CN111401687A - Power module fault diagnosis method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a power module fault diagnosis method, a power module fault diagnosis device, power module equipment and a storage medium. The power module fault diagnosis method comprises the following steps: s1, acquiring an evaluation index system corresponding to the power module, wherein the evaluation index system is used for representing the running state of the power module and comprises a plurality of primary evaluation indexes; s2, determining the weight and the relative closeness of each primary evaluation index respectively, and determining the state value of the running state of the target power module according to the state value, the weight and the relative closeness of each primary evaluation index; the state value of the first-level evaluation index represents the evaluation of main parameters of the power module, the weight of the first-level evaluation index is determined jointly according to an entropy weight method and an analytic hierarchy process, and the relative closeness of the first-level evaluation index is determined according to a good-bad solution distance method; s3, judging whether the state value of the operation state of the power module is lower than a threshold value; if yes, a warning signal is sent out.
Description
Technical Field
The invention relates to the technical field of power module equipment, in particular to a power module fault diagnosis method, a power module fault diagnosis device, power module fault diagnosis equipment and a storage medium.
Background
With the rapid development of science and technology and the outstanding energy crisis and environmental problems, electric automobiles are favored by people. Compared with the traditional fuel oil automobile, the electric automobile has the advantages of low noise, small pollution, high energy utilization rate, various energy sources and the like. The vigorous development of electric automobiles is an effective way to relieve environmental pollution and energy consumption pressure. And as the important corollary equipment of electric automobile, fill electric pile and also turn into by oneself. Along with more and more fill electric pile and put into operation, the power module needs to detect it as the important component part of filling electric pile, confirms its trouble reason in order to maintain it in time when the power module breaks down.
Among the prior art, power module is because install inside the device, if adopt artifical periodic maintenance mode can not carry out convenient and fast's maintenance to it, other current and voltage detection mode, the parameter is too single, can not be to its fault reason analysis well.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a power module fault diagnosis method, a device, equipment and a storage medium, wherein the method, the device, the equipment and the storage medium are combined with an entropy weight method, an analytic hierarchy process and a good-bad solution distance method to determine the integral score of the running state of a target power module; the obtained state value of the running state of the power module is closer to the real situation of the power module, so that the fault detection is better carried out.
In order to achieve the above purpose, the invention provides the following technical scheme:
a power module fault diagnosis method comprising the steps of:
s1, acquiring an evaluation index system corresponding to the power module, wherein the evaluation index system is used for representing the running state of the power module, and the evaluation index system comprises a plurality of primary evaluation indexes which are main parameters of the power module;
s2, determining the weight and the relative closeness of each primary evaluation index respectively, and determining the state value of the running state of the power module according to the state value, the weight and the relative closeness of each primary evaluation index; the state value of the first-level evaluation index represents the evaluation of main parameters of the power module, the weight of the first-level evaluation index is determined jointly according to an entropy weight method and an analytic hierarchy process, and the relative closeness of the first-level evaluation index is determined according to a good-bad solution distance method;
s3, judging whether the state value of the running state of the target power module is lower than a threshold value; if yes, a warning signal is sent out.
Preferably, the main parameter is a combination of one or more of input voltage deviation, input current, output voltage, output current deviation, current sharing unbalance, module temperature, ambient humidity, family defect, maintenance replacement record and service life.
Preferably, the state value acquiring step is as follows:
a1, respectively determining the parameter values of the main parameters corresponding to each primary evaluation index;
and A2, mapping the parameter values corresponding to the primary evaluation indexes to different intervals by applying a sigmoid function to obtain the state values of the primary evaluation indexes.
Preferably, the step of calculating the primary evaluation index weight is as follows:
b1, calculating subjective weight based on analytic hierarchy process; dividing the influencing factors of the target state value into a plurality of classes according to an evaluation index system, and carrying out weight calculation of an analytic hierarchy process to obtain a subjective weight vi；
Based on m groups of test data, calculating objective weights of n primary evaluation indexes by applying an entropy weight method, taking each group of data as a column vector, wherein each column vector consists of state values of n primary evaluation indexes, standardizing each column vector by using expressions (1) and (2), calculating the entropy value by using expression (3), and finally calculating the weight of each index by using expression ⑷;
(1) the expression corresponds to an index having a larger numerical value, and (2) the expression corresponds to an index having a smaller numerical value, wherein fijRepresenting the state value of the jth primary evaluation index of the ith group of data for data in a column vector, dijThe data obtained after standardization; ejEntropy values calculated for the normalized column vectors; wherein p isijAs shown in formula (5); u. ofjIs the objective weight of the calculated jth first-level evaluation index;
b3, calculating the comprehensive weight based on a standard deviation method;
the proportion between the subjective weight and the objective weight in the comprehensive weight is calculated by a standard deviation method;
in the formula (5), i is 1 or 2, and σ is a weight vector mainly for distinguishing two weight vectors1Is the standard deviation, σ, of the subjective weight vector2Is the standard deviation, mu, of the objective weight vector1Is a proportion of the subjective weight, mu2Is a proportion of the objective weight;
the comprehensive weight calculation formula is as follows:
Wi＝μ1vi+μ2ui(i＝1，2，......，n) (6)
in the formula (6), WiFor the i-th primary evaluation index integrated weight vector, v, determined jointly by the entropy weight method and the analytic hierarchy processiIs the objective weight of the ith primary evaluation index, uiIs the objective weight of the ith primary evaluation index; mu.s1Is a proportion of the subjective weight, mu2Is a proportion of the objective weight.
Preferably, the step of determining the relative closeness according to the good-bad solution distance method is as follows:
g1, calculating the distance D between each evaluation object and the optimal solutioni +And distance to the worst solutionDi-ion;
wherein x isiIs the mapped index data, and represents the state value of the evaluation object, Ri +And the solution is the optimal solution of the mapped indexes, and Ri-is the worst solution of the mapped indexes.
G2, calculating the relative closeness C of each evaluation objecti，CiThe larger the value is, the more excellent the representation evaluation object is;
preferably, according to the state value, the weight, and the relative closeness of each primary evaluation index, a specific calculation process of determining the state value of the operating state of the target power module is as follows:
wherein Score is a state value of the operating state of the power module, WiIs the weight of the ith primary evaluation index,
is the transpose of the relative closeness matrix of the ith primary evaluation index.
A power module fault diagnostic apparatus comprising:
the evaluation index acquisition module is used for acquiring an evaluation index system corresponding to the target power module, wherein the evaluation index system is used for representing the running state of the power module, and the evaluation index system comprises a plurality of primary evaluation indexes;
the target power module state value calculation module is used for respectively determining the weight and the relative closeness of each primary evaluation index and further determining the state value of the running state of the target power module;
the fault judgment module is used for judging whether the state value of the running state of the target power module is lower than a threshold value or not; if yes, a warning signal is sent out.
Preferably, the power module fault diagnosis device further includes a data acquisition module, configured to acquire a parameter value, convert the parameter value into a state value, and send the state value to the evaluation index acquisition module.
A power module fault diagnosis apparatus comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the power module fault diagnosis method when executing the computer program.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described power module fault diagnosis method.
Compared with the prior art, the invention has the beneficial effects that: determining the weight of the evaluation index by an entropy weight method and an analytic hierarchy process, and determining the integral score of the running state of the target power module by combining the relative closeness determined by a good-bad solution distance method; the obtained overall score of the running state of the power module is closer to the real condition of the power module, so that fault detection is better carried out.
Description of the drawings:
fig. 1 is a flowchart of a power module fault diagnosis method of an exemplary embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a power module fault diagnosis method apparatus in exemplary embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of a power module fault diagnosis method apparatus in exemplary embodiment 3 of the present invention;
fig. 4 is a schematic structural diagram of a power module fault diagnosis method apparatus in exemplary embodiment 3 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a power module fault diagnosis method, including the following steps:
s1, acquiring an evaluation index system corresponding to the target power module, wherein the evaluation index system is used for representing the running state of the power module, and the evaluation index system comprises a plurality of primary evaluation indexes which represent main parameters of the power module;
s2, determining the weight and the relative closeness of each primary evaluation index respectively, and determining the state value of the running state of the target power module according to the state value, the weight and the relative closeness of each primary evaluation index; the state value of the first-level evaluation index represents the evaluation of each main parameter of the power module, the weight of the first-level evaluation index is determined jointly according to an entropy weight method and an analytic hierarchy process, and the relative closeness of the first-level evaluation index is determined according to a good-bad solution distance method;
s3, judging whether the state value of the running state of the target power module is lower than a threshold value; if yes, a warning signal is sent out.
As can be seen from the above description, the power module fault diagnosis method provided in this embodiment obtains an evaluation index system corresponding to a target power module and including a plurality of primary evaluation indexes; then determining the weight of each primary evaluation index based on an entropy weight method and an analytic hierarchy process, determining relative closeness according to a good-bad solution distance method, determining the state value of the running state of the target power module according to the weight and the relative closeness of each primary evaluation index, and then judging whether the power module has a fault and needs to be overhauled; the power module fault diagnosis method can accurately and timely evaluate the running state of the power module, judge whether faults occur, further effectively improve the running reliability of the power module, effectively improve the working efficiency of operation and maintenance personnel, reduce the operation and maintenance working pressure of the power module, meanwhile, the evaluation process of the running state of the power module is simple and has scientific basis, effective data support can be provided for daily operation and maintenance work of the power module, and the power module fault diagnosis method has strong scientificity, reliability and operability, can effectively guide intelligent operation and maintenance of the power module, and improves the running stability and the running life of a charging facility.
An effective way for converting semi-qualitative and semi-quantitative problems into quantitative problems is by Analytic Hierarchy Process (AHP). The AHP levels various factors, and provides a comparable quantitative basis for analyzing and predicting the development of things, and although pairwise comparison data can be obtained by objective absolute data conversion in the calculation process, the pairwise comparison data is generally subjectively given by field experts, so the AHP is a subjective weighting method in general; the entropy weight method is an objective weighting method, and the weights of all evaluation indexes are obtained by using the existing objective data. If a weighting method is used alone, the obtained weight value may be biased to an objective or subjective aspect, so that the embodiment determines the weight of the first-level evaluation index through an entropy weight method and an analytic hierarchy process, determines the relative closeness of the first-level evaluation index through a good-bad solution distance method, and determines the overall score of the running state of the target power module according to the state value, the weight and the relative closeness of each first-level evaluation index; the obtained overall score of the running state of the power module is closer to the real condition of the power module, so that fault detection is better carried out.
The state value of the primary evaluation index represents the evaluation of each main parameter of the power module, and the main parameters comprise input voltage deviation, input current, output voltage, output current deviation, current sharing unbalance, module temperature, environment humidity, family defects, maintenance and replacement records or service life and the like.
The state value of the primary evaluation index is obtained by the following steps:
a1, determining the parameter values of the influence factors corresponding to the primary evaluation indexes respectively;
and A2, mapping the parameter values corresponding to the primary evaluation indexes to different intervals by applying a sigmoid function and other calculation methods to obtain the state values of the primary evaluation indexes.
The method comprises the following steps of determining the weight according to an entropy weight method and an analytic hierarchy process:
b1, calculating subjective weight based on analytic hierarchy process; dividing the influencing factors of the target state value into a plurality of classes according to an evaluation index system, and carrying out weight calculation of an analytic hierarchy process to obtain a subjective weight vi；
Based on m groups of test data (historical data), calculating objective weights of n primary evaluation indexes by applying an entropy weight method, taking each group of data as a column vector, wherein each column vector consists of state values of n primary evaluation indexes, standardizing each column vector by using expressions (1) and (2), calculating the entropy value by using expression (3), and finally calculating the weight of each index by using expression ⑷;
(1) the expression corresponds to an index having a larger numerical value, and (2) the expression corresponds to an index having a smaller numerical value, wherein fijRepresenting the state value of the jth primary evaluation index of the ith group of data for data in a column vector, dijThe data obtained after standardization; ejEntropy values calculated for the normalized column vectors; u. ofjIs the objective weight of the calculated jth first-level evaluation index;
b3, calculating the comprehensive weight based on a standard deviation method;
the proportion between the subjective weight and the objective weight in the comprehensive weight is calculated by a standard deviation method;
in the formula (5), i is 1 or 2, and σ is a weight vector mainly for distinguishing two weight vectors1Is the standard deviation, σ, of the subjective weight vector2Is the standard deviation, mu, of the objective weight vector1Is a proportion of the subjective weight, mu2Is a proportion of the objective weight;
the comprehensive weight calculation formula is as follows:
Wi＝μ1vi+μ2ui(i＝1，2，......，n) (6)
in the formula (6), WiDetermining the comprehensive weight vector, v, of the ith primary evaluation index according to the entropy weight method and the analytic hierarchy processiIs the objective weight of the ith primary evaluation index, uiIs the objective weight of the ith primary evaluation index; mu.s1Is a proportion of the subjective weight, mu2Is a proportion of the objective weight.
The step of determining the relative closeness according to the good and bad solution distance method is as follows:
based on the mapped index data, the relative closeness of each evaluation index is calculated by applying a good-bad solution distance method TOPSIS (technique for order preference by Similarity to an Ideal solution), and the basic steps are as follows:
g1, calculating the distance D between each evaluation object and the optimal solutioni +And the distance D to the worst solutioni -；
Wherein x isiFor the mapped index data, Ri +For the mapped optimal solution of the index, Ri -Is the worst solution of the mapped indexes.
G2, calculating the relative closeness C of each evaluation objecti，CiThe larger the value is, the more excellent the representation evaluation object is;
the above embodiment relates to determining the state value of the operating state of the target power module according to the state value, the weight and the relative closeness of each primary evaluation index. In this embodiment, the state value of the operating state of the power module is determined by linearly combining the weight and the relative closeness of the primary index and combining the state value, and the specific calculation process is as follows:
wherein Score is a state value of the operating state of the power module, WiIs the weight of the ith primary evaluation index,
is the transpose of the relative closeness matrix of the ith primary evaluation index.
Example 2
Corresponding to the above method embodiments, the present embodiment further provides a power module fault diagnosis device, and the power module fault diagnosis device described below and the power module fault diagnosis method described above may be referred to in correspondence with each other.
Referring to fig. 2, the apparatus includes the following modules:
the evaluation index acquisition module 101 is configured to acquire an evaluation index system corresponding to the target power module, where the evaluation index system is used to represent an operating state of the power module, and the evaluation index system includes a plurality of first-level evaluation indexes;
a target power module state value calculating module 102, configured to determine weights and relative closeness of the primary evaluation indicators, respectively, and further determine a state value of an operating state of the target power module;
a fault judgment module 103, configured to judge whether a state value of an operating state of the target power module is lower than a threshold; if yes, a warning signal is sent out.
As can be seen from the above description, in the power module fault diagnosis apparatus provided in this embodiment, the evaluation index acquisition module 101 is used to acquire an evaluation index system corresponding to the target power module and including a plurality of primary evaluation indexes; then, determining the weight and the relative closeness of each primary evaluation index through a target power module state value calculation module 102, determining the state value of the running state of the target power module according to the weight and the relative closeness of each primary evaluation index, and finally, judging whether the power module has a fault or not and whether the power module needs to be overhauled or not through a fault judgment module 103; through the evaluation of the running state of the power module that foretell power module fault diagnosis device can be accurate timely, judge whether break down, and then can effectively improve power module's operational reliability, and can effectively improve operation and maintenance personnel work efficiency, alleviate the operation and maintenance operating pressure to power module, and simultaneously, power module running state evaluation process is simple and have the scientific foundation, can provide effectual data support for power module daily operation and maintenance work, have very strong scientificity, reliability and maneuverability, can guide power module's intelligent operation and maintenance effectively, improve the operating stability and the running life of the facility of charging.
In order to provide a more accurate data basis for the power module running state evaluation process, in an embodiment of the present application, the power module fault diagnosis apparatus further includes a data acquisition module, configured to acquire a parameter value in real time, convert the parameter value into a state value, and send the state value to the evaluation index acquisition module. .
Example 3
Corresponding to the above method embodiment, the present embodiment further provides a power module fault diagnosis device, and a power module fault diagnosis device described below and a power module fault diagnosis method described above may be referred to in correspondence with each other.
Referring to fig. 3, the power module fault diagnosis apparatus includes:
a memory D l for storing computer programs;
a processor D2 for implementing the steps of the power module fault diagnosis of the above method embodiments when executing the computer program.
Specifically, referring to fig. 4, a specific structural diagram of the power module fault diagnosis device provided in this embodiment is shown, where the power module fault diagnosis device may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 322 (e.g., one or more processors) and a memory 332, and one or more storage media 330 (e.g., one or more mass storage devices) storing an application 342 or data 344. Memory 332 and storage media 330 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions operating on a data processing device. Still further, the central processor 322 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the power module fault diagnosis device 301.
The power module fault diagnostic device 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input-output interfaces 358, and/or one or more operating systems 341, for example, Windows Server, Mac OS XTM, UnixTM, &lTtTtranslation = L "&gTt L &/T &gTt inuxTM, FreeBSDTM, or the like.
The steps in the power module failure diagnosis method described above may be implemented by the structure of the power module failure diagnosis identification apparatus.
Example 4
Corresponding to the above method embodiments, the present embodiment further provides a readable storage medium, and a readable storage medium described below and a power module fault diagnosis method described above may be referred to in correspondence with each other.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the power module fault diagnosis method of the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The foregoing is merely a detailed description of specific embodiments of the invention and is not intended to limit the invention. Various alterations, modifications and improvements will occur to those skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. A power module fault diagnosis method, comprising the steps of:
s1, acquiring an evaluation index system corresponding to the power module, wherein the evaluation index system is used for representing the running state of the power module, and the evaluation index system comprises a plurality of primary evaluation indexes which are main parameters of the power module;
s2, determining the weight and the relative closeness of each primary evaluation index respectively, and determining the state value of the running state of the power module according to the state value, the weight and the relative closeness of each primary evaluation index; the state value of the first-level evaluation index represents the evaluation of main parameters of the power module, the weight of the first-level evaluation index is determined jointly according to an entropy weight method and an analytic hierarchy process, and the relative closeness of the first-level evaluation index is determined according to a good-bad solution distance method;
s3, judging whether the state value of the running state of the target power module is lower than a threshold value; if yes, a warning signal is sent out.
2. The method of claim 1, wherein the primary parameter is a combination of one or more of input voltage deviation, input current, output voltage, output current deviation, current share imbalance, module temperature, ambient humidity, family defect, service replacement record, and age.
3. The power module fault diagnosis method according to claim 1, characterized in that the state value acquisition step is as follows:
a1, respectively determining the parameter values of the main parameters corresponding to each primary evaluation index;
and A2, mapping the parameter values corresponding to the primary evaluation indexes to different intervals by applying a sigmoid function to obtain the state values of the primary evaluation indexes.
4. The power module fault diagnosis method according to claim 1, wherein the calculation of the primary evaluation index weight is as follows:
b1, calculating subjective weight based on analytic hierarchy process; dividing the influencing factors of the target state value into a plurality of classes according to an evaluation index system, and carrying out weight calculation of an analytic hierarchy process to obtain a subjective weight vi；
Based on m groups of test data, calculating objective weights of n primary evaluation indexes by applying an entropy weight method, taking each group of data as a column vector, wherein each column vector consists of state values of n primary evaluation indexes, standardizing each column vector by using expressions (1) and (2), calculating the entropy value by using expression (3), and finally calculating the weight of each index by using expression ⑷;
(1) the expression corresponds to an index having a larger numerical value, and (2) the expression corresponds to an index having a smaller numerical value, wherein fijRepresenting the state value of the jth primary evaluation index of the ith group of data for data in a column vector, dijThe data obtained after standardization; ejEntropy values calculated for the normalized column vectors; wherein p isijAs shown in formula (5); u. ofjIs the objective weight of the calculated jth first-level evaluation index;
b3, calculating the comprehensive weight based on a standard deviation method;
the proportion between the subjective weight and the objective weight in the comprehensive weight is calculated by a standard deviation method;
in the formula (5), i is 1 or 2, and σ is a weight vector mainly for distinguishing two weight vectors1Is the standard deviation, σ, of the subjective weight vector2Is the standard deviation, mu, of the objective weight vector1Is a proportion of the subjective weight, mu2Is a proportion of the objective weight;
the comprehensive weight calculation formula is as follows:
Wi＝μ1vi+μ2ui(i＝1，2，......，n) (6)
in the formula (6), WiFor the i-th primary evaluation index integrated weight vector, v, determined jointly by the entropy weight method and the analytic hierarchy processiIs the objective weight of the ith primary evaluation index, uiIs the objective weight of the ith primary evaluation index; mu.s1Is a proportion of the subjective weight, mu2Being objective weightsSpecific gravity.
5. The method for diagnosing the fault of the charging pile power module according to claim 4, wherein the step of determining the relative closeness according to a good-bad solution distance method is as follows:
g1, calculating the distance D between each evaluation object and the optimal solutioni +And the distance D to the worst solutioni -；
Wherein x isiIs the mapped index data, and represents the state value of the evaluation object, Ri +For the mapped optimal solution of the index, Ri -Is the worst solution of the mapped indexes.
G2, calculating the relative closeness C of each evaluation objecti，CiThe larger the value is, the more excellent the representation evaluation object is;
6. the power module fault diagnosis method according to claim 5, wherein a specific calculation process of determining the state value of the operating state of the target power module according to the state value, the weight and the relative closeness of each primary evaluation index is as follows:
wherein Score is a state value of the operating state of the power module, WiIs the weight of the ith primary evaluation index,
is the transpose of the relative closeness matrix of the ith primary evaluation index.
7. A power module fault diagnosis apparatus characterized by comprising:
the evaluation index acquisition module is used for acquiring an evaluation index system corresponding to the target power module, wherein the evaluation index system is used for representing the running state of the power module, and the evaluation index system comprises a plurality of primary evaluation indexes;
the target power module state value calculation module is used for respectively determining the weight and the relative closeness of each primary evaluation index and further determining the state value of the running state of the target power module;
the fault judgment module is used for judging whether the state value of the running state of the target power module is lower than a threshold value or not; if yes, a warning signal is sent out.
8. The power module fault diagnosis device according to claim 7, further comprising a data acquisition module for acquiring parameter values, converting the parameter values into state values, and sending the state values to the evaluation index acquisition module.
9. A power module fault diagnosis apparatus characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the power module fault diagnosis method according to any one of claims l to 6 when executing the computer program.
10. A readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the power module fault diagnosis method according to any one of claims 1 to 6.
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