CN109763944A - A kind of contactless monitoring system of offshore wind turbine blade fault and monitoring method - Google Patents
A kind of contactless monitoring system of offshore wind turbine blade fault and monitoring method Download PDFInfo
- Publication number
- CN109763944A CN109763944A CN201910081150.2A CN201910081150A CN109763944A CN 109763944 A CN109763944 A CN 109763944A CN 201910081150 A CN201910081150 A CN 201910081150A CN 109763944 A CN109763944 A CN 109763944A
- Authority
- CN
- China
- Prior art keywords
- wind turbine
- aeroacoustics
- offshore wind
- acquisition
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000005540 biological transmission Effects 0.000 claims abstract description 9
- 238000012806 monitoring device Methods 0.000 claims abstract description 9
- 238000004891 communication Methods 0.000 claims abstract description 6
- 239000003643 water by type Substances 0.000 claims abstract description 5
- 238000003062 neural network model Methods 0.000 claims description 12
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 4
- 230000004927 fusion Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 230000004888 barrier function Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000001556 precipitation Methods 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 6
- 238000012423 maintenance Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005684 electric field Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
Abstract
The invention discloses a kind of contactless monitoring system of offshore wind turbine blade fault and monitoring methods, the system includes being arranged in offshore wind turbine foundation, meteorological information collection system on column foot or independent platform, it is arranged in the hydrological information collection system of offshore wind turbine surrounding waters, the aeroacoustics acquisition system being arranged on offshore wind turbine foundation or pylon, meteorological information collection system, hydrological information collection system and aeroacoustics acquisition system real-time data transmission collected are to acquisition node, wireless terminal transmission to data center on the bank is passed through by acquisition node again, data center, pass through Industrial Ethernet connection communication between monitoring system and monitoring device.Monitoring system disclosed in this invention does not need the original structure for destroying fan blade, while facilitating the maintenance of detection device yet.Both it had been able to satisfy traditional monitoring room detection requirement, while there are also remote computers and handheld device can be convenient on-site maintenance.
Description
Technical field
The invention belongs to instrument fields, in particular to sea of one of the field based on aeroacoustics signal characteristic
The contactless monitoring system of upper fan blade fault and monitoring method.
Background technique
Traditional energy is the sun it is impossible to meet the sustainable development of resource and environment requirement, the final source of wind energy resources
Can, it is inexhaustible, it is increasingly valued by people and pays close attention to, the progress of wind generating technology and maturation make
Offshore wind farm becomes the important directions of wind generating technology development.
However, since marine wind electric field is generally built in intertidal zone or offshore sea waters, high construction cost, complicated ocean ring
Border increases the operation and maintenance difficulties of offshore wind turbine.The device that fan blade is obtained as wind energy, reliability directly affect
The power generating quality and efficiency of offshore wind farm unit, in addition, the more land fan blade size of offshore wind turbine blade is bigger, cost more
Height, correspondingly, occur after catastrophe failure it is destructive it is bigger, economic loss is also higher.However, at present both at home and abroad about fan blade
Fault detection and diagnosis method remains in the more rudimentary stage.Therefore, develop it is a kind of fan blade break down just
Phase is detected in time is out of order and accurately identifies fault type, provides the real time on-line monitoring of support and foundation for the exclusion of failure
System and monitoring method have important value.
The needs of sensor used in existing fan blade On-line Fault Detection mode are installed in fan blade in advance,
On the one hand, this can have an adverse effect to the structure of fan blade, reduce the intensity of blade, increase the manufacture difficulty of blade；Separately
On the one hand, for prefabricated sensor once breaking down, maintenance cost is very high.
Summary of the invention
The technical problem to be solved by the invention is to provide a kind of offshore wind turbine leaves based on aeroacoustics signal characteristic
The contactless monitoring system of piece failure and monitoring method.
The present invention adopts the following technical scheme:
A kind of contactless monitoring system of offshore wind turbine blade fault, thes improvement is that: the system includes being arranged in
Meteorological information collection system on offshore wind turbine foundation, column foot or independent platform, is arranged in the hydrology of offshore wind turbine surrounding waters
Information acquisition system, the aeroacoustics acquisition system being arranged on offshore wind turbine foundation or pylon, meteorological information collection system, water
Literary information acquisition system and aeroacoustics acquisition system real-time data transmission collected lead to acquisition node, then by acquisition node
Wireless terminal transmission is crossed to data center on the bank, data center monitors and connects between system and monitoring device by Industrial Ethernet
Connect letter.
Further, the quantity of offshore wind turbine be two or more, every Taiwan Straits upper fan be equipped with meteorological information collection system,
Hydrological information collection system, aeroacoustics acquisition system, acquisition node and wireless terminal.
Further, the meteorological information collection system includes temperature sensor, humidity sensor, wind sensor, rain
Quantity sensor and lightning sensors.
Further, the hydrological information collection system includes Wave Sensor, ocean current sensor and tide sensor.
Further, the aeroacoustics acquisition system includes acoustic sensor and acoustics signal processing unit, acoustics
The measurement range of sensor is 20Hz -20kHz, and the sample frequency of acoustics signal processing unit digital analog converter is not less than
200kHz, acquisition digit are not less than 16, and acquisition channel is no less than 4 channels.
Further, wireless terminal sends data to data center on the bank in the way of microwave communication.
Further, the monitoring device includes central control room's computer monitoring platform, remote computer and hand-held sets
It is standby.
A kind of contactless monitoring method of offshore wind turbine blade fault, using above-mentioned monitoring system, improvements exist
In including the following steps:
(1) weather information is acquired by meteorological information collection system, correction wind is made an uproar, the noise of extreme storm, thunder, the patter of rain is dry
It disturbs；By hydrological information collection system acquisition hydrographic information, the noise jamming of marine wave, stream, tide is corrected；It is adopted by aeroacoustics
The aeroacoustics information of collecting system acquisition fan blade；Collection process is controlled by acquisition node and is passed through acquisition data wirelessly eventually
End is transmitted to data center on the bank；
(2) the aeroacoustics information of weather information, hydrographic information and fan blade is subjected to the fusion of decision level information, corrects ring
Interference of the border noise to aeroacoustics information collection, the multiple dimensioned sample by computational aeroacoustics information based on variation mode decomposition
This entropy, i.e., the Sample Entropy of extraction time sequence on different scale are constructed the state set of eigenvectors of fan blade with this, passed through
The time-frequency characteristics of building aeroacoustics information, the multiple dimensioned Sample Entropy that will be established with test sample are extracted in time frequency analyzing tool analysis
Set of eigenvectors, which is input in neural network, to be learnt and is trained, and network weight and structure are constantly updated, and final convergence obtains
The neural network model of fan blade fault identification and classification；
(3) neural network model is downloaded in monitoring system, monitoring system-computed newly collects aeroacoustics information
Time-frequency characteristics are input in the neural network model and are identified and classified, while the neural network model can be according to new spy
Levy timely update weight and neural network structure, the generalization ability of strength neural network；
(4) state and fault message of each Taiwan Straits upper fan blade are provided in real time by monitoring system, in the event of a failure to monitoring
Equipment alert, at the same in monitoring device can with the state of each Taiwan Straits upper fan blade of real time inspection, and therefore
Barrier is handled it when occurring.
Further, aeroacoustics acquisition system removes ocean using adaptive filter method according to the time-frequency characteristic of signal
Environmental background noise interference, obtains pure fan blade aeroacoustics information.
The beneficial effects of the present invention are:
Monitoring system disclosed in this invention does not need the original structure for destroying fan blade, while facilitating detection device yet
Maintenance.Both it had been able to satisfy traditional monitoring room detection requirement, while there are also remote computers and handheld device can be convenient on-site maintenance.
Monitoring method disclosed in this invention comprehensively and accurately describes blower gas by multi-sensor information fusion technology
Moving noise signal and environmental background noise correct interference of the ambient noise to aeroacoustics information collection.Pass through self-adapting signal
Processing method establishes offshore wind turbine blade aerodynamic acoustic feature vector set, is then realized by machine learning method to offshore wind turbine
Blade state monitoring and fault identification.
Detailed description of the invention
Fig. 1 is the composition schematic diagram that system is monitored disclosed in the embodiment of the present invention 1；
Fig. 2 is the flow diagram of monitoring method disclosed in the embodiment of the present invention 1.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, right below in conjunction with drawings and examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Embodiment 1, as shown in Figure 1, present embodiment discloses a kind of contactless monitoring system of offshore wind turbine blade fault,
The system includes the meteorological information collection system being arranged on offshore wind turbine foundation, column foot or independent platform, is arranged in sea
The hydrological information collection system of upper fan surrounding waters, the aeroacoustics acquisition system being arranged on offshore wind turbine foundation or pylon
System, meteorological information collection system, hydrological information collection system and aeroacoustics acquisition system real-time data transmission collected are extremely
Acquisition node, then wireless terminal transmission is passed through to data center on the bank by acquisition node, data center, monitoring system and monitoring are set
Pass through Industrial Ethernet connection communication between standby.
In the present embodiment, the quantity of offshore wind turbine is two or more, and every Taiwan Straits upper fan is equipped with meteorological information acquisition
System, hydrological information collection system, aeroacoustics acquisition system, acquisition node and wireless terminal, so that data center can on the bank
To obtain the data of each Taiwan Straits upper fan, so that it is every so that monitoring system is read marine wind electric field at any time by data center
The every terms of information of Taiwan Straits upper fan.The meteorological information collection system includes temperature sensor, humidity sensor, wind sensing
Device, precipitation rain fall sensor and lightning sensors.The hydrological information collection system includes Wave Sensor, ocean current sensor and tide
Nighttide sensor.The aeroacoustics acquisition system includes acoustic sensor and acoustics signal processing unit, acoustic sensor
Measurement range is 20Hz -20kHz, and the sample frequency of acoustics signal processing unit digital analog converter is not less than 200kHz, acquires position
Number is not less than 16, and for the accuracy for guaranteeing acquisition acoustic signal, acquisition channel is no less than 4 channels.Wireless terminal utilizes microwave
The mode of communication sends data to data center on the bank.The monitoring device includes that central control room's computer monitoring is flat
Platform, remote computer and handheld device, wherein handheld device is intelligent mobile phone equipment, and various monitoring devices are able to achieve blower
The functions such as blade state data check, malfunction monitoring and alert process.
As shown in Fig. 2, the present embodiment also discloses a kind of contactless monitoring method of offshore wind turbine blade fault, in use
The monitoring system stated, includes the following steps:
(1) weather information is acquired by meteorological information collection system, correction wind is made an uproar, the noise of extreme storm, thunder, the patter of rain is dry
It disturbs；By hydrological information collection system acquisition hydrographic information, the noise jamming of marine wave, stream, tide is corrected；It is adopted by aeroacoustics
Collecting system acquires the aeroacoustics information of fan blade, specifically, aeroacoustics acquisition system is by signal in the present embodiment
Variation mode decomposition is carried out, by comparing relevance and subband time-frequency characteristic with fan blade swing circle, removes ocean
The subband of environmental background noise interference, obtains pure fan blade aeroacoustics information；Collection process is controlled by acquisition node
And acquisition data are passed through into wireless terminal transmission to data center on the bank；
(2) the aeroacoustics information of weather information, hydrographic information and fan blade is subjected to the fusion of decision level information, corrects ring
Interference of the border noise to aeroacoustics information collection, the multiple dimensioned sample by computational aeroacoustics information based on variation mode decomposition
This entropy, i.e., the Sample Entropy of extraction time sequence on different scale, not only can metric signal on the whole complexity, but also can be with
The minutia that signal profound level is excavated from different scale, the state set of eigenvectors of fan blade, blower leaf are constructed with this
When piece breaks down, the time-frequency characteristics of aeroacoustics information will change therewith, be analyzed by time frequency analyzing tool and extract structure
The multiple dimensioned Sample Entropy set of eigenvectors established with test sample is input to nerve net by the time-frequency characteristics for building aeroacoustics information
Learnt in network and trained, constantly update network weight and structure, final convergence obtains fan blade fault identification and classification
Neural network model；
(3) neural network model is downloaded in monitoring system, monitoring system-computed newly collects aeroacoustics information
Time-frequency characteristics are input in the neural network model and are identified and classified, while the neural network model can be according to new spy
Levy timely update weight and neural network structure, the generalization ability of strength neural network；
(4) state and fault message of each Taiwan Straits upper fan blade are provided in real time by monitoring system, in the event of a failure to monitoring
Equipment alert, at the same in monitoring device can with the state of each Taiwan Straits upper fan blade of real time inspection, and therefore
Barrier is handled it when occurring.
In the present embodiment, aeroacoustics acquisition system is removed according to the time-frequency characteristic of signal using adaptive filter method
The interference of marine environment ambient noise, obtains pure fan blade aeroacoustics information.
Claims (9)
1. a kind of contactless monitoring system of offshore wind turbine blade fault, it is characterised in that: the system includes being arranged in sea
Meteorological information collection system on upper fan basis, column foot or independent platform is arranged in the hydrology letter of offshore wind turbine surrounding waters
Cease acquisition system, the aeroacoustics acquisition system being arranged on offshore wind turbine foundation or pylon, meteorological information collection system, the hydrology
Information acquisition system and aeroacoustics acquisition system real-time data transmission collected pass through to acquisition node, then by acquisition node
Wireless terminal transmission to data center on the bank, data center monitors and is connected between system and monitoring device by Industrial Ethernet
Communication.
2. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: offshore wind turbine
Quantity be two or more, every Taiwan Straits upper fan is equipped with meteorological information collection system, hydrological information collection system, aeroacoustics
Acquisition system, acquisition node and wireless terminal.
3. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: the gas
Image information acquisition system includes temperature sensor, humidity sensor, wind sensor, precipitation rain fall sensor and lightning sensors.
4. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: the water
Literary information acquisition system includes Wave Sensor, ocean current sensor and tide sensor.
5. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: the gas
Dynamic acoustics acquisition system includes acoustic sensor and acoustics signal processing unit, and the measurement range of acoustic sensor is 20Hz-
20kHz, the sample frequency of acoustics signal processing unit digital analog converter are not less than 200kHz, and acquisition digit is not less than 16, adopts
Collection channel is no less than 4 channels.
6. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: wireless terminal
Data center on the bank is sent data in the way of microwave communication.
7. the contactless monitoring system of offshore wind turbine blade fault according to claim 1, it is characterised in that: the prison
Controlling equipment includes central control room's computer monitoring platform, remote computer and handheld device.
8. a kind of contactless monitoring method of offshore wind turbine blade fault, uses monitoring system described in claim 1, feature
It is, includes the following steps:
(1) weather information is acquired by meteorological information collection system, correction wind is made an uproar, the noise of extreme storm, thunder, the patter of rain is dry
It disturbs；By hydrological information collection system acquisition hydrographic information, the noise jamming of marine wave, stream, tide is corrected；It is adopted by aeroacoustics
The aeroacoustics information of collecting system acquisition fan blade；Collection process is controlled by acquisition node and is passed through acquisition data wirelessly eventually
End is transmitted to data center on the bank；
(2) the aeroacoustics information of weather information, hydrographic information and fan blade is subjected to the fusion of decision level information, corrects ring
Interference of the border noise to aeroacoustics information collection, the multiple dimensioned sample by computational aeroacoustics information based on variation mode decomposition
This entropy, i.e., the Sample Entropy of extraction time sequence on different scale are constructed the state set of eigenvectors of fan blade with this, passed through
The time-frequency characteristics of building aeroacoustics information, the multiple dimensioned Sample Entropy that will be established with test sample are extracted in time frequency analyzing tool analysis
Set of eigenvectors, which is input in neural network, to be learnt and is trained, and network weight and structure are constantly updated, and final convergence obtains
The neural network model of fan blade fault identification and classification；
(3) neural network model is downloaded in monitoring system, monitoring system-computed newly collects aeroacoustics information
Time-frequency characteristics are input in the neural network model and are identified and classified, while the neural network model can be according to new spy
Levy timely update weight and neural network structure, the generalization ability of strength neural network；
(4) state and fault message of each Taiwan Straits upper fan blade are provided in real time by monitoring system, in the event of a failure to monitoring
Equipment alert, at the same in monitoring device can with the state of each Taiwan Straits upper fan blade of real time inspection, and therefore
Barrier is handled it when occurring.
9. the contactless monitoring method of offshore wind turbine blade fault according to claim 8, it is characterised in that: aeroacoustics
Acquisition system is interfered according to the time-frequency characteristic of signal using adaptive filter method removal marine environment ambient noise, is obtained pure
Fan blade aeroacoustics information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910081150.2A CN109763944B (en) | 2019-01-28 | 2019-01-28 | Non-contact monitoring system and monitoring method for blade faults of offshore wind turbine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910081150.2A CN109763944B (en) | 2019-01-28 | 2019-01-28 | Non-contact monitoring system and monitoring method for blade faults of offshore wind turbine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109763944A true CN109763944A (en) | 2019-05-17 |
| CN109763944B CN109763944B (en) | 2021-03-12 |
Family
ID=66454515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910081150.2A Expired - Fee Related CN109763944B (en) | 2019-01-28 | 2019-01-28 | Non-contact monitoring system and monitoring method for blade faults of offshore wind turbine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109763944B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111555437A (en) * | 2020-05-15 | 2020-08-18 | 中国船舶工业系统工程研究院 | Underwater data center powered by offshore wind power |
| CN111596647A (en) * | 2020-06-01 | 2020-08-28 | 国电联合动力技术有限公司 | Efficient and intelligent test system and method for wind turbine generator |
| CN111967486A (en) * | 2020-06-02 | 2020-11-20 | 安徽三禾一信息科技有限公司 | Complex equipment fault diagnosis method based on multi-sensor fusion |
| CN112067701A (en) * | 2020-09-07 | 2020-12-11 | 国电电力新疆新能源开发有限公司 | Fan blade remote auscultation method based on acoustic diagnosis |
| CN112539142A (en) * | 2020-11-12 | 2021-03-23 | 中国电建集团华东勘测设计研究院有限公司 | Analysis and identification method for noise of monitoring data of offshore wind power structure state |
| CN112727707A (en) * | 2021-01-08 | 2021-04-30 | 国家电投集团东北电力有限公司 | Wind driven generator blade monitoring system and method based on wireless attitude sensor |
| CN113154642A (en) * | 2021-02-22 | 2021-07-23 | 河北建投海上风电有限公司 | Dehumidifier control system for offshore wind turbine |
| TWI742959B (en) * | 2020-12-09 | 2021-10-11 | 國立臺灣大學 | Detection equipment, detection system and wind turbine assembly |
| CN113623144A (en) * | 2021-09-01 | 2021-11-09 | 五凌电力有限公司 | Blade state monitoring system based on acoustic algorithm and monitoring method thereof |
| CN114000989A (en) * | 2021-11-30 | 2022-02-01 | 中国华能集团清洁能源技术研究院有限公司 | Method and system for detecting aerodynamic performance attenuation of blades of wind generating set |
| CN114738205A (en) * | 2022-04-28 | 2022-07-12 | 北京千尧新能源科技开发有限公司 | Method, device, equipment and medium for monitoring state of floating fan foundation |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102434388A (en) * | 2011-11-17 | 2012-05-02 | 高丙团 | Health status online monitoring device of wind generating set and monitoring method of monitoring device |
| CN105351152A (en) * | 2015-11-18 | 2016-02-24 | 电子科技大学 | Remote offshore wind power monitoring device based on ZigBee and GPRS techniques |
| CN105649896A (en) * | 2016-01-07 | 2016-06-08 | 太原科技大学 | Intelligent monitoring operation control system for wind turbine unit and control method thereof |
| WO2017198270A1 (en) * | 2016-05-18 | 2017-11-23 | Vestas Wind Systems A/S | Analysis of wind turbine noise |
| CN107829885A (en) * | 2017-10-25 | 2018-03-23 | 西安锐益达风电技术有限公司 | A kind of blade of wind-driven generator vibration monitoring and system for considering ambient parameter amendment |
| CN207195098U (en) * | 2017-07-31 | 2018-04-06 | 上海绿孚科技有限公司 | A kind of blade state monitoring system of the wind power generating set based on acoustic processing |
| CN108760316A (en) * | 2018-08-16 | 2018-11-06 | 苏州大学 | Information fusion method is joined in the change of variation mode decomposition |
-
2019
- 2019-01-28 CN CN201910081150.2A patent/CN109763944B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102434388A (en) * | 2011-11-17 | 2012-05-02 | 高丙团 | Health status online monitoring device of wind generating set and monitoring method of monitoring device |
| CN105351152A (en) * | 2015-11-18 | 2016-02-24 | 电子科技大学 | Remote offshore wind power monitoring device based on ZigBee and GPRS techniques |
| CN105649896A (en) * | 2016-01-07 | 2016-06-08 | 太原科技大学 | Intelligent monitoring operation control system for wind turbine unit and control method thereof |
| WO2017198270A1 (en) * | 2016-05-18 | 2017-11-23 | Vestas Wind Systems A/S | Analysis of wind turbine noise |
| CN207195098U (en) * | 2017-07-31 | 2018-04-06 | 上海绿孚科技有限公司 | A kind of blade state monitoring system of the wind power generating set based on acoustic processing |
| CN107829885A (en) * | 2017-10-25 | 2018-03-23 | 西安锐益达风电技术有限公司 | A kind of blade of wind-driven generator vibration monitoring and system for considering ambient parameter amendment |
| CN108760316A (en) * | 2018-08-16 | 2018-11-06 | 苏州大学 | Information fusion method is joined in the change of variation mode decomposition |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111555437A (en) * | 2020-05-15 | 2020-08-18 | 中国船舶工业系统工程研究院 | Underwater data center powered by offshore wind power |
| CN111596647A (en) * | 2020-06-01 | 2020-08-28 | 国电联合动力技术有限公司 | Efficient and intelligent test system and method for wind turbine generator |
| CN111596647B (en) * | 2020-06-01 | 2021-08-06 | 国电联合动力技术有限公司 | Efficient and intelligent test system and method for wind turbine generator |
| CN111967486A (en) * | 2020-06-02 | 2020-11-20 | 安徽三禾一信息科技有限公司 | Complex equipment fault diagnosis method based on multi-sensor fusion |
| CN112067701A (en) * | 2020-09-07 | 2020-12-11 | 国电电力新疆新能源开发有限公司 | Fan blade remote auscultation method based on acoustic diagnosis |
| CN112067701B (en) * | 2020-09-07 | 2024-02-02 | 国电电力新疆新能源开发有限公司 | Fan blade remote auscultation method based on acoustic diagnosis |
| CN112539142B (en) * | 2020-11-12 | 2022-09-13 | 中国电建集团华东勘测设计研究院有限公司 | Analysis and identification method for noise of monitoring data of offshore wind power structure state |
| CN112539142A (en) * | 2020-11-12 | 2021-03-23 | 中国电建集团华东勘测设计研究院有限公司 | Analysis and identification method for noise of monitoring data of offshore wind power structure state |
| TWI742959B (en) * | 2020-12-09 | 2021-10-11 | 國立臺灣大學 | Detection equipment, detection system and wind turbine assembly |
| CN112727707A (en) * | 2021-01-08 | 2021-04-30 | 国家电投集团东北电力有限公司 | Wind driven generator blade monitoring system and method based on wireless attitude sensor |
| CN113154642A (en) * | 2021-02-22 | 2021-07-23 | 河北建投海上风电有限公司 | Dehumidifier control system for offshore wind turbine |
| CN113623144A (en) * | 2021-09-01 | 2021-11-09 | 五凌电力有限公司 | Blade state monitoring system based on acoustic algorithm and monitoring method thereof |
| CN114000989A (en) * | 2021-11-30 | 2022-02-01 | 中国华能集团清洁能源技术研究院有限公司 | Method and system for detecting aerodynamic performance attenuation of blades of wind generating set |
| CN114738205A (en) * | 2022-04-28 | 2022-07-12 | 北京千尧新能源科技开发有限公司 | Method, device, equipment and medium for monitoring state of floating fan foundation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109763944B (en) | 2021-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109763944A (en) | A kind of contactless monitoring system of offshore wind turbine blade fault and monitoring method | |
| WO2022077605A1 (en) | Wind turbine blade image-based damage detection and localization method | |
| CN110837137A (en) | Typhoon prediction alarm method | |
| CN103953490A (en) | Implementation method for monitoring status of hydraulic turbine set based on HLSNE | |
| CN113298134B (en) | System and method for remotely and non-contact health monitoring of fan blade based on BPNN | |
| CN114993669B (en) | Multi-sensor information fusion transmission system fault diagnosis method and system | |
| CN204666839U (en) | Wind energy turbine set weather detection devices | |
| US20190114725A1 (en) | Utility network monitoring with a device and an unmanned aircraft | |
| CN105914877A (en) | Remote intelligent monitoring apparatus for the running of an electric power system terminal | |
| CN114089091A (en) | Power transmission line on-line monitoring method and system based on non-contact sensor | |
| CN111404274B (en) | Transmission system displacement on-line monitoring and early warning system | |
| Dhanraj et al. | Statistical data mining through credal decision tree classifiers for fault prediction on wind turbine blades using vibration signals | |
| KR20220146158A (en) | Apparatus for predicting solar radiation and method thereof | |
| CN204066357U (en) | A kind of data transmission set of transmission line long-distance Intelligent line patrolling | |
| CN114893390B (en) | Pump equipment fault detection method based on attention and integrated learning mechanism | |
| Murugan et al. | AI based Weather Monitoring System | |
| CN115102287A (en) | Intelligent management and control system for new energy power station centralized area | |
| CN114500613A (en) | Online monitoring Internet of things system for power grid | |
| KR102443585B1 (en) | Solar power generation system that estimates failure using artificial intelligence | |
| CN204679664U (en) | A kind of Design of meteorological data collection based on GPRS network | |
| CN114898527A (en) | Wearable old man falling detection system and method based on voice assistance | |
| CN209372138U (en) | A kind of air on-line monitoring system | |
| CN114062839A (en) | Railway power line fault positioning device and method thereof | |
| CN207799414U (en) | A kind of site construction monitoring system | |
| CN112464151A (en) | Wind turbine generator yaw system abnormal sound diagnosis method based on acoustic diagnosis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210312 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |