CN109611288B - Wind power operation and maintenance platform based on big data - Google Patents
Wind power operation and maintenance platform based on big data Download PDFInfo
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- CN109611288B CN109611288B CN201811631770.0A CN201811631770A CN109611288B CN 109611288 B CN109611288 B CN 109611288B CN 201811631770 A CN201811631770 A CN 201811631770A CN 109611288 B CN109611288 B CN 109611288B
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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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- 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
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
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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
- F03D9/00—Adaptations of wind motors for special use; Combinations of wind motors with apparatus driven thereby; Wind motors specially adapted for installation in particular locations
- F03D9/20—Wind motors characterised by the driven apparatus
- F03D9/25—Wind motors characterised by the driven apparatus the apparatus being an electrical generator
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
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- Combustion & Propulsion (AREA)
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Abstract
The invention provides a wind power operation and maintenance platform based on big data, which is characterized in that: it includes: the intelligent monitoring system comprises an intelligent monitoring module, a power module and a software platform, wherein the power module comprises a 24V power module and a UPS (uninterrupted power supply), the software platform is installed on a main server, and the software platform comprises a cluster control module, a data receiving module, a display storage module, a data mining module, a data analysis module, an operation and maintenance management module and a data statistics module; the intelligent monitoring module comprises a sensor unit, an intelligent acquisition unit, an Ethernet switch, a network gate and a field server; the field server is provided with field monitoring software which comprises a parameter setting module, a data acquisition module, a data processing module, a data storage module and a data transmission module. The invention can realize the information of fan monitoring, fault prediction, health management, maintenance schemes, spare part storage, personnel dispatching and the like under a big data platform, thereby improving the operation and maintenance efficiency and reducing the operation and maintenance cost.
Description
Technical Field
The invention relates to a wind power operation maintenance platform technology, in particular to the technical field of the platform used for operation and maintenance service of a wind generating set.
Background
The operation and maintenance of wind generating sets (called fans for short) can be generally classified into the following types: 1. daily point inspection and routing inspection are carried out, and the inspection is carried out one by one; 2. according to alarm information fed back by a fan SCADA system (a data acquisition and monitoring control system) or a CMS system (a state monitoring system) additionally arranged on the fan, pertinently checking; 3. and (4) after-the-fact maintenance after the fan is stopped seriously due to failure.
The first prior art has the following disadvantages:
1) the daily spot inspection and routing inspection workload is large, the requirement on the skills of operation and maintenance personnel is high, potential safety hazards exist, and potential faults are easy to ignore;
2) after the fan SCADA system and the CMS system give an alarm, a manufacturer is required to assist in giving out a fault position and fault severity, or the manufacturer is required to prepare personnel and equipment to perform on-site inspection, so that unnecessary shutdown waiting time is easily caused;
3) after-the-fact maintenance after the fan is shut down after serious faults easily cause major safety accidents, secondary damage to equipment is caused, and economic loss is large.
Disclosure of Invention
The invention provides a wind power operation and maintenance platform based on big data, which aims to solve the defects of the prior art, realize the information of fan monitoring, fault prediction, health management, maintenance schemes, spare part storage, personnel dispatching and the like under the big data platform, reduce unnecessary manual detection and shutdown waiting time, avoid serious equipment accidents, ensure the safety of units and personnel, improve the operation and maintenance efficiency and reduce the operation and maintenance cost.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the utility model provides a wind-powered electricity generation fortune dimension platform based on big data which characterized in that: it includes: the intelligent monitoring system comprises an intelligent monitoring module, a power module and a software platform, wherein the power module comprises a 24V power module and a UPS (uninterrupted power supply), the software platform is installed on a main server, and the software platform comprises a cluster control module, a data receiving module, a display storage module, a data mining module, a data analysis module, an operation and maintenance management module and a data statistics module;
the intelligent monitoring module comprises a sensor unit, an intelligent acquisition unit, an Ethernet switch, a network gate and a field server, wherein the sensor unit is connected with the intelligent acquisition unit through a shielded cable, the intelligent acquisition unit is connected with the Ethernet switch, and the Ethernet switch is connected with the field server;
the field server is provided with field monitoring software which comprises a parameter setting module, a data acquisition module, a data processing module, a data storage module and a data transmission module;
the field server is connected to the main server software platform through an Interent network, the software platform architecture is developed based on LabVIEW, and the software platform realizes the driving of each functional module.
The invention has the advantages that:
the wind power operation and maintenance service platform based on the big data realizes online synchronization of fan state monitoring, fault prediction, health management, spare part storage, maintenance schemes, personnel assignment and the like, constructs a closed-loop operation and maintenance service platform, improves the intelligent level of fan state monitoring through deep excavation of mass data, reduces unnecessary manual detection and shutdown waiting time, avoids serious equipment accidents, ensures the safety of units and personnel, finally reduces power generation loss and improves power generation capacity. The wind power operation and maintenance enterprise realizes the online interconnection of a plurality of functions of technical support, operation and maintenance service, supply chain management and customer relationship management on a platform, greatly improves the operation efficiency of the enterprise, and reduces the operation cost.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a diagram of the platform hardware architecture of the present invention;
FIG. 2 is a diagram of the field monitoring software architecture of the present invention;
FIG. 3 is a diagram of the big data platform software architecture of the present invention.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained according to the drawings without inventive labor.
As shown in fig. 1:
the sensor unit of the intelligent monitoring module comprises a plurality of sensors and a signal switching device, wherein more than one vibration acceleration sensor is fixed on a fan transmission chain through a magnetic seat and is used for collecting vibration signals under real-time working conditions; more than one microphone sensor is arranged on a magnetic seat, and the magnetic seat is fixed at the position with the maximum radial distance from the main shaft, the gear box and the generator and is used for collecting noise signals caused by vibration of a fan transmission chain under the real-time working condition; a sloshing sensor is arranged at the top of the tower barrel, namely on the base of the engine room, and is used for measuring the radial inclination angle of the tower barrel; a tachometer is arranged on the fixed bracket and used for measuring the output rotating speed of the gearbox; and the signal switching device is arranged in the electric control cabinet and is connected with a wind speed signal, a power signal, a pressure signal and a temperature signal which are acquired by the fan system.
The wind generating set is an existing wind generating set and comprises a whole fan system, a fan transmission chain, a cabin and the like.
The vibration acceleration sensor, the microphone sensor, the shaking degree sensor, the tachometer, the wind speed, power, pressure, temperature and other sensors in the fan system and the signal switching device connected with the sensors are all subordinate to the sensor unit.
The signal switching device of the sensor unit is connected with the intelligent acquisition unit through a shielded cable, the intelligent acquisition unit is connected with the Ethernet switch through an optical fiber ring network on the fan, and the Ethernet switch is connected with the field server through a local area network.
The site servers of each wind farm are provided with site monitoring software, and are connected with the software platform of the main server through an Interent network by a network cable connecting network gate;
the power supply module comprises a 24V power supply module and a UPS. The 24V power module supplies power to the sensor unit and the intelligent acquisition unit, and the UPS supplies power to the main server and the field server.
As shown in fig. 2:
the field monitoring software comprises a parameter setting module, a data acquisition module, a data processing module, a data storage module and a data transmission module.
The parameter setting module defines acquisition parameters and sets a calibration period to automatically calibrate the sensor; alarm definitions such as kurtosis, total value, effective value, envelope value and the like are defined for vibration signals, online correction is supported, and alarm limit values are set for other signals; the sampling parameter setting comprises sampling frequency, sampling point number, spectral line number and the like; the parameter setting of the transmission chain comprises the correlation of all bearing models, the tooth numbers of all levels of the gear box, the pole pair number of the generator and the like with the rotating speed signal, the automatic calculation of the characteristic frequency of the bearing, the meshing frequency and the rotating frequency of all levels of the gear box, the electrical characteristic frequency of the generator and the like; the data acquisition module reads the data uploaded by the intelligent acquisition unit according to the acquisition parameters defined by the parameter setting module; the data processing module comprises basic signal processing functions of alarm parameter trend analysis, time frequency analysis, envelope analysis and the like; the data storage module stores the data uploaded by the intelligent acquisition unit on a field server and stores the data in the formats of TXT, EXCEL, TDMS and the like; the data transmission module synchronously compresses and transmits the data uploaded by the intelligent acquisition unit.
As shown in fig. 3:
the software platform is installed on the main server and comprises a cluster control module, a data receiving module, a display storage module, a data mining module, a data analysis module, an operation and maintenance management module and a data statistics module.
The software platform architecture is developed based on LabVIEW, and the software platform realizes the driving of each functional module.
The cluster control module is used for remotely controlling field monitoring software of each wind farm, carrying out operations such as parameter setting and the like, managing and distributing communication ports of each wind farm, issuing operation designation, receiving response signals and triggering the data receiving module;
the data receiving module reads and decompresses the unit state data uploaded by the field monitoring software according to the instruction of the cluster control module, provides an open interface which can be accessed to the data uploaded by other manufacturer software, and can also be accessed to enterprise management systems such as ERP, CRM, SCM and the like, and has strong compatibility;
the display storage module has the functions of displaying a wind field distribution diagram, a wind field unit distribution diagram, a data statistical analysis chart and the like, can perform high-compression-ratio processing on data, and stores unit state data read by the data receiving module;
and the data mining module reads the original data in the data storage module and performs decompression and post-processing. The method comprises various algorithm models such as machine learning, pattern recognition, regression analysis and expert system, useful information in mass data is retrieved, and intelligent functions of a data analysis module are enriched.
The data analysis module comprises an alarm setting unit, a data processing unit and an intelligent diagnosis unit, and can read original data uploaded by field monitoring software in real time, perform manual diagnosis and perform secondary processing on historical data. The alarm setting unit sets alarm limit values of all parameters, sets kurtosis, total value, effective value, envelope value and the like aiming at the alarm value of the vibration signal, and supports online correction; the data processing unit mainly comprises various data processing functions such as trend analysis, time-frequency analysis, envelope analysis, waterfall graph, order tracking, long-time waveform and the like; the intelligent diagnosis unit is triggered by an alarm value to perform impact retrieval, frequency positioning and map identification according to a pre-implanted failure mode and failure information uploaded by the data mining module, automatically judges failure positions and failure severity and supports manual secondary confirmation.
The operation and maintenance management module comprises a wind field information management unit, an operation and maintenance personnel management unit, an operation and maintenance scheme library and a spare part storage library; the wind field information management unit comprises wind field basic information, a unit running state and a unit maintenance condition; the wind farm operation and maintenance personnel management unit comprises personnel basic information, a skill matrix and a real-time state; the operation and maintenance scheme library comprises information such as processing schemes and safety measures related to unit maintenance and replacement; the spare part storage warehouse comprises group spare part storage, single wind farm spare part storage and a spare part purchasing period;
the diagnosis result of the data analysis module triggers the operation and maintenance management module, the operation and maintenance scheme library is triggered according to the operation state and the fault form of the wind field unit to upload the corresponding operation and maintenance scheme to the mailbox of the wind field management personnel, meanwhile, the operation and maintenance personnel management unit is triggered to notify the operation and maintenance personnel preparation tool which is idle at present and has the maintenance skill to go to the wind field for maintenance, the spare part storage library is updated in real time according to the maintenance state of the wind field unit, the spare part storage is guaranteed to be reasonable, and the purchase period is shortened.
The data statistics module reads a diagnosis result generation report of the data analysis module, reads maintenance information in the operation and maintenance management module, and generates a wind field operation and maintenance report, a unit fault distribution report, a high-occurrence fault statistics report, a batch fault statistics report and the like, wherein the report forms comprise EXCEL, WORD, PDF and the like.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. The utility model provides a wind-powered electricity generation fortune dimension platform based on big data which characterized in that: it includes:
the intelligent monitoring system comprises an intelligent monitoring module, a power module and a software platform, wherein the power module comprises a 24V power module and a UPS (uninterrupted power supply), the software platform is installed on a main server, and the software platform comprises a cluster control module, a data receiving module, a display storage module, a data mining module, a data analysis module, an operation and maintenance management module and a data statistics module;
the intelligent monitoring module comprises a sensor unit, an intelligent acquisition unit, an Ethernet switch, a network gate and a field server, wherein the sensor unit is connected with the intelligent acquisition unit through a shielded cable, the intelligent acquisition unit is connected with the Ethernet switch, and the Ethernet switch is connected with the field server;
the field server is provided with field monitoring software which comprises a parameter setting module, a data acquisition module, a data processing module, a data storage module and a data transmission module;
the field server is connected to the main server software platform through an Interent network, the software platform architecture is developed based on LabVIEW, and the software platform realizes the driving of each functional module;
the parameter setting module defines acquisition parameters and sets a calibration period to automatically calibrate the sensor; defining kurtosis, a total value, an effective value and an envelope value aiming at a vibration signal alarm limit value, carrying out online correction, and setting alarm limit values aiming at other signals;
the data acquisition module reads the data uploaded by the intelligent acquisition unit according to the acquisition parameters defined by the parameter setting module;
the data processing module comprises alarm parameter trend analysis, time frequency analysis and envelope analysis;
the data storage module stores the data uploaded by the intelligent acquisition unit on a field server and stores the data in a format of TXT, EXCEL or TDMS;
the data transmission module synchronously compresses and transmits the data uploaded by the intelligent acquisition unit;
the cluster control module remotely controls field monitoring software of each wind farm, performs parameter setting operation, manages and distributes communication ports of each wind farm, issues an operation instruction, receives a response signal and triggers the data receiving module;
the data receiving module reads and decompresses the unit state data uploaded by the field monitoring software according to the instruction of the cluster control module, provides an open interface which can be accessed to the data uploaded by other manufacturer software and can be accessed to an enterprise management system;
the display storage module displays a wind field distribution diagram, a wind field unit distribution diagram and a data statistical analysis chart, performs high compression ratio processing on the data, and stores the unit state data read by the data receiving module;
the data mining module reads the original data in the data storage module and carries out decompression and post-processing; the data mining module comprises a machine learning module, a pattern recognition module, a regression analysis module and an expert system algorithm model, and useful information in the mass data is retrieved;
the data analysis module comprises an alarm setting unit, a data processing unit and an intelligent diagnosis unit, and can read original data uploaded by field monitoring software in real time, perform manual diagnosis and perform secondary processing on historical data;
the alarm setting unit sets alarm limit values of all parameters, sets kurtosis, a total value, an effective value and an envelope value aiming at the alarm limit values of the vibration signals, and supports online correction;
the data processing unit comprises trend analysis, time-frequency analysis, envelope analysis, a waterfall graph, order tracking and a long-time waveform;
the intelligent diagnosis unit is triggered by an alarm limit value to perform impact retrieval, frequency positioning and map identification according to a pre-implanted failure mode and failure information uploaded by the data mining module, automatically judge a failure part and failure severity and support manual secondary confirmation;
the operation and maintenance management module comprises a wind field information management unit, an operation and maintenance personnel management unit, an operation and maintenance scheme library and a spare part storage library;
the wind field information management unit comprises wind field basic information, a unit running state and a unit maintenance condition;
the operation and maintenance personnel management unit comprises personnel basic information, a skill matrix and a real-time state;
the operation and maintenance scheme library comprises processing schemes and safety measure information related to unit maintenance and replacement;
the spare part storage warehouse comprises group spare part storage, single wind farm spare part storage and a spare part purchasing period;
the diagnosis result of the data analysis module triggers the operation and maintenance management module, the operation and maintenance scheme library is triggered according to the operation state and the fault form of the wind field unit to upload the corresponding operation and maintenance scheme to a wind field manager mailbox, meanwhile, the operation and maintenance personnel management unit is triggered to notify an operation and maintenance personnel preparation tool which is idle at present and has maintenance skills to go to the wind field for maintenance, and the spare part storage library is updated in real time according to the maintenance state of the wind field unit;
the data statistics module reads a diagnosis result generation report of the data analysis module, reads maintenance information in the operation and maintenance management module, and generates a wind field operation and maintenance report, a unit fault distribution report, a high-occurrence fault statistics report and a batch fault statistics report, wherein the report forms comprise EXCEL, WORD and PDF.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110173399B (en) * | 2019-06-06 | 2023-07-04 | 上海电力学院 | Bolt loosening detection system and method for offshore wind turbine generator set |
| CN110469462B (en) * | 2019-08-21 | 2020-08-18 | 北京天泽智云科技有限公司 | Wind turbine generator system intelligent state monitoring system based on multiple templates |
| CN111322206B (en) * | 2020-02-28 | 2021-05-04 | 唐智科技湖南发展有限公司 | Intelligent operation and maintenance system and method for large mechanical part of wind turbine generator |
| CN114137925B (en) * | 2020-04-24 | 2024-04-02 | 山东省邱集煤矿有限公司 | Intelligent management system for coal mine production safety |
| CN111478979B (en) * | 2020-05-26 | 2022-04-12 | 国电联合动力技术有限公司 | Reliable wind power data acquisition method and system |
| CN111878322B (en) * | 2020-08-03 | 2021-04-20 | 广东工业大学 | Wind power generator device |
| CN114323642A (en) * | 2020-09-29 | 2022-04-12 | 北京金风慧能技术有限公司 | Wind turbine generator vibration data processing system and data dilution method |
| CN112529365B (en) * | 2020-11-19 | 2023-08-18 | 华电电力科学研究院有限公司 | Wind power generation company spare part reserve analysis management device and method |
| CN112594142B (en) * | 2020-11-23 | 2022-04-12 | 东方电气集团科学技术研究院有限公司 | Terminal cloud collaborative wind power operation and maintenance diagnosis system based on 5G |
| CN113482863B (en) * | 2021-07-16 | 2022-07-01 | 南京安维士传动技术股份有限公司 | State evaluation and health management system of wind generating set |
| CN116628437A (en) * | 2023-04-13 | 2023-08-22 | 南京轩果基础建筑工程有限公司 | Data monitoring method for sewage circulation deep purification treatment |
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| CN102621971B (en) * | 2012-04-17 | 2014-04-30 | 上海探能实业有限公司 | Sharing maintenance system ensuring normal operation of wind turbines and realization method thereof |
| CN106471247B (en) * | 2014-06-24 | 2019-06-28 | Ntn株式会社 | Condition monitoring system and the wind generator system for using the system |
| CN106662072B (en) * | 2014-11-18 | 2019-10-25 | Abb瑞士股份有限公司 | Wind-driven generator method for monitoring state and system |
| CN108252873B (en) * | 2017-05-18 | 2020-09-04 | 北京铭峰科技有限公司 | System for wind generating set on-line data monitoring and performance evaluation |
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