CN107748701B - A Reliability Analysis Method of Electric Energy Metering Automation System - Google Patents
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Abstract
本申请公开了一种电能计量自动化系统的可靠性分析方法，该方法包括：建立电能计量自动化系统失效的故障树；将故障树转化为贝叶斯网络；根据贝叶斯网络计算导致电能计量自动化系统失效的各类故障发生概率。本申请提供的电能计量自动化系统的可靠性分析方法，通过建立导致失效的故障因素之间的逻辑关系，计算不同故障发生概率，解决了现有技术中统计电力计量自动化终端的故障发生概率无法得知故障因素，即电力计量自动化终端的实际缺陷的问题，进一步的，根据不同故障发生概率，可找出导致电能计量自动化系统失效的主要因素，由此可针对性的提高电能计量自动化系统的可靠性。
The present application discloses a reliability analysis method of an electric energy metering automation system. The method includes: establishing a fault tree for the failure of the electric energy metering automation system; converting the fault tree into a Bayesian network; and calculating the power metering automation according to the Bayesian network. The probability of occurrence of various types of system failures. The reliability analysis method of the electric energy metering automation system provided by the present application, by establishing the logical relationship between the failure factors leading to failure, and calculating the probability of occurrence of different failures, it solves the problem that the probability of occurrence of failures of the statistical electric power metering automation terminal in the prior art cannot be obtained. Knowing the failure factors, that is, the actual defects of the power metering automation terminal, further, according to the probability of different failures, the main factors leading to the failure of the power metering automation system can be found out, so that the reliability of the power metering automation system can be improved in a targeted manner. sex.
Description
Technical Field
The application relates to the field of electric power measurement, in particular to a reliability analysis method of an electric energy measurement automation system.
Background
The electric energy metering automation system is a system consisting of a metering automation master station, a communication channel and a plurality of electric energy metering automation terminals, wherein each electric energy metering automation terminal is respectively responsible for electric energy information acquisition, data management, data transmission of each level of electric energy metering points, and execution or retransmission of tasks such as control commands issued by the metering automation master station. Currently, common electric power metering automation terminals mainly include distribution transformer monitoring and metering terminals, station electric energy acquisition terminals, concentrators, collectors, load management terminals, interaction terminals and the like.
Due to the complex operation environment of the electric energy metering automation system, each electric energy metering automation terminal is easy to break down, the electric energy metering automation system is invalid, and finally the reliability of the electric energy metering automation system is reduced. For example, communication failures occur between two power metering automation terminals, which are specifically expressed as data loss, data content errors, data delay, data disorder, protocol standard disorder and the like, and thus, communication reliability is reduced. Reliability analysis is carried out, the stability of each electric power metering automation terminal can be mastered, and the fault rate can be reduced. Existing reliability analysis methods include reliability cartography, adjacency matrix method, graph theory, and discrete time markov chain.
The existing reliability analysis methods are all based on the statistical analysis of the failure occurrence times of each electric power metering automation terminal, and the failure occurrence probability of the electric power metering automation terminal is calculated. However, because the cause of the fault is complicated, analyzing the fault occurrence probability of the power metering automation terminal alone cannot reflect the actual defects of the power metering automation terminal, and cannot improve the functions of the power metering automation terminal in a targeted manner, which is of limited help to improve the reliability of the power metering automation system.
Disclosure of Invention
The application provides a reliability analysis method of an electric energy metering automation system, which aims to solve the problem that the existing reliability analysis method is poor in analysis effect.
The application provides a reliability analysis method of an electric energy metering automation system, which comprises the following steps:
establishing a fault tree of the failure of the electric energy metering automatic system;
converting the fault tree into a Bayesian network;
and calculating the probability of occurrence of various faults causing the failure of the automatic electric energy metering system according to the Bayesian network.
Preferably, the establishing of the fault tree of the failure of the automatic system for electric energy metering comprises:
acquiring a plurality of primary faults causing the failure of the electric energy metering automation system;
analyzing the primary fault to obtain at least one secondary fault causing the primary fault;
and establishing a logical relationship between the primary fault and the secondary fault.
Preferably, the method further comprises:
analyzing the secondary fault to obtain a tertiary fault causing the secondary fault;
and establishing a logic relation between the third-level fault and the second-level fault.
Preferably, converting the fault tree into a bayesian network comprises:
establishing Bayesian network nodes respectively corresponding to the primary fault, the secondary fault and the tertiary fault;
and establishing a logical relationship between the Bayesian network nodes according to the logical relationship between the secondary faults and the primary faults and the logical relationship between the tertiary faults and the secondary faults.
Preferably, the calculating, according to the bayesian network, the probability of occurrence of each type of fault causing the failure of the electric energy metering automation system includes:
obtaining all bottom events causing the failure of the power metering automation terminal according to the primary fault, the secondary fault and the tertiary fault;
respectively acquiring the occurrence probability of all the bottom events;
and respectively calculating the probability of occurrence of a primary fault, a secondary fault and a tertiary fault according to the probability of occurrence of all the bottom events respectively and the logical relationship between the Bayesian network nodes.
Preferably, the method further comprises:
and calculating the failure minimum cut set of the automatic electric energy metering system according to all the bottom events.
The reliability analysis method of the electric energy metering automation system has the advantages that:
according to the reliability analysis method of the electric energy metering automation system, firstly, a fault tree of the electric energy metering automation system, which fails, is established, then, Bayesian network conversion is carried out on the fault tree, and finally, the probability of occurrence of various failures of the electric power metering automation terminal, which fails, can be calculated according to the Bayesian network. The process of establishing the fault tree is the process of combing the logical relationship between the fault and a plurality of fault factors of the electric energy metering automation system; and converting the fault tree into a Bayesian network, and calculating the probability of occurrence of various faults causing the failure of the electric energy metering automatic system by utilizing an algorithm of the Bayesian network. The utility model provides a reliability analysis method of electric energy measurement automation system, through establishing the logical relationship between the fault factor that leads to failing, calculate different trouble probability of occurrence, the problem of the fault probability of statistics electric power measurement automation terminal among the prior art can't learn the fault factor, the actual defect at electric power measurement automation terminal promptly is solved, furthermore, according to different trouble probability of occurrence, can find out the main factor that leads to the electric energy measurement automation system to fail, can pertinence improvement electric energy measurement automation system's reliability from this.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a schematic flowchart of a reliability analysis method of an automatic system for electric energy metering according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a process for establishing a fault tree of an automatic system for electric energy metering, according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a fault tree structure of a failure of an automatic system for electric energy metering according to an embodiment of the present application;
fig. 4A is a schematic structural diagram of an or gate fault tree according to an embodiment of the present disclosure;
fig. 4B is a schematic structural diagram of an or-mendbesl network according to an embodiment of the present disclosure;
fig. 4C is a schematic structural diagram of an and gate fault tree according to an embodiment of the present disclosure;
fig. 4D is a schematic structural diagram of an and gate bayesian network according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a bayesian network according to an embodiment of the present application.
Detailed Description
Referring to fig. 1, a schematic flow chart of a reliability analysis method of an automatic system for electric energy metering provided in an embodiment of the present application is shown, and as shown in fig. 1, the reliability analysis method of the automatic system for electric energy metering provided in the embodiment of the present application includes the following steps:
step S110: and establishing a fault tree of the failure of the electric energy metering automatic system.
Specifically, referring to fig. 2, a process of establishing a fault tree is a schematic flow chart of establishing a fault tree in which an electric energy metering automation system fails according to an embodiment of the present application, and the process includes the following steps:
step S111: acquiring a plurality of primary faults causing the failure of the electric energy metering automation system;
in this embodiment, the failure T of the automatic electric energy metering system is represented, and faults occurring in daily use of the automatic electric energy metering system are classified to obtain a primary fault Ma, where a is a number serial number and E is a total number of the primary faults. The first-order fault is a fault occurring when the electric energy metering automation system completes basic functions such as local communication, remote communication, data monitoring and the like, and specifically comprises the following steps: the method comprises the following steps of remote communication network failure M1, local communication failure M2, data detection failure M3, turnover box transmission and table verification failure M4, image recognition failure M5, channel detection failure M6 and automatic seal failure M7.
Step S112: analyzing the primary fault to obtain at least one secondary fault causing the primary fault;
and performing failure analysis on the primary failure Ma to obtain a secondary failure causing the primary failure. In this embodiment, the telecommunication network fault M1 may be decomposed into two-level faults: cellular communication failure a 1; the local communication failure M2 may be decomposed into secondary failures: micropower wireless communication failure a 2; the data detection failure M3 may be broken down into secondary failures: internal interface failure A3, external interface failure a 4; the turnover box transmission and table verification fault M4 can be decomposed into secondary faults: code reading failure A19, laser damage A5, wiring error A6 and electric meter morton A7; the image recognition fault M5 may be decomposed into secondary faults: damage of the liquid crystal screen A8 and software failure A9; the channel detection failure M6 may be decomposed into secondary failures: ethernet test failure A10, GPRS failure A11, RS485 failure A1, disc washer suction error A15, and laser engraving failure A16.
Step S113: establishing a logical relationship between the primary fault and the secondary fault;
and establishing a logical relation between the primary fault and the secondary fault according to the causal relation between the occurrence of the secondary fault and the primary fault. In this embodiment, the occurrence of any one stage of fault may cause the occurrence of the failure T of the automatic energy metering system, and therefore, an or gate relationship is established between the failure T of the automatic energy metering system and each one stage of fault; any secondary fault occurrence will result in the occurrence of a corresponding primary fault, and thus there is an or-gate relationship between each primary fault and the secondary fault.
Further, since part of the secondary faults may be caused by different factors, the present embodiment further includes the following steps:
step S114: analyzing the secondary fault to obtain a tertiary fault causing the secondary fault;
and respectively carrying out failure analysis on all secondary faults to obtain secondary faults of which the parts can be decomposed into three levels: the code reading failure A19 can be decomposed into an RFID failure A17 and a barcode label failure A18.
Step S115: and establishing a logic relation between the third-level fault and the second-level fault.
And establishing a logic relation between the primary fault and the secondary fault according to the causal relation between the occurrence of the tertiary fault and the secondary fault. In this embodiment, only RFID failure a17 and barcode tag failure a18 occurred, resulting in: code reading failure A19 occurs, and as long as at least one of RFID failure A17 and barcode label failure A18 does not occur, code reading failure A19 does not occur, so that the AND gate relations among code reading failure A19, RFID failure A17 and code reading failure A19 can be obtained.
It should be noted that, although the sequence of first establishing a primary fault, then correspondingly establishing a secondary fault, and finally establishing a tertiary fault is adopted when establishing the fault tree in the embodiment, other implementation manners are also possible in the embodiment, for example, first establishing a primary fault, performing failure analysis on one of the primary faults, establishing a secondary fault, and then establishing a tertiary fault; and then, performing failure analysis on the next primary fault, establishing a secondary fault, establishing a tertiary fault, and so on until all the primary faults are subjected to failure analysis to obtain the corresponding secondary fault or the corresponding tertiary fault. In addition, in the present embodiment, the failure T of the automatic energy metering system is decomposed into the first-level fault, the second-level fault, and the third-level fault, but is not limited to this division manner, and according to the actual situation of the automatic energy metering system, the failure T of the automatic energy metering system may need to be further decomposed into the fourth-level fault or the more-level faults, or only needs to be decomposed into the second-level faults, as long as the failure T is decomposed in a manner similar to that of the present embodiment, all of which belong to the protection scope of the present application.
Referring to fig. 3, a schematic diagram of a fault tree structure for an automatic system failure for electric energy metering provided in an embodiment of the present application is shown in fig. 3, where a completed fault tree is established, where the fault tree includes a top node automatic system failure T, a primary fault Ma, a secondary fault and a tertiary fault, and the top node is connected to the primary fault Ma, the primary fault Ma is connected to the secondary fault, and the secondary fault is connected to the tertiary fault through logic gates.
Further, after the fault number is established, a minimal cut set of the fault tree can be solved by using a Forssel algorithm, wherein the minimal cut set comprises all bottom events causing the fault.
Step S120: and converting the fault tree into a Bayesian network.
Specifically, firstly, establishing a bayesian network node corresponding to a primary fault, a secondary fault and a tertiary fault respectively; and then, establishing a logical relationship between the nodes of the Bayesian network according to the logical relationship between the secondary faults and the primary faults and the logical relationship between the tertiary faults and the secondary faults.
Referring to fig. 4A, a structural diagram of an or gate fault tree provided in the embodiment of the present application is shown in fig. 4A, where the logical relationship represented by the or gate fault tree is:
P(C＝1|A＝0，B＝0)＝0
P(C＝1|A＝0，B＝1)＝1
P(C＝1|A＝1，B＝0)＝1
P(C＝1|A＝1，B＝1)＝1 (1)
referring to fig. 4B, a schematic structural diagram of an or-mendbis network provided in the embodiment of the present application. The or gate fault tree shown in fig. 4A is converted into the or gate bayes net shown in fig. 4B, and the or gate logical relationship is input into the or gate bayes net.
Referring to fig. 4C, a structural schematic diagram of an and gate fault tree provided in the embodiment of the present application is shown in fig. 4C, where logical relations represented by the and gate fault tree are:
P(C＝1|A＝0，B＝0)＝0
P(C＝1|A＝0，B＝1)＝0
P(C＝1|A＝1，B＝0)＝0
P(C＝1|A＝1，B＝1)＝1 (2)
referring to fig. 4D, in order to provide a schematic structural diagram of an and gate bayesian network according to an embodiment of the present application, the and gate fault tree shown in fig. 4C is converted into the and gate bayesian network shown in fig. 4D, and the logical relationship between the and gates is input into the and gate bayesian network.
By using the transformation methods shown in fig. 4A to 4B and fig. 4C to 4D, the bayesian network established by the transformation methods is shown in fig. 5, which is a schematic structural diagram of a bayesian network provided in the embodiment of the present application.
Step S130: and calculating the probability of occurrence of various faults causing the failure of the electric energy metering automatic system according to the Bayesian network.
Specifically, first, all the bottom events causing the failure of the power metering automation terminal are obtained by using the primary fault, the secondary fault and the tertiary fault obtained in step S110, and the minimum cut set consisting of the bottom event sets is obtained as follows:
{A1},{A2},{A3},{A4},{A5},{A6},{A7},{A8},{A9},{A10},{A11},{A12},{A13},{A14},{A15},{A16},
{A17,A18}；
and then, respectively acquiring the occurrence probability of all bottom events, wherein the occurrence probability of the bottom events is obtained by decomposing faults when the electric energy metering automation system fails in daily operation and counting the faults.
And finally, respectively calculating the probability of occurrence of the primary fault, the secondary fault and the tertiary fault according to the probability of occurrence of all bottom events and the logical relationship between the Bayesian network nodes. For example, the process of calculating the probability of occurrence of the data detection fault M3 is as follows:
since the data detection failure M3 can be decomposed into the internal interface failure A3 and the external interface failure a4, the strength of the internal interface failure A3 is first determined, and the strength of the occurrence can be represented by the probability of whether the internal interface failure A3 occurs:
c(A3)＝P(A3)＝(0.6,0.4) (3)
in the formula, c (A)3) Indicates the strength of occurrence of internal interface failure A3, P (A)3) The probability of occurrence of the internal interface fault A3 is shown, and in the present embodiment, the probability of occurrence of the internal interface fault A3 is 0.4, and the probability of non-occurrence is 0.6.
Similarly, the strength of the occurrence of external interface fault a4 can be derived:
c(A4)＝P(A4)＝(0.9,0.1) (4)
then, the connection strength between the data detection fault M3 and the internal interface fault A3 and the external interface fault A4 is respectively obtained,
finally, since A3 is connected to a4 through an or gate, M3 coupling strength P (M) can be obtained3|A3,A4) Can find out
Therefore, the probability of occurrence of the data detection fault M3 is 0.46, the probability of occurrence of other faults is respectively obtained, and the maximum influence factor of the fault proportion of the electric energy metering automatic system can be obtained, so that great guidance contribution is made to improvement of the electric energy metering automatic system.
According to the reliability analysis method for the electric energy metering automation system, the fault tree of the electric energy metering automation system failure is established, then the Bayesian network transformation is carried out on the fault tree, and finally the probability of various failures of the electric power metering automation terminal failure can be calculated according to the Bayesian network. The process of establishing the fault tree is the process of combing the fault of the electric energy metering automation system and the logical relation between a plurality of fault factors. After the fault tree is established, qualitative analysis can be performed on fault factors of different levels, for example, a minimal cut set is obtained, so that basic events causing faults are obtained, and the minimal cut set is obtained. And converting the fault tree into a Bayesian network, and calculating the probability of occurrence of various faults causing the failure of the electric energy metering automatic system by utilizing an algorithm of the Bayesian network. The utility model provides a reliability analysis method of electric energy measurement automation system, through establishing the logical relationship between the fault factor that leads to failing, calculate different trouble probability of occurrence, the problem of the fault probability of statistics electric power measurement automation terminal among the prior art can't learn the fault factor, the actual defect at electric power measurement automation terminal promptly is solved, furthermore, according to different trouble probability of occurrence, can find out the main factor that leads to the electric energy measurement automation system to fail, can pertinence improvement electric energy measurement automation system's reliability from this.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention.
Claims (5)
1. A reliability analysis method of an electric energy metering automation system is characterized by comprising the following steps:
establishing a fault tree of the failure of the electric energy metering automatic system;
converting the fault tree into a Bayesian network;
obtaining all bottom events causing the failure of the power metering automation terminal according to the primary fault, the secondary fault and the tertiary fault in the Bayesian network, wherein,
the primary faults comprise a remote communication network fault M1, a local communication fault M2, a data detection fault M3, a turnover box transmission and table verification fault M4, an image recognition fault M5, a channel detection fault M6 and an automatic sealing fault M7;
the secondary fault corresponding to the telecommunication network fault M1 comprises a cellular communication fault A1;
the secondary fault corresponding to the local communication fault M2 comprises a micro-power wireless communication fault A2;
the secondary faults corresponding to the data detection fault M3 comprise an internal interface fault A3 and an external interface fault A4;
the second-level faults corresponding to the turnover box transmission and table verification fault M4 comprise a code reading failure A19, laser damage A5, a wiring error A6 and an electric meter morton A7;
the secondary faults corresponding to the image recognition fault M5 comprise a liquid crystal display damage A8 and a software failure A9;
the second-level faults corresponding to the channel detection fault M6 comprise an Ethernet test failure A10, a GPRS fault A11, an RS485 fault A1, a disc washer suction error A15 and a laser engraving failure A16;
the three-level faults corresponding to the code reading failure A19 comprise an RFID failure A17 and a barcode label failure A18;
respectively acquiring the occurrence probability of all the bottom events;
respectively calculating the probability of occurrence of a primary fault, a secondary fault and a tertiary fault according to the probability of occurrence of all the bottom events respectively and the logical relationship between the Bayesian network nodes, wherein the calculating the probability of occurrence of the primary fault, the secondary fault and the tertiary fault comprises the following steps:
if the bottom event is a secondary fault, calculating the occurrence intensity of the bottom event;
calculating the connection strength of the primary fault according to the probability of the occurrence of the bottom event and the logical relationship between the bottom event and the primary fault;
and obtaining the probability of the occurrence of the primary fault according to the joint strength of the primary fault.
2. The reliability analysis method of claim 1, wherein the establishing a fault tree of the power metering automation system failure comprises:
acquiring a plurality of primary faults causing the failure of the electric energy metering automation system;
analyzing the primary fault to obtain at least one secondary fault causing the primary fault;
and establishing a logical relationship between the primary fault and the secondary fault.
3. The reliability analysis method of claim 2, wherein the method further comprises:
analyzing the secondary fault to obtain a tertiary fault causing the secondary fault;
and establishing a logic relation between the third-level fault and the second-level fault.
4. The reliability analysis method of claim 3, wherein converting the fault tree into a Bayesian network comprises:
establishing Bayesian network nodes respectively corresponding to the primary fault, the secondary fault and the tertiary fault;
and establishing a logical relationship between the Bayesian network nodes according to the logical relationship between the secondary faults and the primary faults and the logical relationship between the tertiary faults and the secondary faults.
5. The reliability analysis method of claim 1, wherein the method further comprises:
and calculating the failure minimum cut set of the automatic electric energy metering system according to all the bottom events.
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| CN105701574A (en) * | 2016-01-18 | 2016-06-22 | 南京邮电大学 | Method for evaluating reliability of distribution system with variable failure rate |
| CN105825317A (en) * | 2016-03-09 | 2016-08-03 | 国网江苏省电力公司电力科学研究院 | DC distribution network reliability assessment method based on Bayesian network |
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