CN106050580B - A kind of driving chain of wind generating set fault diagnosis method and system - Google Patents

A kind of driving chain of wind generating set fault diagnosis method and system Download PDF

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CN106050580B
CN106050580B CN201610681478.4A CN201610681478A CN106050580B CN 106050580 B CN106050580 B CN 106050580B CN 201610681478 A CN201610681478 A CN 201610681478A CN 106050580 B CN106050580 B CN 106050580B
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fault
event
tree
probability
fault tree
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CN106050580A (en
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褚景春
王飞
李永战
董健
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Guodian United Power Technology Co Ltd
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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
    • F05B2260/40Transmission of power
    • 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 driving chain of wind generating set method for diagnosing faults, including:Establish the driving chain of wind generating set fault message knowledge base of fault tree synthesis;It is retrieved in the knowledge base according to the fault characteristic information detected in real time, positions corresponding fault tree;According to preset data and Bayesian Network Inference in the knowledge base, each fault tree F is found outnPosterior probability P (FnShu S), obtain each fault tree probability distribution sequence;According to the fault tree probability distribution sequence order, the bottom event of all triggerings under all fault trees is retrieved and judged to obtain successively, forms failure cause diagnosis set.The present invention also provides the transmission chain fault diagnosis systems of the above-mentioned method for diagnosing faults of application.The present invention on the basis of Fault Tree Analysis by introducing Bayesian networks technique; extremely favourable foundation is provided quickly and effectively to diagnose blowing machine transmission chain failure cause; Wind turbines downtime is reduced, manual maintenance's cost is reduced, improves Generation Rate and economic benefit.

Description

A kind of driving chain of wind generating set fault diagnosis method and system
Technical field
The present invention relates to wind power generating set fault diagnosis technology fields, are driven more particularly to a kind of wind power generating set Chain fault diagnosis method and system.
Background technology
Wind Power Generation Industry is also faced with the multiple circumstances of unit failure while booming at present, wherein due to transmission The working order of chain directly affects the performance and safety of wind power generating set, all diagnosis and detection for transmission chain failure It is particularly important.Existing transmission chain fault detection method predominantly stays in the detection sensor hardware by increasing more redundancies On, it relies on artificial experience and the redundant state detection information diagnosis of equipment is analyzed so that the diagnosis and detection of the transmission chain failure Method is inefficient, reliability is not strong, complex steps, cannot obtain the fault message of the transmission chain in time.
Also in industrial control field equipment fault diagnosis, the Fault Tree Analysis (FTA) mostly used, be study system Target of the malfunction as accident analysis of the mostly undesired generation of system, then look for causing the generation of this failure it is whole because Element, then find out the next stage whole direct factor for causing these factors to occur, traces always that those are original, no longer need to go into seriously Factor until.The various approach that can be broken down with analysis system using the fault tree, calculate each characteristic quantities, can The safety and reliability of system is evaluated.But the fault tree does not have description event polymorphism and fault logic relationship is non- Deterministic ability is not suitable for analyzing the safety and reliability of complication system.
For the system of this complexity of wind power generating set, the certain failures occurred on transmission chain may be with multiple failures Tree is related, and it is comprehensive and accurate outstanding to failure cause positioning how quickly to carry out screening and sequencing, system to multiple fault trees It is important.
It can be seen that above-mentioned existing driving chain of wind generating set method for diagnosing faults is upper in structure, method and use, It obviously has inconveniences and defects, and needs to be further improved.How a kind of new fast and effectively intelligence is founded The driving chain of wind generating set fault diagnosis method and system of transmission chain failure cause are diagnosed to be, it is real to belong to current important research and development class One of topic.
Invention content
The technical problem to be solved in the present invention is to provide a kind of driving chain of wind generating set method for diagnosing faults, can Fast and effectively intelligent diagnostics go out transmission chain failure cause, to overcome the shortcomings of existing transmission chain method for diagnosing faults.
In order to solve the above technical problems, the present invention provides a kind of driving chain of wind generating set method for diagnosing faults, it is described Method includes the following steps:
(1) the driving chain of wind generating set fault message knowledge base of fault tree synthesis is established;
(2) correlation is carried out in the transmission chain fault message knowledge base according to the fault characteristic information detected in real time Retrieval, orients corresponding fault tree number n, i.e. fault tree F1…Fn, wherein the fault characteristic information includes to be diagnosed The fault status information S of fault message and wind power generating set drive apparatus;
(3) probability P (S), the failure that fault status information S occurs are retrieved in the transmission chain fault message knowledge base Set FnPrior probability P (Fn), and work as fault tree FnWhen generation, probability P (the S Shu F of fault status information S generationsn), further according to Bayesian formula P (FnShu S)=P (Fn) P (S Shu Fn)/P (S) finds out fault status information S in the corresponding fault tree Fn Posterior probability P (FnShu S), then according to each fault tree F1…FnObtained posterior probability values being ranked sequentially from big to small, obtains To each fault tree F1…FnProbability distribution sequence;
(4) according to each fault tree F1…FnThe sequence of probability distribution sequence from big to small, retrieves each failure successively The criterion of the corresponding all next stage subevent triggerings of top event of tree, and itself and presence states to be diagnosed fault are judged respectively Whether value matches, if judging to mismatch, which does not trigger, if being judged as matching, event triggering, and continue to judge to be somebody's turn to do Whether event is bottom event, if bottom event, then retrieves stopping, if non-bottom event, continues retrieval downwards, until retrieving institute The bottom event of all triggerings under faulty tree, and by the bottom event of all triggerings with the descending sequence shows of its probability, shape It diagnoses and gathers at failure cause.
As an improvement of the present invention, the step (1) is established the driving chain of wind generating set fault message and is known Know library specific method be:
(1) fault tree models are established according to wind power generating set historical failure knowledge information, wherein with trouble location be therefore Hinder the top event set, sifting sort is carried out to fault message, to cause the event that current event occurs to establish event for its subevent Hinder the set membership between information;
(2) the reason of causing upper level event to occur next stage event in the fault tree, is sentenced as what event triggered According to, while setting up the relationship between each fault status information S and fault tree F;
(3) experience occurred according to known historical failure provides the probability P (S) and each failure that fault status information S occurs Set FnPrior probability P (the F of generationn), it converts fault tree to Bayesian network, is then gone out when event using Bayesian network analysis Barrier tree FnWhen generation, probability P (the S Shu F of fault status information S generationsn), that is, it completes to pass the wind power generating set of fault tree synthesis The foundation of dynamic chain fault message knowledge base.
The present invention also provides a kind of transmission chain failures using above-mentioned driving chain of wind generating set method for diagnosing faults to examine Disconnected system, the diagnostic system include:Fault message base module, fault information acquisition module, fault information analysis module, Fault message judgment module and fault message output module,
The fault message base module, the driving chain of wind generating set fault message for establishing fault tree synthesis Knowledge base;
The fault information acquisition module, the fault signature for detecting and acquiring driving chain of wind generating set in real time are believed Breath, the fault information analysis module is sent to by collected fault characteristic information;
The fault information analysis module, the fault characteristic information for that will receive is in the transmission chain fault message knowledge It is retrieved in library, orients corresponding fault tree number n, and each according to known wind turbine in the transmission chain fault message knowledge base The Prior Probability of each node of fault tree is calculated and breaks down relevant each fault tree probability distribution sequence;
The fault message judgment module, for presence states value that will be to be diagnosed fault and the transmission chain fault message Each event triggering criterion in knowledge base carries out matching judgment, obtains trigger event, and further judges to obtain all relevant The bottom event of all triggerings under fault tree;
The fault message output module, all relevant fault trees for obtaining the fault message judgment module Under all triggerings bottom event, according to its probability of happening size output be failure cause diagnosis set.
By adopting such a design, the present invention has at least the following advantages:
The present invention can be obtained fast and effectively by introducing Bayesian networks technique on the basis of Fault Tree Analysis Failure cause diagnosis set carries out troubleshooting for Maintenance Engineer and provides extremely favourable foundation, and efficient intelligent diagnostics go out The failure cause of wind turbine transmission chain reduces the downtime of Wind turbines critical component, reduces manual maintenance's cost, improves Generation Rate and economic benefit solve the situation that existing fault diagnosis manually will diagnose fault message and position, are Improve operation maintenance, enhancing automatization level provides extremely favourable help.
Description of the drawings
The above is merely an overview of the technical solutions of the present invention, in order to better understand the technical means of the present invention, below In conjunction with attached drawing, the present invention is described in further detail with specific implementation mode.
Fig. 1 is the flow diagram of driving chain of wind generating set method for diagnosing faults of the present invention.
Fig. 2, which is wind power generating set rotating speed of the present invention, to transfinite the result schematic diagram of fault tree.
Fig. 3 is the result schematic diagram that wind power generating set rotating speed of the present invention mismatches fault tree.
Specific implementation mode
The present invention introduces Bayesian networks technique on the basis of existing Fault Tree Analysis, the Bayesian networks technique From inference mechanism and system mode description, there is prodigious similitude with fault tree, but the Bayesian networks technique also has It is described the ability of event polymorphism and fault logic relationship uncertainty, is more suitable for safety to complication system and can It is analyzed by property.
With reference to shown in attached drawing 1, the step of driving chain of wind generating set method for diagnosing faults of the present invention, is as follows:
(1) the driving chain of wind generating set fault message knowledge base of fault tree synthesis is established.
Specific method is:Fault tree models (FT) are set up according to the historical failure knowledge information of wind power generating set, Using trouble location as top event, to fault message carry out sifting sort, using cause the event occur event as The set membership between fault message is established in the subevent of current event;Upper level event is caused to occur next stage event The criterion that reason is triggered as event, while the relationship between each fault status information S and fault tree F is set up, and by special Family provides the probability P (S) and each fault tree F that fault status information S occurs according to the experience that history known fault occursnOccur Probability P (Fn), it then converts fault tree to Bayesian network, is gone out as fault tree F using Bayesian network analysisnWhen generation, Probability P (the S Shu F that fault status information S occursn), and so on, it is established that the driving chain of wind generating set of fault tree synthesis Fault message knowledge base.
(2) correlation is carried out in above-mentioned transmission chain fault message knowledge base according to the fault characteristic information detected in real time Property retrieval, orient corresponding fault tree number n, i.e. fault tree F1…Fn, wherein the fault characteristic information includes to be diagnosed The fault status information S of fault message and wind power generating set drive apparatus.
(3) probability P (S), the fault tree that fault status information S occurs are retrieved in the transmission chain fault message knowledge base FnPrior probability P (Fn), and work as FnWhen generation, probability P (the S Shu F of fault status information S generationsn), further according to Bayes's public affairs Formula (1) finds out fault status information S in the fault tree FnPosterior probability P (FnShu S),
P(FnShu S)=P (Fn) P (S Shu Fn)/P(S) (1)
Then according to each fault tree F1…FnObtained posterior probability values being ranked sequentially from big to small, obtains each fault tree F1…FnProbability distribution sequence.
(4) according to obtained each fault tree F1…FnProbability distribution sequence, each fault tree of ordered retrieval from big to small The corresponding all next stage subevents triggering of top event criterion, and judge itself and presence states value to be diagnosed fault respectively Whether match, if fault status information S is mismatched with the criterion in fault message knowledge base, which does not trigger, If fault status information S is matched with the criterion in fault message knowledge base, event triggering, and continue to judge the thing Whether part is bottom event, if the event is bottom event, retrieves stopping, if non-bottom event, system will continue retrieval downwards, Until retrieving the bottom event of all triggerings under the fault tree, then the bottom of all triggerings in all fault trees is retrieved successively Event, and the bottom event of all triggerings is formed failure cause diagnosis set, be follow-up with the descending sequence shows of probability Physical fault processing provides foundation.
Using the transmission chain fault diagnosis system of above-mentioned driving chain of wind generating set method for diagnosing faults, including:Failure Information knowledge library module, fault information acquisition module, fault information analysis module, fault message judgment module and fault message are defeated Go out module.
The fault message base module, the driving chain of wind generating set fault message for establishing fault tree synthesis are known Know library;
The fault information acquisition module, the fault signature for detecting and acquiring driving chain of wind generating set in real time are believed Breath, the fault information analysis module is sent to by collected fault characteristic information;
The fault information analysis module, the fault characteristic information for that will receive is in the transmission chain fault message knowledge base Retrieval, orients corresponding fault tree number n, and according to the known each fault tree of wind turbine in the transmission chain fault message knowledge base The Prior Probability of each node is calculated and breaks down relevant each fault tree probability distribution sequence;
The fault message judgment module, for presence states value and the transmission chain fault message knowledge that will be to be diagnosed fault Each event triggering criterion in library carries out matching judgment, obtains trigger event, and further judges to obtain all relevant failures The bottom event of all triggerings under tree;
The fault message output module, institute under all relevant fault trees for obtaining the fault message judgment module The bottom event for having triggering is failure cause diagnosis set according to the output of its probability of happening size.
The detailed process of driving chain of wind generating set method for diagnosing faults of the present invention is as follows:When wind power generating set is driven When chain breaks down, the intelligent Fault Diagnose Systems of Wind turbines receive fault characteristic information (including:Failure letter to be diagnosed The fault status information of breath and wind power generating set drive apparatus), while the fault status information is denoted as S.Existing It carries out retrieving with the correlation of fault status information S in fault message knowledge base, orients corresponding fault tree number n, i.e.,:Therefore Barrier tree F1…Fn, and retrieve the probability P (S) that fault status information S occurs, fault tree FnPrior probability be P (Fn), work as FnHair When raw, probability P (the S Shu F of fault status information S generationsn), its posteriority probability P (F is found out according to Bayesian formulanShu S), then according to According to the ordered retrieval fault tree F of its posterior probability from big to small1…Fn, and the corresponding institute of each top event is retrieved successively There is the criterion that next stage subevent is triggered to be matched with presence states value to be diagnosed fault.If fault status information S with Criterion matching in fault knowledge library, then the event triggers, and continues to judge whether the event is bottom event, if the thing Part is bottom event, then retrieves stopping, if non-bottom event, system will continue retrieval downwards, until retrieving under the fault tree The bottom event of all triggerings.By the bottom event triggered under all fault trees with the descending sequence shows of probability, failure is formed Cause diagnosis set, so far diagnosis terminate.
Specific embodiment is as follows:
When wind power generating set quotes wheel speed failure, intelligent Fault Diagnose Systems are believed with the malfunction first pair Cease the relevant fault tree F of S1…FnGo through time, retrieve with the relevant all fault trees of the fault message, i.e. wheel speed is super Limit fault tree F1 and rotating speed and mismatch fault tree F2, such as the fault tree F2 in the fault tree F1 and attached drawing 3 in attached drawing 2, and from therefore The probability P (S)=0.22 that fault status information S occurs, fault tree F are searched in barrier information knowledge library1Prior probability P (F1) =0.15;Work as F1When generation, probability P (the S Shu F of fault status information S generations1)=0.9 obtains its posteriority according to bayesian algorithm Probability P (F1Shu S)=0.61;Fault tree F2Prior probability P (F2)=0.1;Work as F2When generation, what fault status information S occurred Probability P (S Shu F2)=0.3 obtains its posteriority probability P (F according to bayesian algorithm2Shu S)=0.14.
According to fault tree F1、F2Probability size distribution situation, detect failure successively according to the sequence of probability from big to small Tree, i.e., the preferentially fault tree F of retrieval maximum probability1, as shown in Fig. 2, " rotation speed of fan transfinites " fault tree, the fault tree F1's All event S1 ... S21 indicate, intelligent Fault Diagnose Systems are from fault tree F1Top event S1 successively retrieve the next stage of S1 Subevent S2 and S3, according to waiting for that failure judgement characteristic information infers subevent S2 and be triggered, S3 is not triggered, and then judges S2 Whether it is bottom event, if it is not, then next stage subevent S4, S5, S6 and S7 of retrieval S2, infer the bottom thing of triggering failure Part, i.e. fault tree F1In the failure there may be reasons.And so on, intelligent Fault Diagnose Systems continue the event to probability second Barrier tree F2All events make inferences, as shown in Fig. 3, be diagnosed to be fault tree F2The middle bottom event that may trigger failure.Most Eventually, the bottom event (i.e. possible failure cause) that intelligent Fault Diagnose Systems trigger all fault trees is with the descending sequence of probability Row are shown, and are accumulated failure cause diagnosis set, are provided Maintenance Engineer and carry out troubleshooting, so far diagnosis terminates.
The present invention is based on the polymorphic Fault Tree Analysis of Bayesian network can solve complication system, diagnostic area greatly and nothing The problem of method quick positioning failure tree, meanwhile, it is successively retrieved using rule-based reasoning method to generating the fault tree belonging to failure Match, reduce the workload of the fault diagnosis to impossible intermediate event, improves the speed and fault diagnosis of fault diagnosis Precision, reduce the downtime of Wind turbines critical component, reduce manual maintenance's cost, improve Generation Rate and economical imitate Benefit.
The above described is only a preferred embodiment of the present invention, be not intended to limit the present invention in any form, this Field technology personnel make a little simple modification, equivalent variations or modification using the technology contents of the disclosure above, all fall within this hair In bright protection domain.

Claims (3)

1. a kind of driving chain of wind generating set method for diagnosing faults, which is characterized in that described method includes following steps:
(1) the driving chain of wind generating set fault message knowledge base of fault tree synthesis is established;
(2) correlation retrieval is carried out in the transmission chain fault message knowledge base according to the fault characteristic information detected in real time, Orient corresponding fault tree number n, i.e. fault tree F1…Fn, wherein the fault characteristic information includes that failure to be diagnosed is believed The fault status information S of breath and wind power generating set drive apparatus;
(3) probability P (S), the fault tree F that fault status information S occurs are retrieved in the transmission chain fault message knowledge basen's Prior probability P (Fn), and work as fault tree FnWhen generation, probability P (the S Shu F of fault status information S generationsn), further according to Bayes Formula P (FnShu S)=P (Fn) P (S Shu Fn)/P (S) finds out fault status information S in the corresponding fault tree FnPosteriority Probability P (FnShu S), then according to each fault tree F1…FnObtained posterior probability values being ranked sequentially from big to small, obtain it is each therefore Barrier tree F1…FnProbability distribution sequence;
(4) according to each fault tree F1…FnThe sequence of probability distribution sequence from big to small, retrieves each fault tree successively The criterion of the corresponding all next stage subevent triggerings of top event, and judge that it is with presence states value to be diagnosed fault respectively No matching, if judging to mismatch, which does not trigger, if being judged as matching, event triggering, and continue to judge the event Whether it is bottom event, if bottom event, then retrieves stopping, if non-bottom event, continue retrieval downwards, until retrieves all events The bottom event of all triggerings under barrier tree, and the bottom event of all triggerings is formed into event with the descending sequence shows of its probability Hinder cause diagnosis set.
2. driving chain of wind generating set method for diagnosing faults according to claim 1, which is characterized in that the step (1) specific method for establishing the driving chain of wind generating set fault message knowledge base is:
(1) fault tree models are established according to wind power generating set historical failure knowledge information, wherein using trouble location as fault tree Top event, to fault message carry out sifting sort, with cause current event occur event be its subevent, establish failure letter Set membership between breath;
(2) criterion that the reason of causing upper level event to occur next stage event in the fault tree triggers as event, together When set up relationship between each fault status information S and fault tree F;
(3) experience occurred according to known historical failure provides the probability P (S) and each fault tree F that fault status information S occursnHair Raw prior probability P (Fn), it converts fault tree to Bayesian network, is then gone out as fault tree F using Bayesian network analysisn When generation, probability P (the S Shu F of fault status information S generationsn), that is, complete the driving chain of wind generating set to fault tree synthesis The foundation of fault message knowledge base.
3. a kind of transmission chain failure of application driving chain of wind generating set method for diagnosing faults as claimed in claim 1 or 2 Diagnostic system, which is characterized in that the diagnostic system includes:Fault message base module, fault information acquisition module, failure Information analysis module, fault message judgment module and fault message output module,
The fault message base module, the driving chain of wind generating set fault message knowledge for establishing fault tree synthesis Library;
The fault information acquisition module, the fault characteristic information for detecting and acquiring driving chain of wind generating set in real time, Collected fault characteristic information is sent to the fault information analysis module;
The fault information analysis module, the fault characteristic information for that will receive is in the transmission chain fault message knowledge base Retrieval, orients corresponding fault tree number n, and according to the known each failure of wind turbine in the transmission chain fault message knowledge base The Prior Probability for setting each node is calculated and breaks down relevant each fault tree probability distribution sequence;
The fault message judgment module, for presence states value that will be to be diagnosed fault and the transmission chain fault message knowledge Each event triggering criterion in library carries out matching judgment, obtains trigger event, and further judges to obtain all relevant failures The bottom event of all triggerings under tree;
The fault message output module, institute under all relevant fault trees for obtaining the fault message judgment module The bottom event for having triggering is failure cause diagnosis set according to the output of its probability of happening size.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108071562B (en) * 2016-11-17 2021-01-15 中国电力科学研究院 Wind turbine generator energy efficiency state diagnosis method based on energy flow
CN108226775B (en) * 2016-12-13 2020-06-30 北京金风科创风电设备有限公司 Fault self-detection method and device of wind driven generator
CN108320040B (en) * 2017-01-17 2021-01-26 国网重庆市电力公司 Acquisition terminal fault prediction method and system based on Bayesian network optimization algorithm
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CN107704933A (en) * 2017-09-01 2018-02-16 新疆金风科技股份有限公司 Wind power generating set fault diagnosis system and method
CN107544462B (en) * 2017-09-07 2019-07-16 新疆金风科技股份有限公司 For diagnosing the method and system of the failure of wind power generating set
CN108876002B (en) * 2018-05-03 2021-08-17 浙江运达风电股份有限公司 Method for optimizing inventory of spare parts of wind generating set
CN110645153B (en) * 2018-06-27 2020-11-24 北京金风科创风电设备有限公司 Wind generating set fault diagnosis method and device and electronic equipment
CN109580215B (en) * 2018-11-30 2020-09-29 湖南科技大学 Wind power transmission system fault diagnosis method based on deep generation countermeasure network

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103375349A (en) * 2012-04-24 2013-10-30 华锐风电科技(集团)股份有限公司 Transmission chain of wind generating set and wind generating set
DE102012024273A1 (en) * 2012-12-12 2014-06-12 Robert Bosch Gmbh Method for tuning load-dependent processes in electric components of wind turbine, involves increasing or reducing individual burdens in one component during remaining term, so as to adjust individual residual duration of term life
CN104005917A (en) * 2014-04-30 2014-08-27 叶翔 Method and system for predicting wind machine state based on Bayesian reasoning mode
CN104019000A (en) * 2014-06-23 2014-09-03 宁夏银星能源股份有限公司 Load spectrum determination and proactive maintenance system of wind generating set
CN204553118U (en) * 2015-04-20 2015-08-12 中国大唐集团新能源股份有限公司 A kind of novel Wind turbines condition monitoring system
CN105569931A (en) * 2015-12-31 2016-05-11 三一重型能源装备有限公司 Transmission chain fault diagnosis method for wind power generator

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5085912B2 (en) * 2006-11-07 2012-11-28 那須電機鉄工株式会社 Fail-safe control device and control method for wind power generator

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103375349A (en) * 2012-04-24 2013-10-30 华锐风电科技(集团)股份有限公司 Transmission chain of wind generating set and wind generating set
DE102012024273A1 (en) * 2012-12-12 2014-06-12 Robert Bosch Gmbh Method for tuning load-dependent processes in electric components of wind turbine, involves increasing or reducing individual burdens in one component during remaining term, so as to adjust individual residual duration of term life
CN104005917A (en) * 2014-04-30 2014-08-27 叶翔 Method and system for predicting wind machine state based on Bayesian reasoning mode
CN104019000A (en) * 2014-06-23 2014-09-03 宁夏银星能源股份有限公司 Load spectrum determination and proactive maintenance system of wind generating set
CN204553118U (en) * 2015-04-20 2015-08-12 中国大唐集团新能源股份有限公司 A kind of novel Wind turbines condition monitoring system
CN105569931A (en) * 2015-12-31 2016-05-11 三一重型能源装备有限公司 Transmission chain fault diagnosis method for wind power generator

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