CN109507517A - The distribution transformer running state analysis method compared based on two-sided power big data - Google Patents
The distribution transformer running state analysis method compared based on two-sided power big data Download PDFInfo
- Publication number
- CN109507517A CN109507517A CN201811491185.5A CN201811491185A CN109507517A CN 109507517 A CN109507517 A CN 109507517A CN 201811491185 A CN201811491185 A CN 201811491185A CN 109507517 A CN109507517 A CN 109507517A
- Authority
- CN
- China
- Prior art keywords
- transformer
- power
- matrix
- running state
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims description 17
- 238000012360 testing method Methods 0.000 claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000002474 experimental method Methods 0.000 claims abstract description 10
- 239000011159 matrix material Substances 0.000 claims description 39
- 238000005259 measurement Methods 0.000 claims description 6
- 101150004974 ACP3 gene Proteins 0.000 claims description 3
- 238000003491 array Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 3
- 229940050561 matrix product Drugs 0.000 claims description 3
- 230000002146 bilateral effect Effects 0.000 abstract description 9
- 230000003442 weekly effect Effects 0.000 abstract description 5
- 230000007257 malfunction Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
本发明提供一种基于双侧功率大数据比对的配电变压器运行状态分析方法，包括变压器出厂功率实验及归档数据的建立、在线测试采集变压器双侧功率并进行记录并形成输入数据库文件。采集的数据文件由双侧功率数据、双侧电压值及双侧计算阻抗曲线组成，通过在线测试变压器双侧功率参数、电压参数与上述变压器出厂功率比对档案比对及与周度，月度，年度参数大数据比对从而得出现阶段变压器运行状态及未来运行状态。利用变压器双侧功率对称原理，有效解决了现有配电变压器状态评估不准确的问题。提高了配电变压器运行的可检测度，减少了配电变压器故障后发生问题的概率。
The invention provides a method for analyzing the operation state of distribution transformers based on the comparison of double-sided power big data, including the establishment of transformer factory power experiments and archived data, online testing to collect and record the transformer double-sided power, and to form an input database file. The collected data files are composed of bilateral power data, bilateral voltage values and bilateral calculated impedance curves. Through online testing of transformer bilateral power parameters, voltage parameters and the above-mentioned transformer factory power comparison files, they are compared with the weekly, monthly, The annual parameter big data comparison is used to obtain the transformer operating status at the current stage and the future operating status. Using the principle of power symmetry on both sides of the transformer, the problem of inaccurate state assessment of the existing distribution transformer is effectively solved. The detectability of the operation of the distribution transformer is improved, and the probability of problems occurring after the failure of the distribution transformer is reduced.
Description
Technical field
The present invention relates to transformer state analysis and operating condition electric powder predictions, in particular to a kind of to be based on bilateral function
The distribution transformer running state analysis method that rate big data compares.
Background technique
The stable operation of distribution transformer is to be related to the essential condition of power supply reliability and power supply safety.All the time,
It is installed and is dispersed due to distribution transformer, single transformer value is low, and reasons constrain transformer stable operation not in time etc. for inspection,
Although having developed a large amount of transformer detection device for many years, for distribution transformer, lack a kind of simple and reliable
Judgment method and decision logic.The present invention is directed to provide one kind by detection to transformer two-sided power and analysis and can analyze
The analysis method of power transformer interior fault and future malfunction outline.
Summary of the invention
In order to solve the problems, such as described in background technique, the present invention provides a kind of distribution compared based on two-sided power big data
Running state of transformer analysis method has invented a kind of simple possible using transformer two-sided power symmetry principle, can examine in real time
The power transformer interior fault of analysis and the analysis method of future malfunction probability are surveyed, existing distribution transformer state is efficiently solved and comments
Estimate the problem of inaccuracy.The detectable degree for improving distribution transformer operation, occurs problem after reducing distribution transformer failure
Probability.
In order to achieve the above object, the present invention is implemented with the following technical solutions:
A kind of distribution transformer running state analysis method compared based on two-sided power big data, including transformer factory
Power experiment and the foundation of filing data, on-line testing acquire transformer two-sided power and are recorded and form input database
File.The foundation and experiment of transformer factory power data filing are to carry out in transformer factory experiment to a group power test.
The data file of acquisition is made of two-sided power data, bilateral voltage value and bilateral computing impedance curve, is become by on-line testing
Depressor two-sided power parameter, voltage parameter and above-mentioned transformer factory power ratio archives are compared and with it is weekly, it is monthly, year ginseng
Number big data compares the method for obtaining running state of transformer at this stage and the following operating status.
Specifically comprise the following steps:
Step 1: carrying out factory power test, test process is as follows in distribution transformer factory:
1) n power within the scope of transformer efficiency is taken to carry out the given experiment of input power；The choosing of load side input power factor
N numerical value between 0.7-0.98 is taken to be tested, power P 1 of experimental record, secondary power P2, load power factor C, one
Secondary voltage effective value U1, secondary voltage virtual value U2；
2) parameter recorded is formed into following rectangular arrays Ac, Ap, and calculates the product Acp of two matrixes；
Wherein: power factor matrixPower voltage matrix
Calculating matrix product Acp, obtain a result matrix and stores together with above-mentioned two matrix；
Step 2: carrying out online distribution transformer power parameter measurement when transformer actual motion, measurement obtains matrix
Acc,
{ C1 } { P1 P2 U1 U2 }=Acc
It is stored in memory；
Decision conclusions are calculated and obtained Step 3: matrix A cc is compared with matrix A cp；
Calculation method are as follows: calculating matrix Acc is overlapped probability Gb with matrix A cp's, obtains certain in matrix A cc and Acp matrix
One row element probability peak Ga；Running state of transformer is evaluated according to the value of Ga and Gb；
Step 4: by the test data set composite matrix at m time point in the period T for storing transformer station high-voltage side bus
Acpt, combined method are as follows:
Step 5: Acpt and Acp is carried out poor bit arithmetic, equal position deviation matrix Acpp is obtained.Calculate removing for Acp and Accp
Method operation obtains COEFFICIENT K, K=Accp/Acp；
Step 6: estimating the failure rate of the operating status in transformer future according to the value of COEFFICIENT K.
The value according to Ga and Gb in the step three evaluates running state of transformer, specific as follows:
1) work as Ga > a% or multiple coincidence probability Gb > b%, be then judged as that the running state of transformer is outstanding；
2) work as Ga > c% or multiple coincidence probability Gb > d%, be then judged as that the running state of transformer is good；
3) work as Ga > e% or multiple coincidence probability Gb > f%, then it is assumed that the transformer station high-voltage side bus shape is passed；
4) remaining situation then thinks that the transformer station high-voltage side bus is failed；
Wherein, a, b, c, d, e, f be percentage specific ray constant, a, b value range: (100,85], c, d value range: (85,
70], e, f value range: (70,60].
The data of on-line testing in the step three and step 4 can be weekly, monthly or annual time section more
The test data at a time point.
Compared with prior art, the beneficial effects of the present invention are:
A kind of big data based on the online two-sided power test record of distribution transformer of the invention compares operating status point
Analysis and future malfunction rate prediction technique are by on-line testing transformer two-sided power parameter, voltage parameter and above-mentioned transformer
Power ratio of dispatching from the factory archives are compared and with it is weekly, monthly, annual parameter big data compares to obtain transformer station high-voltage side bus at this stage
The method of state and the following operating status.
The present invention using transformer two-sided power symmetry principle provide a kind of simple possible, can real-time detection analysis change
The analysis method of depressor internal fault and future malfunction probability efficiently solves existing distribution transformer status assessment inaccuracy
Problem.The detectable degree for improving distribution transformer operation reduces the probability of generation problem after distribution transformer failure.
Detailed description of the invention
Fig. 1 is a kind of distribution transformer running state analysis method stream compared based on two-sided power big data of the invention
Cheng Tu.
Specific embodiment
Specific embodiment provided by the invention is described in detail below.
As shown in Figure 1, a kind of distribution transformer running state analysis method compared based on two-sided power big data, including
Transformer factory power experiment and the foundation of filing data, on-line testing acquire transformer two-sided power and are recorded and formed
Input data library file.The foundation and experiment of transformer factory power data filing are to carry out in transformer factory experiment to group
Power test.The data file of acquisition is made of two-sided power data, bilateral voltage value and bilateral computing impedance curve.
Specifically comprise the following steps:
Step 1: carrying out factory power test, test process is as follows in distribution transformer factory:
1) n power within the scope of transformer efficiency is taken to carry out the given experiment of input power；The choosing of load side input power factor
N numerical value between 0.7-0.98 is taken to be tested, power P 1 of experimental record, secondary power P2, load power factor C, one
Secondary voltage effective value U1, secondary voltage virtual value U2；
2) parameter recorded is formed into following rectangular arrays Ac, Ap, and calculates the product Acp of two matrixes；
Wherein: power factor matrixPower voltage matrix
Calculating matrix product Acp, obtain a result matrix and stores together with above-mentioned two matrix；
Step 2: carrying out online distribution transformer power parameter measurement when transformer actual motion, measurement obtains matrix
Acc,
{ C1 } { P1 P2 U1 U2 }=Acc
It is stored in memory；
Decision conclusions are calculated and obtained Step 3: matrix A cc is compared with matrix A cp；
Calculation method are as follows: calculating matrix Acc is overlapped probability Gb with matrix A cp's, obtains certain in matrix A cc and Acp matrix
One row element probability peak Ga；Running state of transformer is evaluated according to the value of Ga and Gb；
Step 4: by the test data set composite matrix at m time point in the period T for storing transformer station high-voltage side bus
Acpt, combined method are as follows:
Step 5: Acpt and Acp is carried out poor bit arithmetic, equal position deviation matrix Acpp is obtained.Calculate removing for Acp and Accp
Method operation obtains COEFFICIENT K, K=Accp/Acp；
Step 6: estimating the failure rate of the operating status in transformer future according to the value of COEFFICIENT K.
The data of on-line testing in the step three and step 4 can be weekly, monthly or annual time section more
The test data at a time point.
The value according to Ga and Gb in the step three evaluates running state of transformer, specific as follows:
1) work as Ga > a% or multiple coincidence probability Gb > b%, be then judged as that the running state of transformer is outstanding；
2) work as Ga > c% or multiple coincidence probability Gb > d%, be then judged as that the running state of transformer is good；
3) work as Ga > e% or multiple coincidence probability Gb > f%, then it is assumed that the transformer station high-voltage side bus shape is passed；
4) remaining situation then thinks that the transformer station high-voltage side bus is failed；
Wherein, a, b, c, d, e, f be percentage specific ray constant, a, b value range: (100,85], c, d value range: (85,
70], e, f value range: (70,60].
In the step six, the failure rate for estimating the operating status in transformer future according to the value of COEFFICIENT K is specific as follows:
As COEFFICIENT K≤m%, then it is assumed that transformer future operating status is outstanding；M%≤K≤n%, then it is assumed that transformer is not
It is good to carry out operating status；N%≤K≤p%, then it is assumed that the operation of transformer future is bad.
Wherein, m, n, p be percentage specific ray constant, m value range: (0,10], n value range: (10,20], f value range:
(20,60].
Above embodiments are implemented under the premise of the technical scheme of the present invention, give detailed embodiment and tool
The operating process of body, but protection scope of the present invention is not limited to the above embodiments.Method therefor is such as without spy in above-described embodiment
Not mentionleting alone bright is conventional method.
Claims (2)
1. a kind of distribution transformer running state analysis method compared based on two-sided power big data, which is characterized in that including
Following steps:
Step 1: carrying out factory power test, test process is as follows in distribution transformer factory:
1) n power within the scope of transformer efficiency is taken to carry out the given experiment of input power；Load side input power factor is chosen
N numerical value is tested between 0.7-0.98, power P 1 of experimental record, secondary power P2, load power factor C, once
Voltage effective value U1, secondary voltage virtual value U2；
2) parameter recorded is formed into following rectangular arrays Ac, Ap, and calculates the product Acp of two matrixes；
Wherein: power factor matrixPower voltage matrixCalculating matrix
Product Acp, obtain a result matrix and stores together with above-mentioned two matrix；
Step 2: carrying out online distribution transformer power parameter measurement when transformer actual motion, measurement obtains matrix A cc,
{ C1 } { P1 P2 U1 U2 }=Acc
It is stored in memory；
Decision conclusions are calculated and obtained Step 3: matrix A cc is compared with matrix A cp；
Calculation method are as follows: calculating matrix Acc is overlapped probability Gb with matrix A cp's, obtains certain a line in matrix A cc and Acp matrix
Element probability peak Ga；Running state of transformer is evaluated according to the value of Ga and Gb；
Step 4: by the test data set composite matrix Acpt at m time point in the period T for storing transformer station high-voltage side bus, group
Conjunction method is as follows:
Step 5: Acpt and Acp is carried out poor bit arithmetic, equal position deviation matrix Acpp is obtained.Calculate the division fortune of Acp and Accp
It calculates, obtains COEFFICIENT K, K=Accp/Acp；
Step 6: estimating the failure rate of the operating status in transformer future according to the value of COEFFICIENT K.
2. a kind of distribution transformer running state analysis side compared based on two-sided power big data according to claim 1
Method, which is characterized in that the value according to Ga and Gb in the step three evaluates running state of transformer, specifically such as
Under:
1) work as Ga > a% or multiple coincidence probability Gb > b%, be then judged as that the running state of transformer is outstanding；
2) work as Ga > c% or multiple coincidence probability Gb > d%, be then judged as that the running state of transformer is good；
3) work as Ga > e% or multiple coincidence probability Gb > f%, then it is assumed that the transformer station high-voltage side bus shape is passed；
4) remaining situation then thinks that the transformer station high-voltage side bus is failed；
Wherein, a, b, c, d, e, f be percentage specific ray constant, a, b value range: (100,85], c, d value range: (85,70], e, f
Value range: (70,60].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811491185.5A CN109507517B (en) | 2018-12-07 | 2018-12-07 | Distribution transformer operation state analysis method based on double-side power big data comparison |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811491185.5A CN109507517B (en) | 2018-12-07 | 2018-12-07 | Distribution transformer operation state analysis method based on double-side power big data comparison |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109507517A true CN109507517A (en) | 2019-03-22 |
| CN109507517B CN109507517B (en) | 2020-10-27 |
Family
ID=65751785
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811491185.5A Active CN109507517B (en) | 2018-12-07 | 2018-12-07 | Distribution transformer operation state analysis method based on double-side power big data comparison |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109507517B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110824289A (en) * | 2019-12-16 | 2020-02-21 | 国网辽宁省电力有限公司鞍山供电公司 | Detection and analysis method for running state of power distribution ring main unit |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101404414A (en) * | 2008-09-28 | 2009-04-08 | 王磊 | Power distribution network electromagnetic optimization dynamic loss reduction method, system and synthetic loss reduction system |
| CN101902077A (en) * | 2010-07-13 | 2010-12-01 | 杭州交联电气工程有限公司 | Operating data monitoring and alarming system of converting stations |
| CN103198175A (en) * | 2013-03-04 | 2013-07-10 | 辽宁省电力有限公司鞍山供电公司 | Transformer fault diagnosis method based on fuzzy cluster |
| CN103713210A (en) * | 2013-11-01 | 2014-04-09 | 天津工业大学 | Dry power transformer monitoring and diagnosis system |
| CN103904657A (en) * | 2014-03-24 | 2014-07-02 | 国家电网公司 | Regional power grid reactive voltage control method based on support vector machine |
| KR101526822B1 (en) * | 2014-12-02 | 2015-06-05 | 백훈종 | Apparatus for examining electrical facilities installation safety for electrical safety manager and method thereof |
| US20150241494A1 (en) * | 2014-02-24 | 2015-08-27 | Panasonic Corporation | Nonlinear distortion detection device and distortion compensation power amplifier |
| CN104914327A (en) * | 2015-05-06 | 2015-09-16 | 北京航空航天大学 | Transformer fault maintenance prediction method based on real-time monitoring information |
| CN104914362A (en) * | 2015-05-26 | 2015-09-16 | 深圳供电局有限公司 | SF (sulfur hexafluoride)6Gas transformer insulation state monitoring system and method |
| CN108334894A (en) * | 2017-12-29 | 2018-07-27 | 泰豪科技股份有限公司 | Temperature of oil in transformer abnormality recognition method based on unsupervised machine learning |
| CN109086484A (en) * | 2018-06-29 | 2018-12-25 | 广东工业大学 | A kind of evidence fusion and Method of Set Pair Analysis of transformer health state evaluation |
| CN109428928A (en) * | 2017-08-31 | 2019-03-05 | 腾讯科技（深圳）有限公司 | Selection method, device and the equipment of information push object |
-
2018
- 2018-12-07 CN CN201811491185.5A patent/CN109507517B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101404414A (en) * | 2008-09-28 | 2009-04-08 | 王磊 | Power distribution network electromagnetic optimization dynamic loss reduction method, system and synthetic loss reduction system |
| CN101902077A (en) * | 2010-07-13 | 2010-12-01 | 杭州交联电气工程有限公司 | Operating data monitoring and alarming system of converting stations |
| CN103198175A (en) * | 2013-03-04 | 2013-07-10 | 辽宁省电力有限公司鞍山供电公司 | Transformer fault diagnosis method based on fuzzy cluster |
| CN103713210A (en) * | 2013-11-01 | 2014-04-09 | 天津工业大学 | Dry power transformer monitoring and diagnosis system |
| US20150241494A1 (en) * | 2014-02-24 | 2015-08-27 | Panasonic Corporation | Nonlinear distortion detection device and distortion compensation power amplifier |
| CN103904657A (en) * | 2014-03-24 | 2014-07-02 | 国家电网公司 | Regional power grid reactive voltage control method based on support vector machine |
| KR101526822B1 (en) * | 2014-12-02 | 2015-06-05 | 백훈종 | Apparatus for examining electrical facilities installation safety for electrical safety manager and method thereof |
| CN104914327A (en) * | 2015-05-06 | 2015-09-16 | 北京航空航天大学 | Transformer fault maintenance prediction method based on real-time monitoring information |
| CN104914362A (en) * | 2015-05-26 | 2015-09-16 | 深圳供电局有限公司 | SF (sulfur hexafluoride)6Gas transformer insulation state monitoring system and method |
| CN109428928A (en) * | 2017-08-31 | 2019-03-05 | 腾讯科技（深圳）有限公司 | Selection method, device and the equipment of information push object |
| CN108334894A (en) * | 2017-12-29 | 2018-07-27 | 泰豪科技股份有限公司 | Temperature of oil in transformer abnormality recognition method based on unsupervised machine learning |
| CN109086484A (en) * | 2018-06-29 | 2018-12-25 | 广东工业大学 | A kind of evidence fusion and Method of Set Pair Analysis of transformer health state evaluation |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110824289A (en) * | 2019-12-16 | 2020-02-21 | 国网辽宁省电力有限公司鞍山供电公司 | Detection and analysis method for running state of power distribution ring main unit |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109507517B (en) | 2020-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yu et al. | Challenges and outlook for lithium-ion battery fault diagnosis methods from the laboratory to real world applications | |
| CN115018139B (en) | Method and system for online identifying error state of current transformer based on interphase characteristics | |
| CN103198175B (en) | Based on the Diagnosis Method of Transformer Faults of fuzzy clustering | |
| CN103197177B (en) | A kind of transformer fault diagnosis analytical approach based on Bayesian network | |
| CN103675525B (en) | DC-DC converter health monitoring and fault prediction method based on multiple SVDD models | |
| CN105740975B (en) | A Method for Evaluation and Prediction of Equipment Defects Based on Data Correlation | |
| CN105719048A (en) | Intermediate-voltage distribution operation state fuzzy integrated evaluation method based on principle component analysis method and entropy weight method | |
| CN104809473B (en) | A kind of classification method towards electric power online acquisition data | |
| CN103278343B (en) | A kind of recognition methods of key structural fatigue component | |
| CN107944349A (en) | GIS partial discharge type-scheme recognition methods based on particle swarm support vector machine | |
| CN105224782A (en) | A kind of converting equipment probability of malfunction computing method based on fault mode | |
| WO2020155233A1 (en) | Method for diagnosing external short circuit fault of lithium-ion battery pack on basis of two-stage model prediction | |
| CN104573315B (en) | A kind of computational methods of the power transmission and transforming equipment fault rate based on repair based on condition of component | |
| CN101299534A (en) | Power system temporary stability and thermal stabilization cooperativeness emergency control method | |
| CN107728028A (en) | GIS partial discharge fault distinguishing method based on one-class support vector machines | |
| Vieira et al. | Characterization of welding defects by fractal analysis of ultrasonic signals | |
| CN104914854B (en) | A Method of Industrial Process Fault Diagnosis Based on KPCA | |
| CN109507517A (en) | The distribution transformer running state analysis method compared based on two-sided power big data | |
| CN114325555B (en) | Metering equipment abnormality online monitoring model and error calculation method | |
| Simeone et al. | Collision damage assessment in lithium-ion battery cells via sensor monitoring and ensemble learning | |
| CN103529337A (en) | Method for recognizing nonlinear correlation between equipment failures and electric quantity information | |
| CN105279315A (en) | Related analysis and Mahalanobis distance based transformer online monitoring information aggregation analysis method | |
| CN117471326A (en) | A method for dynamic inconsistency and health status assessment of lithium-ion battery packs | |
| CN108681835B (en) | Method for evaluating degradation state of transformer insulating oil | |
| CN115758284B (en) | Photovoltaic hot spot fault detection method and system based on fusion kernel entropy and information gain |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |