CN116593883A - Breaker body fault diagnosis method, device and equipment of intelligent high-voltage switch and storage medium - Google Patents
Breaker body fault diagnosis method, device and equipment of intelligent high-voltage switch and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
- G01R31/3275—Fault detection or status indication
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The application discloses a breaker body fault diagnosis method, device, equipment and storage medium of an intelligent high-voltage switch, wherein the method comprises the following steps: acquiring historical fault data of a circuit breaker in a high-voltage switch, and determining a suspected fault element set according to the historical fault data; classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element; performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information; analyzing the mapping relation between the fault reasons and the alarm information of each fault element by using a fault tree module; based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker. Effectively improves the fault diagnosis efficiency.
Description
Technical Field
The application relates to a breaker body fault diagnosis method, device and equipment of an intelligent high-voltage switch and a storage medium, belonging to the technical field of intelligent high-voltage switch fault diagnosis.
Background
The optical cable connection in the intelligent high-voltage switch has replaced the hard-wired cable connection in the traditional secondary circuit, and the signal transmission is mainly realized through the virtual circuit. The information transmission network, that is, a certain line transmission signal is not the only receiving end, so that the fault condition cannot be directly judged according to the line topology. Meanwhile, a comprehensive information analysis system with high automation is arranged in the station, so that information sharing is realized, and when the system fails, a monitoring information table reflecting abnormal conditions of all devices in the station can be obtained. The large amount of shared data has important significance for the proposal of a fault diagnosis method of the breaker in the high-voltage switch. Therefore, for the fault conditions of some important functional structures in the station, a new feature based on the intelligent high-voltage switch is needed to provide a more intelligent and adaptive fault diagnosis method.
The important equipment and functional structure in the intelligent high-voltage switch mainly comprises a high-voltage breaker, a protection device, a communication network and the like, and at present, expert students have more researches on the relay protection of the intelligent high-voltage switch and the reliability of the communication network, but have fewer systematic researches on the fault diagnosis of the high-voltage breaker in the intelligent station.
Disclosure of Invention
In the intelligent high-voltage switch, the main functions of the circuit breaker are to realize load control and fault protection, the occurrence of incorrect actions of the circuit breaker can influence the stable operation of the system, and the timely and accurate analysis of the fault cause of the high-voltage circuit breaker is the basis for ensuring the safe operation of a power grid and realizing the self-healing function of the intelligent power grid. A plurality of information acquisition modules are arranged in the intelligent high-voltage switch, so that sufficient data can be acquired, and the running states of various functional modules in the station can be mastered in real time. When equipment fails, massive abnormal signals appear in the station, the abnormal signals are the external manifestation form of the failure, and the actual failure cause can be quickly and effectively found through correlation analysis, so that the failure diagnosis efficiency is effectively improved.
The purpose is as follows: in order to overcome the defects in the prior art, the application provides a breaker body fault diagnosis method, device, equipment and storage medium of an intelligent high-voltage switch, wherein a weighted fuzzy Petri network model is adopted to represent the physical connection and fault propagation process inside the high-voltage breaker, so that the calculation complexity is low and the accuracy is high.
The technical scheme is as follows: in order to solve the technical problems, the application adopts the following technical scheme:
in a first aspect, the present application provides a fault diagnosis method for a breaker body of an intelligent high-voltage switch, including:
step 1: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
step 2: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
step 3: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
step 4: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element, namely the alarm information types corresponding to different fault causes;
step 5: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
In some embodiments, the step 2 includes:
and carrying out preliminary analysis on the historical alarm information and the fault reasons by utilizing the FMEA model so as to determine the alarm information and the fault reasons corresponding to each part of fault elements.
In some embodiments, step 5 further comprises: and carrying out time sequence information constraint checking on the alarm information of the target fault event to remove the error fault information.
In some embodiments, the timing information constraint check comprises:
step 301: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
step 302: for each suspected fault element, respectively dividing alarm information related to the element to form an alarm information set of each element;
step 303: respectively carrying out reverse time sequence reasoning on the alarm information set of each element by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
step 304: merging all the unitary time point constraints of the faults to obtain a total unitary time point constraint T of the faults;
step 305: forward time sequence reasoning is carried out on the total unitary time point constraint T of the faults to obtain unitary time point constraints of all the circuit breakers;
step 306: and comparing the unitary time point constraint of each breaker with the actually obtained alarm time sequence information through an operator, and screening error alarm information which does not meet the time sequence information.
In some embodiments, the step 5 includes:
firstly, calculating the confidence coefficient of an initial breaker based on a preset initial matrix;
secondly, calculating the confidence coefficient of the equivalent fuzzy input of the fault event;
thirdly, comparing the confidence level of the equivalent fuzzy input of the fault event with the magnitude of a preset threshold value;
fourthly, eliminating fault events with the confidence coefficient of the equivalent fuzzy input smaller than a preset threshold value, and calculating fault events with the confidence coefficient of the equivalent fuzzy input larger than the preset threshold value;
fifthly, calculating the confidence coefficient of all the currently available circuit breakers;
and sixthly, replacing the original breaker confidence coefficient with the newly obtained breaker confidence coefficient, repeating the second step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficients of all the breakers are not changed any more, and obtaining the fault probability representation of the high-voltage switch breaker.
In a second aspect, the present application provides a fault diagnosis apparatus for a breaker body of an intelligent high-voltage switch, comprising:
a data acquisition module configured to: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
a fault classification module configured to: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
a timing constraint checking module configured to: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
the mapping relation acquisition module is configured to: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element, namely the alarm information types corresponding to different fault causes;
a fault result acquisition module configured to: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
In a third aspect, the application provides an apparatus comprising,
a memory;
a processor;
and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of the first aspect described above.
In a fourth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method of the first aspect.
The beneficial effects are that: the breaker body fault diagnosis method, device and equipment and storage medium of the intelligent high-voltage switch provided by the application have the following advantages: by utilizing historical fault data to perform correlation analysis on alarm information and fault reasons, a breaker body fault diagnosis model of the intelligent high-voltage switch with low calculation complexity and high accuracy is established, and the actual fault reasons can be quickly and effectively found by utilizing the model, so that the fault diagnosis efficiency is effectively improved.
Drawings
Fig. 1 is a schematic flow chart of a fault diagnosis method for a breaker body of an intelligent high-voltage switch according to an embodiment of the application.
Fig. 2 is a schematic diagram of a circuit breaker body fault diagnosis timing information constraint checking step of an intelligent high-voltage switch according to an embodiment of the present application.
Fig. 3 is a schematic diagram of an iterative flow chart in matrix form in fault recognition of a fuzzy Petri network for fault diagnosis of a circuit breaker of an intelligent high-voltage switch.
Detailed Description
The application is further described below with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the present application, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
In a first aspect, as shown in fig. 1, the present embodiment provides a fault diagnosis method for a breaker body of an intelligent high-voltage switch, including:
step 1: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
step 2: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
step 3: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
step 4: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element, namely the alarm information types corresponding to different fault causes;
step 5: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
In some embodiments, the step 2 includes:
and carrying out preliminary analysis on the historical alarm information and the fault reasons by utilizing the FMEA model so as to determine the alarm information and the fault reasons corresponding to each part of fault elements.
In some embodiments, step 5 further comprises: and carrying out time sequence information constraint checking on the alarm information of the target fault event to remove the error fault information.
In some embodiments, the timing information constraint check comprises:
step 301: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
step 302: for each suspected fault element, respectively dividing alarm information related to the element to form an alarm information set of each element;
step 303: respectively carrying out reverse time sequence reasoning on the alarm information set of each element by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
step 304: merging all the unitary time point constraints of the faults to obtain a total unitary time point constraint T of the faults;
step 305: forward time sequence reasoning is carried out on the total unitary time point constraint T of the faults to obtain unitary time point constraints of all the circuit breakers;
step 306: and comparing the unitary time point constraint of each breaker with the actually obtained alarm time sequence information through an operator, and screening error alarm information which does not meet the time sequence information.
In some embodiments, the step 5 includes:
firstly, calculating the confidence coefficient of an initial breaker based on a preset initial matrix;
secondly, calculating the confidence coefficient of the equivalent fuzzy input of the fault event;
thirdly, comparing the confidence level of the equivalent fuzzy input of the fault event with the magnitude of a preset threshold value;
fourthly, eliminating fault events with the confidence coefficient of the equivalent fuzzy input smaller than a preset threshold value, and calculating fault events with the confidence coefficient of the equivalent fuzzy input larger than the preset threshold value;
fifthly, calculating the confidence coefficient of all the currently available circuit breakers;
and sixthly, replacing the original breaker confidence coefficient with the newly obtained breaker confidence coefficient, repeating the second step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficients of all the breakers are not changed any more, and obtaining the fault probability representation of the high-voltage switch breaker.
In some embodiments, a breaker body fault diagnosis method of an intelligent high-voltage switch includes the steps of:
step 1: and storing the historical fault event of the breaker in the high-voltage switch and the corresponding alarm information thereof into a database, and determining a suspected fault element set.
Step 2: and classifying the data after the faults occur, and determining alarm information and fault reasons corresponding to each part of functional structure.
And carrying out preliminary analysis on the historical alarm information and the fault reasons by utilizing the FMEA model so as to determine a main association relation.
The FMEA method can be used for evaluating the potential faults of the product and the relation between the potential faults and the influence factors thereof, and by researching the internal structure and the processing process of the product, all possible fault conditions are found out, and the relations between various factors and fault events causing different faults of the system are analyzed, so that the possible combination mode of the fault influence factors when the potential faults occur is determined. The method is beneficial to further improving the performance and the reliability of the product and reducing the failure risk in the design and development of the product and the later maintenance. The method graphically represents the influence relation among the events and can be applied to multi-factor multi-target complex system fault analysis.
Step 3: the weighted fuzzy Petri network model effectively utilizes the relevance and redundancy of the alarm time sequence information, screens and fully excavates the fault time sequence information by utilizing the time sequence constraint relation, can automatically screen error fault information and reassign the initial breaker confidence.
In the power grid fault diagnosis method based on the weighted fuzzy Petri network, a breaker represented by a library, a Token represented by the state of the library and a transition represented by an occurred event. According to the relay protection principle, when the high-voltage switch fails, the electric quantity changes along with the failure, the protection device carries out setting calculation on the detected electric quantity, if the detected electric quantity meets the action condition, an action command is sent out, the corresponding high-voltage circuit breaker trips immediately after receiving the action command, and the failure element is cut off. The occurrence of each event is distributed in a certain time range, and is mutually matched and constrained in time, so that a certain time sequence constraint relationship exists. Therefore, the fault diagnosis performance can be effectively improved by reasonably utilizing the fault alarm time sequence information.
Whether the time sequence constraint condition is met or not is judged through time sequence reasoning analysis, so that the error alarm information is screened, and fault tolerance of fault diagnosis can be effectively improved.
The timing information constraint checking step is shown in fig. 2, and includes:
the first step: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
and a second step of: for each suspected fault element, respectively dividing alarm information related to the element to form a set of different elements;
and a third step of: respectively carrying out reverse time sequence reasoning on each set by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
fourth step: merging the unitary time point constraints of fault occurrence obtained by reverse time sequence reasoning of each set to obtain the total constraints of the unitary time points of fault occurrence;
fifth step: and forward time sequence reasoning is carried out on the T to obtain unitary time point constraint of each library, and then the unitary time point constraint is compared with the time sequence information obtained in practice through an operator, so that false alarm information which does not meet the time sequence information can be screened.
Step 4: the fault tree module is utilized to analyze the mapping relation between the fault reasons and the alarm information of each part, namely, all the alarm information types possibly occurring corresponding to different fault reasons are obtained, and the main mapping relation is obtained after the fault tree module is removed;
step 5: and obtaining fault probability representation of the high-voltage switch breaker through iterative operation in a matrix form by a fuzzy reasoning mechanism.
The flow of reasoning for matrix form iteration in fault recognition of a fuzzy Petri net is shown in figure 3,
first, writing in initial matrix, calculating state value of initial library.
And secondly, calculating the reliability of the synthesized input of the transition, namely calculating the confidence of the equivalent fuzzy input.
And thirdly, comparing the synthesized input reliability with the transition threshold value.
And fourthly, eliminating equivalent fuzzy input items with confidence coefficient smaller than the transition threshold value, namely calculating the items with the confidence coefficient larger than the transition threshold value in the synthesized input confidence coefficient.
Fifth, the confidence of all libraries currently available is calculated.
And sixthly, replacing the original confidence coefficient of the library with the newly obtained confidence coefficient of the library, repeating the first step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficient of all libraries is not changed any more, and ending the reasoning operation.
Example 2
In a second aspect, based on embodiment 1, the present embodiment provides a breaker body fault diagnosis apparatus of an intelligent high-voltage switch, including:
a data acquisition module configured to: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
a fault classification module configured to: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
a timing constraint checking module configured to: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
the mapping relation acquisition module is configured to: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element, namely the alarm information types corresponding to different fault causes;
a fault result acquisition module configured to: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
In some embodiments, the timing constraint checking module is specifically configured to:
step 301: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
step 302: for each suspected fault element, respectively dividing alarm information related to the element to form an alarm information set of each element;
step 303: respectively carrying out reverse time sequence reasoning on the alarm information set of each element by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
step 304: merging all the unitary time point constraints of the faults to obtain a total unitary time point constraint T of the faults;
step 305: forward time sequence reasoning is carried out on the total unitary time point constraint T of the faults to obtain unitary time point constraints of all the circuit breakers;
step 306: and comparing the unitary time point constraint of each breaker with the actually obtained alarm time sequence information through an operator, and screening error alarm information which does not meet the time sequence information.
In some embodiments, the fault result obtaining module is specifically configured to:
firstly, calculating the confidence coefficient of an initial breaker based on a preset initial matrix;
secondly, calculating the confidence coefficient of the equivalent fuzzy input of the fault event;
thirdly, comparing the confidence level of the equivalent fuzzy input of the fault event with the magnitude of a preset threshold value;
fourthly, eliminating fault events with the confidence coefficient of the equivalent fuzzy input smaller than a preset threshold value, and calculating fault events with the confidence coefficient of the equivalent fuzzy input larger than the preset threshold value;
fifthly, calculating the confidence coefficient of all the currently available circuit breakers;
and sixthly, replacing the original breaker confidence coefficient with the newly obtained breaker confidence coefficient, repeating the second step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficients of all the breakers are not changed any more, and obtaining the fault probability representation of the high-voltage switch breaker.
Example 3
In a third aspect, based on embodiment 1, the present embodiment provides an apparatus, comprising,
a memory;
a processor;
and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of embodiment 1.
Example 4
In a fourth aspect, based on embodiment 1, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method described in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is only a preferred embodiment of the application, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the application.
Claims (10)
1. A breaker body fault diagnosis method of an intelligent high-voltage switch, the method comprising:
step 1: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
step 2: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
step 3: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
step 4: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element;
step 5: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
2. The method for diagnosing faults of the circuit breaker body of the intelligent high-voltage switch according to claim 1, wherein the step 2 comprises:
and carrying out preliminary analysis on the historical alarm information and the fault reasons by utilizing the FMEA model so as to determine the alarm information and the fault reasons corresponding to each part of fault elements.
3. The method for diagnosing a fault in a circuit interrupter body of an intelligent high voltage switch as recited in claim 1, wherein step 5 further comprises: and carrying out time sequence information constraint checking on the alarm information of the target fault event to remove the error fault information.
4. A breaker body fault diagnosis method for an intelligent high voltage switch according to claim 1 or 3, wherein the timing information constraint check comprises:
step 301: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
step 302: for each suspected fault element, respectively dividing alarm information related to the element to form an alarm information set of each element;
step 303: respectively carrying out reverse time sequence reasoning on the alarm information set of each element by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
step 304: merging all the unitary time point constraints of the faults to obtain a total unitary time point constraint T of the faults;
step 305: forward time sequence reasoning is carried out on the total unitary time point constraint T of the faults to obtain unitary time point constraints of all the circuit breakers;
step 306: and comparing the unitary time point constraint of each breaker with the actually obtained alarm time sequence information through an operator, and screening error alarm information which does not meet the time sequence information.
5. The method for diagnosing faults in a circuit breaker body of an intelligent high voltage switch according to claim 1, wherein the step 5 includes:
firstly, calculating the confidence coefficient of an initial breaker based on a preset initial matrix;
secondly, calculating the confidence coefficient of the equivalent fuzzy input of the fault event;
thirdly, comparing the confidence level of the equivalent fuzzy input of the fault event with the magnitude of a preset threshold value;
fourthly, eliminating fault events with the confidence coefficient of the equivalent fuzzy input smaller than a preset threshold value, and calculating fault events with the confidence coefficient of the equivalent fuzzy input larger than the preset threshold value;
fifthly, calculating the confidence coefficient of all the currently available circuit breakers;
and sixthly, replacing the original breaker confidence coefficient with the newly obtained breaker confidence coefficient, repeating the second step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficients of all the breakers are not changed any more, and obtaining the fault probability representation of the high-voltage switch breaker.
6. An intelligent high-voltage switch's circuit breaker body fault diagnosis device, characterized in that includes:
a data acquisition module configured to: acquiring historical fault data of a circuit breaker in a high-voltage switch, wherein the historical fault data comprises historical fault events, corresponding alarm information and fault reasons, and determining a suspected fault element set according to the historical fault data;
a fault classification module configured to: classifying historical alarm information and fault reasons based on the suspected fault element set, and determining the alarm information and the fault reasons corresponding to each fault element;
a timing constraint checking module configured to: performing time sequence information constraint checking on alarm time sequence information in the historical fault data to remove error fault information;
the mapping relation acquisition module is configured to: according to the historical fault data after the error fault information is removed, a fault tree module is utilized to analyze the mapping relation between the fault cause and the alarm information of each fault element;
a fault result acquisition module configured to: based on the mapping relation, the alarm information of the target fault event is subjected to iterative operation in a matrix form by a fuzzy reasoning mechanism to obtain the fault probability representation of the high-voltage switch breaker, and a fault diagnosis result is determined according to the fault probability representation of the high-voltage switch breaker.
7. The fault diagnosis device for the circuit breaker body of the intelligent high-voltage switch according to claim 6, wherein the time sequence constraint checking module is specifically configured to:
step 301: determining a power failure area, wherein elements in the power failure area are regarded as suspicious fault elements;
step 302: for each suspected fault element, respectively dividing alarm information related to the element to form an alarm information set of each element;
step 303: respectively carrying out reverse time sequence reasoning on the alarm information set of each element by utilizing the actually obtained alarm time sequence information to obtain unitary time point constraint of fault occurrence;
step 304: merging all the unitary time point constraints of the faults to obtain a total unitary time point constraint T of the faults;
step 305: forward time sequence reasoning is carried out on the total unitary time point constraint T of the faults to obtain unitary time point constraints of all the circuit breakers;
step 306: and comparing the unitary time point constraint of each breaker with the actually obtained alarm time sequence information through an operator, and screening error alarm information which does not meet the time sequence information.
8. The fault diagnosis device for the circuit breaker body of the intelligent high-voltage switch according to claim 6, wherein the fault result acquisition module is specifically configured to:
firstly, calculating the confidence coefficient of an initial breaker based on a preset initial matrix;
secondly, calculating the confidence coefficient of the equivalent fuzzy input of the fault event;
thirdly, comparing the confidence level of the equivalent fuzzy input of the fault event with the magnitude of a preset threshold value;
fourthly, eliminating fault events with the confidence coefficient of the equivalent fuzzy input smaller than a preset threshold value, and calculating fault events with the confidence coefficient of the equivalent fuzzy input larger than the preset threshold value;
fifthly, calculating the confidence coefficient of all the currently available circuit breakers;
and sixthly, replacing the original breaker confidence coefficient with the newly obtained breaker confidence coefficient, repeating the second step to the fifth step until the calculation result meets the iteration termination condition, namely, the confidence coefficients of all the breakers are not changed any more, and obtaining the fault probability representation of the high-voltage switch breaker.
9. An apparatus, comprising:
a memory;
a processor;
and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 5.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1 to 5.
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