration environment influence evaluation set;
and carrying out unit vibration influence identification on the unit environment monitoring data based on the vibration environment influence evaluation set to obtain the environment vibration influence coefficient.
8. An intelligent monitoring and early warning system of a wind generating set, which is characterized by comprising an intelligent monitoring module, wherein the system comprises:
the unit information acquisition module is used for acquiring information of the target fan unit to obtain unit basic information;
the unit monitoring index obtaining module is used for obtaining multi-stage unit monitoring indexes, wherein the multi-stage unit monitoring indexes comprise unit environment monitoring indexes, unit vibration monitoring indexes, unit noise monitoring indexes and unit temperature monitoring indexes;
the monitoring planning analysis module is used for carrying out monitoring planning analysis on the target fan set based on the set basic information and the multi-level set monitoring indexes to obtain a set monitoring scheme;
The real-time monitoring module is used for carrying out real-time monitoring on the target fan set through the intelligent monitoring module based on the set monitoring scheme to obtain a multi-dimensional set monitoring data set, wherein the multi-dimensional set monitoring data set comprises set environment monitoring data, set vibration monitoring data, set noise monitoring data and set temperature monitoring data;
the risk assessment module is used for carrying out risk assessment on the unit vibration monitoring data, the unit noise monitoring data and the unit temperature monitoring data through a unit monitoring assessment model to obtain multidimensional risk assessment data;
the data correction module is used for carrying out unit environment influence analysis based on the unit environment monitoring data to obtain a multi-dimensional unit environment influence coefficient, correcting the multi-dimensional risk assessment data based on the multi-dimensional unit environment influence coefficient to obtain corrected risk assessment data;
the early warning condition acquisition module is used for acquiring early warning constraint conditions of a preset unit;
the early warning module is used for judging whether the corrected risk assessment data meets the preset unit early warning constraint conditions, if so, acquiring an early warning signal, and carrying out early warning on the target fan unit according to the early warning signal.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
and the processor is used for realizing the intelligent monitoring and early warning method of the wind generating set according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method for intelligent monitoring and pre-warning of a wind power plant according to any one of claims 1 to 7.
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| CN116881658A (en) * | 2023-07-12 | 2023-10-13 | 南方电网调峰调频发电有限公司检修试验分公司 | Intelligent state evaluation method and system for hydroelectric generating set |
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