CN111579941B - Online steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction - Google Patents
Online steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction Download PDFInfo
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- 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
- G01R31/1263—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 of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B61K9/08—Measuring installations for surveying permanent way
Abstract
The invention discloses an online steel rail insulation defect diagnosis method based on whole-line steel rail potential feature extraction, which belongs to the field of urban rail transit fault detection application, and specifically comprises the following steps: firstly, managing historical data to form different reference groups; then, extracting feature quantity of the real-time data, and performing insulation diagnosis on the real-time data by matching condition features to a reference group; and finally, carrying out probability statistics on the diagnosis result in a discrimination period, and finally realizing defect positioning through a given limit value. The invention can not only diagnose the defect condition rapidly, but also position the defect accurately, and provide reference for stray current protection.
Description
Technical Field
The invention belongs to the field of urban rail transit fault detection application. In particular to an on-line steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction.
Background
The number of city of urban rail transit in 2019 is increased to more than 40, and the total operating mileage exceeds 6700 kilometers. Urban rail transit is generally 1500V or 750V direct current power supply. Most urban rail systems use steel rails as return rails. The train draws current from the contact net through the pantograph, the locomotive consumes energy, and the current returns to the negative pole of the traction power supply station from the steel rail. Due to the transition resistance of the rail to ground, current leaks to the ground when flowing through the rail, and this part of current is called stray current. The stray current flows into the ground, inevitably flows in a metal structure in the ground, and finally flows back to the negative pole of the traction station through a transition resistor and the like.
The current flows from the cathode region to the anode region in the underground pipeline, the metal is dissolved after the oxidation reaction of the anode region, and the pipeline wall becomes thin and even is perforated. In general, the pipelines have spontaneous natural corrosion, and the corrosion speed is slow. Because of the interference of stray current, electrochemical corrosion is generated, when the current value is larger, the corrosion speed is high, and the perforation time is short. Current flows in soil near a transformer, and the current is left in the transformer through the transformer with the neutral point grounded, so that the transformer generates direct current magnetic biasing. After the direct-current magnetic biasing, the harmonic waves generated in the exciting current of the transformer can cause the transformer to be abnormal, and the problems of temperature rise, vibration, noise and the like can occur. The large stray current interference causes additional economic losses.
The stray current protection design adopts the principle of 'taking blockage as a main part, taking drainage as an auxiliary part, preventing drainage and combining, and strengthening monitoring'. Blocking: reducing the current flowing into the ground from the source. Reducing the distance between the tractions; the longitudinal resistance of the steel rail is reduced; increasing the transition resistance of the steel rail to the ground; the voltage level is increased. And (4) row: the current flowing into the ground is reduced by means of drainage measures. And arranging a drainage system, and establishing a loop in which the stray current flows back to the negative pole of the traction station. And (3) measurement: and the line condition is monitored, and a basis is provided for operation and maintenance of stray current.
Important parameters influencing the distribution of stray current of the subway are the longitudinal resistance of the steel rail and the transition resistance of the steel rail to the ground. After the subway is built and enters an operation stage, transition resistance of a steel rail to the ground is reduced along with time accumulation, line pollution accumulation and the like, so that stray current is increased. CJJ49-1992 subway stray current corrosion protection technical code specifies: the transition resistance value (the resistance value which is measured according to the block section and is converted into the length of 1 km) between the subway running rail which is also used as the backflow and the tunnel main body structure (or the ground) is not less than 15 omega km for a newly-built line and not less than 3 omega km for a running line. Monitoring of rail-to-ground transition resistance is particularly important. The transition resistance is regarded as the insulation defect when the transition resistance is not in accordance with the requirement, and the leakage of the ground current of the steel rail is aggravated at the moment, so that the corrosion of the pipeline and the direct current magnetic bias are aggravated.
The existing line transition resistance detection method adopts a method in GB28026.2-2018 appendix B.2, is influenced by an uplink and downlink average flow line and an interline average flow line in detection, and the detection length generally does not exceed 400 m. The single detection efficiency is low, the detection times are increased when the line is longer, and a large amount of manpower and material resources are consumed. The detection of the transition resistance of the service line can only be carried out in the early morning. In view of the above, there is a need for an on-line insulation defect diagnosis technique. When the insulation of the steel rail to the ground on a certain section is changed, the distribution of the full-line steel rail to the ground potential is obviously changed. The most direct characterizing factor for the insulation situation should be the distribution of the full line rail potential.
Disclosure of Invention
The invention aims to monitor the rail potential and the like in an urban rail power supply system and record and analyze data on line in time, thereby realizing the diagnosis of the line insulation condition and providing reference for stray current protection.
Therefore, the invention provides an online steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction, which comprises the following steps of:
step 1: and acquiring and storing data. The real-time steel rail potential data information of each measuring point is obtained through the steel rail potential measuring device of each measuring point, and information including departure intervals, load current, states of related equipment and the like is obtained through information interaction. Storing the acquired data to a real-time database; converting the data into data which can be identified by a monitoring system through a protocol of the server; the system processes data, provides historical data management, data query (including various characteristic quantities), statistical calculation and insulation diagnosis, and sends control instructions such as alarm to the system.
Step 2: and managing historical data, selecting a stage with initial line operation and good line condition, and processing the historical data of the stage to form different reference groups.
For different departure intervals t in historical datahSatisfy ZT in calculation cyclehWhen the load current is 00 hours, a plurality of groups of short-time average load currents AVI are arrangedqhShort-time average AVE of rail potential of each corresponding measuring pointihShort-time rail potential maximum MAXihMinimum value MIN of short-time rail potentialihShort term absolute average ABVihAs different reference groups, the short-time characteristic quantities here all take the corresponding departure intervals as calculation periods, and the calculation formula is as follows:
MAXih＝max(Iihn,n＝1,2,3....N),i＝1,2,...I (3)
MINih＝min(Iihn,n＝1,2,3....N),i＝1,2,...I (4)
wherein N represents the nth data in a calculation period, and N represents the total number of the data in the calculation period; q represents the Q-th traction station on the line, and Q represents the total number of the traction stations on the line; i represents the ith measuring point of the line, and I represents the total number of the measuring points of the line.
And step 3: and extracting the characteristic quantity of the real-time data, and performing insulation diagnosis on the real-time data by matching the condition characteristics to a reference group.
3.1 real-time data processing:
taking the operating time within 24h as a diagnosis period, and carrying out different departure intervals t in the periodsSatisfy the ZT in the cyclesWhen the load current exceeds 00, the average load current AVI of each traction station q is calculatedqsAs a condition quantity matching the reference value; average value AVE of rail potential at each measurement point iisShort-time rail potential maximum MAXisMinimum value MIN of short-time rail potentialisABV, arithmetic mean of absolute values in short timeisAs the characteristic amount of the insulation diagnosis, the calculation formula is as follows:
MAXis＝max(Iisn,n＝1,2,3....N),i＝1,2,...I (8)
MINis＝min(Iisn,n＝1,2,3....N),i＝1,2,...I (9)
3.2 matching reference groups, ts＝th、ZTsAVI calculated in real time 00 ═ 00qsAnd AVIqhIs used as a matching condition for matching the reference group, and E is a minimization target, wherein E is expressed as follows:
3.3 data comparison: comparing the characteristic quantity calculated by each measuring point with the matched reference group, and calculating the deviation delta UixjAnd offsetRate sigmaiThe following were used:
ΔUixj＝Uixj-me-Uix-ref,i＝1,2,...I (12)
wherein j represents the jth comparison within the diagnostic cycle, j being 1,2,3 …; x represents a selected diagnostic feature.
3.4 abnormality diagnosis: for different measurement points i, when the offset ratio σi≤uxWhen u is turned onxIs an offset rate limit; considering that the j-th comparison measurement point i has abnormal steel rail potential characteristic quantity x and abnormal times kix＝kix+1。
3.5 Single diagnosis of suspected defects: in the j comparison, when different diagnosis characteristic quantities x are analyzed, if any measurement point diagnosis characteristic quantity x is abnormal, comparing the delta U of different measurement points iixj(ii) a If the maximum value or the absolute average value of the short-time rail potential is compared, the maximum delta U is takenixjIs the abnormal comparison result of the index x; if the rail potential minimum value is short, the minimum delta U is takenixjIs the abnormal comparison result of the index x; if the abnormal comparison result points to the measurement point i as the suspicious defect measurement point when the diagnostic feature quantity x is compared for the jth time, the suspicious defect statistic m of the diagnostic feature quantity x of the measurement point is determinedix+1。
3.6 the comprehensive diagnosis characteristic quantity is used for defect single diagnosis: during the jth comparison, aiming at different selected diagnosis characteristic quantities, the measurement point i pointed by the abnormal comparison result of the diagnosis characteristic quantity x satisfies the following conditions:
when the arithmetic mean value of the maximum value, the minimum value and the absolute value is taken as the diagnosis characteristic quantity, at least two indexes exist and point to the same i after the comparison result of the two indexes, and the suspicious defect statistic n of the measuring pointi+1。
When the maximum value and the minimum value are taken as diagnosis indexes, two possibilities exist, i pointed by the maximum value and i pointed by the minimum value are the same i, ni+ 1; i directed at the maximum is different from i directed at the minimumN will all be differenti+1。
When the maximum value or the minimum value is taken as a diagnosis index, the index abnormal result points to i, ni+1。
3.7 the next set of data processing and comparison during the diagnostic period: and updating j to j +1, and repeating the steps 3.1 to 3.6 until the diagnosis period is ended.
And 4, step 4: and carrying out probability statistics on the diagnosis result, and finally realizing defect positioning through a given limit value.
The comparison frequency of each diagnosis period is J, a matrix [ K ] represents the abnormal frequency, a matrix [ M ] represents the suspicious defect frequency, and a matrix [ N ] represents the defect frequency; the initial matrix elements are all 0; wherein, the middle lines of [ K ] and [ M ] represent measuring points, and the columns represent compared characteristic quantities; the [ N ] middle row represents a measurement point;
counting abnormal rate of each measuring point in diagnosis period
To defect ratio
When beta isi≥βuI of 10 is a measured point with defects, if all β are locatedi＜βuThen there are no defect points; wherein beta isuIs the nominal defect rate.
Further, the related equipment comprises a steel rail potential limiter OVPD and a drainage device; when the device state is off, it is denoted as "0"; when the device state is closed, it is denoted as "1". The state of the device is represented by a state value ZT as shown in table 1:
TABLE 1 Equipment status Table
| History/real-time device status | ZTh/ZTs |
| OVPD and drainage device are both disconnected | 00 |
| OVPD open and drainage device close | 01 |
| OVPD closing and drainage device opening | 10 |
| OVPD and drainage device are both closed | 11 |
Furthermore, various calculation periods exist in the diagnosis period, data are required to interact with PSCADA real-time information, the total number of the data in one diagnosis period is J groups, namely the comparison times are J times.
Further, the diagnostic characteristic quantities are a short-term rail potential maximum value, a short-term rail potential minimum value, and a short-term absolute average value.
Further, uxDifferent values are taken according to different lines, and the diagnostic characteristic quantity is negative no matter what is taken.
Compared with the prior art, the invention has the beneficial technical effects that:
1. by using the online diagnosis method, the defect position can be quickly positioned.
2. The stray current is closely related to the transition resistance, and when a problem occurs in insulation of a certain part, the stray current is increased. The method can find defects in time and provide reference for stray current protection.
Drawings
FIG. 1 is a schematic diagram of inventive data acquisition.
FIG. 2 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
The flow chart of the online steel rail insulation defect diagnosis method based on the whole-line steel rail potential feature extraction is shown in fig. 2, and the embodiment of the invention diagnoses the potential change condition of the whole-line steel rail and locates the defect position by taking the steel rail potential as the feature. Data collected by each measuring point data collecting device in the figure 1 is transmitted to a monitoring system, and real-time other information such as train departure interval time, equipment state and the like is obtained from a PASCDA system.
The historical data and the real-time data are processed according to the method of the invention, and the short-time average load current of each traction station, the short-time average value of the rail potential of each measuring point, the maximum value of the short-time rail potential, the minimum value of the short-time rail potential, the arithmetic average value of the short-time absolute value and the like are calculated. And calculating deviation values and deviation rates after matching the reference groups, and performing single defect diagnosis aiming at different diagnosis characteristic quantities. And after the diagnosis period is finished, counting. The defect diagnosis is performed with the defect ratio as a limit.
The specific embodiment is as follows:
reference value acquisition:
ZThthe historical data processing results at 00 are shown in table 2:
table 2 reference group details table
Data processing:
ZTsa set of real-time data processing results at 00 are shown in table 3.
TABLE 3 real-time data processing results
Insulation diagnosis:
1. matching the reference group. Due to ts＝150s＝th1And E is calculated from the E values of the real-time data and reference groups1＝0.4<E2＝9.57e+04<E3＝1.72e+05<E3 ═ 2.14E + 05. Matched to reference group 1.
2. And (6) comparing the data. The deviation values and the deviation ratios are calculated as shown in Table 4:
TABLE 4 calculation of deviation values and deviation ratios
3. And (4) diagnosing the abnormality. This line gets uxAfter this comparison, k is-20%31+1，k33+1。
4. A single diagnosis of suspected defects. m is31+1、m33+1。
5. And (5) integrating the diagnosis characteristic quantity to perform defect single diagnosis. N under different comparison rulesiThe situation is as follows:
(1) three indexes are integrated, namely short-time maximum rail potential, short-time minimum rail potential and short-time absolute average rail potential: n is3+1；
(2) Synthesizing the short-time maximum rail potential and the short-time minimum rail potential: n is3+1。
(3) Using the maximum rail potential as the diagnostic characteristic quantity, n3+ 1; after using only the minimum rail potential as a diagnostic feature, ni+0。
6. Statistics were taken for single diagnosis during the day. Taking the comprehensive maximum steel rail potential and the minimum steel rail potential as diagnosis characteristic quantities, and counting the results as follows:
7. and comparing the total times J to 4, and obtaining the diagnosis result after data statistics that the rail potentials of the stations 1,2,3 and 4 are abnormal. The maximum anomaly rate for station 3 is 100%. Defect ratio beta3＝100％、β450%, measurement point 3 and measurement point 4 are located.
The method realizes the diagnosis of the insulation defect of the rail-ground transition resistance by utilizing the characteristic of the change of the potential of the steel rail, makes up the gap of an online insulation diagnosis method, indirectly reflects the stray current condition by the diagnosis, and can be used as reference for the stray current protection.
Claims (5)
1. The online steel rail insulation defect diagnosis method based on the whole-line steel rail potential feature extraction is characterized by comprising the following steps of:
step 1: data acquisition and storage: acquiring real-time steel rail potential data information of each measuring point through a steel rail potential measuring device of each measuring point, and acquiring departure intervals, load current and related equipment states through information interaction; storing the acquired data to a real-time database;
step 2: managing historical data, selecting a stage with initial line operation and good line condition, processing the historical data of the stage to form different reference groups:
for different departure intervals t in historical datahSatisfy ZT in calculation cyclehWhen the average load current is 0, the average load current AVI is sorted outqhShort-time average AVE of rail potential of each corresponding measuring pointihShort-time rail potential maximum MAXihMinimum value MIN of short-time rail potentialihShort term absolute average ABVihAs different reference groups, the short-time characteristic quantities here all take the corresponding departure intervals as calculation periods, and the calculation formula is as follows:
MAXih＝max(Iihn,n＝1,2,3....N),i＝1,2,...I (3)
MINih＝min(Iihn,n＝1,2,3....N),i＝1,2,...I (4)
wherein N represents the nth data in a calculation period, and N represents the total number of the data in the calculation period; q represents the Q-th traction station on the line, and Q represents the total number of the traction stations on the line; i represents the ith measuring point of the line, and I represents the total number of the measuring points of the line;
and step 3: extracting feature quantity of the real-time data, and performing insulation diagnosis on a reference group through condition feature matching:
3.1 real-time data processing:
taking the operating time within 24h as a diagnosis period, and carrying out different departure intervals t in the periodsSatisfy the ZT in the cyclesWhen the load current is 0, calculating the short-time average load current AVI of each traction station qqsAs a condition quantity matching the reference value; average value AVE of rail potential at each measurement point iisShort-time rail potential maximum MAXisMinimum value MIN of short-time rail potentialisABV, arithmetic mean of absolute values in short timeisAs the characteristic amount of the insulation diagnosis, the calculation formula is as follows:
MAXis＝max(Iisn,n＝1,2,3....N),i＝1,2,...I (8)
MINis＝min(Iisn,n＝1,2,3....N),i＝1,2,...I (9)
3.2 matching reference groups, ts＝th、ZTsAVI calculated in real time 0 ═ 0qsAnd AVIqhIs used as a matching condition for matching the reference group, and E is a minimization target, wherein E is expressed as follows:
3.3 data comparison: comparing the characteristic quantity calculated by each measuring point with the matched reference group, and calculating the deviation delta UixjAnd offset ratio sigmaiThe following were used:
ΔUixj＝Uixj-me-Uix-ref,i＝1,2,...I (12)
wherein j represents the jth comparison within the diagnostic cycle, j being 1,2,3 …; x represents the selected diagnostic characteristic quantity; u shapeixj-meIs the data of the characteristic quantity x of the ith measuring point at the jth comparison in the diagnosis period, Uix-refThe characteristic quantity x of the ith measuring point corresponds to a compared reference value;
3.4 abnormality diagnosis: for different measurement points i, when the offset ratio σi≤uxWhen u is turned onxIs an offset rate limit; considering that the j-th comparison measurement point i has abnormal steel rail potential characteristic quantity x and abnormal times kixAdding 1;
3.5 Single diagnosis of suspected defects: in the j-th comparison, different diagnostic features are comparedWhen the quantity x is analyzed, if any one measuring point diagnosis characteristic quantity x is abnormal, comparing the delta U of different measuring points iixj(ii) a If the maximum value or the absolute average value of the short-time rail potential is compared, the maximum delta U is takenixjIs the abnormal comparison result of the index x; if the rail potential minimum value is short, the minimum delta U is takenixjIs the abnormal comparison result of the index x; if the abnormal comparison result points to the measurement point i as the suspicious defect measurement point when the diagnostic feature quantity x is compared for the jth time, the suspicious defect statistic m of the diagnostic feature quantity x of the measurement point is determinedixAdding 1;
3.6 the comprehensive diagnosis characteristic quantity is used for defect single diagnosis: during the jth comparison, aiming at different selected diagnosis characteristic quantities, the measurement point i pointed by the abnormal comparison result of the diagnosis characteristic quantity x satisfies the following conditions:
when the arithmetic mean value of the maximum value, the minimum value and the absolute value is taken as the diagnosis characteristic quantity, at least two indexes exist and point to the same i after the comparison result of the two indexes, and the suspicious defect statistic n of the measuring pointiAdding 1;
when the maximum value and the minimum value are taken as diagnosis indexes, two possibilities exist, i pointed by the maximum value and i pointed by the minimum value are the same i, ni+ 1; i pointed by the maximum value is not the same as i pointed by the minimum value, and n is differentiAdding 1;
when the maximum value or the minimum value is taken as a diagnosis index, the index abnormal result points to i, niAdding 1;
3.7 the next set of data processing and comparison during the diagnostic period: updating j, adding 1 to j, and repeating the steps from 3.1 to 3.6 until the diagnosis period is ended;
and 4, step 4: carrying out probability statistics on the diagnosis result, and finally realizing defect positioning through a given limit value:
the comparison frequency of each diagnosis period is J, a matrix [ K ] represents the abnormal frequency, a matrix [ M ] represents the suspicious defect frequency, and a matrix [ N ] represents the defect frequency; the initial matrix elements are all 0; wherein, the middle lines of [ K ] and [ M ] represent measuring points, and the columns represent compared characteristic quantities; the [ N ] middle row represents a measurement point;
counting the measurement points within a diagnostic cycleAbnormal ratio of each feature quantity
To the defect ratio of each measuring point
When beta isi≥βuI of 10 is a measured point with defects, if all β are locatedi＜βuThen there are no defect points; wherein beta isuIs the nominal defect rate.
2. The online steel rail insulation defect diagnosis method based on the whole-line steel rail potential feature extraction is characterized in that the related equipment is a steel rail potential limiter (OVPD) and a drainage device; when the device state is off, it is denoted as "0"; when the device state is closed, it is denoted as "1".
3. The online steel rail insulation defect diagnosis method based on the whole-line steel rail potential feature extraction is characterized in that multiple calculation periods exist in the diagnosis period, data are interacted with PSCADA in real time, the total number of data in one diagnosis period is J groups, and the comparison times are J times.
4. The online steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction is characterized in that the diagnosis feature quantity is a short-time steel rail potential maximum value, a short-time steel rail potential minimum value and a short-time absolute average value.
5. The on-line steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction as claimed in claim 1, wherein u isxDifferent values are taken according to different lines, and the diagnostic characteristic quantity is negative no matter what is taken.
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