CN110555049A - fault reason suspected degree analysis method based on measurement abnormality correlation degree model - Google Patents
fault reason suspected degree analysis method based on measurement abnormality correlation degree model Download PDFInfo
- Publication number
- CN110555049A CN110555049A CN201810278867.1A CN201810278867A CN110555049A CN 110555049 A CN110555049 A CN 110555049A CN 201810278867 A CN201810278867 A CN 201810278867A CN 110555049 A CN110555049 A CN 110555049A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- degree
- correlation
- occurrence
- reason
- 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.)
- Withdrawn
Links
- 230000005856 abnormality Effects 0.000 title claims abstract description 32
- 238000004458 analytical method Methods 0.000 title abstract description 15
- 238000005259 measurement Methods 0.000 title abstract description 6
- 230000002159 abnormal effect Effects 0.000 claims abstract description 177
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000004364 calculation method Methods 0.000 claims abstract description 19
- 238000011084 recovery Methods 0.000 claims description 10
- 238000012935 Averaging Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 230000006698 induction Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000013024 troubleshooting Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 abstract description 6
- 230000001364 causal effect Effects 0.000 abstract description 2
- 238000012423 maintenance Methods 0.000 abstract description 2
- 238000005065 mining Methods 0.000 abstract description 2
- 238000010219 correlation analysis Methods 0.000 description 6
- 230000005611 electricity Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 206010042635 Suspiciousness Diseases 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Primary Health Care (AREA)
- Marketing (AREA)
- Evolutionary Biology (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Water Supply & Treatment (AREA)
- Algebra (AREA)
- Public Health (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Debugging And Monitoring (AREA)
Abstract
A failure cause suspected degree analysis method based on a metering abnormality correlation model relates to a failure cause suspected degree analysis method; the measurement abnormality in daily operation is various and complex, and effective analysis and application are difficult to perform. According to the method, the association degree of every two abnormal events is calculated through classification and screening of historical data, whether the two abnormal events have a causal relationship or not is visually found, the result is screened, and a basis is provided for subsequent calculation of the fault suspicion degree; analyzing and mining a large amount of historical data, and calculating the posterior probability of a certain anomaly caused by a certain reason; the correlation degree and the posterior probability are combined to obtain the fault suspicion degree of the concurrent abnormal event caused by a certain reason. The suspected degree of trouble calculation of this technical scheme has made things convenient for the maintenance greatly, and when the electric wire netting system broke down the warning, can direct online operation, and the comparatively accurate quick reason that results in the unusual emergence of obtaining of this technique of application.
Description
Technical Field
The invention relates to a fault cause suspicion degree analysis method, in particular to a fault cause suspicion degree analysis method based on a metering abnormality correlation degree model.
Background
The existing metering anomaly analysis technology mainly aims at analyzing a single metering anomaly event. The current electricity consumption information acquisition system can give out 26 metering abnormity warning information, and can be divided into six abnormity types of abnormal electricity quantity, abnormal voltage and current, abnormal load, abnormal indication value and abnormal wiring. For a single metering abnormal event, through the research summary of abnormal reason classification, processing flow, abnormal phenomena and processing steps, a more standard and uniform processing flow and a more standard and uniform processing method are provided. However, the measurement abnormality in daily operation is various and complicated, and it is difficult to perform effective analysis and application.
A commonly used correlation analysis method is a gray correlation calculation method. The basic idea of the grey correlation analysis method is to judge whether the relation is tight according to the similarity degree between the geometric shapes of the sequence curves. The closer the curves are, the greater the degree of correlation between the corresponding sequences and vice versa. The degree of correlation is greatly affected by the two-stage minimum absolute value difference and the two-stage maximum absolute error. Once a certain maximum value point or minimum value point appears in the data column, the correlation coefficient of each feature point is greatly influenced, so that the correlation value has large fluctuation, and the data value is extremely unstable. On the other hand, sample data required by the grey correlation degree model for calculating the correlation degree is a specific number, but the metering abnormal event only occurs in a yes or no mode, namely the input is only 0 and 1, so the grey correlation degree calculation model is not suitable for calculating the correlation degree of the metering abnormal event.
because the technology for analyzing the suspected degree of the fault reason of the metering abnormal event is few, the method for analyzing the suspected degree of the fault reason based on the combination of the metering abnormal events is researched to realize the accurate analysis of the suspected degree of the fault reason, and the power utilization behaviors of users under various abnormal events are systematically analyzed, so that the method has important practical significance.
Disclosure of Invention
The invention aims to solve the technical problems and provide the technical task of perfecting and improving the prior technical scheme and providing a fault cause suspected degree analysis method based on a measurement abnormity association degree model so as to achieve the purpose of accurately analyzing the association degree. Therefore, the invention adopts the following technical scheme.
The method for analyzing the suspected degree of the fault reason based on the metering abnormality correlation degree model is characterized by comprising the following steps of:
The method comprises the following steps:
1) Data pre-processing
Acquiring database data, wherein the data in the database comprise various abnormal events of each user, the occurrence time and recovery time of each abnormality and the cause of the abnormality occurrence;
searching out a blank place in a column of the abnormal event occurrence date, and deleting the data of the whole line where the blank place is located;
Counting the frequency of each reason under the abnormal event, and dividing the frequency by the total frequency of the reasons to obtain the probability of the abnormal occurrence under the condition that a certain reason occurs, namely the conditional probability;
2) Calculating the correlation degree of two abnormal events
a) According to the sequence of the two metering abnormal events, calculating to obtain an abnormal event AiPreceded by an exception event AjOccurring, abnormal event AiAnd abnormal event AjA correlation coefficient between; calculating to obtain abnormal event AjPreceded by an exception event AiOccurring, abnormal event AiAnd anomaly AjThe correlation coefficient between;
r＝1-d/15，
Wherein d is two abnormal events A of the same useri、AjDays between the occurrence dates, wherein d has a value in the range of [0, 15%]；
b) when the relevance is calculated, a plurality of obtained abnormal events A are obtainediAveraging the correlation coefficients to obtain the concurrent abnormal event AiThe degree of correlation which occurs first and a plurality of abnormal events A which are obtained simultaneouslyjAveraging the correlation degrees which occur first to obtain the concurrent abnormal event AjA degree of correlation that occurs first;
3) Calculating the posterior probability of a certain anomaly caused by a certain reason
for AiAbnormality is caused by BjThe posterior probability caused by the reasons is calculated by using probability theory and mathematical statistics, and is calculated by using formulaCalculating posterior probability; wherein P (B)j) For the possibility of various causes, according to formulaObtaining the total frequency of occurrence of all abnormal causes in the formula a and bjThe number of times of occurrence of the j-th type abnormal reason; p (A)i|Bj) For the reason of BjUnder the conditions that occur, AiProbability of occurrence of an anomaly, i.e. conditional probability, according to the formulaobtaining in the formula cjIs an abnormality AiThe number of occurrences of the j-th type of abnormality under the occurrence condition; p (A)i) For the total probability of a single abnormal event, by the formula of the total probabilityObtaining;
4) Comprehensively calculating the fault doubtful degree of a certain concurrent abnormal event caused by a certain reason
The influence factor of the fault doubtful degree of the metering abnormal event combination comprises the correlation degree of every two abnormal events, AiAbnormality is caused by BjThe posterior probability caused by the reasons is obtained according to the relevance and the posterior probability to obtain the reason B of the concurrent abnormal eventsjThe suspected degree of the caused fault; the greater the degree of certainty, the greater the probability that the abnormal event is caused by the cause.
as a further improvement and supplement to the above technical solutions, the present invention also includes the following additional technical features.
As a preferable technical means: in step 4), when the abnormal event A occurs1And A2When in concurrence, the formula of the fault suspected degree is as follows: e ═ D ═ R ═ E1e2]wherein D ═ P (B)j|A1)P(Bj| A2)]Wherein P (B)j|A1) And P (B)j|A2) Calculating the obtained posterior probability for the step 3);
Wherein e is1＝P(Bj|A1)+P(Bj|A2)r12indicates an anomaly A1First occurrence, anomaly A2Cause of the latter occurrence BjThe degree of suspicion of induction; e.g. of the type2＝P(Bj|A2)+P(Bj|A1)r21Indicates an anomaly A2First occurrence, anomaly A1Cause of the latter occurrence BjThe degree of doubtful induction.
As a preferable technical means: in the step 2), when the association rate is greater than a threshold value, calculating an abnormal reason set of concurrent abnormal events; and if the association rate is greater than the threshold value, processing according to a single abnormal event.
As a preferable technical means: processing abnormal events needing to be examined within one week, and setting the threshold value to be 0.6 at the moment; and for abnormal event combinations which are not examined, setting the threshold value to be 0.4 according to the corresponding relation between the correlation coefficient and the strength of the correlation.
As a preferable technical means: in step 2), the degree of association RijThe calculation formula of (2) is as follows:
In the formula mi≤niWhen the number of days in interval is less than 6, the total number niAt 50% of (a), mi＝niAt this time ripIs' i.e. rip(ii) a On the other hand, when the ratio of the number of days between intervals is not less than 6% and not more than 50%, mi<niat this time rip' is ripA moiety other than 0; the correlation degree R can be obtained in the same wayjithe calculation formula of (2) is as follows:
As a preferable technical means: in the step c), when the ratio of the correlation coefficient is more than 0.68 and is more than 50%, removing the data with the correlation coefficient of 0 to obtain the mean value of the correlation coefficient and obtain the correlation degree; when the ratio of the correlation coefficient larger than 0.68 is smaller than 50%, all data are kept for averaging, and the correlation degree is obtained.
As a preferable technical means: in the step 1), the data sources of the database comprise power quality monitoring data of the transformer substation and archive data filled by maintainers after abnormal troubleshooting, reason determination and overhaul.
Has the advantages that: according to the method, the association degree of every two abnormal events is calculated through classification and screening of historical data, whether a causal relationship exists between the two abnormal events is visually found, the result is screened, the accuracy is improved, and a basis is provided for subsequent calculation of fault suspicion; analyzing and mining a large amount of historical data, and calculating the posterior probability of a certain anomaly caused by a certain reason by combining the probability theory and the knowledge of mathematical statistics; the relevance and the posterior probability jointly act to obtain the fault suspicion degree caused by a certain reason of the concurrent abnormal event. The fault doubt degree calculation of the technical scheme greatly facilitates maintenance, when a power grid system fails and gives an alarm, the power grid system can be directly operated on a line, and the technology is applied to the reason that abnormity occurs due to accurate and quick obtaining.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
as shown in fig. 1, the present invention comprises the steps of:
1) Data pre-processing
Acquiring database data, wherein the data in the database comprise various abnormal events of each user, the occurrence time and recovery time of each abnormality and the cause of the abnormality occurrence;
Searching out a blank place in a column of the abnormal event occurrence date, and deleting the data of the whole line where the blank place is located;
Counting the frequency of each reason under the abnormal event, and dividing the frequency by the total frequency of the reasons to obtain the probability of the abnormal occurrence under the condition that a certain reason occurs, namely the conditional probability;
2) Calculating the correlation degree of two abnormal events
a) According to the sequence of the two metering abnormal events, calculating to obtain an abnormal event AiPreceded by an exception event AjOccurring, abnormal event AiAnd abnormal event Aja correlation coefficient between; calculating to obtain abnormal event AjPreceded by an exception event AiOccurring, abnormal event AiAnd anomaly AjThe correlation coefficient between;
The correlation coefficient is calculated by the formula:
r＝1-d/15，
Wherein d is two abnormal events A of the same useri、AjDays between the occurrence dates, wherein d has a value in the range of [0, 15%]；
b) When the relevance is calculated, a plurality of obtained abnormal events A are obtainediCoefficient of pre-occurrence correlationTaking the average value to obtain the concurrent abnormal event AiThe degree of correlation which occurs first and a plurality of abnormal events A which are obtained simultaneouslyjAveraging the correlation degrees which occur first to obtain the concurrent abnormal event AjA degree of correlation that occurs first;
3) Judging whether the correlation degree is greater than a threshold value, and calculating an abnormal reason set of the concurrent abnormal events when the correlation rate is greater than the threshold value; if the association rate is greater than the threshold value, processing according to a single abnormal event;
4) acquiring abnormal reason set { B) of concurrent common eventsj}
5) Calculating the posterior probability of a certain anomaly caused by a certain reason
For AiAbnormality is caused by Bjthe posterior probability caused by the reasons is calculated by using probability theory and mathematical statistics, and is calculated by using formulaCalculating posterior probability; wherein P (B)j) For the possibility of various causes, according to formulaObtaining the total frequency of occurrence of all abnormal causes in the formula a and bjThe number of times of occurrence of the j-th type abnormal reason; p (A)i|Bj) For the reason of BjUnder the conditions that occur, AiProbability of occurrence of an anomaly, i.e. conditional probability, according to the formulaObtaining in the formula cjIs an abnormality AiThe number of occurrences of the j-th type of abnormality under the occurrence condition; p (A)i) For the total probability of a single abnormal event, by the formula of the total probabilityObtaining;
6) Comprehensively calculating the fault doubtful degree of a certain concurrent abnormal event caused by a certain reason
Fault doubtful degree shadow for metering abnormal event combinationThe response factor includes the degree of association between two abnormal events, Aiabnormality is caused by BjThe posterior probability caused by the reasons is obtained according to the relevance and the posterior probability to obtain the reason B of the concurrent abnormal eventsjThe suspected degree of the caused fault; the greater the degree of certainty, the greater the probability that the abnormal event is caused by the cause.
Some steps of the present invention will be further described with reference to specific examples.
The following examples are specifically described.
The first embodiment is as follows:
1 data Source
the data source used for the relevancy analysis in the embodiment is historical alarm data of multiple anomalies of a specialized transformer user and a low-voltage user in a same month in a certain city in 2015-2016 and in the same month. The data mainly comprises information such as user numbers, abnormal type names, abnormal occurrence time, abnormal recovery time, abnormal filing time and the like. The abnormal recovery time is influenced by various factors, and the abnormal metering events to be evaluated are generally recovered manually within one week; for abnormal events which are not checked, the recovery time is not required, and the recovery can be carried out according to the field operation condition and the work arrangement of workers; the recovery time given in the historical alarm data may be an archive time instead of an actual recovery time, which cannot be determined. Considering that the occurrence time of the abnormal events in the existing historical data is determined, the association degree is calculated by taking the occurrence time of the abnormal events as a reference when the association degree between every two abnormal events is calculated and the number of days between the two abnormal events.
The data sources for analyzing the suspected degree of the fault are electric energy quality monitoring data of 389 transformer substations in a certain city from 2015 7 to 2016 8 and archive data filled by a maintainer after abnormal examination, reason determination and overhaul. The data in the database includes information such as various abnormal events occurred by each user, the occurrence time and recovery time of each abnormality, and the cause of the abnormality occurrence.
2 correlation analysis method
2.1 correlation coefficient function construction
for the same user, theoretically, if two metering abnormal events occur immediately before and after, and the second abnormal event occurs before the first abnormal event is recovered, the two abnormal events can be considered to have a certain correlation; the larger the number of days between the two abnormality occurrence dates is, the smaller the correlation coefficient between the two is. The abnormal assessment time is one week, 2 times of assessment time is taken as a boundary, when the interval of two measurement abnormal occurrence dates exceeds 15 days, no correlation is considered, and correlation coefficient calculation is not carried out; the number of days between occurrence dates is within 15 days, one exception is recovered, and the other exception occurs, and the correlation coefficient is 0; the number of days between occurrence of the data is within 15 days and when one abnormality occurs before the other abnormality recovers, the smaller the number of days between the occurrence, the larger the correlation coefficient, and the correlation coefficient is calculated according to the formula (1).
r＝1-d/15 (1)
Wherein d is the interval days of two abnormal events of the same user.
2.2 Association analysis flow
Firstly, historical data is analyzed and processed to obtain all the metering abnormal event types which appear for two years and are marked as Ak(k ═ 1,2, …, n); and then, pairwise combination is carried out on all abnormal events, and correlation analysis is respectively carried out. According to the sequence of the two metering abnormal events, calculating the correlation degree R between the two abnormal events based on the correlation coefficientijAnd Rji。RijIndicates an anomaly AiFirst occurrence, anomaly AiAnd anomaly Ajthe degree of association between; rjiIndicates an anomaly Ajfirst occurrence, anomaly AiAnd anomaly Ajthe degree of association between them.
obtaining anomaly A based on historical dataiAnd anomaly AjThe occurrence date interval is within 15 days and is the same user, exception Ainumber of first occurrences (including same day occurrence) niAnd anomaly AjNumber of first occurrence (including same day occurrence) nj(ii) a Then, the n is respectively calculated according to the formula (1)iAnd njIs combined with anotherThe correlation coefficient of abnormal events is recorded as rip(p＝1,2,3,…,ni) And rjq(q＝1,2,3,…, nj)。
Get the degree of correlation RijThe calculation formula of (2) is as follows:
The correlation degree R can be obtained in the same wayjiThe calculation formula of (2) is as follows:
3, screening concurrent events
The range of the degree of association calculated by the formulas (2) and (3) is [0,1], and the closer to 1, the stronger the association; the closer to 0, the weaker the correlation. And screening the concurrent abnormal events by setting a threshold, and when the calculated association degree is higher than the threshold, considering the association as a group of concurrent abnormal event combinations. Table 1 shows the correspondence between the correlation coefficient and the strength of the correlation.
TABLE 1 correlation coefficient and correlation strength
For abnormal events needing to be assessed, the abnormal events are generally processed within one week, and the threshold value can be set to be 0.618; and for abnormal event combinations which are not examined, setting the threshold value to be 0.4 according to the corresponding relation between the correlation coefficient and the correlation strength. And screening the concurrent abnormal events through correlation calculation and threshold setting, and screening abnormal event combinations needing to be subjected to fault doubtful degree analysis in one step.
4 failure suspicion degree calculation
4.1 finding the fault suspicion degree of a single abnormal event caused by some kind of reasons
Analyzing and processing the abnormal cause data to obtain the abnormal AiThe causes of (i ═ 1,2, …, n) share m groups, and are denoted by Bj(j ═ 1,2, …, m); then, the probability P (B) of the occurrence of various causes is obtained according to the formula (4)j)；
Wherein a is the total number of occurrences of all abnormal causes, bjThe number of times the j-th type abnormality has occurred.
Each abnormal event has a plurality of causes causing the abnormal event, and when a certain cause occurs, the probability P (A) of the abnormal event is obtained according to the formula (5)i|Bj) -in reason BjUnder the conditions occurring, anomaly AiThe probability of occurrence;
in the formula cjIs an abnormality AiThe number of occurrences of the j-th type abnormality under the occurrence condition.
Then, the probability P (A) of each abnormal event occurrence is obtained by the total probability formulai)。
Finally, the Bayes formula of formula (7) is used to calculate P (B)j|Ai) -abnormal event AiIs caused by the cause Bjthe probability of the single abnormal event is the fault suspiciousness of the single abnormal event caused by some kind of reasons.
4.2 calculating the fault doubtful degree of the concurrent abnormal event caused by some kind of reasons
The intersection of the fault reason subsets of the single abnormal event is calculated, the abnormal reason set forming the abnormal concurrent event combination is determined, and when abnormal alarms are concurrent, the probability that the reason triggering the abnormality is one of the abnormal reason intersection is higher. And calculating the fault suspicion degree of each abnormal reason under a single abnormal event based on the historical data, and combining the fault suspicion degrees with the association degree of the combination of the concurrent abnormal events to obtain the fault suspicion degrees of each abnormal reason under the concurrent abnormal events.
Combination with concurrent exceptions A1And A2For example, a single exception A1And A2From reason BjThe probability of causing is P (B) respectivelyj|A1) And P (B)j|A2) If the single abnormal event fault suspicion degree matrix is:
D＝[P(Bj|A1)P(Bj|A2)] (8)
Anomaly A1first occurrence, anomaly A2the degree of correlation between the latter two is R12(ii) a Anomaly A2First occurrence, anomaly A1The degree of the later-occurring correlation is R21. The matrix of the degree of association is:
Then when exception A1And anomaly A2When the two are concurrent, the abnormal reason is BjThe suspicion degree matrix is:
Wherein e1Is an abnormality A1First occurrence, anomaly A2Cause of the latter occurrence BjThe degree of suspicion of induction; e.g. of the type2Is an abnormality A2First occurrence, anomaly A1Cause of the latter occurrence BjThe degree of suspicion of induction; anomaly A1and anomaly A2On the same day, due to reason BjThe suspected degree of the fault is (e)1+e2)/2。
The fault suspected degree range calculated by the formula (10) is [0,1], and the more close to 1, the higher the suspected degree is; the closer to 0, the lower the doubtness.
5 example analysis
In order to prove the accuracy and reliability of the correlation analysis method, two groups of metering abnormal events of other miswiring lines and tidal current reversal are selected for analysis.
From the calculation result of the suspected degree of failure, when other wrong wiring and abnormal warning of reversed trend are concurrent, whether other wrong wiring occurs first, or the reversed trend occurs first, or other wrong wiring and the reversed trend occur on the same day, the probability that the reason for triggering the abnormal condition is the file error is the largest, and the probability that the reason for triggering the abnormal condition is the electricity stealing person is the smallest.
According to the on-site worker investigation experience, when other wrong wiring and tide reverse abnormal alarms are concurrent, the abnormal reason is that the condition of file errors is the most, and the condition that the abnormal reason is artificial electricity stealing is very few. Substantially consistent with the calculation result of the fault suspicion.
example two:
The same as the embodiment is not repeated, except in the correlation analysis process.
Considering that the correlation coefficient takes a value of 0 in many cases, the accuracy of the correlation degree of most two abnormal events with short occurrence interval days is reduced, and therefore whether a part with the correlation coefficient of 0 is included needs to be considered according to specific situations when the correlation degree is obtained. With a degree of association RijFor example, when exception AiFirst, and the number of two abnormal interval days within 6 days is less than the total number niWhen the correlation coefficient is 50 percent, the correlation degree between two abnormal events can be considered to be small according to statistical experience, a term with the correlation coefficient of 0 is reserved when the correlation degree is calculated, and the correlation degree R is obtained by directly averaging all the correlation coefficientsij(ii) a Otherwise, eliminating the item with the correlation coefficient of 0 when averaging. From this, the degree of correlation R can be obtainedijThe calculation formula of (2) is as follows:
In the formula mi≤niWhen the number of days in interval is less than 6, the total number niAt 50% of (a), mi＝niAt this time ripIs' i.e. rip(ii) a On the other hand, when the ratio of the number of days between intervals is not less than 6% and not more than 50%, mi<niAt this time rip' is ripThe portion other than 0. The correlation degree R can be obtained in the same wayjithe calculation formula of (2) is as follows:
The method for analyzing the suspected degree of a fault cause based on the metric anomaly correlation model shown in fig. 1 is an embodiment of the present invention, and it is intended to embody the essential features and advantages of the present invention, and it is within the scope of the present invention to modify the same according to the actual application requirements.
Claims (7)
1. The method for analyzing the suspected degree of the fault reason based on the metering abnormality correlation degree model is characterized by comprising the following steps of:
1) data pre-processing
Acquiring database data, wherein the data in the database comprise various abnormal events of each user, the occurrence time and recovery time of each abnormality and the cause of the abnormality;
Searching out a blank place in a column of the abnormal event occurrence date, and deleting the data of the whole line where the blank place is located;
counting the frequency of each reason under the abnormal event, and dividing the frequency by the total frequency of the reasons to obtain the probability of the abnormal occurrence under the condition that a certain reason occurs, namely the conditional probability;
2) Calculating the correlation degree of two abnormal events
a) According to the sequence of the two metering abnormal events, calculating to obtain an abnormal event Aipreceded by an exception event AjOccurring, abnormal event AiAnd abnormal event AjIs close toA joint coefficient; calculating to obtain abnormal event AjPreceded by an exception event AiOccurring, abnormal event AiAnd anomaly AjA correlation coefficient between;
The correlation coefficient is calculated by the formula:
r＝1-d/15，
Wherein d is two abnormal events A of the same useri、Ajdays between the occurrence dates, wherein d has a value in the range of [0, 15%]；
b) When the relevance is calculated, a plurality of obtained abnormal events A are obtainediAveraging the correlation coefficients to obtain the concurrent abnormal event AiThe first occurring correlation degree and a plurality of obtained abnormal events AjAveraging the correlation degrees which occur first to obtain the concurrent abnormal event Ajdegree of association that occurs first;
3) Calculating the posterior probability of a certain anomaly caused by a certain reason
For AiAbnormality is caused by BjThe posterior probability caused by the reasons is calculated by using probability theory and mathematical statistics, and formulaCalculating posterior probability; wherein P (B)j) For the possibility of various causes, according to formulaThe total number of occurrences of all the causes of the abnormality, b, is obtainedjthe number of times of occurrence of the j-th type abnormal reason; p (A)i|Bj) For the reason of BjUnder the conditions that occur, AiProbability of occurrence of an anomaly, i.e. conditional probability, according to the formulaObtaining in the formula cjIs an abnormality AiThe number of occurrences of the j-th type of abnormality under the occurrence condition; p (A)i) For the total probability of a single abnormal event, by the formula of the total probabilityObtaining;
4) comprehensively calculating the fault doubtful degree of a certain concurrent abnormal event caused by a certain reason
The influence factor of the fault doubtful degree of the metering abnormal event combination comprises the correlation degree of every two abnormal events, AiAbnormality is caused by BjThe posterior probability caused by the reasons is obtained according to the relevance and the posterior probability to obtain the reason B of the concurrent abnormal eventsjThe suspected degree of the caused fault; the greater the degree of certainty, the greater the probability that the abnormal event is caused by the cause.
2. The method for analyzing the suspected degree of the fault cause based on the abnormal metering correlation model of claim 1, wherein:
In step 4), when the abnormal event A occurs1And A2When in concurrence, the formula of the fault suspicion degree is as follows: e ═ D ═ R ═ E1e2]wherein D ═ P (B)j|A1)P(Bj|A2)]Wherein P (B)j|A1) And P (B)j|A2) Calculating the obtained posterior probability for the step 3);
Wherein e is1＝P(Bj|A1)+P(Bj|A2)r12Indicates an anomaly A1First occurrence, anomaly A2Cause of the latter occurrence BjThe degree of suspicion of induction; e.g. of the type2＝P(Bj|A2)+P(Bj|A1)r21Indicates an anomaly A2First occurrence, anomaly A1Cause of the latter occurrence BjThe degree of doubtful induction.
3. The method for analyzing the suspected degree of the fault cause based on the abnormal metering correlation model as claimed in claim 2, wherein: in the step 2), judging whether the association degree is greater than a threshold value, and calculating an abnormal reason set of the concurrent abnormal events when the association rate is greater than the threshold value; and if the association rate is greater than the threshold value, processing according to a single abnormal event.
4. The method according to claim 3, wherein the method comprises: processing abnormal events needing to be examined within one week, and setting the threshold value to be 0.6 at the moment; and for abnormal event combinations which are not examined, setting the threshold value to be 0.4 according to the corresponding relation between the correlation coefficient and the relevance strength.
5. The method for analyzing the suspected degree of the fault cause based on the abnormal metering correlation model of claim 1, wherein: in step 2), the degree of association RijThe calculation formula of (2) is as follows:
In the formula mi≤niWhen the number of days in interval is less than 6, the total number niAt 50% of (a), mi＝niAt this time ripIs' i.e. rip(ii) a On the other hand, when the ratio of the number of days between intervals is not less than 6% and not more than 50%, mi<niat this time rip' is ripA moiety other than 0; the correlation degree R can be obtained in the same wayjiThe calculation formula of (2) is as follows:
。
6. The method of claim 4, wherein the method comprises: in the step c), when the ratio of the correlation coefficient is more than 0.68 and is more than 50%, removing the data with the correlation coefficient of 0 to obtain the mean value of the correlation coefficient and obtain the correlation degree; when the ratio of the correlation coefficient is more than 0.68 and less than 50%, all data are kept for averaging to obtain the correlation degree.
7. The method for analyzing the suspected degree of the fault cause based on the abnormal metering correlation model of claim 1, wherein: in the step 1), the data sources of the database comprise power quality monitoring data of the transformer substation and archive data filled by maintainers after abnormal troubleshooting, reason determination and overhaul.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810278867.1A CN110555049A (en) | 2018-03-30 | 2018-03-30 | fault reason suspected degree analysis method based on measurement abnormality correlation degree model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810278867.1A CN110555049A (en) | 2018-03-30 | 2018-03-30 | fault reason suspected degree analysis method based on measurement abnormality correlation degree model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110555049A true CN110555049A (en) | 2019-12-10 |
Family
ID=68733603
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810278867.1A Withdrawn CN110555049A (en) | 2018-03-30 | 2018-03-30 | fault reason suspected degree analysis method based on measurement abnormality correlation degree model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110555049A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884170A (en) * | 2020-12-17 | 2021-06-01 | 上海智大电子有限公司 | Predictive intelligent operation and maintenance system and method for comprehensive pipe gallery |
| CN112949677A (en) * | 2021-01-04 | 2021-06-11 | 杭州恒朴电子科技有限公司 | Sorting backflow abnormal relevance analysis method based on proximity algorithm |
| CN113281059A (en) * | 2021-05-18 | 2021-08-20 | 潍柴动力股份有限公司 | SCR fault assessment method, SCR device and vehicle |
| CN113296011A (en) * | 2021-04-15 | 2021-08-24 | 汇智道晟(舟山)科技有限公司 | Circuit conducted signal active detection analysis system |
| CN116415423A (en) * | 2023-03-10 | 2023-07-11 | 山东亿仓教育科技有限公司 | Computer simulation data processing system and method based on big data analysis |
-
2018
- 2018-03-30 CN CN201810278867.1A patent/CN110555049A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884170A (en) * | 2020-12-17 | 2021-06-01 | 上海智大电子有限公司 | Predictive intelligent operation and maintenance system and method for comprehensive pipe gallery |
| CN112949677A (en) * | 2021-01-04 | 2021-06-11 | 杭州恒朴电子科技有限公司 | Sorting backflow abnormal relevance analysis method based on proximity algorithm |
| CN113296011A (en) * | 2021-04-15 | 2021-08-24 | 汇智道晟(舟山)科技有限公司 | Circuit conducted signal active detection analysis system |
| CN113281059A (en) * | 2021-05-18 | 2021-08-20 | 潍柴动力股份有限公司 | SCR fault assessment method, SCR device and vehicle |
| CN116415423A (en) * | 2023-03-10 | 2023-07-11 | 山东亿仓教育科技有限公司 | Computer simulation data processing system and method based on big data analysis |
| CN116415423B (en) * | 2023-03-10 | 2024-03-26 | 宁夏新立电子有限公司 | Computer simulation data processing system and method based on big data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110555049A (en) | fault reason suspected degree analysis method based on measurement abnormality correlation degree model | |
| CN106779505B (en) | Power transmission line fault early warning method and system based on big data driving | |
| CN106780121B (en) | Power consumption abnormity identification method based on power consumption load mode analysis | |
| CN108830510B (en) | Power data preprocessing method based on mathematical statistics | |
| US8868985B2 (en) | Supervised fault learning using rule-generated samples for machine condition monitoring | |
| CN109948877B (en) | Power consumption anomaly accurate analysis method based on anomaly event combination | |
| CN106054104A (en) | Intelligent ammeter fault real time prediction method based on decision-making tree | |
| CN103761173A (en) | Log based computer system fault diagnosis method and device | |
| CN105574669B (en) | Power transmission and transformation equipment state abnormity detection method based on time-space joint data clustering analysis | |
| CN110321912A (en) | More metering anomalous event correlation analysis methods | |
| CN107436277A (en) | The single index data quality control method differentiated based on similarity distance | |
| CN110059845B (en) | Metering device clock error trend prediction method based on time sequence evolution gene model | |
| CN112686405A (en) | Power distribution network fault area division method based on fault tree | |
| CN108287327A (en) | Metering automation terminal fault diagnostic method based on Bayes's classification | |
| CN104949840A (en) | Diesel engine fault diagnosis method based on vibration analysis | |
| CN113948159B (en) | Fault detection method, device and equipment for transformer | |
| CN111737035A (en) | Fault diagnosis method and system based on server log | |
| CN110555016A (en) | Multi-metering abnormal event correlation degree analysis method | |
| CN109308225A (en) | A kind of virtual machine method for detecting abnormality, device, equipment and storage medium | |
| CN113485305A (en) | Aircraft outwork fault diagnosis system and method | |
| CN110555048A (en) | Fault reason suspected degree analysis method based on measurement abnormality correlation degree model | |
| CN113010394A (en) | Machine room fault detection method for data center | |
| CN112286987A (en) | Electric power internet of things abnormal alarm compression method based on Apriori algorithm | |
| CN107506832A (en) | The hidden danger method for digging aided in is maked an inspection tour to monitoring | |
| CN106228248A (en) | A kind of system automatic trouble diagnosis method analyzed based on fuzzy FMEA |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20191210 |