CN107895194B - Fault diagnosis method for main coolant system of nuclear power plant - Google Patents

Fault diagnosis method for main coolant system of nuclear power plant Download PDF

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CN107895194B
CN107895194B CN201710984407.6A CN201710984407A CN107895194B CN 107895194 B CN107895194 B CN 107895194B CN 201710984407 A CN201710984407 A CN 201710984407A CN 107895194 B CN107895194 B CN 107895194B
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龚永健
苏晓燕
钱虹
张栋良
陈秋冬
石峰建
白宛灵
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Shanghai University of Electric Power
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Abstract

The invention relates to a fault diagnosis method for a main coolant system of a nuclear power plant, which comprises the steps that a model building module, a BPA generator, an evidence fusion module and a decision diagnostor are sequentially connected, data obtained by calculation of the model built by the module through the BPA generator is used as input of the evidence fusion module, a result obtained by fusion of the evidence fusion module is transmitted to the decision diagnostor, and finally, a judgment result is output by the decision diagnostor. The invention analyzes and expresses the uncertainty of fault diagnosis from the angle of evidence, continuously reduces a hypothesis set by depending on the accumulation of the evidence, and distinguishes unknown and uncertain, thereby leading the diagnosis result to be more objective and accurate. The BPA is generated by utilizing the triangular fuzzy number, and a specific numerical value is obtained through quantitative calculation, so that the analysis is convenient, and the calculation is simple and convenient; and secondly, the method has stronger flexibility and is helpful for an analyst to make a decision.

Description

Fault diagnosis method for main coolant system of nuclear power plant
Technical Field
The invention relates to a fault diagnosis technology, in particular to a fault diagnosis method for a main coolant system of a nuclear power plant.
Background
Nuclear energy is a safe, clean, and efficient source of energy. China is developing the nuclear power industry vigorously to meet the power demand, optimize the energy structure and promote the sustainable development of economy. At present, the installed capacity of 24 nuclear power generating units under construction is 2654.9 ten thousand kilowatts, and the installed capacity of the nuclear power generating units under construction accounts for 37.77 percent of the installed capacity of the nuclear power generating units under construction in the world in the first construction scale. The nuclear power safety is always the focus of attention of people, and the safety of nuclear power plants can practically and reliably ensure the safety of surrounding residents and nuclear power workers, so that the possibility of potential nuclear power accidents must be reduced to the greatest extent. The nuclear power fault diagnosis system is researched and perfected, is a requirement for improving the operation reliability of a nuclear power plant, and has important effects and significance for nuclear power safety and economic operation. In a nuclear power system, a main coolant system is one of important components, which is also called a reactor coolant system, and is a plurality of closed cooling loops which are connected in parallel and are composed of a reactor, a coolant pump, a voltage stabilizer, a steam generator, pipelines and valves according to the capacity of the closed cooling loops. Its main function is to cool the core and transfer the heat generated by the core to the steam generator to generate steam, and at the same time, it can effectively prevent radioactive substances from leaking out as a secondary barrier.
The fault diagnosis system is an important system for ensuring safe and stable operation of nuclear power, is an identification process of multiple fault modes of equipment, and contains a large amount of uncertainty in the process. The traditional fault diagnosis technology has a mature theoretical basis and some practical operation experiences, but in the main coolant system of the nuclear power plant, the fault of equipment is not only various but also has different fault reasons. A study of distributed fault diagnosis technology of a main coolant system of a nuclear power plant provides a distributed fault diagnosis method of a neural network based on feature extraction, but the method cannot accurately describe the difference between diagnosis results when each symptom parameter takes different values, so that the influence of the change of the symptom parameter on the diagnosis results cannot be well reflected, the decision support of an operator is not facilitated, and the problem is widely existed in the fault diagnosis of the actual main coolant system and needs to be solved urgently.
Disclosure of Invention
The invention provides a fault diagnosis method of a main coolant system of a nuclear power plant, aiming at the problem that the fault diagnosis is difficult to be accurate due to various faults and different fault reasons of equipment in the main coolant system of the nuclear power plant, and the fault diagnosis method can describe the change of a fault diagnosis confidence coefficient along with the change of a symptom parameter value more accurately, effectively reflect the relation between the fault and the symptom and make accurate judgment and decision.
The technical scheme of the invention is as follows: a fault diagnosis method for a main coolant system of a nuclear power plant specifically comprises the following steps:
1) the model building module builds the corresponding relation between the symptoms and the faults: setting a fault type and a corresponding symptom thereof, and establishing a fault space model to obtain a corresponding relation between the symptom and the fault;
2) BPA was generated by a basic probability assignment function BPA generator: firstly, establishing a triangular fuzzy function by using the minimum value, the optimum value and the maximum value of fault symptom parameters, comparing the specific measured values of the input symptoms on the basis, and generating a basic probability assignment function of each fault type according to a BPA (Business Process analysis) generation algorithm;
3) fusing BPA of each fault symptom generated by the BPA generator according to the symptom measurement value by using an evidence fusion module to obtain a fused BPA, and converting the BPA into probability distribution so as to facilitate subsequent decision making;
4) the decision diagnotor judges the fault type and outputs a certainty value: and inputting the probability distribution obtained by the evidence fusion module into a decision diagnoser, wherein the maximum probability is the identified fault type, and the reliability of the conclusion is given, namely the probability value of the maximum fault mode.
The BPA generator in the step 2) consists of the following devices:
(1) a history database input device for inputting the maximum, minimum and optimal normal values of the symptom parameters;
(2) a symptom parameter input device for inputting each symptom parameter measurement value, namely real-time data;
(3) the membership function generating device generates a proper membership function according to the model constructed by the module and calculates to obtain the distribution of the basic probability function;
the BPA generator generates BPA as follows:
establishing a triangular fuzzy function: longitudinal axis muARepresenting membership values in which three points X are arranged in succession on the abscissaij1，Xij0，Xij2Respectively representing the minimum, the best and the maximum normal values corresponding to a fault symptom parameter, wherein the best normal value X of the symptom parameterij0Corresponding to the longitudinal axis membership value of 1, establishing the fault symptom triangular fuzzy function for the vertex of the triangle, and establishing all fault symptom triangular fuzzy functions by the method;
if the fault type identification frame theta ═ { F ═ F1，F2，F3，F4N }, wherein F1，F2，F3，F44 fault types, N is normal operation, and a certain symptom parameter measured value is set as XiAnd the parameter is a fault FiTypical symptom of (1), XiMembership to target FiIs expressed as μiDegree of membership to the other three faults is μi', degree of membership to normal case N is μNIn which μNHas a value of XiSubstituting the value of (A) into the triangular blur number Xij1，Xij0，Xij2The obtained membership value yNAnd calculating each membership value by the following three formulas:
Figure GDA0002883465020000031
then, the 3 membership values are normalized, and the BPA of the measurement value is established as follows:
Figure GDA0002883465020000032
wherein Fa、FbAnd FcRespectively is except for FiThree other faults.
The invention has the beneficial effects that: the invention relates to a fault diagnosis method for a main coolant system of a nuclear power plant, which takes fault symptoms as evidence of an identification target, obtains a fusion result by an evidence synthesis method and judges the fault type. BPA is generated by utilizing a triangular fuzzy function, a specific numerical value is obtained through quantitative calculation, so that the analysis is convenient, and the calculation is simple and convenient; and secondly, the method has stronger flexibility and is helpful for an analyst to make a decision.
Drawings
FIG. 1 is a flow chart of a nuclear power plant primary coolant system fault diagnosis method of the present invention;
FIG. 2 is a block diagram of a BPA generator;
FIG. 3 is a diagram of a triangular blur function used in the present invention.
Detailed Description
Fig. 1 shows a flow chart of a method for diagnosing a fault of a main coolant system of a nuclear power plant, which includes the following steps:
1. the model building module builds the corresponding relation between the symptoms and the faults: setting a fault type and a corresponding symptom thereof, and establishing a fault space model to obtain a corresponding relation between the symptom and the fault;
2. BPA (basic probability assignment function) generator BPA: firstly, establishing a triangular fuzzy function by using the minimum value, the optimum value and the maximum value of fault symptom parameters, comparing the specific measured values of the input symptoms on the basis, and generating a basic probability assignment function of each fault type according to a BPA (Business Process analysis) generation algorithm;
3. fusing BPA of each fault symptom generated by the BPA generator according to the symptom measurement value by using an evidence fusion module to obtain a fused BPA, and converting the BPA into probability distribution so as to facilitate subsequent decision making;
4. and the decision diagnotor judges the fault type and outputs a certainty value. And inputting the probability distribution obtained by the evidence fusion module into a decision diagnoser, wherein the maximum probability is the identified fault type, and the reliability of the conclusion is given, namely the probability value of the maximum fault mode.
The model building module, the BPA generator, the evidence fusion module and the decision diagnostor are sequentially connected, data obtained by calculation of the model built by the module through the BPA generator is used as input of the evidence fusion module, a result fused by the evidence fusion module is transmitted to the decision diagnostor, and finally a judgment result is output by the decision diagnostor.
The BPA generator is shown in FIG. 2 and comprises the following devices:
1) a history database input device for inputting the maximum, minimum and optimal normal values of the symptom parameters;
2) a symptom parameter input device for inputting each symptom parameter measurement value, namely real-time data;
3) and the membership function generating device generates a proper membership function according to the model constructed by the module and calculates to obtain the distribution of the basic probability function.
When the value of a certain symptom parameter is closer to the optimal normal value, the probability of the corresponding fault is lower, and the probability of the normal operation of the system is higher; on the contrary, the higher the probability of occurrence of the corresponding fault, the lower the probability of normal operation of the system. Based on this idea, BPA was generated as follows:
establishing a triangular blur number (X)ij1，Xij0，Xij2) As shown in fig. 3, the vertical axis μARepresenting membership values in which three points X are arranged in succession on the abscissaij1，Xij0，Xij2Respectively representing the minimum, the best and the maximum normal values corresponding to a fault symptom parameter, wherein the best normal value X of the symptom parameterij0And establishing the fault symptom triangular fuzzy function for the vertex of the triangle with the corresponding longitudinal axis membership value of 1, and establishing all fault symptom triangular fuzzy functions by the method. The system fault type identification framework theta ═ F is known1，F2，F3，F4N }, wherein F1，F2，F3，F4There are 4 fault types, and N is normal operation. Let a certain symptom parameter measure be XiAnd the parameter is a faultFiTypical symptom of (1), XiMembership to target FiIs expressed as μiDegree of membership to the other three faults is μi', degree of membership to normal case N is μN. Wherein muNHas a value of XiSubstituting the value of (A) into the triangular blur number (X)ij1，Xij0，Xij2) The obtained membership value yN(see FIG. 3). Calculating each membership value by the following three formulas:
Figure GDA0002883465020000051
then, the 3 membership values are normalized, and the BPA of the measurement value is established as follows:
Figure GDA0002883465020000052
wherein Fa、FbAnd FcRespectively is except for FiThree other faults.
And inputting the generated BPA into an evidence fusion module for fusion through an evidence combination rule, and outputting the BPA to a decision diagnoser for decision judgment through conversion of a gambling probability formula into probability distribution.
The invention is further described below with reference to the accompanying drawings:
firstly, establishing a fault space model, wherein F is { F ═ F1，F2，F3，F4，N}(F1A rupture accident of heat transfer tube of left loop steam generator, F2A right loop steam generator tube burst accident, F3Rupture accident of main steam pipeline of left loop in containment vessel, F4A failure accident of the main pipeline of the left loop and N-normal operation), each sign parameter has a normal value range when the system is in normal operation, and the table of the fault sign parameter and the normal range is shown in table 1. Then extracting the characteristic of each symptom parameter, using the characteristic value 1 to represent abnormal condition, using 0 to represent normal condition, converting each parameter measured value into {0,1} characteristicParameters from which the correspondence between several faults and corresponding typical symptoms can be found, such as a list of fault monitoring parameters and feature extractions shown in table 2 and the correspondence between symptoms and faults shown in table 3.
TABLE 1
Figure GDA0002883465020000061
TABLE 2
Figure GDA0002883465020000062
TABLE 3
Figure GDA0002883465020000063
Figure GDA0002883465020000071
Obviously, when a certain symptom parameter value is closer to the optimal normal value, the probability of the fault corresponding to the symptom parameter value is lower, and the probability of the normal operation of the system is higher; on the contrary, the higher the probability of occurrence of the corresponding fault, the lower the probability of normal operation of the system. The BPA generation model herein was built on this basis as follows:
establishing a triangular blur number (X)ij1，Xij0，Xij2) As shown in FIG. 3, wherein Xij1，Xij0，Xij2Respectively, the minimum, optimum and maximum normal values of the symptom parameter. The system of the present invention recognizes the frame Θ ═ F1，F2，F3，F4N, and setting the measured value of a certain symptom parameter as XiAnd the parameter is a fault FiTypical symptom of (1), XiMembership to target FiIs expressed as μiDegree of membership to the other three faults is μi', degree of membership to normal case N is μN. Wherein muNHas a value of XiSubstituting the value of (A) into the triangular blur number (X)ij1，Xij0，Xij2) The obtained membership value yN(see FIG. 3). Calculating each membership value by the following three formulas:
Figure GDA0002883465020000072
then, the 3 membership values are normalized, and the BPA of the measurement value is established as follows:
Figure GDA0002883465020000073
wherein Fa、FbAnd FcRespectively is except for FiThree other faults.
And then fusing the BPA by using a Dempster combination method to obtain a fusion result. The Dempster combination formula is as follows:
Figure GDA0002883465020000081
wherein
Figure GDA0002883465020000082
Wherein A isi、Bj、ClIs the focal element, m, in the frame of evidence theory identification1(Ai) Is the first BPA mesogen AiAssigned value of the basic probability of m2(Bj) Is the second BPA mesogen BjAssigned value of the basic probability of m3(Cl) Is the third BPA middle coke element ClA base probability assignment of.
And finally, identifying and judging the fault type through a decision diagnotor. Assuming that m is a BPA function on theta, the corresponding betting probability conversion formula BetPm:Θ→[0,1]The definition is as follows:
Figure GDA0002883465020000083
where | A | is the value of the set A (i.e., the number of elements in A).
And after the probability distribution is obtained, finding out the fault with the maximum probability, and outputting a confidence value.
Where w is the failure mode when the Basic Probability Assignment (BPA) is converted into a probability distribution, i.e. w belongs to F1, F2, F3, F4 and N. A is the focal element of the BPA and can be any subset of the recognition frame power set, i.e., A is any subset of the set { F1, F2, F3, F4, N }. m { A } is the basic probability assignment for Focus A.
Figure GDA0002883465020000084
Is an empty album
Figure GDA0002883465020000085
A base probability assignment of.
Through verification, the method not only can obtain correct diagnosis results, but also can better reflect the difference between the diagnosis results when each symptom parameter takes different values, can more easily see the change of the confidence coefficient of the diagnosis results, and can provide certain reference and reference for fault diagnosis research in an actual nuclear power system in the future.

Claims (1)

1. A fault diagnosis method for a main coolant system of a nuclear power plant specifically comprises the following steps:
1) the model building module builds the corresponding relation between the symptoms and the faults: setting a fault type and a corresponding symptom thereof, and establishing a fault space model to obtain a corresponding relation between the symptom and the fault;
2) BPA was generated by a basic probability assignment function BPA generator: firstly, establishing a triangular fuzzy function by using the minimum value, the optimum value and the maximum value of fault symptom parameters, comparing the specific measured values of the input symptoms on the basis, and generating a basic probability assignment function of each fault type according to a BPA (Business Process analysis) generation algorithm;
3) fusing BPA of each fault symptom generated by the BPA generator according to the symptom measurement value by using an evidence fusion module to obtain a fused BPA, and converting the BPA into probability distribution so as to facilitate subsequent decision making;
4) the decision diagnotor judges the fault type and outputs a certainty value: inputting the probability distribution obtained by the evidence fusion module into a decision diagnoser, wherein the maximum probability is the identified fault type, and the reliability of the conclusion is given, namely the probability value of the maximum fault mode;
the BPA generator in the step 2) is characterized by comprising the following devices:
(1) a history database input device for inputting the maximum, minimum and optimal normal values of the symptom parameters;
(2) a symptom parameter input device for inputting each symptom parameter measurement value, namely real-time data;
(3) the membership function generating device generates a proper membership function according to the model constructed by the module and calculates to obtain the distribution of the basic probability function;
the BPA generator generates BPA as follows:
establishing a triangular fuzzy function: longitudinal axis muARepresenting membership values in which three points X are arranged in succession on the abscissaij1，Xij0，Xij2Respectively representing the minimum, the best and the maximum normal values corresponding to a fault symptom parameter, wherein the best normal value X of the symptom parameterij0Corresponding to the longitudinal axis membership value of 1, establishing the fault symptom triangular fuzzy function for the vertex of the triangle, and establishing all fault symptom triangular fuzzy functions by the method;
if the fault type identification frame theta ═ { F ═ F1，F2，F3，F4N }, wherein F1，F2，F3，F44 fault types, N is normal operation, and a certain symptom parameter measured value is set as XiAnd the parameter is a fault FiTypical symptom of (1), XiMembership to target FiIs expressed as μiUnder the other three categoriesDegree of failure is mui', degree of membership to normal case N is μNIn which μNHas a value of XiSubstituting the value of (A) into the triangular blur number Xij1，Xij0，Xij2The obtained membership value yNAnd calculating each membership value by the following three formulas:
Figure FDA0002883465010000021
then, the 3 membership values are normalized, and the BPA of the measurement value is established as follows:
Figure FDA0002883465010000022
wherein Fa、FbAnd FcRespectively is except for FiThree other faults.
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