CN106980922A - A kind of power transmission and transformation equipment state evaluation method based on big data - Google Patents
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Abstract
The present invention relates to a kind of power transmission and transformation equipment state evaluation method based on big data, its technical characterstic is to comprise the following steps：Set up and optimizing evaluation forecast model group, the valuation prediction models group includes Fault tree, state evaluation model and trend-analyzing model；Set up and optimize the equipment typical defect Sample Storehouse based on image data processing technique；State evaluation and risk trend analysis are carried out according to Fault tree, state evaluation model, trend-analyzing model and image deflects Sample Storehouse.The present invention is reasonable in design, and incidence relation carries out data mining and model analysis between it makes full use of a large amount of status informations of equipment, with the health status of pre- measurement equipment, and the accuracy rate of its automatic Evaluation is lifted to 85% from 50% or so；Meanwhile, the present invention can will be seen that the image measurement result of the detection means such as light, infrared, ultraviolet is brought into the evaluation of equipment state, lifting means state analysis and status predication level.
Description
Technical field
The invention belongs to grid equipment state inspection field, especially a kind of power transmission and transforming equipment shape based on big data
State evaluation method.
Background technology
At present, the state evaluation to grid equipment is typically to use grade form mode, and what equipment was likely to occur is all kinds of scarce
Fall into, scored according to its importance and degradation for occurring position, by deduction of points number characterize the healthy shape of equipment
State.But, the above method can not be evaluated for the defect being not included in table, that is, equipment of problems may also be by
It is chosen as normal condition；Additionally, due to not analyzing defect Producing reason, the defect phenomenon only in accordance with generation is evaluated,
The state of evaluation may be inconsistent with the virtual condition of equipment；, can only be by artificial for the image detection result such as infrared, ultraviolet
Go to be evaluated.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide one kind is reasonable in design, evaluation is accurate and automates
The high power transmission and transformation equipment state evaluation method based on big data of degree.
The present invention solves its technical problem and takes following technical scheme to realize：
A kind of power transmission and transformation equipment state evaluation method based on big data, comprises the following steps：
Step 1, foundation and optimizing evaluation forecast model group, the valuation prediction models group include Fault tree, state and commented
Valency model and trend-analyzing model；
Step 2, set up and optimize the equipment typical defect Sample Storehouse based on image data processing technique；
Step 3, according to Fault tree, state evaluation model, trend-analyzing model and image deflects Sample Storehouse carry out shape
State is evaluated and risk trend analysis.
The foundation of the Fault tree is with optimization method：According to the equipment working condition information of equipment running status, environment
Information, monitoring of equipment information and equipment regular inspection Test Information, are analyzed at link by the first data prediction and the first big data
Reason is obtained, wherein, the first data prediction link includes data pick-up, data cleansing, data selection and data conversion, and it passes through
Dimension, which is reduced, after increase dimension determines data area, will be unstructured with semi-structured data structure by structural means
Change, by filling a vacancy, smooth noise means serialized data and remove abnormal data and repeated data；First big data is analyzed
Link realizes the foundation of valuation prediction models, and combination failure case by classification, cluster, regression analysis and association discovery method
The mode of study sets up Fault tree.
The state evaluation model includes carrying out status data packet, carried out according to diagnostic rule and judgement formula
Fault diagnosis, obtains probability of malfunction, main parameters, accident analysis progress state evaluation, final to carry out equipment state grading, equipment
Failure risk and cause influence analysis.
The trend-analyzing model is included according to all previous evaluation result data progress data sorting, according to early warning threshold values one
Fix time to be spaced and carry out trend analysis, so as to obtain pre-warning time and trend analysis matrix of consequence.
The concrete methods of realizing of the step 2 is：Equipment light visual, equipment infrared imaging picture and equipment is ultraviolet
Imaging picture is set up by two links of the second data prediction and second largest data analysis, the second data prediction link
Calculated using smoothing processing, medium filtering, gradient operator and rim detection complete the detection of picture quality, noise reduction optimization, duplicate removal
Work, second largest data analysis link is contrasted by reference base picture, Similarity Measure, key area identification, aberration identification technology
Realize normal condition, attention state, abnormality, severe conditions and the different orders of severity of attention above tri-state of distinct device
The foundation of defect sample.
The concrete methods of realizing of the step 3 is：When external input device equipment essential information, structural state data and
During picture unstructured data, the 3rd data prediction link is carried out to it first, the data prediction link is to above-mentioned data
The noise reduction optimization of examination duplicate removal, rejecting abnormal data, seasonal difference and picture is carried out, pretreated data will call state to comment
Valency model, Fault tree and image deflects picture sample storehouse are specifically assessed the state of equipment, and quantitative analyzes
The current state of equipment, the probability of happening of defect failure, and combine manual confirmation and revision value feed back to the analysis of the first big data,
It is perfect that second largest data analysis link carries out model；After multiple diagnostic evaluation is carried out to single equipment, and then application trend
Analysis model combines all previous evaluation result and the being predicted property of development of defects trend of the equipment is judged.
Advantages and positive effects of the present invention are：
1st, the various information such as present invention fusion electric network state, equipment operation, maintenance, experiment and natural environment, foundation is based on
The equipment state evaluation model and trend-analyzing model of big data, incidence relation enters between making full use of a large amount of status informations of equipment
Row data mining and model analysis, with the health status of pre- measurement equipment, the accuracy rate of its automatic Evaluation from 50% or so lifting to
85%.The present invention relies on the analysis and the continuous study to fault case of big data, it is not necessary to defect is carried out again exhaustive, it is to avoid
Because not being included in defect table, and the equipment that there will be problem is chosen as the situation of normal condition, broken away from due to defect phenomenon
Occur causality with failure not knowing about, and situation about can not evaluate.
2nd, the present invention uses the place of the unstructured view data such as visible light video picture, infrared thermal imaging, ultraviolet imagery
Reason technology, sets up the image pattern storehouse of capital equipment typical defect, can be by the image measurement result of the detection means such as infrared, ultraviolet
Bring into the evaluation of equipment state, lifting means state analysis and status predication level.
Brief description of the drawings
Fig. 1 is the process chart of the present invention；
Fig. 2 is the state evaluation model of the present invention；
Fig. 3 is the trend-analyzing model of the present invention.
Embodiment
The embodiment of the present invention is further described below in conjunction with accompanying drawing：
A kind of power transmission and transformation equipment state evaluation method based on big data, as shown in figure 1, comprising the following steps：
Step 1, foundation and valuation prediction models group of the optimization based on Fault tree.
This step be according to different device classes, using reflect such equipment running status full dimension data and utilize
The analysis method of big data is modeled the result with optimization.Valuation prediction models group includes Fault tree, state evaluation mould
Type and trend-analyzing model.
The foundation of Fault tree is with optimization method：The full dimension data of equipment running status include equipment working condition information,
Environmental information, monitoring of equipment information and equipment regular inspection Test Information etc..Wherein big data analysis method is mainly reflected in " the first number
Data preprocess " and " analysis of the first big data " link.First data prediction (data prediction 1) link by data pick-up,
Data cleansing, data selection and data convert several steps, dimension is reduced after dimension determine data area by increasing, pass through
Unstructured and semi-structured data structuring is easy to discriminance analysis by structural means, by filling a vacancy, smooth noise hand
Section serialized data simultaneously removes abnormal data and repeated data, is that the big data analysis of next link lays the foundation.First big number
Then realized according to (big data analysis 1) link is analyzed by big data method for digging such as classification, cluster, regression analysis and association discoveries
The foundation of valuation prediction models, and the mode of combination failure case study sets up Fault tree.
By the analysis and training to fault case, combination failure tree analysis method builds the growing fault tree of equipment,
Establish the overall dendroid fault system for arriving part.During the analysis at initial stage by the way of semi-intelligent is semi-artificial, deeply tie
The experience of domain experts accumulation is closed, branch and the leaf node of each level of device tree is specified, and clear and definite interdependent node
Logical relation, is coupled using gate progressive, builds the Fault tree based on fault case.For failing clear failure mould
Formula but possess abnormal Rule of judgment then individually set up fault branch, it is ensured that comprehensive diagnostic exhaustive.Follow-up improves and supplement
Deep excavation is then carried out to fault case dependent on complete intelligentized big data analysis (correlation analysis and causality analysis)
With continuous study.Such a analysis and application process not needing to carry out defect it is exhaustive, it is to avoid because not being included in defect table,
And the equipment that there will be problem is chosen as the situation of normal condition.
As shown in Fig. 2 state evaluation model mainly includes to status data progress packet, according to diagnostic rule and sentenced
Determine formula and carry out fault diagnosis, obtain probability of malfunction, main parameters, accident analysis progress state evaluation, finally give equipment shape
The results such as state grading, equipment fault risk and cause influence analysis.
As shown in figure 3, trend-analyzing model mainly carries out data sorting, according to early warning according to all previous evaluation result data
Threshold values P carries out trend analysis at certain time intervals, so as to obtain pre-warning time and trend analysis matrix of consequence.
Step 2, equipment typical defect Sample Storehouse of the foundation based on image data processing technique.
This step brings the result of the image detecting technique such as infrared, ultraviolet, visible ray in appraisement system into, and foundation is based on
The equipment typical defect Sample Storehouse of image data processing technique.Sample Storehouse is according to equipment light visual, equipment infrared imaging figure
Piece, equipment ultraviolet imagery picture.Sample Storehouse passes through second using a large amount of infrared, ultraviolet, visible ray pictures as the basis of analysis
Two links of data prediction and second largest data analysis are set up.Wherein the second data prediction (data prediction 2) ring
Saving the technology mainly used includes：Smoothing processing, medium filtering calculating, gradient operator and rim detection etc., picture is completed with this
Detection, noise reduction optimization, the duplicate removal work of quality.Second largest data analysis (big data analysis 2) link then passes through reference base picture pair
Than normal condition, attention state, the exception that the technologies such as, Similarity Measure, key area identification, aberration identification realize distinct device
The foundation of the defect sample of state, severe conditions and the different orders of severity of attention above tri-state.
Step 3, according to Fault tree, state evaluation model, trend-analyzing model and image deflects Sample Storehouse carry out shape
State is evaluated and risk trend analysis.
Fault tree, state evaluation model, trend-analyzing model and image deflects Sample Storehouse are to carry out state evaluation
Basis, while being the guarantee for carrying out constantly correcting, optimizing to it again.Its specific method is：
When various dimensions information (the non-knot such as equipment essential information, structural state data, picture of a certain equipment of outside input
Structure data) it is carried out state evaluation and risk trend prediction when, it is necessary first to the 3rd data prediction (data are carried out to it
3) link is pre-processed, needs to carry out noise reduction optimization of examination duplicate removal, rejecting abnormal data, seasonal difference and picture etc. in the link
Work.Pretreated data will call state evaluation model, Fault tree and image deflects picture sample storehouse to equipment
State is specifically assessed, and quantitative analyzes the current state of equipment, probability of happening of defect failure etc., with reference to artificial true
Recognize and revision value can feed back to the analysis of the first big data, second largest data analysis link (model is perfect) to model inside correlation
Coefficient is weighted amendment.After multiple diagnostic evaluation is carried out to single equipment, and then application trend analysis model is combined and gone through
Secondary evaluation result is judged the being predicted property of development of defects trend of the equipment, plays a part of preventing trouble before it happens.
It is emphasized that embodiment of the present invention is illustrative, rather than it is limited, therefore present invention bag
Include and be not limited to embodiment described in embodiment, it is every by those skilled in the art's technique according to the invention scheme
The other embodiment drawn, also belongs to the scope of protection of the invention.
Claims (6)
1. a kind of power transmission and transformation equipment state evaluation method based on big data, it is characterised in that comprise the following steps：
Step 1, foundation and optimizing evaluation forecast model group, the valuation prediction models group include Fault tree, state evaluation mould
Type and trend-analyzing model；
Step 2, set up and optimize the equipment typical defect Sample Storehouse based on image data processing technique；
Step 3, according to Fault tree, state evaluation model, trend-analyzing model and image deflects Sample Storehouse carry out state comment
Valency and risk trend analysis.
2. a kind of power transmission and transformation equipment state evaluation method based on big data according to claim 1, it is characterised in that：Institute
The foundation and optimization method for stating Fault tree is：According to the equipment working condition information, environmental information, Supervision of equipment running status
Measurement information and equipment regular inspection Test Information, analyze link processing by the first data prediction and the first big data and obtain, wherein,
First data prediction link includes data pick-up, data cleansing, data selection and data conversion, and it is subtracted by increasing after dimension
Lack dimension to determine data area, by structural means by unstructured and semi-structured data structuring, by filling up sky
Scarce, smooth noise means serialized data simultaneously removes abnormal data and repeated data；First big data analyze link by classification,
Cluster, regression analysis and association discovery method realize the foundation of valuation prediction models, and the mode of combination failure case study is built
Vertical Fault tree.
3. a kind of power transmission and transformation equipment state evaluation method based on big data according to claim 1, it is characterised in that：Institute
Stating state evaluation model includes carrying out status data packet, carries out fault diagnosis according to diagnostic rule and judgement formula,
Probability of malfunction, main parameters, accident analysis progress state evaluation are obtained, it is final to carry out equipment state grading, equipment fault risk
With cause influence analysis.
4. a kind of power transmission and transformation equipment state evaluation method based on big data according to claim 1, it is characterised in that：Institute
Stating trend-analyzing model is included according to all previous evaluation result data progress data sorting, according to early warning threshold values at certain time intervals
Trend analysis is carried out, so as to obtain pre-warning time and trend analysis matrix of consequence.
5. a kind of power transmission and transformation equipment state evaluation method based on big data according to claim 1, it is characterised in that：Institute
The concrete methods of realizing for stating step 2 is：Equipment light visual, equipment infrared imaging picture and equipment ultraviolet imagery picture are led to
Cross two links of the second data prediction and second largest data analysis to set up, the second data prediction link is using smooth place
Reason, medium filtering are calculated, gradient operator and rim detection complete the detection of picture quality, noise reduction optimization, duplicate removal work, second largest
Data analysis link is contrasted by reference base picture, Similarity Measure, key area identification, aberration identification technology realize distinct device
Normal condition, attention state, abnormality, severe conditions and notice that the defect sample of the above tri-state different orders of severity is built
It is vertical.
6. a kind of power transmission and transformation equipment state evaluation method based on big data according to claim 1, it is characterised in that：Institute
The concrete methods of realizing for stating step 3 is：When external input device equipment essential information, structural state data and picture non-structural
When changing data, the 3rd data prediction link is carried out to it first, the data prediction link carries out examination to above-mentioned data and gone
Weight, rejecting abnormal data, the noise reduction optimization of seasonal difference and picture, pretreated data will call state evaluation model, set
Standby fault tree and image deflects picture sample storehouse are specifically assessed the state of equipment, quantitative to analyze equipment at present
The probability of happening of state, defect failure, and combination manual confirmation and revision value feed back to the analysis of the first big data, the second big data
Analyze link progress model perfect；After multiple diagnostic evaluation is carried out to single equipment, and then application trend analysis model knot
All previous evaluation result is closed to judge the being predicted property of development of defects trend of the equipment.
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| CN108052626A (en) * | 2017-12-19 | 2018-05-18 | 号百信息服务有限公司 | One kind realizes data deduplication device and method based on dynamic programming method |
| CN108335044A (en) * | 2018-02-06 | 2018-07-27 | 国网天津市电力公司电力科学研究院 | A kind of power transmission and transformation equipment state evaluation method |
| CN108664538A (en) * | 2017-11-30 | 2018-10-16 | 全球能源互联网研究院有限公司 | A kind of automatic identification method and system of the doubtful familial defect of power transmission and transforming equipment |
| CN108681814A (en) * | 2018-05-10 | 2018-10-19 | 北京鼎泰智源科技有限公司 | A kind of big data quality standard management control method |
| CN108846511A (en) * | 2018-06-04 | 2018-11-20 | 国家电网公司 | A kind of defect of transformer equipment trend analysis based on regulation big data platform |
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Application publication date: 20170725 |