CN103217286A - Wind power unit transmission system failure identification method and system based on failure data - Google Patents
Wind power unit transmission system failure identification method and system based on failure data Download PDFInfo
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Abstract
The invention provides a wind power unit transmission system failure identification method and system based on failure data. The wind power unit transmission system failure identification method and system based on the failure data includes the steps: step1, generating a plurality of failure state models of a wind power unit according to history working data of the wind power unit; and step2, generating a current state model according to real-time working data of the wind power unit, and carrying out estimation on failures of the wind power unit according to similarity of the current state model and the failure state models. According to the wind power unit transmission system failure identification method and system based on the failure data, comparison can be carried out on wind power unit transmission system failure models and current wind power unit transmission system state models, then development tendency of early failures of a wind power unit transmission system is identified and judged according to the similarity between the wind power unit transmission system failure models and current wind power unit transmission system state models, and preventative measures can be carried out.
Description
Technical field
The present invention relates to technical field of data processing, particularly a kind of driving unit fault recognition methods of wind-powered electricity generation unit and system based on fault data.
Background technology
Along with the exhaustion day by day of the energy and increasingly sharpening of polluting, regenerative resource and clean energy resource more and more come into one's own.Wind power generation or claim that wind-power electricity generation is a kind of regenerative resource of cleaning, and the operating cost of wind-force unit is low, so the wind power technology equipment is the important component part of wind-powered electricity generation industry also is the basis of wind-powered electricity generation industry development and ensure.
Though wind-powered electricity generation has cleaning, renewable, low cost and other advantages, need be arranged in defectives such as field but have equally to geography requirement height, equipment, therefore how the data of wind power equipment being collected and handled is the basis of automatic management, data monitoring.The service data of just simple record wind-powered electricity generation unit kinematic train in the prior art, and can't carry out early warning in advance.Cause only when wind-powered electricity generation unit kinematic train breaks down, just going maintenance like this, very big for the normal influence on system operation of equipment.
Summary of the invention
The technical problem to be solved in the present invention is that the present invention proposes a kind of more reliable initial failure recognition methods of wind-powered electricity generation unit kinematic train and system based on fault data.
In order to solve the problems of the technologies described above, embodiments of the invention provide a kind of wind-powered electricity generation unit driving unit fault recognition methods based on fault data, comprise
Step 1, according to the history data of wind-powered electricity generation unit kinematic train, generate a plurality of failure state model of wind-powered electricity generation unit kinematic train;
Preferred as technique scheme, described step 1 specifically comprises:
Step 11, obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Step 12, a state parameter set forming at the state parameter in each time period are as the failure state model in this time period.
Preferred as technique scheme, described step 1 specifically comprises:
Step 11, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter set that described state parameter is formed is to set up first failure state model;
Step 12, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
Step 13, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
Preferred as technique scheme, described step 2 specifically comprises:
Step 21, obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generate the current state model;
Step 22, described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Step 23, according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
Preferred as technique scheme all generates a plurality of failure state model respectively at each dissimilar fault in the described step 1.
In order to solve the problems of the technologies described above, embodiments of the invention also provide a kind of wind-powered electricity generation unit driving unit fault recognition system based on fault data, comprising:
The fault model MBM is used for the history data according to wind-powered electricity generation unit kinematic train, generates a plurality of failure state model of wind-powered electricity generation unit kinematic train;
Estimate module, be used for generating the current state model according to the real-time running data of described wind-powered electricity generation unit kinematic train, and according to the similarity of described current state model and described failure state model so that described wind-powered electricity generation unit driving unit fault is estimated.
Preferred as technique scheme, described fault model MBM specifically comprises:
Acquiring unit is used to obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Modeling unit, a state parameter set that is used for forming at the state parameter in each time period is as the failure state model in this time period.
Preferred as technique scheme, described fault model MBM specifically comprises:
First modeling unit is used to obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter that described state parameter forms is gathered to set up first failure state model;
Second modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
The 3rd modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
Preferred as technique scheme, the described module of estimating specifically comprises:
The current state model unit is used to obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generates the current state model;
The contrast unit is used for described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Estimate the unit, be used for according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
Preferred as technique scheme, described fault model MBM all generates a plurality of failure state model respectively at each dissimilar fault.
The beneficial effect of technique scheme of the present invention is as follows:
The embodiment of the invention can compare by the failure state model of wind-powered electricity generation unit kinematic train and the current state model of current wind-powered electricity generation unit kinematic train, to discern and to judge the development trend of the initial failure of system according to their similarity degree, prevent trouble before it happens.
Description of drawings
Fig. 1 is the schematic flow sheet based on the wind-powered electricity generation unit driving unit fault recognition methods of fault data of the embodiment of the invention;
Fig. 2 is the structural representation based on the wind-powered electricity generation unit driving unit fault recognition system of fault data of the embodiment of the invention.
Embodiment
For making the technical problem to be solved in the present invention, technical scheme and advantage clearer, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.
Embodiments of the invention provide a kind of wind-powered electricity generation unit driving unit fault recognition methods based on fault data, and its flow process comprises as shown in Figure 1:
Step 1, according to the history data of wind-powered electricity generation unit kinematic train, generate a plurality of failure state model of wind-powered electricity generation unit kinematic train;
In the said method, be to adopt current state model and a plurality of failure state model that generate according to history data to compare, with the similarity of failure state model fault estimated with basis.Whether this mode can be estimated wind-powered electricity generation unit kinematic train according to historical data and can break down, and prevents trouble before it happens, and improves the stability of system.Concrete, because that the kinematic train of wind-powered electricity generation unit may comprise is multiple, therefore can all generate one or more failure state model respectively to each fault.The real-time running data that obtains can be generated a current state model like this, then current state model and each failure state model be compared respectively to determine similarity.If with some failure state model similarities than higher, can think that then this fault may take place this wind-powered electricity generation unit.
In one embodiment of the invention, adopt following method to generate the failure state model of wind-powered electricity generation unit kinematic train.Be that described step 1 specifically comprises:
Step 11, obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Step 12, a state parameter set forming at the state parameter in each time period are as the failure state model in this time period.
In one embodiment of the invention, the week before fault takes place, the history data in two weeks, month are obtained in concrete can adopting, and generate three failure state model respectively.Be that described step 1 specifically comprises:
Step 11, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter set that described state parameter is formed is to set up first failure state model;
Step 12, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
Step 13, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
Can utilize the failure state model of real-time running data and a plurality of time periods to compare respectively like this, can determine whether to exist potential faults by the model in the fault generating process like this.
After having obtained a plurality of failure state model, in step 2, need to utilize the current real-time running data of wind-powered electricity generation unit kinematic train to generate the current state model.Be that described step 2 specifically comprises:
Step 21, obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generate the current state model;
Step 22, described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Step 23, according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
Because the fault of wind-powered electricity generation unit kinematic train may be multiple, therefore can all carry out abovementioned steps at fault in each.Promptly each fault is all generated a plurality of failure state model, compare by current state model and each failure state model then.Be all to generate a plurality of failure state model respectively at each dissimilar fault in the described step 1.
Embodiments of the invention also provide a kind of wind-powered electricity generation unit driving unit fault recognition system based on fault data, and its structure comprises as shown in Figure 2:
The fault model MBM is used for the history data according to wind-powered electricity generation unit kinematic train, generates a plurality of failure state model of wind-powered electricity generation unit kinematic train;
Estimate module, be used to obtain the real-time running data of described wind-powered electricity generation unit kinematic train, the real-time running data of described wind-powered electricity generation unit kinematic train and described failure state model are compared with the similarity of determining described real-time running data and described failure state model so that described wind-powered electricity generation unit driving unit fault is estimated.
Wherein, described fault model MBM specifically comprises:
Acquiring unit is used to obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Modeling unit, a state parameter set that is used for forming at the state parameter in each time period is as the failure state model in this time period.
Wherein, described fault model MBM specifically comprises:
First modeling unit is used to obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter that described state parameter forms is gathered to set up first failure state model;
Second modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
The 3rd modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
Wherein, the described module of estimating specifically comprises:
The current state model unit is used to obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generates the current state model;
The contrast unit is used for described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Estimate the unit, be used for according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
Wherein, described fault model MBM all generates a plurality of failure state model respectively at each dissimilar fault.
The embodiment of the invention can compare by the failure state model of wind-powered electricity generation unit kinematic train and the current state model of current wind-powered electricity generation unit kinematic train, to discern and to judge the development trend of the initial failure of system according to their similarity degree, prevent trouble before it happens.
The above is a preferred implementation of the present invention; should be pointed out that for those skilled in the art, under the prerequisite that does not break away from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. based on the wind-powered electricity generation unit driving unit fault recognition methods of fault data, it is characterized in that, comprising:
Step 1, according to the history data of wind-powered electricity generation unit kinematic train, generate a plurality of failure state model of wind-powered electricity generation unit kinematic train;
Step 2, generate the current state model according to the real-time running data of described wind-powered electricity generation unit kinematic train, and according to the similarity of described current state model and described failure state model so that described wind-powered electricity generation unit driving unit fault is estimated.
2. the wind-powered electricity generation unit driving unit fault recognition methods based on fault data according to claim 1 is characterized in that described step 1 specifically comprises:
Step 11, obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Step 12, a state parameter set forming at the state parameter in each time period are as the failure state model in this time period.
3. the wind-powered electricity generation unit driving unit fault recognition methods based on fault data according to claim 1 is characterized in that described step 1 specifically comprises:
Step 11, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter set that described state parameter is formed is to set up first failure state model;
Step 12, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
Step 13, obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
4. according to claim 1 or 2 or 3 described wind-powered electricity generation unit driving unit fault recognition methodss, it is characterized in that described step 2 specifically comprises based on fault data:
Step 21, obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generate the current state model;
Step 22, described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Step 23, according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
5. the wind-powered electricity generation unit driving unit fault recognition methods based on fault data according to claim 1 is characterized in that, all generates a plurality of failure state model respectively at each dissimilar fault in the described step 1.
6. the wind-powered electricity generation unit driving unit fault recognition system based on fault data is characterized in that, comprising:
The fault model MBM is used for the history data according to wind-powered electricity generation unit kinematic train, generates a plurality of failure state model of wind-powered electricity generation unit kinematic train;
Estimate module, be used for generating the current state model according to the real-time running data of described wind-powered electricity generation unit kinematic train, and according to the similarity of described current state model and described failure state model so that described wind-powered electricity generation unit driving unit fault is estimated.
7. the wind-powered electricity generation unit driving unit fault recognition system based on fault data according to claim 6 is characterized in that described fault model MBM specifically comprises:
Acquiring unit is used to obtain the state parameter of the different time sections of the unit of wind-powered electricity generation described in history data kinematic train before fault takes place;
Modeling unit, a state parameter set that is used for forming at the state parameter in each time period is as the failure state model in this time period.
8. the wind-powered electricity generation unit driving unit fault recognition system based on fault data according to claim 6 is characterized in that described fault model MBM specifically comprises:
First modeling unit is used to obtain the state parameter of the unit of wind-powered electricity generation described in history data kinematic train in fault takes place by the last week, and the state parameter that described state parameter forms is gathered to set up first failure state model;
Second modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up second failure state model;
The 3rd modeling unit is used for obtaining history data, the state parameter of described wind-powered electricity generation unit kinematic train in fault generation the last fortnight, and the state parameter set that described state parameter is formed is to set up the 3rd failure state model.
9. according to claim 6 or 7 or 8 described wind-powered electricity generation unit driving unit fault recognition systems, it is characterized in that the described module of estimating specifically comprises based on fault data:
The current state model unit is used to obtain the real-time running data of described wind-powered electricity generation unit kinematic train, and generates the current state model;
The contrast unit is used for described current state model and described a plurality of failure state model are compared respectively to obtain the similarity of described current state model and each failure state model;
Estimate the unit, be used for according to described similarity so that described wind-powered electricity generation unit driving unit fault is estimated.
10. the wind-powered electricity generation unit driving unit fault recognition system based on fault data according to claim 6 is characterized in that described fault model MBM all generates a plurality of failure state model respectively at each dissimilar fault.
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Application publication date: 20130724 |