CN107179503A - The method of Wind turbines intelligent fault diagnosis early warning based on random forest - Google Patents
The method of Wind turbines intelligent fault diagnosis early warning based on random forest Download PDFInfo
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Abstract
The invention discloses a kind of method of the Wind turbines intelligent fault diagnosis early warning based on random forest, including：The historical data for extracting Wind turbines state is used as sample data；Exploratory analysis and pretreatment are carried out to the sample data；The Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, is carried out analyzing and evaluation model according to model result；Real-time diagnosis is carried out to Wind turbines equipment using the model after assay, if diagnostic result is abnormal, the model will send warning information.The present invention uses random forests algorithm, it is considered to the overall feature of index, the problem of this method not only can solve single index decision device state, and can consider hiding knowledge correlation between numerous indexs, and comprehensive judgement is made to output result.
Description
Technical field
It is that the Wind turbines intelligent fault based on random forest is examined more specifically the present invention relates to technical field of electric power
The method of disconnected early warning.
Background technology
Wind-power electricity generation is one of closest commercialized renewable energy technologies generally acknowledged in the world.Protection is emphasized current
Under environment, the background of sustainable development, consumption of fossil fuels, the wind-power electricity generation of non-environmental-pollution are not considered as the energy most cleaned
Source utilizes form.In past 10 years, because annual average rate of increase is close to 28%, wind-power electricity generation turns into and increased in the world most
Fast regenerative resource.
With the fast development of wind energy and putting into operation for large-scale wind power unit, and because most of units' installation is inclined
Remote area, the factor such as load is unstable, many Wind turbines of China all occur in that operation troubles, directly affects wind-power electricity generation
Security and economy.To keep the long-term stability development of wind-powered electricity generation, strengthen the competitiveness of it and traditional energy, it is necessary to improve constantly
Wind power generation efficiency, reduction wind power equipment maintenance cost and operation cost, promote the economic interests of enterprise to maximize.
Traditional method for diagnosing faults, although employ certain scientific and technological means, but the sum of maintenance personal's technology
The factor weight that experience is accounted for is substantially the experience for first relying on maintenance personal to the positioning of failure, is then being subject to section than larger
Learn diagnosis of technique method to be accurately positioned, the place of failure problems is found, to a certain extent in the presence of artificial subjective errors
With the shortcoming of extension unit maintenance time；
Also, because wind power plant their location is far away, relatively more severe, it is necessary to attendant's length plus natural environment
Time value is kept, and as wind-powered electricity generation station quantity increases, the waste of personnel's capital is will result in a certain extent.
The content of the invention
Instant invention overcomes traditional manual method in trouble-shooting time length, deviation present on fault diagnosis and monitoring
The problem of big and personnel waste.Therefore, the invention provides a kind of Wind turbines intelligent fault diagnosis based on random forest
The method of early warning.
To achieve these goals, the present invention provides following technical scheme：
The method of Wind turbines intelligent fault diagnosis early warning based on random forest, including：
The historical data for extracting Wind turbines state is used as sample data；To the sample data carry out exploratory analysis and
Pretreatment；
The Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, is analyzed simultaneously according to model result
Evaluation model；
Real-time diagnosis is carried out to Wind turbines equipment using the model after assay, should if diagnostic result is abnormal
Model will send warning information.
Preferably, the historical data of Wind turbines state is extracted as sample data, including：
By analyzing Wind turbines most common failure, Wind turbines fault indices system is built；
Wind turbines status history data conduct is extracted from Wind turbines automated system or equipment background control system
Sample data.
Preferably, exploratory analysis and pretreatment are carried out to the sample data, including：
Exploratory analysis is carried out to the sample data, refers to reject aging equipment, equipment of just going into operation, tentatively examines wind-powered electricity generation closely
The feature situation of unit normally with abnormality；
The sample data is pre-processed, including：Data cleansing, missing values processing, Data Discretization, attitude layer
With data conversion.
Preferably, the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, including：
Pretreated sample data is divided into training sample and test sample, Random Forest model expert's sample is built
Collection；
Wind turbines intelligent fault diagnosis Early-warning Model is built using sample set.
Preferably, analyze and evaluate Wind turbines intelligent fault diagnosis Early-warning Model, including：
To predict the outcome carry out analyzing and diagnosing of the model on test set,
If prediction is all correct, illustrate that the diagnosis effect of model is more satisfactory；
If diagnostic result has error, further Optimized model.
Preferably, real-time diagnosis is carried out to Wind turbines equipment using the model trained, including：
Online data is accessed from Wind turbines automated system or equipment background control system；
The attribute in Wind turbines fault indices system is selected from online data as input attribute；
Online data is pre-processed, the model that the online data of pretreatment is accessed after assay, real-time pair sets
Standby data are monitored diagnosis.
Preferably, in addition to the more historical failures of Wind turbines and normal data are collected, periodically model are trained,
Upgrade in time model.
Compared with prior art, beneficial effects of the present invention：
There is generation problem the present invention be directed to Wind turbines failure frequency, construct Wind turbines intelligent fault diagnosis early warning mould
Type, study is trained using random forests algorithm to Wind turbines historical data, excavates influence Wind turbines failure and occurs
Each factor feature mode, the threshold value for each index that breaks down is determined, so that according to equipment real time execution to Wind turbines
On-line fault diagnosis early warning is carried out, Wind turbines maintenance cost is effectively reduced, the utilization ratio of Wind turbines is improved；
The present invention uses random forests algorithm, it is considered to which the overall feature of index, this method can not only solve single finger
The problem of marking decision device state, and hiding knowledge correlation between numerous indexs can be considered, output result is done
Go out comprehensive judgement.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
The embodiment of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is the method for the Wind turbines intelligent fault diagnosis early warning provided in an embodiment of the present invention based on random forest
Flow chart；
The schematic diagram for the decision tree that Fig. 2 provides for real-time example of the invention；
The schematic diagram for the random forest that Fig. 3 provides for real-time example of the invention；
Fig. 4 is in the method for the Wind turbines intelligent fault diagnosis early warning based on random forest that example is provided in real time of the invention
Build the flow chart of the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest；
The graph of a relation of the false determination ratio and decision tree number of the random forest that Fig. 5 provides for real-time example of the invention；
Fig. 6 is in the method for the Wind turbines intelligent fault diagnosis early warning based on random forest that example is provided in real time of the invention
Wind turbines intelligent fault diagnosis Early-warning Model based on random forest realizes the flow chart of real-time diagnosis.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Embodiment 1：
The method of Wind turbines intelligent fault diagnosis early warning based on random forest, including：
Wind turbines fault indices system is built, Wind turbines status history data is extracted and is used as sample data；
Wind turbines state history sample data to extraction carries out Data Mining and pretreatment；
The Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, is analyzed simultaneously according to model result
Evaluation model；
Real-time diagnosis is realized using the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest of structure；
The more historical failures of Wind turbines and normal data are collected, periodically model is trained, upgrade in time model.
It is preferred that, the structure Wind turbines fault indices system extracts Wind turbines status history data and is used as sample
Data, refer to：
By analyzing Wind turbines most common failure, Wind turbines fault indices system is built；
Part Wind turbines state is selectively extracted from Wind turbines automated system, equipment background control system to go through
History data.
It is preferred that, the Wind turbines state history sample data of described pair of extraction carries out Data Mining and pretreatment, including：
Exploratory analysis is carried out to sample, aging equipment, equipment of just going into operation is rejected, preliminary close examination Wind turbines normally with it is different
The feature situation of normal state；
Sample is pre-processed, including data cleansing, missing values processing, Data Discretization, attitude layer and data become
Change.
It is preferred that, the Wind turbines intelligent fault diagnosis Early-warning Model of the structure based on random forest, according to model knot
Fruit analyze and evaluation model, including：
Build Random Forest model expert's sample set；
Build Wind turbines intelligent fault diagnosis Early-warning Model；
Model result is analyzed and evaluated, Wind turbines failure intelligence is further optimized according to analysis and the result evaluated
Early-warning Model can be diagnosed.
It is preferred that, it is described to be realized in fact using the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest built
When diagnose, including：
According to the Wind turbines intelligent fault diagnosis model (after the completion of training) of structure, by Wind turbines equipment-related data
It is linked into model, is monitored diagnosis to device data in real time, once find that diagnostic result is " abnormal ", just to wind turbine
Group sends warning information.
It is preferred that, the collection more historical failures of Wind turbines and normal data are periodically trained to model, and
When more new model, including：
Collect the more historical failures of Wind turbines and normal data so that model covers the spy of all situations as far as possible
Levy, and can regular one month to model training once, upgrade in time model, improve constantly model accuracy.
The invention discloses the method for the Wind turbines intelligent fault diagnosis early warning based on random forest, including：For wind
Group of motors failure frequency has generation problem, Wind turbines intelligent fault diagnosis Early-warning Model is constructed, using random forests algorithm pair
Wind turbines historical data is trained study, excavates the feature mode for each factor that influence Wind turbines failure occurs, really
The threshold value for each index that breaks down is made, so that on-line fault diagnosis early warning is carried out to Wind turbines according to equipment real time execution,
Wind turbines maintenance cost is effectively reduced, the utilization ratio of Wind turbines is improved.
It is that diversified, conventional research often focuses on list due to constituting running of wind generating set abnormal state
The threshold range of individual index determines whether Wind turbines send warning information, and this method has certain defect, right herein
Some key indexs (often direct decision device state) do not do independent research, but use random forests algorithm, it is considered to index
Overall feature, the problem of this method not only can solve single index decision device state, and crowd can be considered
Knowledge correlation is hidden between multi objective, comprehensive judgement is made to output result.
Embodiment 2：
Referring to Fig. 1, being examined it illustrates the Wind turbines intelligent fault provided in an embodiment of the present invention based on random forest
The flow chart of the method for disconnected early warning, can include：
S101：Wind turbines fault indices system is built, Wind turbines status data is extracted and is used as sample data.
By analyzing Wind turbines most common failure, 13 attributes (gear_temp, gear_rate are chosen
Rotor_vol ..., motor_temp_sd, power_mean, is_running) as input attribute, just whether Wind turbines
Often operation builds Wind turbines fault indices system as output attribute；
Part Wind turbines state is selectively extracted from Wind turbines automated system, equipment background control system to go through
History data are used as sample data.It may be noted that selectively extraction part Wind turbines status history data is due in this step
The achievement data that some need in equipment background control system may be imperfect, the selection data integrity so we should try one's best
Compare high data as sample data.
S102：Wind turbines state history sample data to extraction carries out Data Mining and pretreatment.
Exploratory analysis is carried out to sample, aging equipment, equipment of just going into operation is rejected, preliminary close examination Wind turbines normally with it is different
The feature situation of normal state；
Sample is pre-processed, including data cleansing, missing values processing, Data Discretization, attitude layer and data become
Change.
S103：The Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, is carried out according to model result
Analyze and evaluation model.
Build Random Forest model expert's sample set；
Build Wind turbines intelligent fault diagnosis Early-warning Model；
Model result is analyzed and evaluated, Wind turbines failure intelligence is further optimized according to analysis and the result evaluated
Early-warning Model can be diagnosed.
S104：Realized and examined in real time using the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest of structure
It is disconnected.
According to the Wind turbines intelligent fault diagnosis model (after the completion of training) of structure, by Wind turbines equipment-related data
It is linked into model, is monitored diagnosis to device data in real time, once find that diagnostic result is " abnormal ", just to wind turbine
Group sends warning information.
S105：The more historical failures of Wind turbines and normal data are collected, periodically model is trained, upgraded in time
Model.
Collect the more historical failures of Wind turbines and normal data so that model covers the spy of all situations as far as possible
Levy, and can regular one month to model training once, upgrade in time model, improve constantly model accuracy.
In the present embodiment, optionally, the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built.As schemed
Shown in 6, including：
Random Forest model expert's sample set is built first, including：
S201：Parameter setting：Random forests algorithm chooses 500 trees, using stratified sampling by S102 pretreatments
Sample is divided into training sample and test sample accounting is (0.8,0.2).
For stratified sampling in S201, it is necessary to which what is illustrated is：
Stratified sampling is to combine Scientific grouping method with sampling, is specifically that will totally be divided into several homogeneities
Layer, then random sampling or mechanical sampling in each layer, stratified sampling ensure that extracted sample has enough representativenesses.
S202：500 sample sets of generation are concentrated from S201 training sample using the random sampling pattern that can be put back to, often
The number of samples of individual sample set is identical with the number of samples of S201 training sample set, in theory 500 sample set coverings
Original sample concentrates 2/3 data instance, and the data not included are referred to as the outer data (Out-Of-Bag, OOB) of bag, and the outer data of bag can
For as test data, estimation can be very good to assess the classifying quality of the assembled classifier in random forests algorithm.
For being carried out in S202 to data after the random sampling that can put back to, sample set covers original sample concentration in theory
2/3 instance data is, it is necessary to which what is illustrated is：
Assuming that carrying out having the stochastical sampling put back to a number of samples for N sample set D, n times are extracted, each sample is not
The probability being extracted is (1-1/N)N, when N is sufficiently large (1-1/N)N1/e ≈ 0.368 will be converged on, therefore it is considered that sample is sub
Collection covers the instance data of original sample 2/3.
Then Wind turbines intelligent fault diagnosis Early-warning Model is built, including：
S203：500 decision trees are grown using 500 self-service sample sets of generation.Here in each node of each tree
On, it is random from 13 features respectively to select m (m<=13) individual feature, generally takes in Practical ProjectOften
It is secondary to select a feature to carry out branch according to certain rule (information gain) from this randomly selected m feature, until this
Tree fully growth, cut operator is not done therebetween.
For the comentropy told in S203 with information gain, it is necessary to which explanation is：
Comentropy：The uncertainty (confusion degree) of information is illustrated, entropy is bigger, and information is more chaotic, be more difficult to prediction, then should
The information content that index is provided is smaller, and the weight of the index is smaller, more inessential.For categorizing system, classification C is variable, it
Possible value is C1,C2,…,Cn, and the probability that each classification occurs is P (C1),P(C2),…,P(Cn), therefore n is exactly
The sum of classification.Now the entropy of categorizing system can be just expressed as：
Information gain is for feature one by one, exactly to see a feature t, system have it and do not have it when
It is respectively how many to wait information content, and both differences are exactly the information content that this feature is brought to system, i.e. gain.System contains feature
Information content is exactly formula above when t, the information content of system when what it was represented is comprising all features.
InfoGain=H (Y)-H (Y | X)
In categorizing system, the selection of attribute and the division of decision tree are selected according to information gain, for root
Node and the maximum attribute variable of child node selection information gain, then using recursive method build whole decision tree and with
Machine forest.
S204：Test sample collection is predicted according to the 500 of above-mentioned generation decision trees, the test knot of comprehensive each tree
Fruit determines final result according to certain voting mechanism.The good of random forests algorithm utilizes randomness (including random generation
Subsample collection, randomly chooses subcharacter), the correlation between each tree is minimized, overall classification performance is improved, together
When, because the generation time of each tree is very short, and forest can realize parallelization, and the classification speed of random forest is very
It hurry up.Assuming that random forest grader { hi(x,θi, i=1 ..., N) }, the class label of classification results is exactly by each decision tree hi
(x,θi) and probability averagely obtain, for test case x, prediction class label cp, then,
Wherein, argmaxcRepresent that finding parameter c, N with maximum scores represents the number of decision tree in random forest, I
(*) represents performance function,Classification results of the decision tree to C classifications are represented,Represent decision tree hiLeaf node number
Mesh, WiRepresent the weight of i-th tree in random forest.
Model result is as follows：
The model output result of table 1
As can be seen from the above table, whether normally run for Wind turbines, it is 2.7%, explanation to wrap outer data error rate OOB
The overall classifying quality of model is ideal.Fig. 5 be random forest OOB false determination ratios and decision tree number, as can be seen from Figure 5 with
Machine forest false determination ratio constantly reduces with the increase of decision tree number, finally converges to a less definite value.Mark in Fig. 5
Number negative sample error rate is represented for 1 dotted line, the dotted line marked as 2 represents positive sample error rate, and solid black lines represent overall mistake
Rate.
Finally, model result is analyzed and evaluated, further optimization is decided whether according to analysis and the result evaluated
Wind turbines intelligent fault diagnosis Early-warning Model, including：
S205：To predict the outcome carry out analyzing and diagnosing, Model Diagnosis interpretation of result of the model on test set：
Table 2：Test data diagnostic result
Diagnostic analysis is carried out by 8 datas to test set, prediction is all correct, illustrates that the diagnosis effect of model compares
Ideal, without further optimization Wind turbines intelligent fault diagnosis Early-warning Model.If the undesirable feelings of the diagnosis effect of model
Under condition, can by adjust decision tree number, tree depth capacity, the parameter such as measure information mode and feature selection approach
Setting come further Optimized model.It can learn：According to test result, Isosorbide-5-Nitrae, 6,8 Wind turbines are normally run, and the 2nd,
3,5,7 Wind turbines send warning information, and should start corresponding prediction scheme measure at once, prevent bigger safety
Accident and economic loss.
In above-described embodiment, optionally, the Wind turbines intelligent fault diagnosis early warning based on random forest of structure is utilized
Model realization real-time diagnosis.As shown in fig. 6, including：
S301：Online data is accessed from Wind turbines automated system, equipment background control system.
S302：Selected from online data in Wind turbines fault indices system 13 attributes (
Gear_temp, gear_rate, rotor_vol ..., motor_temp_sd, power_mean, is_running)
It is used as input attribute.
S303：Online data is pre-processed, including data cleansing, missing values processing, Data Discretization, attitude layer
With data conversion etc..
S304：Pretreated online data is linked into model, diagnosis is monitored to device data in real time, once
It was found that diagnostic result is " abnormal ", just Wind turbines are sent with warning information, staff can be directed to parameters at once
Take corresponding shutdown measure and alternative.
The foregoing description of the disclosed embodiments, enables those skilled in the art to realize or using the present invention.To this
A variety of modifications of a little embodiments will be apparent for a person skilled in the art, and generic principles defined herein can
Without departing from the spirit or scope of the present invention, to realize in other embodiments.Therefore, the present invention will not be limited
It is formed on the embodiments shown herein, and is to fit to consistent with features of novelty with principles disclosed herein most wide
Scope.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
The specific implementation of the present invention is confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of not departing from present inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the present invention's
Protection domain.
Claims (7)
1. the method for the Wind turbines intelligent fault diagnosis early warning based on random forest, it is characterised in that including：
The historical data for extracting Wind turbines state is used as sample data；Exploratory analysis and pre- place are carried out to the sample data
Reason；
The Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, is analyzed and is evaluated according to model result
Model；
Real-time diagnosis is carried out to Wind turbines equipment using the model after assay, if diagnostic result is abnormal, the model
Warning information will be sent.
2. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, the historical data of Wind turbines state is extracted as sample data, including：
By analyzing Wind turbines most common failure, Wind turbines fault indices system is built；
Wind turbines status history data is extracted from Wind turbines automated system or equipment background control system and is used as sample
Data.
3. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, exploratory analysis and pretreatment are carried out to the sample data, including：
Exploratory analysis is carried out to the sample data, refers to reject aging equipment, equipment of just going into operation, tentatively examines Wind turbines closely
Normally with the feature situation of abnormality；
The sample data is pre-processed, including：Data cleansing, missing values processing, Data Discretization, attitude layer sum
According to conversion.
4. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, the Wind turbines intelligent fault diagnosis Early-warning Model based on random forest is built, including：
Pretreated sample data is divided into training sample and test sample, Random Forest model expert's sample set is built；
Wind turbines intelligent fault diagnosis Early-warning Model is built using sample set.
5. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, analyze and evaluate Wind turbines intelligent fault diagnosis Early-warning Model, including：
To predict the outcome carry out analyzing and diagnosing of the model on test set,
If prediction is all correct, illustrate that the diagnosis effect of model is more satisfactory；
If diagnostic result has error, further Optimized model.
6. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, real-time diagnosis is carried out to Wind turbines equipment using the model trained, including：
Online data is accessed from Wind turbines automated system or equipment background control system；
The attribute in Wind turbines fault indices system is selected from online data as input attribute；
Online data is pre-processed, the model that the online data of pretreatment is accessed after assay, in real time to number of devices
According to being monitored diagnosis.
7. the method for the Wind turbines intelligent fault diagnosis early warning as claimed in claim 1 based on random forest, its feature exists
In, in addition to the more historical failures of Wind turbines and normal data are collected, periodically model is trained, upgrade in time mould
Type.
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| CN107967485A (en) * | 2017-11-16 | 2018-04-27 | 国网冀北电力有限公司电力科学研究院 | Electro-metering equipment fault analysis method and device |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104747368A (en) * | 2015-01-27 | 2015-07-01 | 风脉（武汉）可再生能源技术有限责任公司 | Method and system for optimizing power of wind turbine generator |
| CN105069476A (en) * | 2015-08-10 | 2015-11-18 | 国网宁夏电力公司 | Method for identifying abnormal wind power data based on two-stage integration learning |
-
2017
- 2017-04-21 CN CN201710266789.9A patent/CN107179503B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104747368A (en) * | 2015-01-27 | 2015-07-01 | 风脉（武汉）可再生能源技术有限责任公司 | Method and system for optimizing power of wind turbine generator |
| CN105069476A (en) * | 2015-08-10 | 2015-11-18 | 国网宁夏电力公司 | Method for identifying abnormal wind power data based on two-stage integration learning |
Non-Patent Citations (1)
| Title |
|---|
| 温延立: "风电机组监测数据可视分析研究", 《东北电力大学硕士学位论文》 * |
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