CN108229827A - A kind of power equipment quality problems modeling and analysis methods - Google Patents
A kind of power equipment quality problems modeling and analysis methods Download PDFInfo
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- CN108229827A CN108229827A CN201810008498.4A CN201810008498A CN108229827A CN 108229827 A CN108229827 A CN 108229827A CN 201810008498 A CN201810008498 A CN 201810008498A CN 108229827 A CN108229827 A CN 108229827A
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Abstract
The present invention discloses a kind of power equipment quality problems modeling and analysis methods, includes the following steps：Step S1, input electric power equipment quality problem information and life cycle management information, establish sample database；Step S2 trains power equipment quality problems sample data based on artificial neural network, obtains power equipment Analysis of Quality Problem model；Step S3 establishes the analysis being made of statistical output, correlation output and the output of single quality problems and exports system.Such method can provide support for constantly improve power sourcing equipment standard, procurement strategy and quality surveillance strategy, it is ensured that equipment quality supervision work specification is efficiently carried out.
Description
Technical field
The invention belongs to power equipment quality surveillance field, more particularly to a kind of power equipment quality problems modeling analysis side
Method.
Background technology
Power equipment has the features such as strongly professional, type is various, amount of purchase is big, Costco Wholesale is high, and the matter of equipment
Amount requirement can occur constantly to change with new technology, new diseases.Equipment intensive management deepens constantly, and is realizing
While centralized purchasing scale effect, it is necessary to maintain strict control over product quality pass, complete equipment guarantee work with guaranteeing both quality and quantity, establish general headquarters
With net provincial company two-stage equipment management organization system and corresponding device service mechanism, carry out equipment unified management, strengthen equipment
Fundamentals of management construction, puopulsion equipment standardization effort, in-depth bid and purchase management, strengthening device Emergency System construction.
Power grid enterprises gradually pay attention to power equipment safety management work, but are more the ranges in policy to product kimonos
The quality of business is constrained, and focuses on the quality of product in the narrow sense rather than by the supervision to process come the quality of management and control product.
Traditional electric power equipment management system is more extensive, especially equipment purchase lack of control unified flow, and buying permission also fails to
Reasonable distribution, uniformity, inaccurate coordination.In equipment bidding management aspect, procurement inquiry, procurement contract negotiation, supplier
The restriction mechanisms such as supervision and management are unsound, it is impossible to realize effectively management etc..
At present, in face of such severe qualitative materiel problem of management, State Grid Corporation of China has also carried out a series of technology
Innovation and administrative reform.General headquarters' pool is established, the qualitative materiel management network that province, city, county's classification are implemented implements project comprehensively
Unit mass is supervised and the main body responsibility of supplier's quality assurance.
With the fast development of science and technology, big data and theoretical emerging as one of artificial intelligence start including commercial affairs,
Each industry such as medicine, management or even electric power has given play to its great function.Therefore it is necessary to be based on intelligent algorithm to electric power
Equipment quality problem big data is modeled, the key factor and reason of analyzing influence power equipment quality problems, and retrospect causes
The link of quality problems provides support for constantly improve procurement criteria, procurement strategy and quality surveillance strategy, promotes equipment quality
Supervision work specification is efficiently carried out.
Invention content
The purpose of the present invention is to provide a kind of power equipment quality problems modeling and analysis methods, can be constantly improve
Power sourcing equipment standard, procurement strategy and quality surveillance strategy provide support, it is ensured that equipment quality supervision work specification, efficiently
Carry out.
In order to achieve the above objectives, solution of the invention is：
A kind of power equipment quality problems modeling and analysis methods, include the following steps：
Step S1, input electric power equipment quality problem information and life cycle management information, establish sample database；
Step S2 trains power equipment quality problems sample data based on artificial neural network, obtains power equipment quality
Case study model；
Step S3 establishes the analysis being made of statistical output, correlation output and the output of single quality problems and exports body
System.
In above-mentioned steps 1, power equipment quality problems information includes：When question number, problem carry declaration form position, problem is found
Between, link of pinpointing the problems, problem category and the analysis of causes.
In above-mentioned steps S1, power equipment life cycle management information includes：The unique code of power equipment, device name, equipment
Type, voltage class, the bid amount of money, the bid duration, supplier, procurement value, the buying duration, prison makes unit, prison makes the duration, prison
Make extension number of days, prison makes working condition, sampling observation ratio, the sampling observation duration, sampling observation extension number of days, Examined, whether rechecks, transports
Mode, the transport duration, transport extension number of days, transport responsible department, check and accept the duration, check and accept whether qualification, installation period, debug work
Phase, installation unit, debugging unit, super quality guarantee period days running, maintenance times and operating status.
In above-mentioned steps S1, when establishing sample database, the power equipment quality problems information to input and full longevity are needed
Life cycle information does data prediction, is specifically divided into data scrubbing, data integration, hough transformation and data transformation.
Above-mentioned data scrubbing includes filling up missing values, cleaning garbage and cleaning exceptional value or wrong data；Data set
Into including deleting amount of redundancy and processing conflict value；Hough transformation includes information normization and reduces data scale；Data transformation packet
It includes numerical value replacement, normalized and changes input form.
In above-mentioned steps S2, the artificial neural network of selection is basic three layers structure, including input layer, hidden layer and
Output layer, the learning rules of use are the nature distributed treatment models of BP algorithm, the sample data in database as input,
It is divided into training sample data collection, assessment sample data set and test sample data set according to a certain percentage.
In above-mentioned steps S3, statistics output includes quality problems number occurs by voltage class analysis power equipment and account for
Than, by link analysis power equipment of pinpointing the problems quality problems number and accounting occurs, by device type analysis power equipment hair
Raw quality problems number and accounting, by putting into operation, quality problems number and accounting occur for time limit analysis power equipment and by factory
Family's analysis power equipment quality problems frequency and accounting.
In above-mentioned steps S3, correlation output refers to each information of life cycle management and power equipment quality problems correlation,
The correlation of the parameter of each information of life cycle management and power equipment quality problems between any two is specifically referred to, including but not limited to
Supplier cause power equipment quality problems correlation, prison make extension number of days cause power equipment quality problems correlation,
Sampling observation ratio causes the correlation of power equipment quality problems, means of transportation causes the correlation of power equipment quality problems.
In above-mentioned steps S3, single quality problems output includes causing the link probability distribution of the quality problems, influences to be somebody's turn to do
The key factor probability distribution of quality problems and the recommendation on improvement for the quality problems and corrective measure.
After using the above scheme, power equipment quality problems information and life cycle management letter that present invention processing was collected
Breath, based on intelligent algorithm, by establishing correlation model and data framework, the key of analyzing influence power equipment quality problems
Factor and reason, retrospect cause the link of quality problems, are constantly improve power sourcing equipment standard, procurement strategy and quality prison
It superintends and directs strategy and support is provided, it is ensured that equipment quality supervision work specification is efficiently carried out.
Description of the drawings
Fig. 1 is the process principle figure of the present invention；
Fig. 2 is Artificial Neural Network Modeling analysis result figure of the present invention；
Fig. 3 is power equipment quality problems link retrospect probability distribution graph of the present invention；
Fig. 4 is the probability distribution graph of the present invention for influencing power equipment quality problems critical issue.
Specific embodiment
Below with reference to the drawings and the specific embodiments, the present invention is described in further detail.But this should not be understood
Range for the above-mentioned theme of the present invention is only limitted to following embodiment, all to belong to this based on the technology that the content of present invention is realized
The range of invention.
As shown in Figure 1, the present invention provides a kind of power equipment quality problems modeling and analysis methods, include the following steps：
Step S1, input electric power equipment quality problem information and life cycle management information, establish sample database.
Wherein, power equipment quality problems information includes：Question number, problem carry declaration form position, problem discovery time, find
Problem link, problem category and the analysis of causes.
In the step S1, power equipment life cycle management information includes：The unique code of power equipment, device name, equipment
Type, voltage class, the bid amount of money, the bid duration, supplier, procurement value, the buying duration, prison makes unit, prison makes the duration, prison
Make extension number of days, prison makes working condition, sampling observation ratio, the sampling observation duration, sampling observation extension number of days, Examined, whether rechecks, transports
Mode, the transport duration, transport extension number of days, transport responsible department, check and accept the duration, check and accept whether qualification, installation period, debug work
Phase, installation unit, debugging unit, super quality guarantee period days running, maintenance times and operating status.
Sample database is established in the step S1, is needed to the power equipment quality problems information of input and life-cycle
Cycle information does data prediction, is specifically divided into data scrubbing, data integration, hough transformation and data transformation；Wherein, data are clear
Reason includes filling up missing values, cleaning garbage and cleaning exceptional value or wrong data；Data integration include delete amount of redundancy and
Handle conflict value；Hough transformation includes information normization and reduces data scale；Data transformation includes numerical value and replaces, at normalization
Reason and change input form.
Step S2 trains power equipment quality problems sample data based on artificial neural network, obtains power equipment quality
Case study model.
In the step S2, the artificial neural network of selection is basic three layers structure, including input layer, hidden layer and
Output layer, the learning rules of use are the nature distributed treatment models of common BP algorithm.Sample data in database is made
For input, it is divided into training sample data collection, assessment sample data set and test sample data set according to a certain percentage.
Step S3 establishes the analysis being made of statistical output, correlation output and the output of single quality problems and exports body
System.
Statistical output in step S3 include by voltage class analysis power equipment occur quality problems number and accounting,
Quality problems number and accounting occurs by link analysis power equipment of pinpointing the problems, matter occurs by device type analysis power equipment
Amount problem number and accounting, by putting into operation, quality problems number and accounting occur for time limit analysis power equipment and by manufacturer point
Analyse power equipment quality problems frequency and accounting.
Correlation output in step S3 refers to each information of life cycle management and power equipment quality problems correlation, specifically
Refer to the correlation of the parameter of each information of life cycle management and power equipment quality problems between any two, including but not limited to supply
Quotient causes the correlation of power equipment quality problems, prison makes extension number of days and causes the correlation of power equipment quality problems, sampling observation
Ratio causes the correlation of power equipment quality problems, means of transportation causes the correlation of power equipment quality problems.
Single quality problems output in step S3 includes causing the link probability distribution of the quality problems, influences the quality
The key factor probability distribution of problem and the recommendation on improvement for the quality problems and corrective measure.
Name an embodiment.
Input electric power equipment quality problem information and life cycle management information first, by data scrubbing, data integration, number
According to stipulations and data preconditioning, sample database is obtained, the following table of partial data in sample database：
1 sample database partial data of table
Then, power equipment Analysis of Quality Problem model is trained based on artificial neural network.Artificial neural network uses
Random methods choose training, assessment and test data set, using Scaled Conjugate Gradient Method (Scaled Conjugate
Gradient, SCG) as training algorithm, using mean square error (Mean Squared Error, MSE) as assessment fitting effect
The index of fruit.To reach the clustering problem for solving power equipment quality problems and causing link, the present embodiment include 29 input quantities,
15 layers of hidden layer and 8 outputs.Select input sample database, by sample data be divided into 80% training dataset, 15%
Assess data set and 5% test data set, model result such as Fig. 2 that training obtains.
Finally, the present embodiment analyzes single quality problems output as a result, the quality problems being analysed to are brought into and had been established
Network model, the links of the exportable quality problems traced as a result, as shown in Figure 3 and Figure 4.Understand that number is WT16090001
Quality problems, calculated by modeling analysis, it is 1.52% to obtain the problem and be happened at the probability of bidding link, is happened at and adopts
The probability for purchasing link is 2.93%, and it is 37.33% to be happened at and supervise the probability for making link, and the probability for being happened at sampling observation link is
9.11%, the probability for being happened at transit link is 16.70%, and the probability for being happened at factory inspection and acceptance link is 0.41%, is happened at
The probability of installation and debugging link is 0.41%, and the probability for being happened at operation and maintenance link is 31.59%.It is wherein happened at prison and makes ring
The possibility highest of section, operation and maintenance link is secondly.
Above example is merely illustrative of the invention's technical idea, it is impossible to protection scope of the present invention is limited with this, it is every
According to technological thought proposed by the present invention, any change done on the basis of technical solution each falls within the scope of the present invention
Within.
Claims (9)
1. a kind of power equipment quality problems modeling and analysis methods, it is characterised in that include the following steps：
Step S1, input electric power equipment quality problem information and life cycle management information, establish sample database；
Step S2 trains power equipment quality problems sample data based on artificial neural network, obtains power equipment quality problems
Analysis model；
Step S3 establishes the analysis being made of statistical output, correlation output and the output of single quality problems and exports system.
2. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step 1
In, power equipment quality problems information includes：Question number, problem carry declaration form position, problem discovery time, link of pinpointing the problems,
Problem category and the analysis of causes.
3. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S1
In, power equipment life cycle management information includes：The unique code of power equipment, device name, device type, voltage class, bid
The amount of money, the bid duration, supplier, procurement value, the buying duration, prison makes unit, prison makes the duration, prison makes extension number of days, prison makes work
Make situation, sampling observation ratio, the sampling observation duration, sampling observation extension number of days, Examined, whether recheck, means of transportation, the transport duration, transport
Defeated extension number of days, transport responsible department, check and accept the duration, check and accept whether qualification, installation period, debug the duration, installation unit, debugging
Unit, super quality guarantee period days running, maintenance times and operating status.
4. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S1
In, when establishing sample database, need to do data to the power equipment quality problems information and life cycle management information of input pre-
Processing is specifically divided into data scrubbing, data integration, hough transformation and data transformation.
5. a kind of power equipment quality problems modeling and analysis methods as claimed in claim 4, it is characterised in that：The data are clear
Reason includes filling up missing values, cleaning garbage and cleaning exceptional value or wrong data；Data integration include delete amount of redundancy and
Handle conflict value；Hough transformation includes information normization and reduces data scale；Data transformation includes numerical value and replaces, at normalization
Reason and change input form.
6. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S2
In, the artificial neural network of selection is basic three layers structure, including input layer, hidden layer and output layer, the study of use
Rule is the nature distributed treatment model of BP algorithm, and the sample data in database is divided into according to a certain percentage as input
Training sample data collection, assessment sample data set and test sample data set.
7. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S3
In, statistics output includes quality problems number and accounting occurring, by link of pinpointing the problems by voltage class analysis power equipment
Analysis power equipment occurs quality problems number and accounting, quality problems number occurs by device type analysis power equipment and accounts for
Than, quality problems number and accounting occur by the time limit analysis power equipment that puts into operation and analyze power equipment quality by manufacturer
Problem frequency and accounting.
8. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S3
In, correlation output refers to each information of life cycle management and power equipment quality problems correlation, in particular to life cycle management
The correlation of the parameter of each information and power equipment quality problems between any two, including but not limited to supplier cause power equipment
The correlation of quality problems, prison make the correlation of extension number of days initiation power equipment quality problems, sampling observation ratio causes electric power and sets
Correlation, the means of transportation of standby quality problems cause the correlation of power equipment quality problems.
9. a kind of power equipment quality problems modeling and analysis methods as described in claim 1, it is characterised in that：The step S3
In, single quality problems output includes the link probability distribution for causing the quality problems, the key factor for influencing the quality problems
Probability distribution and the recommendation on improvement for the quality problems and corrective measure.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110118900A (en) * | 2019-03-27 | 2019-08-13 | 南京航空航天大学 | A kind of remained capacity and power frequency series arc faults detection method |
| CN112488456A (en) * | 2020-11-12 | 2021-03-12 | 南方电网科学研究院有限责任公司 | Digital data modeling method for power equipment |
| CN113239032A (en) * | 2021-06-02 | 2021-08-10 | 云南电网有限责任公司电力科学研究院 | Power distribution network power distribution equipment operation and maintenance monitoring method, device and system |
| CN113379005A (en) * | 2021-08-12 | 2021-09-10 | 新风光电子科技股份有限公司 | Intelligent energy management system and method for power grid power equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110118900A (en) * | 2019-03-27 | 2019-08-13 | 南京航空航天大学 | A kind of remained capacity and power frequency series arc faults detection method |
| CN112488456A (en) * | 2020-11-12 | 2021-03-12 | 南方电网科学研究院有限责任公司 | Digital data modeling method for power equipment |
| CN113239032A (en) * | 2021-06-02 | 2021-08-10 | 云南电网有限责任公司电力科学研究院 | Power distribution network power distribution equipment operation and maintenance monitoring method, device and system |
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