CN107061183A - A kind of automation method for diagnosing faults of offshore wind farm unit - Google Patents

A kind of automation method for diagnosing faults of offshore wind farm unit Download PDF

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Publication number
CN107061183A
CN107061183A CN201710032036.1A CN201710032036A CN107061183A CN 107061183 A CN107061183 A CN 107061183A CN 201710032036 A CN201710032036 A CN 201710032036A CN 107061183 A CN107061183 A CN 107061183A
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China
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data
analysis
signal
sensor
automation
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Pending
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CN201710032036.1A
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Chinese (zh)
Inventor
黄林冲
梁禹
黄帅
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Sun Yat Sen University
National Sun Yat-sen University
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National Sun Yat-sen University
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Priority to CN201710032036.1A priority Critical patent/CN107061183A/en
Publication of CN107061183A publication Critical patent/CN107061183A/en
Pending legal-status Critical Current

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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05BINDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00Function
    • F05B2260/80Diagnostics

Abstract

The invention discloses a kind of automation method for diagnosing faults of offshore wind farm unit, it the described method comprises the following steps:The sensor for wanting monitoring project is installed in the sea turn electric machine structure to be diagnosed;The signal extracted to sensor is pre-processed, and removes the useless or interference signal extracted and contained in signal;Specific data analysis is carried out to the described pretreated later signal of progress, eliminated the false and retained the true;Signal is carried out into the fault threshold that gained amplitude is set with data analysis after data analysis to be analyzed, it is then determined that whether wind turbine structure is in failure;Determined to use corresponding counter-measure according to the situation of failure.The present invention can carry out instant alarming, accident analysis to offshore wind farm operating states of the units and be predicted alarm to potential failure, have important value for unnecessary loss of reduction Wind turbines catastrophic failure, reduction etc..

Description

A kind of automation method for diagnosing faults of offshore wind farm unit
Technical field
The present invention relates to a kind of method for diagnosing faults, more particularly to a kind of automation fault diagnosis side of offshore wind farm unit Method.
Background technology
Failure when wind power generating set is run is to influence one of key of wind-powered electricity generation operation enterprise benefit.In general, wind Group of motors operation troubles is to determine the key factor that can unit run steadily in the long term, and its mechanism of production is complicated, is related to factor many It is many.The operation maintenance of current wind power plant mainly unit master control system monitor and regular visit by way of understand the fortune of unit Row state.Due to constituting containing much information for annex running status, calculation process process is complicated, especially for high sample frequency institute The processing of the mass data of acquisition needs cycle regular hour;And fault diagnosis afterwards is often not as good as retrieving failure to wind-powered electricity generation The damage that unit is caused.Therefore, Wind turbines on-line monitoring, Realtime Alerts and fault diagnosis system are built, is run for monitoring Operating mode, take appropriate measures in time, prevent and reduce the generation of unit failure, and by depth data analysis to carry out failure pre- It is alert, pre-emptive maintenace is realized, is all highly important.
The content of the invention
It is an object of the invention to overcome the defect of prior art to be examined there is provided a kind of automation failure of offshore wind farm unit Disconnected method, using wireless sensing data communication network, with reference to advanced computer information technology, to solve prior art and system pair The problem of real time fail is alarmed not in time, realize to the trend analysis of offshore wind farm operating states of the units and fault pre-alarming.
To achieve these goals, present invention employs following technical scheme:
A kind of automation method for diagnosing faults of offshore wind farm unit, it is characterised in that the diagnostic method comprises the following steps:
A, the sensor for wanting in the offshore wind farm units' installation to be diagnosed monitoring project;
B, the signal extracted to sensor are pre-processed, and remove the useless or interference signal extracted and contained in signal;
C, specific data analysis is carried out to the pretreated later signal of described progress, eliminated the false and retained the true;
D, the fault threshold for setting institute's value and data analysis after signal progress data analysis are analyzed, then really Whether Wind turbines are determined in failure;
If e, income analysis data break down exceedes set fault threshold, system is alarmed, it is then determined that wind Mechanism and the fault signature of appearance that motor breaks down, and then determine location of fault occur, decision-making is finally carried out, decision-making is entered Row maintenance down is to continue with monitoring;If do not broken down, system is not alarmed, then can proceed inspection with decision-making Survey;If having partial data to show in income analysis data, there is slight failure or mill in possible machine Wind turbines Damage, then can be given warning in advance or protected in advance according to trouble location and associated components.
Sensor in the step a includes low frequency piezoelectric acceleration sensor, is mainly used in monitoring the master of generating set The input shaft bearing of axle and gear-box;Universal piezoelectric acceleration sensor, is mainly used in monitoring gearbox planetary train, output shaft With the vibration signal of dynamo bearing;Speed probe, is mainly used in measuring the rotating speed of blower fan main shaft and turning for generator amature Speed, strain transducer and displacement transducer, are respectively used to the displacement of blade and tower, topple.
Self-organized network communication module is installed additional in the existing digital interface of described sensor and communication protocol.
The wireless data acquisition system that the diagnostic method is used includes data acquisition device and short-distance wireless receives hair Injection device, the ZigBee module of IEEE 802.15.4 wireless protocols is met built in the data acquisition device, can be to being gathered Data pre-processed, reject substantial amounts of useless or interference data, and processing data is transferred to short-distance wireless connect Receive and dispatch injection device.The self-organized network communication module installed additional in described wireless transmitting and receiving device and sensor is filled by data acquisition Put and configure one by one, form the wireless distributed communication network of a pair of multiple spots, described wireless distributed network system can timing or The automatic data collection to measuring point real time data is realized at any time, and described data acquisition device has dormancy and backstage arousal function, energy Enough by the original loading interface agreement of situ configuration or data management end, background acquisition is performed in real time and instruction is sent, wireless hair Penetrate after reception device receives the data that data acquisition device is sent and Users'Data Analysis platform is transferred to by WCDMA.
Data Analysis Services system in the diagnosis algorithm c is the Data Analysis Platform based on LabVIEW.
Described Data Analysis Platform method includes:Vibration time domain parameter analysis includes the amplitude domain analysis and signal of signal Time-domain analysis;Vibration signal frequency-domain analysis includes the power spectrum of FFT spectrum analysis, the cepstrum analysis of signal and the signal of signal Analysis;Signal rank spectrum analysis;Signal wavelet analysis.
The automation method for diagnosing faults for the Wind turbines that the present invention is provided can on-line real time monitoring Wind turbines operation State, and can when failure occurs instant alarming, it is to avoid the serious consequence brought not in time by fault alarm;Meanwhile, pass through The data analysis carries out anticipation with fault diagnosis module to running of wind generating set state, is diagnosed to be and has existed or will produce Running of wind generating set or component failure, in time remind relevant unit maintained, raising the running of wind generating set life-span, The fault rate of running of wind generating set is reduced, is reduced because of the economic loss that Wind turbines failure is brought.
Brief description of the drawings
The present invention is described in further detail with reference to the accompanying drawings and detailed description.
Fig. 1 is the cardinal principle figure of the automation method for diagnosing faults of offshore wind farm unit of the present invention;
Fig. 2 is the structural representation of the automation method for diagnosing faults of offshore wind farm unit of the present invention.
Embodiment
A kind of automation method for diagnosing faults of disclosed offshore wind farm unit is logical using wireless sensing data Believe net, with reference to advanced computer information technology, to solve the problem of real time fail is alarmed not in time for prior art and system, Realize to the trend analysis of offshore wind farm operating states of the units and fault pre-alarming.
As shown in figure 1, a kind of automation method for diagnosing faults of disclosed offshore wind farm unit, methods described Key step include:
A, the sensor for wanting in the offshore wind farm units' installation to be diagnosed monitoring project;
B, the signal extracted to sensor are pre-processed, and remove the useless or interference signal extracted and contained in signal;
C, specific data analysis is carried out to the pretreated later signal of described progress, eliminated the false and retained the true;
D, the fault threshold for setting gained amplitude and data analysis after signal progress data analysis are analyzed, then really Whether Wind turbines are determined in failure;
If e, income analysis data break down exceedes set fault threshold, system is alarmed, it is then determined that wind Mechanism and the fault signature of appearance that motor breaks down, and then determine location of fault occur, decision-making is finally carried out, decision-making is entered Row maintenance down is to continue with monitoring;If do not broken down, system is not alarmed, then can proceed inspection with decision-making Survey;If having partial data to show in income analysis data, there is slight failure or mill in possible machine Wind turbines Damage, then can be given warning in advance or protected in advance according to trouble location and associated components.
As shown in Fig. 2 a kind of specific reality of the automation method for diagnosing faults of disclosed offshore wind farm unit Example is applied, the part to be monitored is installed into corresponding sensor first, low frequency piezoelectric acceleration sensor is such as arranged on hair At the main shaft of group of motors and the input shaft bearing of gear-box, universal piezoelectric acceleration sensor is arranged on gear case planet wheel At system, output shaft and dynamo bearing, speed probe is arranged at blower fan main shaft and generator amature, by strain transducer Tip, middle part and root with blade is installed, displacement transducer is separately mounted to tower both vertically and horizontally; The existing digital interface of the sensor and communication protocol install self-organized network communication module additional.
Data obtained by monitoring are transferred to wireless data acquisition system by sensor.
Wireless data acquisition system is pre-processed to the data of reception, rejects substantial amounts of useless or interference data, And transfer data to short-distance wireless receiving and transmitting unit.
Above-mentioned wireless data acquisition system includes data acquisition device and short-distance wireless receiving and transmitting unit, the number According to the ZigBee module for meeting IEEE 802.15.4 wireless protocols built in harvester, wireless transmitting and receiving device and sensor In the self-organized network communication module that installs additional configured one by one by data acquisition device, form the wireless distributed communication of a pair of multiple spots Net, wireless distributed network system timing or can realize automatic data collection to measuring point real time data at any time, data acquisition device With dormancy and backstage arousal function, can in real time it be performed by the original loading interface agreement of situ configuration or data management end Background acquisition and transmission are instructed, and wireless transmitting and receiving device receives long-range by WCDMA after the data that data acquisition device is sent It is transferred to Users'Data Analysis platform.
Above-mentioned Users'Data Analysis platform is the Data Analysis Platform based on LabVIEW, and the data that received can be entered Row storage, inquiry and analysis, LabVIEW data analysis systems are by including time-domain analysis, frequency-domain analysis and other analysis methods The fault threshold that data are carried out with the Wind turbines that the numeral needed for being obtained after analyzing is set in itself with system is carried out to score Analysis, it is then determined that whether Wind turbines are faulty.
When faulty, startup separator warning system, the number for the warning system that staff can then be provided according to system According to the feature and its concrete reason and position for learning Wind turbines launching failure, to determine to take corresponding counter-measure to repair Failure;When fault-free, and staff can also judge whether Wind turbines have mild wear according to the data of analysis system Or it is abnormal, and then take corresponding protection and counter-measure in advance;When fault-free and each item data is in normal range (NR), then may be used System is allowed to continue the decision-making monitored to take.
The technology contents and technical characteristic of the present invention have revealed that as above those skilled in the art are still potentially based on this The teaching of invention and make the replacement and modification without departing substantially from essence of the invention, therefore, the scope of the present invention is not limited to embodiment Disclosed content, also including various replacements and modification without departing substantially from essence of the invention.

Claims (6)

1. a kind of automation method for diagnosing faults of offshore wind farm unit, it is characterised in that the diagnostic method includes following step Suddenly:
A, the sensor for wanting in the offshore wind farm units' installation to be diagnosed monitoring project;
B, the signal extracted to sensor are pre-processed, and remove the useless or interference signal extracted and contained in signal;
C, specific data analysis is carried out to the pretreated later signal of described progress, eliminated the false and retained the true;
D, the fault threshold for setting institute's value and data analysis after signal progress data analysis are analyzed, then really Whether Wind turbines are determined in failure;
If e, income analysis data break down exceedes set fault threshold, system is alarmed, it is then determined that wind Mechanism and the fault signature of appearance that motor breaks down, and then determine location of fault occur, decision-making is finally carried out, decision-making is entered Row maintenance down is to continue with monitoring;If do not broken down, system is not alarmed, then can proceed inspection with decision-making Survey;If having partial data to show in income analysis data, there is slight failure or mill in possible machine Wind turbines Damage, then can be given warning in advance or protected in advance according to trouble location and associated components.
2. automation method for diagnosing faults according to claim 1, it is characterised in that:Sensor bag in the step a Low frequency piezoelectric acceleration sensor is included, is mainly used in monitoring the main shaft of generating set and the input shaft bearing of gear-box;Universal pressure Electric acceleration transducer, is mainly used in monitoring the vibration signal of gearbox planetary train, output shaft and dynamo bearing;Rotating speed is passed Sensor, is mainly used in monitoring the rotating speed of blower fan main shaft and the rotating speed of generator amature;Strain transducer and displacement transducer, respectively Displacement for blade and tower, topple.
3. automation method for diagnosing faults according to claim 2, it is characterised in that:In the existing number of described sensor Word interface and communication protocol install self-organized network communication module additional.
4. automation method for diagnosing faults according to claim 1, it is characterised in that:It is wireless that the diagnostic method is used Data collecting system includes data acquisition device and short-distance wireless receiving and transmitting unit, meets built in the data acquisition device The data gathered can be pre-processed by the ZigBee module of IEEE 802.15.4 wireless protocols, be rejected substantial amounts of useless Or interference data, and processing data is transferred to short-distance wireless receiving and transmitting unit;Described wireless transmitter receiver dress Put and configured one by one by data acquisition device with the self-organized network communication module installed additional in sensor, form wireless point of a pair of multiple spots Cloth communication network, described wireless distributed network system can timing or realize at any time the automatic of measuring point real time data is adopted Collection, described data acquisition device has dormancy and backstage arousal function, can be original by situ configuration or data management end Loading interface agreement, performs background acquisition and sends instruction, wireless transmitting and receiving device receives data acquisition device and sent in real time Data after Users'Data Analysis platform is transferred to by WCDMA.
5. automation method for diagnosing faults according to claim 1, it is characterised in that:Data in the diagnosis algorithm c Analysis process system is the Data Analysis Platform based on LabVIEW.
6. method for diagnosing faults is automated according to claim 1 or 5, it is characterised in that:Described Data Analysis Platform Method includes:Vibration time domain parameter analysis includes the time-domain analysis of the amplitude domain analysis and signal of signal;Vibration signal frequency-domain analysis The power spectrumanalysis of FFT spectrum analysis, the cepstrum analysis of signal and signal including signal;Signal rank spectrum analysis;Signal Wavelet analysis.
CN201710032036.1A 2017-01-17 2017-01-17 A kind of automation method for diagnosing faults of offshore wind farm unit Pending CN107061183A (en)

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Cited By (8)

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CN107909157A (en) * 2017-10-31 2018-04-13 中海油能源发展股份有限公司 Offshore oilfield moves device clusters monitoring diagnosis system
CN108020406A (en) * 2017-11-21 2018-05-11 常州大学 A kind of veneer reeling machine health status monitoring and source of early warning
CN109269801A (en) * 2018-09-27 2019-01-25 中北大学 A kind of gearbox fault monitoring device for wind-powered electricity generation
CN109444709A (en) * 2018-09-07 2019-03-08 南京理工大学 Wind turbines Design of Test System method based on virtual instrument technology
CN109580217A (en) * 2018-09-27 2019-04-05 中北大学 A kind of fault monitoring method of wind turbine gearbox
CN110362045A (en) * 2019-06-14 2019-10-22 上海电力学院 A kind of marine double-fed fan motor unit fault distinguishing method considering maritime meteorology factor
CN110513252A (en) * 2019-08-30 2019-11-29 湘电风能有限公司 A kind of wind power plant SCADA system data abnormality alarming repair system and method
CN111579001A (en) * 2020-06-02 2020-08-25 珠海格力智能装备有限公司 Fault detection method and device for robot

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CN102156043A (en) * 2010-12-31 2011-08-17 北京四方继保自动化股份有限公司 Online state monitoring and fault diagnosis system of wind generator set
CN102200186A (en) * 2011-05-10 2011-09-28 大连理工大学 Remote on-line state monitoring and fault diagnosis system of gear box of wind generating set
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107909157A (en) * 2017-10-31 2018-04-13 中海油能源发展股份有限公司 Offshore oilfield moves device clusters monitoring diagnosis system
CN108020406A (en) * 2017-11-21 2018-05-11 常州大学 A kind of veneer reeling machine health status monitoring and source of early warning
CN109444709A (en) * 2018-09-07 2019-03-08 南京理工大学 Wind turbines Design of Test System method based on virtual instrument technology
CN109269801A (en) * 2018-09-27 2019-01-25 中北大学 A kind of gearbox fault monitoring device for wind-powered electricity generation
CN109580217A (en) * 2018-09-27 2019-04-05 中北大学 A kind of fault monitoring method of wind turbine gearbox
CN110362045A (en) * 2019-06-14 2019-10-22 上海电力学院 A kind of marine double-fed fan motor unit fault distinguishing method considering maritime meteorology factor
CN110362045B (en) * 2019-06-14 2021-07-16 上海电力学院 Marine doubly-fed wind turbine generator fault discrimination method considering marine meteorological factors
CN110513252A (en) * 2019-08-30 2019-11-29 湘电风能有限公司 A kind of wind power plant SCADA system data abnormality alarming repair system and method
CN111579001A (en) * 2020-06-02 2020-08-25 珠海格力智能装备有限公司 Fault detection method and device for robot

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