CN110095661A - A kind of distribution transformer high-pressure side open-phase fault emergency repair method - Google Patents
A kind of distribution transformer high-pressure side open-phase fault emergency repair method Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention discloses a kind of distribution transformer high-pressure side open-phase fault emergency repair methods, are related to phase shortage judgment method.Currently, phase shortage judgement there is a problem of lacking real-time and intelligence degree is not deep enough.The present invention includes that step obtains data, data matrix, high dimensional feature extraction, multiple features fusion, fault source tracing, high voltage phase loss fault diagnosis;Spectrum analysis based on MP-Law and the average spectral radius index based on Ring-Law further establish LES system for statistical indices and its visualization, and then construct phase shortage criterion index;Power distribution network operation and environmental information are merged, failure influence factor is analyzed, equipment state evolution process is modeled by time series, index of correlation is formed with random matrix and the state of power network line is assessed, provide guidance for power distribution network service arrangement.The technical program rationally to all types of distribution respective resources, realizes the reasonable disposition of maintenance resource;Optimize power distribution network and overhaul resource distribution, so that emergency first-aid repair is more accurate.
Description
Technical field
The present invention relates to phase shortage judgment method more particularly to a kind of distribution transformer high-pressure side open-phase fault emergency repair methods.
Background technique
In the normal course of operation of transformer, the foreign matter of environment fall or overload etc. due to, lead to height
Press a certain phase line in side to disconnect or fuse blows, further will cause other biphase currents increase suddenly, transformer coil
Temperature increases, and easily transformer is caused to burn out, causes some unsafe accidents, can all caused to resident and factory user great
Personnel and economic loss.It is most of by manually patrolling since automation degree of equipment is low in most traditional transformer O&M
Look into, the mode of manual report checks problem, efficiency is extremely low.It is general with automatically-monitored equipment and cloud computing in recent years
And monitoring terminal periodic on send the monitoring data such as real-time voltage to cloud main website, main website passes through the analysis continuous 2-3 period
Monitoring data carry out problem analysis, due to communication flows limitation and mitigate Cloud Server burden, above the period is sent at least to limit
It is scheduled on 15 minutes, therefore, the time of fault discovery is at least half an hour, is not able to satisfy wanting for power grid O&M and power supply reliability
It asks.
Currently, being related generally in the application of low-voltage distribution network to the emergency repair method of distribution transformer high-pressure side open-phase fault
To intelligent distribution transformer terminals (referred to as: distribution transformer terminals), the public change monitoring system of intelligence (referred to as: main station system) and it is three artificial in terms of,
And include from process: given in intelligent distribution transformer terminals timing, the public change monitoring system timing judgement of intelligence, manual analysis and publication and
Artificial treatment four processes, detailed process are as shown in Figure 1.
Firstly, the three-phase voltage of the intelligent distribution transformer terminals real-time measurement distribution transformer low-pressure side mounted in scene, and with 15
Minute is the mission profile in period, gives the data to intelligence public affairs change monitoring system in timing remotely;Then, intelligence is public becomes monitoring system
System judges high-pressure side phase shortage using the data (that is: sending the period at least two) continuously received three times, and then forms alarm thing
Part information, while alarm event information is sent to related personnel in the form of short message;Finally, according to short message content, artificial point
The information such as equipment involved in the alarm event, user are precipitated, to make a report on corresponding outage information and be issued, simultaneously also
Work of sending someone is to in-situ processing.The process has the following disadvantages:
One, lack real-time.Send a data to intelligence public affairs change monitoring system on intelligent distribution transformer terminals 15 minutes are just long-range,
And the public change monitoring system of intelligence needs at least 30 minutes (sending the period on two) could judge high-pressure side phase shortage, is next connected
For artificial link in time there is also very big uncertainty, whole flow process is far from satisfying requirement in real-time;
Two, intelligence degree is not deep enough.In whole flow process, the content for needing manually to participate in is more, for example analysis is accused
Alert event information content, publication outage information, worksheet processing maintenance etc., intelligence employed in process, information system can not be right
The treatment process of the problem carries out all-the-way tracking and monitoring.
Summary of the invention
The technical problem to be solved in the present invention and the technical assignment of proposition are to be improved and improved to prior art,
A kind of distribution transformer high-pressure side open-phase fault emergency repair method is provided, to achieve the purpose that improve emergency first-aid repair accuracy.For this purpose,
The present invention takes following technical scheme.
A kind of distribution transformer high-pressure side open-phase fault emergency repair method, comprising the following steps:
1) data are obtained:
High Dimensional Data Set is obtained using existing communication mechanism, the data collection of the relevant sensor of fault detection in intelligence
Energy distribution transformer terminals, transfer to edge from cloud;
2) data matrix:
Sampled data forms random matrix by stochastic matrix models;Stochastic matrix models are based on time series to equipment shape
The modeling of state evolution process converts random matrix problem analysis for electric network state cognition by stochastic matrix models;To split only
The part area data that need to be handled;
3) high dimensional feature extracts
High dimensional feature is extracted to pretreated random matrix based on Ring-Law and MP-Law, obtains average spectral radius
MSR, linear statistical characteristic value LES, and visualization processing is carried out, it is united by the higher-dimension that spectrum analysis figure intuitively embodies fault data collection
Metering;
4) multiple features fusion
It is that time reference quantity progress multiple features melt with electrical special reason amount and general statistic with higher-dimension statistic for main reference quantity
It closes, constructs phase shortage criterion;
5) fault source tracing
By splicing matrix disposal, trace to the source failure, positioning failure phase;Index is recalculated through data splicing and is led to
The situation of change for crossing New Set judges the influence of spliced data to event, analyzes the statistical property of criterion, according to setting
Fixed criterion threshold value carries out high voltage phase loss breakdown judge;Statistical property includes convergence, confidence level;The criterion threshold value set up includes
Omission factor threshold value, false alarm rate threshold value;
Since network system state depends on multiple influence factors, it is assumed that the state of certain power grid is the change of a N-dimensional parameter
It measures and has K potential influence factors, by certain time period tiThe measurement of (i=1,2 ..., T), the N-dimensional of the state of power grid
Vector can form basic status matrix naturallyAnd each influence factor can also obtain the value of the period, constitute because
Plain vector
Two matrixes (vector) with equal length can form a new matrix by concatenation;By basic status
Matrix B and factor vector cjIt is spliced into composite matrix Aj。
For the ease of analyzing its impact factor to the influence power of basic status, need to amplify the influence power of impact factor;Choosing
Determine factor vector cj, replicating the factor vector K times by certain mode, (K can use 0.4 × N) forms one and state matrix rule
The matched matrix D of mouldj, as shown in formula (4):
In DjMiddle introducing white noise is to eliminate interdependency, as shown in formula (5):
Cj=Dj+ηjR (j=1,2 ..., m) (5)
In formula (5), R is standard gaussian random matrix, ηjWith signal-to-noise ratio (SNR, signal-to-noise ratio) ρ phase
It closes:
Concurrently to each factor vector cjComposite matrix A is spliced to form by matrixj:
Compare each AjStatistical indicator LES can find out different data, that is, influence the sensible factor of state;
6) high voltage phase loss fault diagnosis
Fault source tracing is obtained as a result, obtaining high voltage phase loss fault diagnosis result, arranges corresponding maintenance to appoint by diagnostic result
Business.
MSR and LES essence is the statistic of the random matrix of matrix, and when system is abnormal, which can significant changes.
It may make using distributed algorithm and distribution global behavior assessed by the statistic in each region, i.e., lacked to what be whether there is in distribution
Mutually make a response.
The technical program lacks high pressure using data in edge calculations and region by redesigning local communication mechanism
Mutually " edge " (the intelligent distribution transforming transferred to from " cloud " (power distribution automation main station system of new generation) closer to the source of trouble is studied and judged in alarm
Terminal), the real-time of high voltage phase loss breakdown repair is substantially increased, is increased in one minute from least half an hour before.?
In function decentralization process, it is based on overall condition initial setting criterion, is based further on localization historical data edge feelings with arround
Condition revises criterion.In conjunction with existing big data platform, " alarm event large data center " is deployed beyond the clouds, is passed through
Big data intellectual analysis is automatically performed the process from equipment fault detailed analysis to auto form delivering.Finally, automatic receive live people
The feedback result of work processing enriches the intelligence degree of entire repairing process.The technical program is for distribution transformer high pressure
Open-phase fault repairing in side is of great significance, and effectively improves the power supply reliability of power distribution network.
Meanwhile the technical program optimization power distribution network overhauls resource distribution;So that emergency first-aid repair is more accurate.Merge power distribution network fortune
Capable and environmental information analyzes failure influence factor, is modeled by time series to equipment state evolution process, with random matrix shape
It is assessed at state of the index of correlation to power network line, provides guidance for power distribution network service arrangement.Index and visual image,
The state of route is assessed, and then provides guidance for line maintenance arrangement.Distribution network line is divided into distribution network
Good segments, easily the hair types such as section and faulty section realize the reasonable disposition of maintenance resource rationally to all types of distribution respective resources.
As optimization technique means: it further include data prediction step, pre-treatment step carries out before high dimensional feature extracts,
Data prediction includes:
Data are translated certain periods to restore nonsynchronous data by translation;
And/or reinforce, a certain column data is replicated, the influence with the selective analysis time to system integrality;
And/or augmentation, abnormal inducement is excavated, to conveniently realize parallel computation;
And/or random wave, normalization, matrix mark is changed to the statistics premise so that its satisfaction.
Different observing matrixes can be set up by different pretreatment modes for analysis.
As optimization technique means: the average spectral radius MSR index is higher-dimension statistical indicator, passes through comparative observation
Value and desired value carry out hypothesis testing.
As optimization technique means: the number that the linear statistical characteristic value LES index system passes through Random Matrices Theory
It is obtained according to processing method, value has statistical nature.
The utility model has the advantages that
1, the technical program is by redesigning local communication mechanism, using data in edge calculations and region, high pressure
Phase shortage alarm, which is studied and judged from " cloud ", transfers to " edge " closer to the source of trouble, substantially increases the real-time of high voltage phase loss breakdown repair
Property, it is increased in one minute from least half an hour before.In function decentralization process, it is automatically performed from equipment fault and divides in detail
Analyse the process of auto form delivering.Finally, the automatic feedback result for receiving live artificial treatment, enriches the intelligence of entire repairing process
Degree can be changed.The technical program is of great significance for the repairing of distribution transformer high-pressure side open-phase fault, effectively improves distribution
The power supply reliability of net.
2, the technical program optimization power distribution network overhauls resource distribution;So that emergency first-aid repair is more accurate.Merge power distribution network operation
And environmental information, failure influence factor is analyzed, equipment state evolution process is modeled by time series, is formed with random matrix
Index of correlation assesses the state of power network line, provides guidance for power distribution network service arrangement.Index and visual image, it is right
The state of route is assessed, and then provides guidance for line maintenance arrangement.Distribution network line is divided into distribution network good
Good section, easily the hair types such as section and faulty section realize the reasonable disposition of maintenance resource rationally to all types of distribution respective resources.
Detailed description of the invention
Fig. 1 is existing procedure figure.
Fig. 2 is flow chart of the invention.
Fig. 3 is Ring Law, MP Law, MSR/LES hypothesis testing effect picture of the invention.
Fig. 4 is fault source tracing partial process view of the invention.
Specific embodiment
Technical solution of the present invention is described in further detail below in conjunction with Figure of description.
As shown in Fig. 2, the present invention the following steps are included:
Step 1: obtaining data
High Dimensional Data Set is obtained using existing communication mechanism, is exactly the data collection the relevant sensor of fault detection
In intelligent distribution transformer terminals, edge is transferred to from cloud.
Step 2: establishing data model for the high dimensional data collected
Using Random Matrices Theory method, based on it, Random Matrices Theory is constructed.Random matrix models its advantage and exists
In the space-time associative correlation (higher-dimension statistical information) for being contemplated that Data In High-dimensional Spaces (data set) itself.The model built can
Contain certain statistical information (depending on actual scene and data cases), further, data matrix can be pre-processed with side
Just high dimensional feature (MSR, LES) extracts;Index of correlation is formed with random matrix to assess the state of power network line, is distribution
The diagnosis of net phase shortage provides guidance.Stochastic matrix models RMM is established based on sampled data, row N represents sampling dimension, column T is represented
Sampling.Random matrix problem analysis is converted by electric network state cognition by RMM.Due to the spy that character matrix is detachable, merges
Property, the detachable data for only handling regional area of the modeling method.
Step 3: extracting its higher-dimension statistic using specific parser for the high dimensional data collected
MSR and LES is extracted based on Ring-Law and MP-Law, and why can put forward this feature and this feature energy
Being used as criterion is because there is Random Matrices Theory ensureing, the conclusion of spectrum analysis has a statistical property, and these
Property can be obtained a priori, as shown in figure 3, the higher-dimension statistic as fault data collection.
Step 4: multiple features fusion
Using higher-dimension statistic as Primary Reference, phase shortage criterion is constructed based on multiple features fusion technology, detects whether that event occurs
Barrier.
Step 5: fault source tracing
By further splicing the processing of matrix, trace to the source failure, i.e. the phase of positioning failure, to the statistics of criterion
Matter (convergence, confidence level) etc. is further analyzed, and sets up criterion threshold value according to engineering demand (omission factor, false alarm rate).
As shown in figure 4, since network system state depends on multiple influence factors, it is assumed that the state of certain power grid is a N
It ties up the variable of parameter and has K potential influence factors, by certain time period tiThe measurement of (i=1,2 ..., T), power grid
The N-dimensional vector of state can form basic status matrix naturallyAnd each influence factor can also obtain the period
Value, constituent element vector
Two matrixes (vector) with equal length can form a new matrix by concatenation;By basic status
Matrix B and factor vector cjIt is spliced into composite matrix Aj。
For the ease of analyzing its impact factor to the influence power of basic status, need to amplify the influence power of impact factor;Choosing
Determine factor vector cj, replicating the factor vector K times by certain mode, (K can use 0.4 × N) forms one and state matrix rule
The matched matrix D of mouldj, as shown in formula (4):
In DjMiddle introducing white noise is to eliminate interdependency, as shown in formula (5):
Cj=Dj+ηjR (j=1,2 ..., m) (5)
In formula (5), R is standard gaussian random matrix, ηjWith signal-to-noise ratio (SNR, signal-to-noise ratio) ρ phase
It closes:
Concurrently to each factor vector cjComposite matrix A is spliced to form by matrixj:
Compare each AjStatistical indicator LES can find out different data, that is, influence the sensible factor of state.
Step 6: high voltage phase loss fault diagnosis and repairing
High voltage phase loss fault diagnosis result is obtained by fault detection and fault source tracing, is arranged according to diagnostic result corresponding
Maintenance plan.
To set up different observing matrixes for analysis, and accuracy is improved, it can be into before extracting its higher-dimension statistic
Row pretreatment.Following basic function: 1. translations is set up in pretreatment --- data are translated into certain periods to restore nonsynchronous
Data;2. reinforcing --- a certain column data is replicated into the influence with the selective analysis time to system integrality;3. increasing
Extensively --- exception inducement can be achieved and excavate, can easily realize parallel computation;4. random wave, normalization --- by matrix mark
Change certain statistics premise so that its satisfaction.
A kind of distribution transformer high-pressure side open-phase fault emergency repair method shown in figure 2 above is specific implementation of the invention
Example, has embodied substantive distinguishing features of the present invention and progress, can be under the inspiration of the present invention, right according to actual using needs
Its equivalent modifications for carrying out shape, structure etc., the column in the protection scope of this programme.
Claims (4)
1. a kind of distribution transformer high-pressure side open-phase fault emergency repair method, it is characterised in that the following steps are included:
1) data are obtained:
High Dimensional Data Set is obtained using existing communication mechanism, the data collection of the relevant sensor of fault detection is matched intelligently
Transformer terminals transfer to edge from cloud;
2) data matrix:
Sampled data forms random matrix by stochastic matrix models;Stochastic matrix models are based on time series and drill equipment state
Become process model building, converts random matrix problem analysis for electric network state cognition by stochastic matrix models;It only needs to locate to split
The part area data of reason;
3) high dimensional feature extracts
High dimensional feature is extracted to pretreated random matrix based on Ring-Law and MP-Law, obtains average spectral radius MSR, line
Property statistical characteristics LES, and visualization processing is carried out, the higher-dimension statistic of fault data collection is intuitively embodied by spectrum analysis figure;
4) multiple features fusion
It is that time reference quantity carries out multiple features fusion with electrical special reason amount and general statistic with higher-dimension statistic for main reference quantity,
Phase shortage criterion is constructed, general statistic includes mean value, variance, low-dimensional transformation index;
5) fault source tracing
By splicing matrix disposal, trace to the source failure, positioning failure phase;Index is recalculated through data splicing and by new
The situation of change of index judges influence of the spliced data to event, analyzes the statistical property of criterion, according to setting
Criterion threshold value carries out high voltage phase loss breakdown judge;Statistical property includes convergence, confidence level;The criterion threshold value set up includes missing inspection
Rate threshold value, false alarm rate threshold value;
Since network system state depends on multiple influence factors, it is assumed that the state of certain power grid be the variable of a N-dimensional parameter and
There are K potential influence factors, by certain time period tiThe measurement of (i=1,2 ..., T), the N-dimensional vector of the state of power grid
Basic status matrix can be formed naturallyAnd each influence factor can also obtain the value of the period, constituent element to
Amount
Two matrixes with equal length form a new matrix by concatenation;By basic status matrix B and factor
Vector cjIt is spliced into composite matrix Aj;
For the ease of analyzing its impact factor to the influence power of basic status, amplify the influence power of impact factor;Selected Factors to
Measure cj, replicate the factor vector K times matrix D for forming one with state matrix Size Matchj, it is shown below:
In DjMiddle introducing white noise is shown below with eliminating interdependency:
Cj=Dj+ηjR (j=1,2 ..., m) (5)
In formula, R is standard gaussian random matrix, ηjIt is related with signal-to-noise ratio (SNR, signal-to-noise ratio) ρ:
Concurrently to each factor vector cjComposite matrix A is spliced to form by matrixj:
Compare each AjStatistical indicator LES can find out different data, that is, influence the sensible factor of state;
6) high voltage phase loss fault diagnosis
Fault source tracing is obtained as a result, obtaining high voltage phase loss fault diagnosis result, arranges corresponding maintenance task by diagnostic result.
2. a kind of distribution transformer high-pressure side open-phase fault emergency repair method according to claim 1, it is characterised in that: also wrap
Data prediction step is included, pre-treatment step carries out before high dimensional feature extracts, and data prediction includes:
Data are translated certain periods to restore nonsynchronous data by translation;
And/or reinforce, a certain column data is replicated, the influence with the selective analysis time to system integrality;
And/or augmentation, abnormal inducement is excavated, to conveniently realize parallel computation;
And/or random wave, normalization, matrix mark is changed to the statistics premise so that its satisfaction.
3. a kind of distribution transformer high-pressure side open-phase fault emergency repair method according to claim 2, it is characterised in that: described
Average spectral radius MSR index be higher-dimension statistical indicator, hypothesis testing is carried out by comparative observation value and desired value.
4. a kind of distribution transformer high-pressure side open-phase fault emergency repair method according to claim 3, it is characterised in that: described
Linear statistical characteristic value LES index system obtained by the data processing method of Random Matrices Theory, value has statistics special
Sign.
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110568275A (en) * | 2019-09-17 | 2019-12-13 | 国网福建省电力有限公司安溪县供电公司 | open-phase fault studying and judging method and system based on public and private power distribution network variable data |
CN111196201A (en) * | 2019-12-31 | 2020-05-26 | 国网北京市电力公司 | Mobile box transformer substation vehicle |
CN112083274A (en) * | 2020-08-21 | 2020-12-15 | 深圳供电局有限公司 | Method and device for monitoring fault information of power grid secondary equipment box |
CN114897035A (en) * | 2021-10-09 | 2022-08-12 | 国网浙江省电力有限公司电力科学研究院 | Multi-source data feature fusion method for 10kV cable state evaluation |
CN116008682A (en) * | 2023-03-22 | 2023-04-25 | 国网江西省电力有限公司电力科学研究院 | Real-time studying and judging method and system for distribution transformer high-voltage open-phase fault position |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101964548A (en) * | 2010-11-12 | 2011-02-02 | 国电南瑞科技股份有限公司 | Comprehensive intelligent assembly of power transformer |
CN105071536A (en) * | 2015-08-19 | 2015-11-18 | 江苏省电力公司扬州供电公司 | Intelligent distribution transformer area system |
CN106571626A (en) * | 2016-08-29 | 2017-04-19 | 上海交通大学 | Power system cognitive method based on random moment theory |
CN108152675A (en) * | 2017-12-21 | 2018-06-12 | 华中科技大学 | It is determined and fault zone localization method based on the fault moment of Random Matrices Theory |
CN108828405A (en) * | 2018-06-06 | 2018-11-16 | 西南交通大学 | A kind of electric transmission line fault detection method based on random matrix |
-
2019
- 2019-04-10 CN CN201910286431.1A patent/CN110095661B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101964548A (en) * | 2010-11-12 | 2011-02-02 | 国电南瑞科技股份有限公司 | Comprehensive intelligent assembly of power transformer |
CN105071536A (en) * | 2015-08-19 | 2015-11-18 | 江苏省电力公司扬州供电公司 | Intelligent distribution transformer area system |
CN106571626A (en) * | 2016-08-29 | 2017-04-19 | 上海交通大学 | Power system cognitive method based on random moment theory |
CN108152675A (en) * | 2017-12-21 | 2018-06-12 | 华中科技大学 | It is determined and fault zone localization method based on the fault moment of Random Matrices Theory |
CN108828405A (en) * | 2018-06-06 | 2018-11-16 | 西南交通大学 | A kind of electric transmission line fault detection method based on random matrix |
Non-Patent Citations (2)
Title |
---|
徐心怡 等: "基于随机矩阵理论的配电网运行状态相关性分析方法", 《电网技术》 * |
贺兴 等: "随机矩阵理论在电力系统认知中的应用初探", 《电网技术》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110568275A (en) * | 2019-09-17 | 2019-12-13 | 国网福建省电力有限公司安溪县供电公司 | open-phase fault studying and judging method and system based on public and private power distribution network variable data |
CN110568275B (en) * | 2019-09-17 | 2022-02-01 | 国网福建省电力有限公司安溪县供电公司 | Open-phase fault studying and judging method and system based on public and private power distribution network variable data |
CN111196201A (en) * | 2019-12-31 | 2020-05-26 | 国网北京市电力公司 | Mobile box transformer substation vehicle |
CN112083274A (en) * | 2020-08-21 | 2020-12-15 | 深圳供电局有限公司 | Method and device for monitoring fault information of power grid secondary equipment box |
CN112083274B (en) * | 2020-08-21 | 2023-10-31 | 深圳供电局有限公司 | Method and device for monitoring fault information of secondary equipment box of power grid |
CN114897035A (en) * | 2021-10-09 | 2022-08-12 | 国网浙江省电力有限公司电力科学研究院 | Multi-source data feature fusion method for 10kV cable state evaluation |
CN116008682A (en) * | 2023-03-22 | 2023-04-25 | 国网江西省电力有限公司电力科学研究院 | Real-time studying and judging method and system for distribution transformer high-voltage open-phase fault position |
CN116008682B (en) * | 2023-03-22 | 2023-08-15 | 国网江西省电力有限公司电力科学研究院 | Real-time studying and judging method and system for distribution transformer high-voltage open-phase fault position |
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