CN109492777A - A kind of Wind turbines health control method based on machine learning algorithm platform - Google Patents
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Abstract
A kind of Wind turbines health control method based on machine learning algorithm platform.The health control method includes: theoretical power generation equilibrium analysis, performance rate assessment, fault diagnosis model and health control modelling, health evaluating and health degree analysis, the realization of health control, that is, repair based on condition of component.According to the deep application of theoretical capacity equilibrium analysis, in conjunction with big data technology, through machine learning in conjunction with artificial intelligence, realize the anticipation and assessment of the health status of blower and its accessory and capital equipment, finally make the maintenance model of Wind turbines from scheduled overhaul, regular inspection periodical repair to repair based on condition of component transition, to reduce Wind turbines scheduled overhaul loss electricity, non-plan repair loss electricity and performance loss electricity, reach the target for improving wind-powered electricity generation station generated energy, improving station economic benefit.
Description
Technical field
The present invention relates to internet big data technology and energy wind power generation fields, and in particular to one kind is based on machine learning
The Wind turbines health control method of algorithm platform.
Background technique
China's wind-powered electricity generation industry is grown rapidly, installed capacity of wind-driven power rapid growth and the special addressing and wind-powered electricity generation of wind power plant
The fluctuation of unit load brings huge challenge to the work of Wind turbines fault alarm.There is alarm in conventional alarm system
It is more, alarm is inaccurate, alarm not in time the problem of, and existing big data system is not fully used, and is lacked there are following
It falls into and insufficient:
1) magnanimity fan operation data, maintenance data and environmental data waste, huge data volume can not but pass through software systems
Reach filtering, processing, analysis within the reasonable time and is formed as the more effective information of wind-powered electricity generation enterprise production and operation management.
2) a large amount of data do not play the role of big data, the low efficiency of data processing and data mining, without accurate
Visual analyzing show.
3) existing alarm causes Threshold Alerts, is not associated associated components, can not achieve the in-advance of alarm
And accuracy.
4) the Wind turbines health control platform based on machine learning algorithm platform is not built, wind power plant Wind turbines are strong
There are management risks for health state unpredictability.
Summary of the invention
The purpose of the present invention is propose that one kind passes through scene for shortcoming existing for current Wind turbines management system
The means such as fault data statistics, expert's assessment, quantification modeling, under the premise of guaranteeing safety and integrity, with the smallest
Repairing shutdown loss and the consumption of the smallest Maintenance Resource is target, provides a kind of Wind turbines based on machine learning algorithm platform
Health control method.
Present general inventive concept are as follows: Wind turbines are health management system arranged, goed deep into according to theoretical capacity equilibrium analysis
Using through machine learning in conjunction with artificial intelligence, realizing the strong of blower and its accessory and capital equipment in conjunction with big data technology
The anticipation and assessment of health state finally make the maintenance model of Wind turbines from scheduled overhaul, regular inspection periodical repair to repair based on condition of component mistake
It crosses, to reduce Wind turbines scheduled overhaul loss electricity, non-plan repair loss electricity and performance loss electricity, reaches raising
Wind-powered electricity generation station generated energy, the target for improving station economic benefit.
The technical scheme is that such:
A kind of Wind turbines health control method based on machine learning algorithm platform.
The health control method includes: theoretical power generation equilibrium analysis, performance rate assessment, fault diagnosis model and strong
The design of health administrative model, health evaluating and health degree analysis, the realization of health control, that is, repair based on condition of component.Each part mentioned above function is such as
Under:
1, theoretical power generation equilibrium analysis.
Calculation formula: theoretical power generation=reality generated energy+scheduled overhaul loses electricity+non-plan repair loses electricity+and rations the power supply
It loses electricity+involvement and loses electricity+performance loss electricity.Scheduled overhaul loses electricity: based on Inspection interval, establishment inspection
The plan of repairing, the electric quantity loss generated during carrying out preventive overhaul to unit are known as scheduled overhaul loss electricity (unit itself
Maintenance)；
Non-plan repair loses electricity: the electric quantity loss during unit failure causes shutdown and defect elimination to handle is known as unplanned inspection
It repairs loss electricity and is called breakdown loss electricity (unit faults itself)；
It rations the power supply and loses electricity: holding two kinds including rationing the power supply to shut down and ration the power supply to drop, the electric quantity loss during scheduling is rationed the power supply is known as rationing the power supply
Lose electricity；
Involvement loss electricity: involvement is divided into involvement and over-the-counter involvement in field.Involvement is shut down in the field other than referring to because of unit and is set in
It is standby stop transport (such as main transformer, the matched facility failure of collection electric line or scheduled overhaul) cause unit be forced it is out of service during make
At electric quantity loss；Over-the-counter involvement, which is shut down, to be referred to because of over-the-counter power grid reason (such as external power lines road, electric power system fault, maintenance)
Cause unit whithin a period of time (non real-time scheduling) be forced it is out of service in a period of electric quantity loss or natural cause
Caused (sleet, frost, typhoon etc. can not resist weather condition) causes equipment downtime, drop to hold beyond the case where fan design grade
Electric quantity loss.
Performance loss electricity: hold and below standard two kinds of unit performance comprising unit from drop.Unit holds from drop refers to unit certainly
Body reason causes unit drop power output operation, as protected operation after main component (bearing) replacement.Unit performance is below standard to refer to fortune
Row power output be not achieved standard value or wind speed reach specified incision wind speed after unit can not cut operation, the electricity in this period damages
It loses and is known as performance loss electricity.
2, performance rate is assessed.
The assessment of Wind turbines performance rate is exactly to establish evaluation mould on the basis of analyzing Wind turbines performance risk factors
Type estimates Wind turbines performance rate score, and single risk size can be protruded in comprehensive assessment to total evaluation
Influence, more objectively respond actual conditions.Dynamic variable weight fuzzy evaluation model mainly includes two parts:
(1) weight of Wind turbines performance rate evaluation index is determined with the theory that dynamic is weighed surely；
(2) then weight is corrected according to the correlation between index, obtains variable weight model assessment models.
Using variable weight Fuzzy evaluation mode, is graded and scored lower than fan performance risk, reflect unit with data
Performance condition, and equipment performance is divided into A, B, C three grades, unit performance risk situation is calculated, unit performance grade is obtained
Evaluate score, by A grade of Wind turbines it is qualitative for performance it is up to standard, by B, C grades of Wind turbines it is qualitative be performance not
It is up to standard；After the evaluation of passage capacity grade, equipment performance is quantified into the qualitative blower not up to standard for performance, into fault diagnosis mould
Type realizes the anticipation of failure primary to performance blower not up to standard.
3, fault diagnosis model and health control modelling.
Enter big data environment by real time data, overhaul data, video data etc., it is different with multi-source by database interfusion
Structure model, is associated Various types of data, then forms knowledge by computation model, recycles machine learning method and algorithm,
Breakdown judge is carried out by failure of the fault diagnosis to equipment, forms fault type, and pass through the index of setting equipment health degree
Initial value constantly carries out deep learning to health degree initial value to carry out intelligent fault alarm, until index value is completely quasi-
Really, alarm is really accurate.And after completing machine learning and deep learning, accurate health evaluating report and equipment health degree are formed
Real-time tracking and the anticipation of equipment health status are realized in analysis.
Quantification treatment is carried out rather than simple qualitative description to the attribute of each component, main includes representing to disagree
Standard value, acceptable value, the value that transfinites etc. of justice, are described the problem with number, become the numerical chracter that computer can identify, currently
Measured value by with standard value, acceptable value, the value that transfinites according to certain algorithm comparison after by intellectual analysis, provide alarm etc.
Grade analyzes whether it is false alarm, finally provides conclusion, provides decision-making foundation for maintenance staff.
4, health evaluating and health degree analysis, the realization of health control, that is, repair based on condition of component.
Complete machine learning, intelligent platform and it is health management system arranged after, realize the anticipation of equipment health, driving equipment
Inspecting state is from completing overhaul of the equipments and changing from regular inspection periodical repair, scheduled overhaul to repair based on condition of component afterwards into thing with transition in advance.
Comprehensive Evaluation is carried out from multi-level, multi-angle, to realize the accurate, comprehensive of evaluation result.Firstly, more current measured value with
Standard value obtains the degree for deviateing standard value.Secondly, the relatively important carry out fuzzy evaluation to current detection component, provides it
Relative weighting.We are also contemplated that the factor of other aspects, provide evaluation result based on various factors.
The invention has the advantages that:
1) the existing wrong report of wind farm device alarm and failing to report phenomenon are solved, realizes the objective and accurate property of alarm, is fault alarm
Treatment people provides objective accurate alert process foundation；
2) wind power plant Wind turbines health status unpredictability is solved, realizes the higher wind farm device health status of confidence level
Prediction, provides scientific basis for State Maintenance, Failure elimination in budding state, realizes repair based on condition of component；
3) it is predetermined to solve the problems, such as that Wind turbines maintenance program is difficult to, realizes Reliability Maintenance；
4) it solves the problems, such as that the shortage of wind farm device maintenance expert and Maintenance Resource can not be shared, establishes the wind constantly improve
Electric field equipment repairs intelligent expert system；
5) wind farm device is solved because of random stop phenomenon caused by failure and the influence to production, production capacity, profit, realizes wind
Electric field equipment realizes the maximization of wind power plant production capacity, profit maximum because of the Modulatory character shut down caused by failure.
Detailed description of the invention
Fig. 1 is Wind turbines health control flow diagram of the present invention.
Specific embodiment
A specific embodiment of the invention combination attached drawing is illustrated.
As shown in Figure 1, a kind of Wind turbines health control method based on machine learning algorithm platform.The management method packet
Include following procedure:
1, performance rate is evaluated.
Utility grade evaluation model carries out performance rate assessment to blower and its capital equipment and accessory, realizes to wind
The division of motor group performance rate will be defined as performance blower up to standard in A grades of blower, will define in B/C grades of blower
For performance blower not up to standard.
2, fault diagnosis model designs.
By step 1, by fan performance blower not up to standard, into fault diagnosis model, in conjunction with big data platform and machine
The application for learning algorithm platform carries out breakdown judge by failure of the fault diagnosis to equipment, forms fault type, and will be faulty
Blower is alarmed.
3, health evaluating and health degree analysis.
Standby redundancy information, service personnel's information, Weather information are equal to health control models coupling, formed accurately strong
Health assessment report and equipment health degree analysis realize real-time tracking and the anticipation of equipment health status, realize the pre- of equipment health
Sentence, driving equipment inspecting state is from completing overhaul of the equipments from regular inspection periodical repair, scheduled overhaul to shape afterwards into thing with transition in advance
State maintenance transformation.
Specific step is as follows:
1) real time data, overhaul data, video data etc. are written to Hadoop big data platform, realize different types of data
Fusion.We do not start with from sensor, start with from blower structure mechanism, using step analysis means, are split to each
Small component, and the function of each widget, relatively important weight, failure performance, fault message, solution etc. is detailed
Information all enumerates out；For be fitted without sensor part of appliance we intend taking correlation rule, using indirect measurement
Technology analyzes part of appliance that is associated therewith and being mounted with sensor, in conjunction with sensorless technology and some advanced biographies
Sensor obtains；Its bright spot is consideration that each component is associated, rather than traditional independence is analyzed.
2) blower data are analyzed and screened with modeling data, the data that model training needs are imported into machine learning
Platform.
3) to the format of data, legitimacy of data etc. carries out relevant processing.
4) it designs and extracts feature and be used for model training.
5) it chooses GBDT algorithm and carries out model training, export trained machine learning model for subsequent pre-estimation
It calculates.
Algorithms selection: GBDT(Gradient Boosting Decision Tree, Gradient Iteration decision tree)
The applicable scene of GBDT:
Continuous feature is more；
Too many Character adjustment is not needed；
Has certain interpretation after visualization；
Model optimization emphasis-parameter；
GBDT model includes 6 adjustable parameters altogether, is respectively as follows: learning rate, the depth capacity of single tree, the number of tree, most leaflet
Sub- weight, minimum division threshold value, L2 canonical；
Adjusting ginseng work is the process that more preferable result is found in continuous modeling, and project team carries out more wheels to GBDT model and adjusts ginseng, final to select
One group of most suitable parameter configuration is selected, so that model is stablized in training set and assessment concentrated expression；
6) online Prediction service framework is built, data processing, and online Prediction interface and service logic research and development, web services and API are real
It is existing, test.
7) model generated to model training step is assessed using recall rate and logarithm loss function.
Recall rate is a kind of Classification Algorithms in Data Mining evaluation index, the accuracy of decision Tree algorithms, neural network algorithm
It is to be judged according to recall rate come calculated result.Recall rate is the information bar number in the correct information item number and sample extracted
Ratio, (TP: being correctly divided into the number of positive example to recall=TP/ (TP+FN), i.e., practical to be positive example and be classified device stroke
It is divided into the instance number of positive example；FN: mistakenly being divided the number of example of being negative, i.e., practical to be positive example but be classified device division and be negative example
Instance number).
Such as: it realizes alarm and the number that should be alarmed is A, the number that alarm is not implemented but should alarm is B, then alarm is sentenced
Fixed recall rate recall=A/ (A+B).
Logarithm loss function (Log-loss): for estimating the predicted value of model and the inconsistent degree of true value.Log-
Loss is a soft classification accuracy measure, and the confidence level of the classification belonging to it is indicated using probability.(yi refers to
True classification 0 or 1 belonging to i sample, pi indicate that i-th of sample belongs to the probability of classification 1).
In classification output, if output is real number value, that is, belong to the probability of each classification, then Log- can be used
Loss evaluates classification results.Such as: when prediction is exactly matched with concrete class, then two parts of formula are all 0(vacations
Determine 0log0=0), then logarithm loss function value is 0.
4, health evaluating and health degree analysis, the realization of health control, that is, repair based on condition of component.
Complete machine learning, intelligent platform and it is health management system arranged after, realize the anticipation of equipment health, driving equipment
Inspecting state is from completing overhaul of the equipments and changing from regular inspection periodical repair, scheduled overhaul to repair based on condition of component afterwards into thing with transition in advance.
We carry out Comprehensive Evaluation from multi-level, multi-angle, to realize the accurate, comprehensive of evaluation result.Firstly, we
Compare current measured value and standard value, obtains the degree for deviateing standard value.Secondly, we are to the relatively important of current detection component
Fuzzy evaluation is carried out, its relative weighting is provided.We are also contemplated that the factor of other aspects, provide judge based on various factors
As a result.
Claims (3)
1. a kind of Wind turbines health control method based on machine learning algorithm platform, which is characterized in that the health control side
Method includes: theoretical power generation equilibrium analysis, performance rate assessment, fault diagnosis model and health control modelling, health
Assessment and health degree analysis, the realization of health control, that is, repair based on condition of component, each part mentioned above function are as follows:
(1) theoretical power generation equilibrium analysis:
Calculation formula: theoretical power generation=reality generated energy+scheduled overhaul loses electricity+non-plan repair loses electricity+loss of rationing the power supply
Electricity+performance loss electricity is lost in electricity+involvement, and scheduled overhaul loses electricity, based on Inspection interval, establishment maintenance meter
It draws, the electric quantity loss generated during carrying out preventive overhaul to unit is known as scheduled overhaul loss electricity, and (unit itself is examined
It repairs)；
(2) performance rate is assessed:
The assessment of Wind turbines performance rate is exactly to establish evaluation model on the basis of analyzing Wind turbines performance risk factors,
Wind turbines performance rate score is estimated, single risk size can be protruded in comprehensive assessment to the shadow of total evaluation
It rings, more objectively responds actual conditions；
(3) fault diagnosis model and health control modelling:
Enter big data environment by real time data, overhaul data, video data etc., passes through database interfusion and multi-source heterogeneous mould
Type is associated Various types of data, then forms knowledge by computation model, recycles machine learning method and algorithm, passes through
Fault diagnosis carries out breakdown judge to the failure of equipment, forms fault type, and initial by the index of setting equipment health degree
Value constantly carries out deep learning to health degree initial value to carry out intelligent fault alarm, until index value entirely accurate, report
It is alert really accurate, and complete machine learning and after deep learning, form accurate health evaluating report and equipment health degree analysis,
Realize real-time tracking and the anticipation of equipment health status；
(4) health evaluating and health degree analysis, the realization of health control, that is, repair based on condition of component:
Complete machine learning, intelligent platform and it is health management system arranged after, realize the anticipation of equipment health, driving equipment is overhauled
State is from completing overhaul of the equipments and changing from regular inspection periodical repair, scheduled overhaul to repair based on condition of component, from more afterwards into thing with transition in advance
Level, multi-angle carry out Comprehensive Evaluation, to realize the accurate, comprehensive of evaluation result.
2. health control method according to claim 1, it is characterised in that: the evaluation that described performance rate assessment is established
Model is dynamic variable weight fuzzy evaluation model, mainly includes two parts: (1) determining Wind turbines with the theory that dynamic is weighed surely
The weight of performance rate evaluation index；(2) then weight is corrected according to the correlation between index, obtains variable weight mould
Type assessment models.
3. health control method according to claim 1, it is characterised in that: described health evaluating and health degree analysis
Specific step is as follows:
1) real time data, overhaul data, video data etc. are written to Hadoop big data platform, realize different types of data
Fusion.We do not start with from sensor, start with from blower structure mechanism, using step analysis means, are split to each
Small component, and the function of each widget, relatively important weight, failure performance, fault message, solution etc. is detailed
Information all enumerates out；For be fitted without sensor part of appliance we intend taking correlation rule, using indirect measurement
Technology analyzes part of appliance that is associated therewith and being mounted with sensor, in conjunction with sensorless technology and some advanced biographies
Sensor obtains；Its bright spot is consideration that each component is associated, rather than traditional independence is analyzed；
2) blower data are analyzed and screened with modeling data, the data that model training needs are imported into machine learning and are put down
Platform.；
3) to the format of data, legitimacy of data etc. carries out relevant processing；
4) it designs and extracts feature and be used for model training；
5) it chooses GBDT algorithm and carries out model training, export trained machine learning model for subsequent and estimate calculating；
Algorithms selection: GBDT(Gradient Boosting Decision Tree, Gradient Iteration decision tree)；
The applicable scene of GBDT:
Continuous feature is more；
Too many Character adjustment is not needed；
Has certain interpretation after visualization；
Model optimization emphasis-parameter；
GBDT model includes 6 adjustable parameters altogether, is respectively as follows: learning rate, the depth capacity of single tree, the number of tree, most leaflet
Sub- weight, minimum division threshold value, L2 canonical；
Adjusting ginseng work is the process that more preferable result is found in continuous modeling, and project team carries out more wheels to GBDT model and adjusts ginseng, final to select
One group of most suitable parameter configuration is selected, so that model is stablized in training set and assessment concentrated expression；
6) online Prediction service framework is built, data processing, and online Prediction interface and service logic research and development, web services and API are real
It is existing, test；
7) model generated to model training step is assessed using recall rate and logarithm loss function.
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| CN111062508A (en) * | 2019-03-28 | 2020-04-24 | 华北电力大学（保定） | Method for evaluating real-time running state of wind turbine generator based on big data technology |
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