CN110470964A - GIS point discharge stage judgment method and judgment means based on maintenance decision purpose - Google Patents
GIS point discharge stage judgment method and judgment means based on maintenance decision purpose Download PDFInfo
- Publication number
- CN110470964A CN110470964A CN201910744225.0A CN201910744225A CN110470964A CN 110470964 A CN110470964 A CN 110470964A CN 201910744225 A CN201910744225 A CN 201910744225A CN 110470964 A CN110470964 A CN 110470964A
- Authority
- CN
- China
- Prior art keywords
- discharge
- stage
- regime
- electric discharge
- fingerprint
- 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.)
- Pending
Links
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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
-
- 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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/16—Construction of testing vessels; Electrodes therefor
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Relating To Insulation (AREA)
Abstract
The present invention relates to a kind of GIS point discharge stage judgment method and judgment means based on maintenance decision purpose, the judgment method is the following steps are included: define point discharge severity stage, dividing discharge fingerprint characteristic vector；To the electric discharge fingerprint characteristic vector that test overall process obtains, the cluster centre of each discharge regime is obtained；Local discharge characteristic fingerprint to be diagnosed is extracted, the distance between the cluster centre of local discharge characteristic fingerprint and each discharge regime is calculated；The corresponding classification of the minimum value of distance is determined as wait diagnose discharge regime locating for partial discharge phenomenon by the distance between the cluster centre for judging local discharge characteristic fingerprint Yu each discharge regime.The present invention has rational design, realizes that the arbitration functions of discharge regime locating for GIS point discharge defect and the extent of injury provide scientific guidance opinion for the purpose of auxiliary repair decision for site examining and repairing decision.
Description
Technical field
The invention belongs to shelf depreciation technical field, especially a kind of GIS point discharge rank based on maintenance decision purpose
Section judgment method and judgment means.
Background technique
Currently, the voltage class of equipment and capacity are continuously improved in electric system with the development of China's power industry, become
Power station place, ambient conditions, equipment and technology requires and the factors such as economy make traditional open type electrical equipment more and more not
Adapt to the construction of modern electric.
The gas-insulating and fully-enclosed combined electrical apparatus of SF6 (Gas Insulated Metal-enclosed Switchigear, letter
Claim GIS) because the advantages that compact-sized, occupied area is small, the time between overhauls(TBO) is long and high safety and reliability, is more and more answered
With.However, GIS device is difficult to analyze and determine its existing defect, especially due to its closure and internal not visible property
Assessment to its extent of injury, this brings very big puzzlement to site examining and repairing decision, or even seriously hinders GIS device application
And the development of power industry.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose a kind of tip GIS based on maintenance decision purpose
Discharge regime judgment method and judgment means can be realized discharge regime locating for GIS point discharge defect, judge to endanger journey
Degree, provides scientific guidance opinion for site examining and repairing decision.
The present invention solves its technical problem and adopts the following technical solutions to achieve:
A kind of GIS point discharge stage judgment method based on maintenance decision purpose, comprising the following steps:
Step 1, the electric discharge fingerprint characteristic vector obtained to test overall process, obtain the cluster centre of each discharge regime；
Step 2 extracts local discharge characteristic fingerprint to be diagnosed, and calculates local discharge characteristic fingerprint and each discharge regime
The distance between cluster centre；
The distance between step 3, the cluster centre for judging local discharge characteristic fingerprint and each discharge regime, by distance
The corresponding classification of minimum value is determined as wait diagnose discharge regime locating for partial discharge phenomenon.
Further include handling before step 1 as follows: defining point discharge stage, dividing discharge fingerprint characteristic vector.
The discharge regime includes: electric discharge initial phase, electric discharge developing stage and electric discharge severe stage.
The electric discharge initial phase are as follows: the corona and meeting is acted on so that tip is electric to the ablation of spine that shelf depreciation is formed
Extremely nearby the degree of irregularity of electric field reduces；The electric discharge developing stage are as follows: electrode gap has part of cation from tip always
Electrode is mobile to ground electrode, and the ion concentration in entire gap can be increasing；The electric discharge severe stage are as follows: positive space charge
It is formed about strong electrical field in plate electrode, and thus excites strong ionization process, so that electric discharge when applying voltage negative half period outside
Process acutely develops.
The electric discharge fingerprint characteristic vector includes: the ratio between shelf depreciation phase width, positive-negative half-cycle discharge time, positive and negative half
The ratio between all averaged discharge amplitudes, electric discharge amplitude entropy, maximum electric discharge amplitude entropy, Vmax- φ spectrogram negative half period degree of skewness, electric discharge
The box counting dimension of mean value and ⊿ ui the distribution spectrogram of time interval.
The step 1 obtains the cluster centre of each discharge regime, including electric discharge starting rank using K-means clustering method
The cluster centre of section, electric discharge developing stage and the severe stage that discharges.
The step 2 is calculated using Euclidean distance square in the cluster of local discharge characteristic fingerprint and each discharge regime
The distance between heart.
A kind of GIS point discharge stage judgment means based on maintenance decision purpose, comprising:
Module is obtained, the electric discharge fingerprint characteristic vector for obtaining to test overall process obtains the poly- of each discharge regime
Class center；
Computing module, for extracting local discharge characteristic fingerprint to be diagnosed, calculate local discharge characteristic fingerprint with it is each
The distance between cluster centre of discharge regime；
Judgment module, for judging the distance between the cluster centre of local discharge characteristic fingerprint Yu each discharge regime,
The corresponding classification of the minimum value of distance is determined as wait diagnose discharge regime locating for partial discharge phenomenon.
The electric discharge fingerprint characteristic vector includes: the ratio between shelf depreciation phase width, positive-negative half-cycle discharge time, positive and negative half
The ratio between all averaged discharge amplitudes, electric discharge amplitude entropy, maximum electric discharge amplitude entropy, Vmax- φ spectrogram negative half period degree of skewness, electric discharge
The box counting dimension of mean value and ⊿ ui the distribution spectrogram of time interval.
The discharge regime includes: electric discharge initial phase, electric discharge developing stage and electric discharge severe stage.
The advantages and positive effects of the present invention are:
The present invention has rational design, the electric discharge fingerprint characteristic vector that is obtained by test overall process and three discharge regimes
The distance of cluster centre judges discharge regime locating for partial discharge phenomenon, realizes the locating hair of GIS point discharge defect
The judgement of exhibition state and the extent of injury provides scientific guidance opinion for the purpose of auxiliary repair decision for site examining and repairing decision.
Detailed description of the invention
Fig. 1 is that point discharge fingerprint characteristic changes over time trend schematic diagram；
Fig. 2 is point discharge discharge regime division principle figure.
Specific embodiment
The embodiment of the present invention is further described below in conjunction with attached drawing.
Design philosophy of the invention is:
Nonlinear variation tendency can be presented in Partial Feature parameter in the development process of shelf depreciation, even each
All there is the case where first increases and then decreases in test section, this explanation relies on the size of nonlinear change parameter, can not put to part
The severity of electricity is judged, and can not accurately be divided to discharge regime, therefore extracting being capable of Efficient Characterization point
The monotone variation characteristic parameter of end electric discharge shelf depreciation different stages of development and severity, it appears particularly necessary.This patent root
According to the discharge data in point discharge each stage, it is extracted 8 classes electric discharge as shown in table 1, that monotone variation is presented, according to these electric discharges
Fingerprint characteristic can further provide the diagnostic method of point discharge Evolution States.
1 point discharge severity characteristic feature fingerprint of table
In addition, Fig. 1 is respectively 8 characteristic parameters of point discharge with the variation tendency of test period, thick line is pair in figure
Curve carries out the result after exponential smoothing.It can be seen that 8 electric discharge fingerprint characteristics with the growth of test period present it is dull increase or
The trend of reduction, thus as the characteristic parameter for characterizing shelf depreciation severity.
From fig. 1, point discharge fingerprint characteristic reaches in test can mutate after a certain period of time, entire experimental stage
Occur to be mutated twice altogether.The reason of being mutated twice occurs from the test of the interpretation of mechanism below:
After voltage reaches certain value, since polar effect occurs the more intensive lesser electric discharge of amplitude in negative half period,
When discharging condition does not deteriorate, corona and meeting is acted on so that point electrode is attached to the ablation of spine that shelf depreciation is formed
The degree of irregularity of nearly electric field reduces, and electric discharge can maintain in this stage；When discharging condition further deteriorates, point electrode exists
Positive half cycle generates excessive cation, and in negative half period, these excessive cations not yet migrate completely, to exacerbate negative half period
When electric discharge, at this moment electric discharge enter second stage, in this stage in, electrode gap has part of cation from point electrode always
It is mobile to ground electrode, and since the electric discharge of negative half period aggravates, the ion concentration in entire gap can be increasing；Work as electrode gap
In cation concentration near the plate electrode can build up very high level, when plate electrode negative polarity, positive space electricity
Lotus is formed about strong electrical field in plate electrode, and thus excites strong ionization process, so that it is (negative to apply voltage negative half period outside
The positive plate of point) when discharge process acutely develop, at this moment electric discharge enters the phase III.
Features described above amount is subjected to clustering to define initial phase Sini, the developing stage Sdev of point discharge and tight
Heavy stage Sser, it is more significant for the division of discharge regime in this way.It is obtained by K-means clustering method to extracted
Characteristic fingerprint is clustered, and the cluster centre of three discharge regimes is obtained.The electric discharge fingerprint characteristic obtained using test is as putting
Electric severity diagnosis cluster sample database, using extracted discharge severity characteristic feature according to minimal distance principle
Automatic discrimination can be carried out to the discharge regime of shelf depreciation.
Based on above-mentioned design philosophy, the GIS point discharge stage judgment method packet of the invention based on maintenance decision purpose
Include following steps:
Step 1 defines point discharge severity stage, dividing discharge fingerprint characteristic vector.
The electric discharge fingerprint characteristic vector that this patent defines include: shelf depreciation phase width/°Positive-negative half-cycle electric discharge
The ratio between the ratio between number (N+/N-), positive-negative half-cycle averaged discharge amplitude (μ (V+)/μ (V-)) are discharged amplitude entropy (En (V)), most
Big electric discharge amplitude entropy (En (Vmax)), Vmax- φ spectrogram negative half period degree of skewnessDischarge time interval it is equal
It is worth the box counting dimension (⊿ ui (DB)) of (μ ⊿ t), ⊿ ui distribution spectrogram.Fingerprint characteristic vector (Cf) mathematic(al) representation that discharges is as follows:
Step 2, the electric discharge fingerprint characteristic vector (Cf) obtained to test overall process obtain three using K-means clustering method
The cluster centre Cfcenter (Cf1, Cf2, Cf3) of a discharge regime, wherein Cf1, Cf2, Cf3 respectively correspond electric discharge starting rank
The cluster centre of section, electric discharge developing stage and the severe stage that discharges.
Step 3 extracts local discharge characteristic fingerprint Cfx to be diagnosed, and calculates the cluster centre of Cfx and three discharge regime
Euclidean distance square di between (Cf1, Cf2, Cf3)2。
Step 4 judges Euclidean distance square di2Size, Euclidean distance square di2The corresponding classification of minimum value be
Wait diagnose discharge regime locating for partial discharge phenomenon.
Fig. 2 gives 8 characteristic fingerprint the first two characteristic fingerprint discharge phase width of point discharge and positive-negative half-cycle electric discharge
The obtained discharge regime judging result of the ratio between number, abscissa indicate that phase width, ordinate indicate positive-negative half-cycle electric discharge time
The ratio between number, in the cluster result of three discharge regimes, bottom shadow part represents electric discharge initial phase, upper light grey color shadow part
Divide and represent electric discharge developing stage, top black shaded area represents electric discharge severe stage.
Based on above-mentioned GIS point discharge stage judgment method, the present invention also provides a kind of GIS based on maintenance decision purpose
Point discharge stage judgment means, the GIS point discharge stage judgment means include:
Module is obtained, the electric discharge fingerprint characteristic vector for obtaining to test overall process obtains the poly- of each discharge regime
Class center；
Computing module, for extracting local discharge characteristic fingerprint to be diagnosed, calculate local discharge characteristic fingerprint with it is each
The distance between cluster centre of discharge regime；
Judgment module, for judging the distance between the cluster centre of local discharge characteristic fingerprint Yu each discharge regime,
The corresponding classification of the minimum value of distance is determined as wait diagnose discharge regime locating for partial discharge phenomenon.
The present invention does not address place and is suitable for the prior art.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore packet of the present invention
Include and be not limited to embodiment described in specific embodiment, it is all by those skilled in the art according to the technique and scheme of the present invention
The other embodiments obtained, also belong to the scope of protection of the invention.
Claims (10)
1. a kind of GIS point discharge stage judgment method based on maintenance decision purpose, it is characterised in that the following steps are included:
Step 1, the electric discharge fingerprint characteristic vector obtained to test overall process, obtain the cluster centre of each discharge regime；
Step 2 extracts local discharge characteristic fingerprint to be diagnosed, and calculating local discharge characteristic fingerprint is poly- with each discharge regime
The distance between class center；
The distance between step 3, the cluster centre for judging local discharge characteristic fingerprint and each discharge regime, by the minimum of distance
It is worth corresponding classification to be determined as wait diagnose discharge regime locating for partial discharge phenomenon.
2. the GIS point discharge stage judgment method according to claim 1 based on maintenance decision purpose, feature exist
In: further include handling before step 1 as follows: defining point discharge stage, dividing discharge fingerprint characteristic vector.
3. the GIS point discharge stage judgment method according to claim 1 based on maintenance decision purpose, feature exist
In: the discharge regime includes: electric discharge initial phase, electric discharge developing stage and electric discharge severe stage.
4. the GIS point discharge stage judgment method according to claim 3 based on maintenance decision purpose, feature exist
In: the electric discharge initial phase are as follows: the corona and meeting is acted on so that point electrode is attached to the ablation of spine that shelf depreciation is formed
The degree of irregularity of nearly electric field reduces；The electric discharge developing stage are as follows: electrode gap has part of cation from point electrode always
Mobile to ground electrode, the ion concentration in entire gap can be increasing；The electric discharge severe stage are as follows: positive space charge is in plate
Electrode is formed about strong electrical field, and thus excites strong ionization process, so that discharge process when applying voltage negative half period outside
Acutely development.
5. the GIS point discharge stage judgment method according to claim 1 based on maintenance decision purpose, feature exist
It include: that the ratio between shelf depreciation phase width, positive-negative half-cycle discharge time, positive-negative half-cycle are flat in: the electric discharge fingerprint characteristic vector
Discharge the ratio between amplitude, electric discharge amplitude entropy, maximum electric discharge amplitude entropy, Vmax- φ spectrogram negative half period degree of skewness, discharge time
The box counting dimension of mean value and ⊿ ui the distribution spectrogram at interval.
6. the GIS point discharge stage judgment method according to claim 1 based on maintenance decision purpose, feature exist
It obtains the cluster centre of each discharge regime using K-means clustering method in: the step 1, including electric discharge initial phase, puts
The cluster centre of electric developing stage and electric discharge severe stage.
7. the GIS point discharge stage judgment method according to claim 1 based on maintenance decision purpose, feature exist
In: the step 2 calculated using Euclidean distance square local discharge characteristic fingerprint and each discharge regime cluster centre it
Between distance.
8. a kind of GIS point discharge stage judgment means based on maintenance decision purpose, it is characterised in that: include:
Module is obtained, for the electric discharge fingerprint characteristic vector to test overall process acquisition, in the cluster for obtaining each discharge regime
The heart；
Computing module calculates local discharge characteristic fingerprint and each electric discharge for extracting local discharge characteristic fingerprint to be diagnosed
The distance between the cluster centre in stage；
Judgment module will be away from for judging the distance between the cluster centre of local discharge characteristic fingerprint Yu each discharge regime
From the corresponding classification of minimum value be determined as wait diagnose discharge regime locating for partial discharge phenomenon.
9. the GIS point discharge stage judgment means according to claim 8 based on maintenance decision purpose, feature exist
It include: that the ratio between shelf depreciation phase width, positive-negative half-cycle discharge time, positive-negative half-cycle are flat in: the electric discharge fingerprint characteristic vector
Discharge the ratio between amplitude, electric discharge amplitude entropy, maximum electric discharge amplitude entropy, Vmax- φ spectrogram negative half period degree of skewness, discharge time
The box counting dimension of mean value and ⊿ ui the distribution spectrogram at interval.
10. the GIS point discharge stage judgment means according to claim 8 based on maintenance decision purpose, feature exist
In: the discharge regime includes: electric discharge initial phase, electric discharge developing stage and electric discharge severe stage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910744225.0A CN110470964A (en) | 2019-08-13 | 2019-08-13 | GIS point discharge stage judgment method and judgment means based on maintenance decision purpose |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910744225.0A CN110470964A (en) | 2019-08-13 | 2019-08-13 | GIS point discharge stage judgment method and judgment means based on maintenance decision purpose |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110470964A true CN110470964A (en) | 2019-11-19 |
Family
ID=68510740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910744225.0A Pending CN110470964A (en) | 2019-08-13 | 2019-08-13 | GIS point discharge stage judgment method and judgment means based on maintenance decision purpose |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110470964A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113533910A (en) * | 2021-06-10 | 2021-10-22 | 中国电力科学研究院有限公司 | Method and system suitable for converter transformer partial discharge early warning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104155585A (en) * | 2014-08-12 | 2014-11-19 | 国家电网公司 | GIS partial discharge type identification method based on GK fuzzy clustering |
| CN104198898A (en) * | 2014-08-04 | 2014-12-10 | 西安交通大学 | Local discharge development process diagnosis method based on pulse-train analysis |
| CN105004972A (en) * | 2015-06-25 | 2015-10-28 | 华北电力大学（保定） | Porcelain insulator insulation state evaluation method based on solar-blind ultraviolet imaging image feature |
| CN108051711A (en) * | 2017-12-05 | 2018-05-18 | 国网浙江省电力公司检修分公司 | Solid insulation surface defect diagnostic method based on state Feature Mapping |
-
2019
- 2019-08-13 CN CN201910744225.0A patent/CN110470964A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104198898A (en) * | 2014-08-04 | 2014-12-10 | 西安交通大学 | Local discharge development process diagnosis method based on pulse-train analysis |
| CN104155585A (en) * | 2014-08-12 | 2014-11-19 | 国家电网公司 | GIS partial discharge type identification method based on GK fuzzy clustering |
| CN105004972A (en) * | 2015-06-25 | 2015-10-28 | 华北电力大学（保定） | Porcelain insulator insulation state evaluation method based on solar-blind ultraviolet imaging image feature |
| CN108051711A (en) * | 2017-12-05 | 2018-05-18 | 国网浙江省电力公司检修分公司 | Solid insulation surface defect diagnostic method based on state Feature Mapping |
Non-Patent Citations (1)
| Title |
|---|
| 王彩雄: "基于特高频法的GIS局部放电故障诊断研究", 《中国博士学位论文全文数据库 工程科技II辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113533910A (en) * | 2021-06-10 | 2021-10-22 | 中国电力科学研究院有限公司 | Method and system suitable for converter transformer partial discharge early warning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110224192B (en) | Method for predicting service life of power battery by gradient utilization | |
| CN109765496A (en) | A kind of cell health state estimation method based on online electrochemical impedance spectrometry | |
| CN106785178B (en) | Battery module reuse detection, screening and matching method and device | |
| Su et al. | Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression | |
| CN109670242B (en) | Transformer winding deformation unsupervised online monitoring method based on elliptical envelope curve | |
| CN112834945A (en) | Evaluation model establishing method, battery health state evaluation method and related product | |
| CN109078871B (en) | Rejection method of retired battery parallel module for echelon utilization | |
| CN105589040A (en) | Battery regulation and control method based on aging adjustment battery operation interval | |
| CN110488164B (en) | High-voltage cable insulation aging state comprehensive assessment early warning method and system | |
| CN112904219A (en) | Big data-based power battery health state prediction method | |
| CN104237750A (en) | GIS insulation defect partial discharge fault graph drawing method | |
| CN110888077A (en) | Accelerated lithium ion battery life evaluation method based on ARIMA time sequence | |
| CN108919003A (en) | A kind of test of sensitive equipment voltage dip resistance characteristics and data processing method | |
| CN110729938A (en) | Asynchronous motor rotor broken bar fault identification method based on machine vision | |
| CN110470964A (en) | GIS point discharge stage judgment method and judgment means based on maintenance decision purpose | |
| CN108061861B (en) | Screening method for self-discharge of lithium ion battery | |
| CN113283474A (en) | SVM-based partial discharge pattern recognition method | |
| Yuwei et al. | Partial discharge pattern recognition method based on Transfer Learning and DenseNet model | |
| CN114091340B (en) | Construction and discrimination method based on multi-physical-field direct-current partial discharge model | |
| CN116774051B (en) | Battery capacity quick estimation method considering time-frequency domain multidimensional data characteristics | |
| CN115508721B (en) | Lithium ion battery lithium separation detection method | |
| CN116224109A (en) | Method for rapidly measuring capacity of lithium ion battery | |
| CN116466067A (en) | Method for early warning residual life of composite insulator silicon rubber material based on gray theory | |
| CN115424077A (en) | Cable defect identification method based on residual error neural network | |
| CN116540117A (en) | Power battery Y capacitance prediction system and prediction method |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191119 |
|
| RJ01 | Rejection of invention patent application after publication |