CN114687930A - Wind turbine generator fault early warning closed-loop management and control system and method - Google Patents
Wind turbine generator fault early warning closed-loop management and control system and method Download PDFInfo
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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
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Abstract
The invention discloses a wind turbine generator fault early warning closed-loop management and control system and a method, wherein the system comprises a data access layer, an early warning analysis layer, a fault diagnosis layer and a data interaction layer, SCADA data and CMS vibration data of a wind turbine generator are accessed into the early warning analysis layer, and when the wind turbine generator is in a fault early stage, an early warning model detects that the wind turbine generator is abnormal on a characteristic value representing a health state, and then the abnormality is caught, so that operation and maintenance personnel are guided to start intervention measures in time, and the further deterioration of the fault of the wind turbine generator is avoided; the method can realize early warning of faults, reduce unplanned shutdown of a wind field, realize deep excavation of fault hidden information, identify frequent faults to optimize a fault maintenance scheme, realize closed-loop management of the faults, form a case library and improve the life cycle health management of the wind turbine generator.
Description
Technical Field
The invention relates to the technical field of wind turbine generator online fault monitoring, in particular to a wind turbine generator fault early warning closed-loop control system and method.
Background
At present, most faults given in a control system of a wind turbine generator are generated, and fault early warning is not really realized; and real closed-loop management is not realized in the aspect of fault treatment, and after field defect treatment is finished, important information such as whether the fault belongs to recent frequent occurrence and frequent occurrence frequency, whether the same fault exists in other similar types of wind fields and the like is not further deeply excavated. Besides the SCADA data, the wind turbine generator also has CMS vibration data, wherein the application is to utilize amplitude change of the wind turbine generator more and identify early and medium faults of the wind turbine generator by data characteristics. The identified faults are mostly reported from a control system, the faults belong to the posterior, the faults are not early warned in advance, and serious faults can cause serious damage to equipment besides shutdown of a unit; after a fault occurs, tracking is generally realized through various online platforms or excel tables and the like, once fault processing is completed and closed, data is not deeply mined, and value information deeply hidden in a database is not effectively extracted; in the dimension of fault overall analysis, the fault overall analysis is more based on a certain wind farm base, fault analysis is not performed on all wind farms from a macroscopic view, particularly for wind turbine generators of the same type, fault feedback is more valuable, as the number of the wind turbine generators is large, the same or similar faults still occur after the faults are solved, the fault treatment is not thorough or radical treatment is not realized, the faults of the type need to be focused by field operation and maintenance personnel, but in the actual operation process, higher attention degree is not brought to the faults due to the lack of corresponding technical means; meanwhile, for operation and maintenance personnel, under the condition of not using advanced technical means, the operation data of the past 1 month or even the past 24 hours is manually analyzed, the abnormality is difficult to identify and capture, and the efficiency is extremely low.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a wind turbine generator fault early warning closed-loop control system and method. Meanwhile, CMS vibration data monitoring is combined, data transmitted back by the wind power plant can be fully utilized, and multi-dimensional fan fault early warning is achieved.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: the utility model provides a wind turbine generator system trouble early warning closed loop management and control system, includes:
the data access layer is used for accessing the SCADA data and the CMS vibration data of the wind turbine generator set in the wind field into the early warning analysis layer through a preset data transmission mechanism;
the early warning analysis layer is used for preprocessing the accessed SCADA data, screening out measuring points from the preprocessed SCADA data through an algorithm to perform modeling of an early warning model, inputting the operation data of the wind turbine generator into the early warning model to obtain a health state conclusion of the wind turbine generator, and outputting the health state conclusion to the fault diagnosis layer; meanwhile, the accessed CMS vibration data are cleaned, and then vibration signal characteristic quantities are extracted, and then normal units are marked in a pre-analysis mode;
the fault diagnosis layer is used for carrying out comprehensive diagnosis and analysis on the conclusion output by the early warning analysis layer and outputting diagnosis information to the data interaction layer;
and the data interaction layer is used for importing data to optimize the system and exporting and displaying the early warning diagnosis report.
Further, the data access layer specifically performs the following operations:
and recording the SCADA data file in the CSV format in the wind field into a MySQL or Oracle relational database, converting the CMS vibration data in the wind field into a CSV format file or a TXT format file through a data conversion tool, and recording the CSV format file or the TXT format file into the MySQL or Oracle relational database.
Further, the early warning analysis layer specifically executes the following operations:
rejecting abnormal data or unusable data in the SCADA data, then screening data of the unit in a normal operation state, carrying out modeling on the preprocessed SCADA data through screened measuring points to carry out early warning model, wherein the measuring points are combinations of parameters capable of representing performance of the wind turbine, and after training of the early warning model is finished, reading other SCADA data to calculate the health degree of the wind turbine in the current time period and setting a health degree critical value;
performing data cleaning on CMS vibration data, and generating new vibration signal data of each measuring point of a transmission chain through frequency resampling, or performing data cleaning on the CMS vibration data through stripping invalid vibration signal data; and then marking characteristic vectors and frequency components of all parts in the vibration signal data by a frequency scanning algorithm, calculating index parameters of all measuring points and frequency components of the transmission chain, and marking the normal unit in a pre-analysis mode according to the characteristic vectors and the frequency components of all parts in the vibration signal data and the indexes of all measuring points and frequency components of the transmission chain.
Further, the fault diagnosis layer specifically performs the following operations:
the method comprises the steps of displaying the measuring points with abnormal conditions and the names of the wind turbine equipment with abnormal conditions of the wind turbine by analyzing a list of all measuring points which cause the health degree of the wind turbine to be smaller than a health degree critical value, analyzing vibration signal characteristics, combining SCADA (supervisory control and data acquisition) process parameters and a conclusion output according to an early warning analysis layer, and finally outputting diagnosis information, wherein the diagnosis information comprises a conclusion of the final diagnosis of the wind turbine, the fault degree of the wind turbine, a maintenance suggestion of the wind turbine and a fault development trend.
Further, the early warning diagnosis report comprises a wind turbine generator fault report, a wind turbine generator health state, wind turbine generator easy-sending and re-sending fault statistics, a major component fault report and a major component degradation trend analysis curve.
The invention provides a wind turbine generator fault early warning closed-loop control method, which uses the wind turbine generator fault early warning closed-loop control system and comprises the following steps:
s1, accessing the wind turbine generator fault early warning closed-loop management and control system to SCADA data and CMS vibration data of the wind turbine generator;
s2, starting early warning analysis of the wind turbine fault early warning closed-loop management and control system according to the data accessed in the step S1, calculating the health degree of the wind turbine through an operation early warning model, and identifying the abnormal vibration fault of the wind turbine according to CMS vibration data;
s3, determining the severity level of the fault according to the identified fault of the wind turbine generator, checking whether the fault occurs on the wind turbine generator in a wind turbine generator fault early warning closed-loop management and control system, namely whether the fault belongs to frequent faults, and if the fault belongs to frequent faults, determining the maintenance scheme in combination with the previous maintenance scheme for maintaining the frequent faults; if the faults do not belong to frequent faults, corresponding maintenance schemes are directly provided for the faults with different severity levels;
and S4, after the maintenance is finished, recording the relevant information of the fault in the wind turbine generator fault early warning closed-loop management and control system for recording.
Further, in step S4, the failure-related information includes a failure picture, a failure cause, and a spare part and an assembly size to be replaced in the actual repair process.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method can realize early warning of faults, reduce unplanned shutdown of a wind field, realize deep excavation of fault hidden information, identify frequent faults to optimize a fault maintenance scheme, realize closed-loop management of the faults, form a case library and improve the life cycle health management of the wind turbine generator.
Drawings
Fig. 1 is a framework diagram of a wind turbine generator fault early warning closed-loop management and control system.
Detailed Description
The present invention will be further described with reference to the following specific examples.
Referring to fig. 1, the wind turbine generator fault early warning closed-loop management and control system provided in this embodiment includes:
the data access layer is used for accessing the SCADA data and the CMS vibration data in the wind field into the early warning analysis layer through a preset data transmission mechanism, and specifically executes the following operations:
and recording the SCADA data file in the CSV format in the wind field into a MySQL or Oracle relational database, converting the CMS vibration data in the wind field into a CSV format file or a TXT format file through a data conversion tool, and recording the CSV format file or the TXT format file into the MySQL or Oracle relational database.
The early warning analysis layer is used for preprocessing the accessed SCADA data, screening out measuring points from the preprocessed SCADA data through an algorithm to perform modeling of an early warning model, inputting the running data of the wind turbine into the early warning model to obtain the health state conclusion of the wind turbine and outputting the health state conclusion to the fault diagnosis layer; simultaneously, CMS vibration data of the access are cleaned and then vibration signal characteristic quantity is extracted, and then normal units are marked in a pre-analysis mode, the early warning analysis layer comprises a fan early warning analysis module and a CMS vibration data analysis module, and the following operations are specifically executed:
rejecting abnormal data or unusable data in the SCADA data, screening data of the set in a normal operation state, carrying out early warning model modeling on the preprocessed SCADA data through screened measuring points, wherein the measuring points are combinations of parameters capable of representing the performance of the wind turbine generator, and after the early warning model training is finished, calculating the health degree of the wind turbine generator in the current time period by reading other SCADA data, and setting a health degree critical value;
performing data cleaning on CMS vibration data, and generating new vibration signal data of each measuring point of a transmission chain through frequency resampling, or performing data cleaning on the CMS vibration data through stripping invalid vibration signal data; and then marking characteristic vectors and frequency components of all parts in the vibration signal data by a frequency scanning algorithm, calculating index parameters of all measuring points and frequency components of the transmission chain, and marking the normal unit in a pre-analysis mode according to the characteristic vectors and the frequency components of all parts in the vibration signal data and the indexes of all measuring points and frequency components of the transmission chain.
The fault diagnosis layer carries out comprehensive diagnosis and analysis on the conclusion output by the early warning analysis layer and outputs diagnosis information to the data interaction layer, and the fault diagnosis layer comprises a fan fault diagnosis module and a CMS (management system) diagnosis module and specifically executes the following operations:
the method comprises the steps of displaying the measuring points with abnormal conditions and the names of the wind turbine equipment with abnormal conditions of the wind turbine by analyzing a list of all measuring points which cause the health degree of the wind turbine to be smaller than a health degree critical value, analyzing vibration signal characteristics, combining SCADA (supervisory control and data acquisition) process parameters and a conclusion output according to an early warning analysis layer, and finally outputting diagnosis information, wherein the diagnosis information comprises a conclusion of the final diagnosis of the wind turbine, the fault degree of the wind turbine, a maintenance suggestion of the wind turbine and a fault development trend.
And the data interaction layer is used for importing data to optimize the system and exporting and displaying early warning diagnosis reports, and the early warning diagnosis reports comprise a wind turbine fault report, a wind turbine health state, wind turbine easy-sending and re-sending fault statistics, a major component fault report and a major component degradation trend analysis curve.
The embodiment discloses a wind turbine generator fault early warning closed-loop management and control method, which takes the occurrence of a generator bearing fault in a wind turbine generator #009 of a wind farm as an example, and uses the wind turbine generator fault early warning closed-loop management and control system, and comprises the following steps:
s1, accessing the wind turbine generator fault early warning closed-loop management and control system to SCADA data and CMS vibration data of wind turbine generator # 009;
s2, starting early warning analysis of the wind turbine generator fault early warning closed-loop management and control system according to the data accessed in the step S1, calculating the health degree of the wind turbine generator #009 through an operation early warning model, identifying the high temperature of the generator bearing of the wind turbine generator #009, identifying the abnormal vibration fault of the wind turbine generator #009 according to CMS vibration data, and identifying the abnormal vibration amplitude and the abnormal frequency spectrum of the bearing of the generator;
s3, determining the severity level of the fault according to the identified fault of the wind turbine generator #009, and if the fault is slight, the wind turbine generator #009 can be in a fault state to wait for a proper window for overhauling; if the fault is serious, the wind turbine generator #009 must be shut down for maintenance, so as to avoid further deterioration of the fault; checking whether the fault occurs on the wind turbine generator #009 in the wind turbine generator fault early warning closed-loop management and control system, namely whether the fault belongs to frequent faults, and if the fault belongs to frequent faults, determining the maintenance scheme by combining the maintenance scheme for maintaining the frequent faults at the last time; if the faults do not belong to frequent faults, corresponding maintenance schemes are directly provided for the faults with different severity levels;
s4, after the maintenance is finished, recording the relevant information of the fault in the wind turbine generator fault early warning closed-loop control system for recording; the relevant information of the fault comprises a fault picture, a fault reason, spare parts and assembly sizes which are replaced in the actual maintenance process. The reason for the generator bearing abnormity is found through field inspection, the generator is poor in lubrication, and the wind turbine generator #009 recovers to normally operate after the lubricating oil is refilled on site.
In addition, whether the faults are frequent faults or not is checked through the system, if yes, the comprehensive general investigation work of a wind field needs to be carried out, meanwhile, whether the faults are frequently sent to the machine type or not can be further mined and analyzed, if yes, the reason that the faults occur is the defects in the design of the machine type, and therefore the machine type needs to be improved subsequently.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.
Claims (7)
1. The utility model provides a wind turbine generator system trouble early warning closed loop management and control system which characterized in that includes:
the data access layer is used for accessing the SCADA data and the CMS vibration data of the wind turbine generator set in the wind field into the early warning analysis layer through a preset data transmission mechanism;
the early warning analysis layer is used for preprocessing the accessed SCADA data, screening out measuring points from the preprocessed SCADA data through an algorithm to perform modeling of an early warning model, inputting the operation data of the wind turbine generator into the early warning model to obtain a health state conclusion of the wind turbine generator, and outputting the health state conclusion to the fault diagnosis layer; meanwhile, the accessed CMS vibration data are cleaned, and then vibration signal characteristic quantities are extracted, and then normal units are marked in a pre-analysis mode;
the fault diagnosis layer is used for carrying out comprehensive diagnosis and analysis on the conclusion output by the early warning analysis layer and outputting diagnosis information to the data interaction layer;
and the data interaction layer is used for importing data to optimize the system and exporting and displaying the early warning diagnosis report.
2. The wind turbine generator system fault early warning closed-loop management and control system according to claim 1, wherein the data access layer specifically performs the following operations:
and recording the SCADA data file in the CSV format in the wind field into a MySQL or Oracle relational database, converting the CMS vibration data in the wind field into a CSV format file or a TXT format file through a data conversion tool, and recording the CSV format file or the TXT format file into the MySQL or Oracle relational database.
3. The wind turbine generator system fault early warning closed-loop management and control system according to claim 1, wherein the early warning analysis layer specifically performs the following operations:
rejecting abnormal data or unusable data in the SCADA data, screening data of the set in a normal operation state, carrying out early warning model modeling on the preprocessed SCADA data through screened measuring points, wherein the measuring points are combinations of parameters capable of representing the performance of the wind turbine generator, and after the early warning model training is finished, calculating the health degree of the wind turbine generator in the current time period by reading other SCADA data, and setting a health degree critical value;
carrying out data cleaning on CMS vibration data, generating new vibration signal data of each measuring point of a transmission chain by frequency resampling, or carrying out data cleaning on the CMS vibration data by stripping ineffective vibration signal data; and then marking characteristic vectors and frequency components of all parts in the vibration signal data by a frequency scanning algorithm, calculating index parameters of all measuring points and frequency components of the transmission chain, and marking the normal unit in a pre-analysis mode according to the characteristic vectors and the frequency components of all parts in the vibration signal data and the indexes of all measuring points and frequency components of the transmission chain.
4. The wind turbine generator system fault early warning closed-loop management and control system of claim 1, wherein the fault diagnosis layer specifically performs the following operations:
the method comprises the steps of displaying the measuring points with abnormal conditions and the names of the abnormal wind turbine equipment by analyzing a list of all measuring points which cause the health degree of the wind turbine to be smaller than a health degree critical value, analyzing vibration signal characteristics, combining SCADA process parameters and a conclusion output according to an early warning analysis layer, and finally outputting diagnosis information, wherein the diagnosis information comprises a conclusion of the final diagnosis of the wind turbine, a fault degree of the wind turbine, a maintenance suggestion of the wind turbine and a fault development trend.
5. The wind turbine generator system fault early warning closed-loop management and control system according to claim 1, wherein the early warning diagnosis report includes a wind turbine generator system fault report, a health state of the wind turbine generator system, statistics of faults which are easy to occur and are repeated, a major component fault report and a major component degradation trend analysis curve.
6. A wind turbine generator fault early warning closed-loop control method is characterized in that the wind turbine generator fault early warning closed-loop control system of any one of claims 1 to 5 is used, and comprises the following steps:
s1, accessing the wind turbine generator fault early warning closed-loop management and control system to SCADA data and CMS vibration data of the wind turbine generator;
s2, starting early warning analysis of the wind turbine fault early warning closed-loop management and control system according to the data accessed in the step S1, calculating the health degree of the wind turbine through an operation early warning model, and identifying the abnormal vibration fault of the wind turbine according to CMS vibration data;
s3, determining the severity level of the fault according to the identified fault of the wind turbine generator, checking whether the fault occurs on the wind turbine generator in a wind turbine generator fault early warning closed-loop management and control system, namely whether the fault belongs to frequent faults, and if the fault belongs to frequent faults, determining the maintenance scheme in combination with the previous maintenance scheme for maintaining the frequent faults; if the faults do not belong to frequent faults, corresponding maintenance schemes are directly provided for the faults with different severity levels;
and S4, after the maintenance is finished, recording the relevant information of the fault in the wind turbine generator fault early warning closed-loop management and control system for recording.
7. The wind turbine generator system fault early warning closed-loop management and control method according to claim 6, wherein in step S4, the fault related information includes a fault picture, a fault reason, spare parts and assembly size for replacement in an actual maintenance process.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057676A (en) * | 2023-10-11 | 2023-11-14 | 深圳润世华软件和信息技术服务有限公司 | Multi-data fusion fault analysis method, equipment and storage medium |
| CN117851956A (en) * | 2024-03-07 | 2024-04-09 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057676A (en) * | 2023-10-11 | 2023-11-14 | 深圳润世华软件和信息技术服务有限公司 | Multi-data fusion fault analysis method, equipment and storage medium |
| CN117057676B (en) * | 2023-10-11 | 2024-02-23 | 深圳润世华软件和信息技术服务有限公司 | Multi-data fusion fault analysis method, equipment and storage medium |
| CN117851956A (en) * | 2024-03-07 | 2024-04-09 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
| CN117851956B (en) * | 2024-03-07 | 2024-05-10 | 深圳市森树强电子科技有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis |
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