CN203879690U - Fault real-time pre-warning and smart control platform for wind power plant - Google Patents
Fault real-time pre-warning and smart control platform for wind power plant Download PDFInfo
- Publication number
- CN203879690U CN203879690U CN201420256668.8U CN201420256668U CN203879690U CN 203879690 U CN203879690 U CN 203879690U CN 201420256668 U CN201420256668 U CN 201420256668U CN 203879690 U CN203879690 U CN 203879690U
- Authority
- CN
- China
- Prior art keywords
- fault
- fan
- real
- module
- blower fan
- 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.)
- Expired - Lifetime
Links
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 241001269238 Data Species 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 16
- 238000013461 design Methods 0.000 abstract description 7
- 238000011156 evaluation Methods 0.000 abstract description 7
- 230000008901 benefit Effects 0.000 abstract description 3
- 230000007547 defect Effects 0.000 abstract description 3
- 230000003449 preventive effect Effects 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract 1
- 230000005611 electricity Effects 0.000 description 11
- 238000003745 diagnosis Methods 0.000 description 6
- 238000000034 method Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- Y02E10/722—
Landscapes
- Wind Motors (AREA)
Abstract
The utility model discloses a fault real-time pre-warning and smart control platform for wind power plant. The fault real-time pre-warning and smart control platform comprises fan state observing system, a fan operation state real-time evaluating system and a fan fault pre-warning and control system. The fault real-time pre-warning and smart control platform has the advantages that state monitoring and fault diagnosing of wind-driven generator sets are achieved, real-time evaluation of equipment state is achieved, pre-warning and diagnosing of faults is achieved, fault-tolerant control of the faults is achieved, fault shutdown is changed into planned shutdown, shutdown is reduced or accident expansion is avoided, equipment maintenance management of enterprises is gradually transited from planned maintenance and accident maintenance to preventive maintenance, safe and reliable operation of the wind-driven generator sets is guaranteed, modernization level of equipment management of wind power operation enterprises is increased, defects of related structural design, unreasonable part shape selection and the like can be discovered timely, and a practical and reliable basis is provided for the optimization design and safety operation of the wind-driven generator sets.
Description
Technical field
The utility model relates to a kind of wind energy turbine set intelligent control system, relates in particular to a kind of wind energy turbine set fault real-time early warning intelligent control platform.
Background technique
When China's Wind Power Generation Industry flourish, be faced with the multiple circumstances of unit fault; how to reduce burst accident rate and the downtime of wind-powered electricity generation unit and critical component; reduce maintenance cost; improve Generation Rate and economic benefit, become the problem that wind-power electricity generation investment, construction, operation maintenance must solve as early as possible.The problem of wind-powered electricity generation unit reliability has become the bottleneck of wind-powered electricity generation development of manufacturing.
Therefore; wind-powered electricity generation unit equipment state is made in real time, evaluated reliably; reduce and shut down or avoid fault spread; ensure that aerogenerator unit safe moves reliably; improve business equipment operation and management level and maintenance usefulness; save recondition expense, there is significant economic benefit, need badly and making research aspect wind energy turbine set fault real-time early warning.
Model utility content
The purpose of this utility model is just to provide in order to address the above problem a kind of wind energy turbine set fault real-time early warning intelligent control platform.
The utility model is achieved through the following technical solutions above-mentioned purpose:
Wind energy turbine set fault real-time early warning intelligent control platform, comprise fan condition viewing system, the real-time evaluating system of fan operation state and fan trouble early warning and control system, described fan condition viewing system comprises multiple sensors and fan condition on-line monitoring module, the real-time evaluating system of described fan operation state comprises fan operation condition diagnosing module, fault blower fan locking module and potential faults blower fan locking module, described fan trouble early warning and control system comprise fan trouble early warning system and the fault-tolerant control module of fault blower fan, multiple described sensors are arranged on respectively on multiple blower fans of described wind energy turbine set, described fan condition on-line monitoring module communicates to connect with multiple described sensor network datas respectively, the data output end of described fan condition on-line monitoring module is connected with the data input pin of described fan operation condition diagnosing module, the data output end of described fan operation condition diagnosing module is connected with the data input pin of described fault blower fan locking module and the data input pin of potential faults blower fan locking module respectively, the data output end of described fault blower fan locking module is connected with the data input pin of described fan trouble early warning system, the control signal port of described potential faults blower fan locking module is connected with the control signal port of the fault-tolerant control module of described fault blower fan.
The beneficial effects of the utility model are:
The utility model provides wind energy turbine set fault real-time early warning intelligent control platform, wind-powered electricity generation unit is carried out to condition monitoring and fault diagnosis work, equipment state is made to Real-Time Evaluation, diagnosis is forecast and made to fault in advance, fault-tolerant control in addition, change disorderly closedown is planned shut-down, reduce and shut down or avoid fault spread, make enterprise to the maintenance management of equipment from planned maintenance, accident maintenance is progressively transitioned into preventive maintenance, ensure that aerogenerator unit safe moves reliably, improve wind-powered electricity generation operation enterprise Plant engineering modernization level, can also find in time the defect in dependency structure design simultaneously, unreasonable etc. in component type selecting, for the optimal design of wind-powered electricity generation unit, safe operation provides solid foundation.
Brief description of the drawings
Fig. 1 is the structured flowchart of the utility model wind energy turbine set fault real-time early warning intelligent control platform.
Embodiment
Below in conjunction with accompanying drawing, the utility model is described in further detail:
As shown in Figure 1, the utility model wind energy turbine set fault real-time early warning intelligent control platform, comprise fan condition viewing system, the real-time evaluating system of fan operation state and fan trouble early warning and control system, described fan condition viewing system comprises multiple sensors and fan condition on-line monitoring module, the real-time evaluating system of described fan operation state comprises fan operation condition diagnosing module, fault blower fan locking module and potential faults blower fan locking module, described fan trouble early warning and control system comprise fan trouble early warning system and the fault-tolerant control module of fault blower fan, multiple described sensors are arranged on respectively on multiple blower fans of described wind energy turbine set, described fan condition on-line monitoring module communicates to connect with multiple described sensor network datas respectively, the data output end of described fan condition on-line monitoring module is connected with the data input pin of described fan operation condition diagnosing module, the data output end of described fan operation condition diagnosing module is connected with the data input pin of described fault blower fan locking module and the data input pin of potential faults blower fan locking module respectively, the data output end of described fault blower fan locking module is connected with the data input pin of described fan trouble early warning system, the control signal port of described potential faults blower fan locking module is connected with the control signal port of the fault-tolerant control module of described fault blower fan.
Multiple described sensor in the utility model is the multiclass sensor that blower fan is carried out to condition monitoring, comprises monitoring states such as temperature, humidity, rotating speeds.
The utility model shows according to a large amount of facts and data, condition monitoring and fault diagnosis technology is to understanding the performance state of modern project technique system and large-scale and complicated device, find early incipient fault, prevent trouble before it happens, to guarantee that satisfactorily completing of every engineering task plays vital effect.Wind-powered electricity generation unit is carried out to condition monitoring and fault diagnosis work, equipment state is made to Real-Time Evaluation, diagnosis is forecast and made to fault in advance, fault-tolerant control in addition, change disorderly closedown is planned shut-down, reduce and shut down or avoid fault spread, make enterprise to the maintenance management of equipment from planned maintenance, accident maintenance is progressively transitioned into preventive maintenance, ensure that aerogenerator unit safe moves reliably, improve equipment Management in Enterprise modernization level, in fault diagnosis, when fault-tolerant control, can also find in time the defect in dependency structure design, unreasonable etc. in component type selecting, for the optimal design of wind-powered electricity generation unit, safe operation provides solid foundation.
A kind of wind energy turbine set fault of the utility model real-time early warning intelligent control platform, by fan condition viewing system, fan operation state real-time evaluation system and fan trouble alarming processing system, totally three parts form.The concrete function of every part is as follows:
Fan condition viewing system: by the online monitoring system of wind field, gather and monitor the data of the parts running statees at different levels of wind power generating set in wind field.
Fan operation state real-time evaluation system: the parts running statees at different levels of the wind power generating set in wind field are carried out to real-time condition diagnosing evaluation, and to there is the locking of classifying of the blower fan (that is: inferior health blower fan) of potential faults and the blower fan of fault.
Fan trouble alarming processing system: process according to the different problem blower fan classification that lock onto, prompting wind field operations staff correspondingly safeguards or keep in repair reference; Or by intelligent control platform control fault blower fan fault-tolerant operation.
The utility model, by gathering wind power generating set unit statuss at different levels, carries out real-time online assessment, classification processing, realizes aerogenerator unit safe and moves reliably, improves wind-powered electricity generation operation enterprise equipment operation management level and maintenance usefulness.The running state data of every Fans arrives fan condition on-line monitoring module by data network transmission, the data sample of the every Fans collecting is carried out to diagnostic analysis, and fan operation state real-time evaluation system can filter out the blower fan of sub-health state and the blower fan of fault state.Process according to the different problem blower fan classification that lock onto, inferior health blower fan is carried out to fault pre-alarming, reference process suggestion need to be safeguarded or keep in repair to certain parts of prompting wind field operations staff Fans.And for fault blower fan, the fault-tolerant control of blower fan substitutes failed equipment by the fault-tolerant device of data communication network control fan trouble position and enters working state, ensure that blower fan can continue normal operation.
Embodiment is above described preferred implementation of the present utility model; not scope of the present utility model is limited; design under spiritual prerequisite not departing from the utility model; various distortion and improvement that the common engineers and technicians in related domain make technical solutions of the utility model, all should fall in the definite protection domain of claims of the present utility model.
Claims (1)
1. wind energy turbine set fault real-time early warning intelligent control platform, it is characterized in that: comprise fan condition viewing system, the real-time evaluating system of fan operation state and fan trouble early warning and control system, described fan condition viewing system comprises multiple sensors and fan condition on-line monitoring module, the real-time evaluating system of described fan operation state comprises fan operation condition diagnosing module, fault blower fan locking module and potential faults blower fan locking module, described fan trouble early warning and control system comprise fan trouble early warning system and the fault-tolerant control module of fault blower fan, multiple described sensors are arranged on respectively on multiple blower fans of described wind energy turbine set, described fan condition on-line monitoring module communicates to connect with multiple described sensor network datas respectively, the data output end of described fan condition on-line monitoring module is connected with the data input pin of described fan operation condition diagnosing module, the data output end of described fan operation condition diagnosing module is connected with the data input pin of described fault blower fan locking module and the data input pin of potential faults blower fan locking module respectively, the data output end of described fault blower fan locking module is connected with the data input pin of described fan trouble early warning system, the control signal port of described potential faults blower fan locking module is connected with the control signal port of the fault-tolerant control module of described fault blower fan.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201420256668.8U CN203879690U (en) | 2014-05-20 | 2014-05-20 | Fault real-time pre-warning and smart control platform for wind power plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201420256668.8U CN203879690U (en) | 2014-05-20 | 2014-05-20 | Fault real-time pre-warning and smart control platform for wind power plant |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN203879690U true CN203879690U (en) | 2014-10-15 |
Family
ID=51680193
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201420256668.8U Expired - Lifetime CN203879690U (en) | 2014-05-20 | 2014-05-20 | Fault real-time pre-warning and smart control platform for wind power plant |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN203879690U (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108204331A (en) * | 2016-12-19 | 2018-06-26 | 北京金风科创风电设备有限公司 | The fault handling method and device of wind power generating set |
| CN108803555A (en) * | 2018-03-20 | 2018-11-13 | 北京航空航天大学 | A kind of inferior health online recognition and diagnostic method based on performance monitoring data |
| CN108845242A (en) * | 2018-05-25 | 2018-11-20 | 北京金风科创风电设备有限公司 | Fault identification method and device, and computer readable storage medium |
| CN108900125A (en) * | 2018-05-25 | 2018-11-27 | 北京金风科创风电设备有限公司 | Fault tolerance method and apparatus, computer readable storage medium |
| WO2019223350A1 (en) * | 2018-05-25 | 2019-11-28 | 北京金风科创风电设备有限公司 | Fault handling method and apparatus for wind power generator set, and computer readable storage medium |
-
2014
- 2014-05-20 CN CN201420256668.8U patent/CN203879690U/en not_active Expired - Lifetime
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108204331A (en) * | 2016-12-19 | 2018-06-26 | 北京金风科创风电设备有限公司 | The fault handling method and device of wind power generating set |
| CN108803555A (en) * | 2018-03-20 | 2018-11-13 | 北京航空航天大学 | A kind of inferior health online recognition and diagnostic method based on performance monitoring data |
| CN108845242A (en) * | 2018-05-25 | 2018-11-20 | 北京金风科创风电设备有限公司 | Fault identification method and device, and computer readable storage medium |
| CN108900125A (en) * | 2018-05-25 | 2018-11-27 | 北京金风科创风电设备有限公司 | Fault tolerance method and apparatus, computer readable storage medium |
| CN108845242B (en) * | 2018-05-25 | 2019-09-13 | 北京金风科创风电设备有限公司 | Fault identification method and device, and computer readable storage medium |
| CN108900125B (en) * | 2018-05-25 | 2019-09-13 | 北京金风科创风电设备有限公司 | Fault tolerance method and apparatus, computer readable storage medium |
| WO2019223350A1 (en) * | 2018-05-25 | 2019-11-28 | 北京金风科创风电设备有限公司 | Fault handling method and apparatus for wind power generator set, and computer readable storage medium |
| US11146192B2 (en) | 2018-05-25 | 2021-10-12 | Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd. | Fault handling method and apparatus for wind power generator set, and computer readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN203879690U (en) | Fault real-time pre-warning and smart control platform for wind power plant | |
| CN103711645B (en) | Based on the wind power generating set state evaluating method of modeling parameters signature analysis | |
| CN104019000B (en) | The loading spectrum of wind power generating set is determined and perspective maintenance system | |
| CN103323772B (en) | Based on the running status of wind generator analytical approach of neural network model | |
| CN103343728A (en) | Wind generating set remote on-line multi-mode health state monitoring and fault diagnosis system | |
| CN103670921B (en) | Wind power generating set intellectual status monitoring system | |
| CN106815771B (en) | Long-term assessment method for wind farm load | |
| CN103234585A (en) | Online monitoring and fault diagnosis system of large wind turbine units | |
| CN105136435A (en) | Wind generating set blade fault diagnosis method and device | |
| CN202326011U (en) | State-monitoring and fault-diagnosis system of wind-power set | |
| CN108072524B (en) | Wind turbine generator gearbox bearing fault early warning method | |
| CN203519144U (en) | Aerogenerator blade and tower-barrel state monitoring system | |
| CN103234753A (en) | Gaussian process modeling based wind turbine shafting state monitoring method | |
| CN103306893B (en) | A kind of wind-driven generator fault pre-alarming and alarm method | |
| CN201835985U (en) | Embedded monitoring instrument and wind generating set thereof | |
| CN108252873A (en) | A kind of wind power generating set online data monitoring and its system of Performance Evaluation | |
| CN202126487U (en) | Testing platform for pitch controlled system of wind turbine | |
| CN102706885A (en) | On-line damage detecting system of blade of wind generating set | |
| CN104018988A (en) | Wind generating set monitoring system based on physical component model and real-time data | |
| CN107065829A (en) | A kind of photovoltaic module pollution diagnosis method supervised based on solar power generation under big data is excavated | |
| CN202453182U (en) | Fault diagnosis device of gearbox of wind generation set | |
| CN204591656U (en) | Based on thermal power plant turbine pumping unit fault pre-alarming and the optimized maintenance system of industry internet | |
| CN103334946B (en) | The online diagnosis of energy saving control system of a kind of thermal power plant's steam-driven induced draft fan | |
| CN205047372U (en) | Recognition device of wind -powered electricity generation thick liquid leaf surface trouble | |
| CN103439970B (en) | A kind of wind power generating set emulation test method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CX01 | Expiry of patent term |
Granted publication date: 20141015 |