CN115564410A - State monitoring method and device for relay protection equipment - Google Patents
State monitoring method and device for relay protection equipment Download PDFInfo
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Abstract
The application discloses a state monitoring method and device of relay protection equipment, which are characterized in that a state monitoring index of the relay protection equipment is generated based on operation data of the relay protection equipment, a preset association rule is utilized, a target weight coefficient of the state monitoring index is determined according to the confidence coefficient of the state monitoring index, and the index weight coefficient is calculated by introducing the confidence coefficient of the association rule, so that the importance degree between indexes is analyzed more objectively and the problem of subjectivity of artificial monitoring is avoided; the membership degree of the state monitoring index at each preset state grade is determined by utilizing a preset cloud model, so that the problem that the boundary division of the index grade interval is over-fixed in the traditional fuzzy comprehensive evaluation is solved; and finally, analyzing the running state grade of the relay protection equipment according to the target weight coefficient and the membership degree, thereby solving the problem of hysteresis existing in the periodic maintenance work and effectively improving the monitoring timeliness of the relay protection equipment and the monitoring precision of the state monitoring result.
Description
Technical Field
The application relates to the technical field of relay protection operation and maintenance, in particular to a state monitoring method and device of relay protection equipment.
Background
The relay protection equipment is used as an important component of the smart grid, and the health state and the operation reliability of the relay protection equipment play an important role in guaranteeing the safe and stable operation of the power system. The operation and maintenance mode of the relay protection equipment mainly focuses on periodic tests and post-fault diagnosis tests, and a reasonable and intelligent equipment operation and maintenance management means is lacked.
At present, data information such as equipment internal state information, operation environment information, professional inspection results, various test results and the like accumulated in a long-term operation and maintenance process of relay protection equipment cannot be effectively mined and utilized, the health state of the relay protection equipment is difficult to judge and predict in time according to historical operation information and real-time online information, the relay protection equipment can be discovered and processed after obvious faults of the relay protection equipment occur, and the overhaul work has hysteresis. Therefore, a real-time status monitoring method for relay protection equipment is needed to realize the transition from the periodic maintenance to the status maintenance of the maintenance mode of the relay protection equipment, so as to improve the lean operation and maintenance management level of the equipment.
Disclosure of Invention
The application provides a state monitoring method and device of relay protection equipment, and aims to solve the technical problem that the current maintenance work of the relay protection equipment has hysteresis.
In order to solve the above technical problem, in a first aspect, the present application provides a method for monitoring a state of a relay protection device, including:
generating a state monitoring index of the relay protection equipment based on the operation data of the relay protection equipment;
determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by using a preset association rule;
determining the membership degree of the state monitoring index at each preset state grade by using a preset cloud model;
and analyzing the operation state grade of the relay protection equipment according to the target weight coefficient and the membership degree.
In some implementations, generating the status monitoring indicator of the relay protection device based on the operation data of the relay protection device includes:
and classifying the operation data according to the equipment body, the secondary circuit and the channel condition to generate a state monitoring index of the relay protection equipment.
In some implementations, determining a target weight coefficient of the state monitoring indicator according to a confidence of the state monitoring indicator by using a preset association rule includes:
calculating the confidence coefficient of the index of the criterion layer according to the operation data by taking the operation data as the index of the index layer, taking the state monitoring index as the index of the criterion layer and taking the operation state grade as the index of the target layer;
according to the confidence coefficient, calculating a first weight coefficient of the criterion layer index relative to the target layer index and a second weight coefficient of the index layer index relative to the criterion layer index;
and calculating a third weight coefficient of the index layer index relative to the target layer index according to the first weight coefficient and the second weight coefficient, wherein the third weight coefficient is a target weight coefficient of the state monitoring index.
In some implementations, calculating a confidence level for the criteria-level indicator based on the operational data includes:
classifying the operational data into a plurality of sets of data items based on the indicator layer indicators;
analyzing the confidence coefficient of the index of the criterion layer according to a plurality of data item sets by using a preset association rule, wherein the preset association rule is as follows:
wherein the content of the first and second substances,
the degree of support is represented by a number of,
and (B) represents the confidence level, wherein theta represents the set of all data item sets, f (A U B) represents the number of times that the data item set A and the data item set B simultaneously appear in theta, and f (A) represents the number of times that the data item set A appears in theta.
In some implementations, determining the membership of the state monitoring indicator at each preset state level using a preset cloud model includes:
calculating cloud digital characteristics of the state monitoring indexes, wherein the cloud digital characteristics comprise expected values, entropy values and super-entropy values;
generating a state grade cloud model corresponding to each state grade based on the cloud digital characteristics;
on the basis of the cloud model, cloud droplet data when the state monitoring indexes are intersected with the cloud models of the state grades are counted;
and calculating the membership degree of the state monitoring index at each preset state grade according to the cloud drop data.
In some implementations, calculating the membership of the state monitoring indicator at each preset state level according to the cloud droplet data includes:
and calculating the membership degree of the state monitoring index at each preset state grade by using a preset membership function according to the cloud drop data, wherein the preset membership function is as follows:
wherein r is membership, m is cloud droplet data, y i Is the membership value corresponding to the cloud drop.
In some implementations, analyzing the operation state level of the relay protection device according to the weight coefficient and the membership degree includes:
forming a weight coefficient matrix by using the target weight coefficients of all the state monitoring indexes, and forming a membership degree matrix by using the membership degrees of all the state monitoring indexes;
and determining the operation state grade of the relay protection equipment according to the weight coefficient matrix and the membership matrix based on the maximum membership principle.
In a second aspect, the present application further provides a state monitoring device for a relay protection device, including:
the generating module is used for generating a state monitoring index of the relay protection equipment based on the operation data of the relay protection equipment;
the first determining module is used for determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by utilizing a preset association rule;
the second determining module is used for determining the membership degree of the state monitoring index at each preset state grade by using the preset cloud model;
and the analysis module is used for analyzing the operation state grade of the relay protection equipment according to the target weight coefficient and the membership degree.
In a third aspect, the present application further provides a computer device, including a processor and a memory, where the memory is used to store a computer program, and when the computer program is executed by the processor, the method for monitoring the state of the relay protection device according to the first aspect is implemented.
In a fourth aspect, the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for monitoring the state of the relay protection device according to the first aspect is implemented.
Compared with the prior art, the application at least has the following beneficial effects:
the method comprises the steps of generating a state monitoring index of the relay protection equipment based on operation data of the relay protection equipment, determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by using a preset association rule, and calculating the index weight coefficient by introducing the confidence coefficient of the association rule, so that the importance degree between indexes is more objectively and quantitatively analyzed, and the problem of subjectivity of artificial monitoring is avoided; the membership degree of the state monitoring index at each preset state grade is determined by utilizing a preset cloud model, so that the problem that the boundary division of the index grade interval is over-fixed in the traditional fuzzy comprehensive evaluation is solved; and finally, analyzing the running state grade of the relay protection equipment according to the target weight coefficient and the membership degree, thereby realizing real-time monitoring, solving the problem of hysteresis existing in the periodic maintenance work, and effectively improving the monitoring timeliness of the relay protection equipment and the monitoring precision of the state monitoring result.
Drawings
Fig. 1 is a schematic flowchart of a state monitoring method for a relay protection device according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a state monitoring method for a relay protection device according to another embodiment of the present application;
FIG. 3 is a schematic diagram of a condition monitoring indicator system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a state monitoring device of a relay protection device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for monitoring a state of a relay protection device according to an embodiment of the present application. The state monitoring method of the relay protection device in the embodiment of the application can be applied to computer devices, and the computer devices include but are not limited to smart phones, notebook computers, tablet computers, desktop computers, physical servers, cloud servers and other devices. As shown in fig. 1, the method for monitoring the state of the relay protection device in this embodiment includes steps S101 to S104, which are detailed as follows:
and S101, generating a state monitoring index of the relay protection equipment based on the operation data of the relay protection equipment.
In this step, the operation data includes body data, secondary loop data, and channel condition data of the relay protection device, which is specifically shown in fig. 3.
In some embodiments, the step S101 includes: and classifying the operation data according to the conditions of the equipment body, the secondary circuit and the channel to generate a state monitoring index of the relay protection equipment.
In this embodiment, in order to make the selected index reflect the actual operation state of the relay protection device as much as possible, a three-layer state monitoring index system of the relay protection device shown in fig. 3 is constructed according to the scientific, comprehensive and feasible index screening principle, and sequentially includes a target layer, a criterion layer and an index layer. The criterion layer classifies the indexes from the three aspects of the equipment body, the secondary loop and the channel condition; the index layer is composed of test and real-time monitoring symptom state index items which are respectively associated with the indexes of the upper layer.
And step S102, determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by using a preset association rule.
In the step, the confidence coefficient of the association rule is introduced to calculate the weight of the monitoring indexes, and the relative importance degree between the monitoring indexes is objectively quantified. The association rule is an important means for knowledge discovery in the field of data mining and is used for describing association relations among different item sets in the transaction database.
In some embodiments, the step S102 includes:
calculating the confidence of the criterion layer index according to the operation data by taking the operation data as an index layer index, taking the state monitoring index as a criterion layer index and taking the operation state grade as a target layer index;
according to the confidence coefficient, calculating a first weight coefficient of the criterion layer index relative to the target layer index and a second weight coefficient of the index layer index relative to the criterion layer index;
and calculating a third weight coefficient of the index layer index relative to the target layer index according to the first weight coefficient and the second weight coefficient, wherein the third weight coefficient is a target weight coefficient of the state monitoring index.
In this embodiment, (1) confidence degrees c corresponding to three indexes of the device body, the secondary loop and the channel condition in the criterion layer are calculated 1 ，c 2 ，c 3 . According to the formula
Normalizing the index confidence coefficient to obtain a weight coefficient w of the index of the criterion layer relative to the target layer 1 ，w 2 ，w 3 ；
(2) Respectively calculating confidence degrees of the subordinate indexes of the indexes in the criterion layer, and obtaining a weight coefficient of the indexes in the index layer relative to the criterion layer after normalization:
(3) calculating according to the following formula to obtain the weight coefficient of the index layer relative to the target layer
In the formula, w i Weight coefficient, w, representing the ith index of the criterion layer ij And the weight coefficient of the j index under the i index of the criterion layer is represented.
In some embodiments, said calculating a confidence level of said criteria-level indicator based on said operational data comprises:
classifying the operational data into a plurality of sets of data items based on the index layer indicators;
analyzing the confidence of the criterion layer index according to a plurality of data item sets by using the preset association rule, wherein the preset association rule is as follows:
wherein the content of the first and second substances,
the degree of support is represented by a number of,
and (B) represents the confidence level, wherein theta represents the set of all data item sets, f (A U B) represents the number of times that the data item set A and the data item set B simultaneously appear in theta, and f (A) represents the number of times that the data item set A appears in theta.
In this embodiment, in order to ensure the validity and credibility of the mined association rule, the support degree and the confidence degree are selected as the measure indexes of the rule. The support degree is used for representing the importance degree of the association rule in the current transaction database, and the higher the support degree is, the higher the association degree between the two item sets is. The confidence level is used to characterize the confidence level of the association rule, i.e. the higher the confidence level, the higher the confidence level of the association rule.
And S103, determining the membership degree of the state monitoring index at each preset state grade by using a preset cloud model.
In this step, the cloud model is a model which can reflect randomness (occurrence probability) and ambiguity (the fact of the same or the same of the boundary) of objective things and can realize unified quantitative description of the determined and uncertain relation between monitoring indexes and monitoring grades. Cloud model pass expectation E x Entropy E n Entropy of H e The three independent parameters jointly express the numerical characteristics of a qualitative concept, thereby reflecting the uncertainty of the concept.
Further, the risk level of the running state of the relay protection equipment and the corresponding comment set are represented as follows: v = { V) 1 ,v 2 ,v 3 ,v 4 } = { normal, note, abnormal, severe }.
In some embodiments, the step S103 includes:
calculating cloud digital characteristics of the state monitoring indexes, wherein the cloud digital characteristics comprise expected values, entropy values and super-entropy values;
generating a state grade cloud model corresponding to each state grade based on the cloud digital features;
on the basis of the cloud model, cloud drop data when the state monitoring indexes are intersected with the state grade cloud models are counted;
and calculating the membership degree of the state monitoring index at each preset state grade according to the cloud droplet data.
In the present embodiment, (1) three cloud digital features C (Ex, en, he) of the monitoring index are determined;
(2) according to the formula
Is generated with E n To expect, H e Normally distributed random number of standard deviation
(3) According to the formula
Is generated with E n In the event that it is desired to do so,
a normally distributed random number x which is a standard deviation;
(4) computing
Obtaining a cloud drop (x, y);
(5) and (4) repeating the steps (2) to (4) until a specified number of cloud droplets are generated, and forming the index state monitoring grade cloud model.
In some embodiments, the calculating, according to the cloud droplet data, a membership of the state monitoring indicator at each preset state level includes:
and calculating the membership degree of the state monitoring index at each preset state grade according to the cloud drop data by using a preset membership degree function.
In this embodiment, the number of cloud droplets at the intersection of the actual observed value of the index and the corresponding cloud model of each state class is counted, and if the number of cloud droplets intersecting the cloud model of the normal state class is m, the membership value corresponding to the cloud droplets is y i (i =1,2,. M), the membership value of the index to the normal state is:
by analogy, the indexes respectively belonging to fourMembership degree of each state grade r = [ r ] 1 ,r 2 ,r 3 ,r 4 ]。
And step S104, analyzing the operation state grade of the relay protection equipment according to the target weight coefficient and the membership degree.
In the step, the membership degree of the monitoring index is determined based on the cloud model, and the comprehensive research and judgment result of the running state of the relay protection equipment is obtained according to the maximum membership degree principle.
In some embodiments, the step S104 includes:
forming a weight coefficient matrix by the target weight coefficients of all the state monitoring indexes, and forming a membership degree matrix by the membership degrees of all the state monitoring indexes;
and determining the operation state grade of the relay protection equipment according to the weight coefficient matrix and the membership matrix based on a maximum membership principle.
In this embodiment, the index weight coefficient matrix is
And multiplying the index state evaluation matrix R to obtain a comprehensive monitoring result vector P of the relay protection equipment.
P＝W·R＝[p 1 ,p 2 ,p 3 ,p 4 ]；
Taking max { p according to the maximum membership rule 1 ,p 2 ,p 3 ,p 4 And the state grade corresponding to the relay protection device is the current state monitoring result of the relay protection device.
As an example and not by way of limitation, as shown in fig. 2, the present embodiment follows the scientific, comprehensive, and feasibility index screening principle, and constructs a monitoring index system from three aspects of an equipment body, a secondary circuit, and a channel condition; calculating the weight of the monitoring indexes by introducing the confidence coefficient of the association rule, and objectively quantifying the relative importance degree between the monitoring indexes; and determining the membership degree of the monitoring index based on the cloud model, and obtaining the comprehensive research and judgment result of the running state of the relay protection equipment according to the maximum membership degree principle.
In this embodiment, in order to enable the selected index to reflect the actual operation state of the relay protection device as much as possible, a three-layer relay protection device state monitoring index system shown in fig. 2 is constructed according to the scientific, comprehensive and feasible index screening principle, and the target layer, the criterion layer and the index layer are sequentially arranged from left to right. The criterion layer classifies the indexes from the three aspects of the equipment body, the secondary loop and the channel condition; the index layer is composed of test and real-time monitoring symptom state index items which are respectively associated with the indexes of the upper layer.
Further, in this embodiment, the monitoring index data of the relay protection device is mined and analyzed based on the association rule, and the confidence of each layer of index and the weight coefficient obtained after normalization are obtained through calculation are shown in the following table.
Further, by formula
Calculating a weight coefficient matrix W of each index of the obtained index layer relative to the target layer:
W＝[0.041,0.043,0.057,0.065,0.061,0.083,0.049,0.017,0.079,0.12,0.066,0.081,0.052,0.052,0.062,0.072]；
further, the risk level of the running state of the relay protection device and the corresponding comment set are represented as follows: v = { V = 1 ,v 2 ,v 3 ,v 4 } = { normal, note, abnormal, severe }. The membership value of the single index belonging to each state grade interval is obtained according to the steps, and is shown in the following table.
Further, the state evaluation matrix R of the relay protection device can be obtained according to the membership value of each single index in table 2.
Further, in this embodiment, the weight coefficient is calculated according to the obtained monitoring indexA matrix W and a state evaluation matrix R, represented by the formula P = W · R = [ P = 1 ,p 2 ，p 3 ,p 4 ]And calculating to obtain the state grade membership of the relay protection equipment, which is shown in the following table.
Further, in this embodiment, the current operating status of the relay protection device is ranked as follows: p (normal) > p (attention) > p (abnormal) > p (serious), according to the maximum membership principle, the current overall running state of the device can be judged to be normal, the device conforms to the actual situation, and the device does not need to be overhauled.
The state monitoring method of the relay protection device corresponding to the embodiment of the method is implemented to achieve corresponding functions and technical effects. Referring to fig. 4, fig. 4 is a block diagram illustrating a structure of a state monitoring device of a relay protection device according to an embodiment of the present application. For convenience of explanation, only the parts related to the present embodiment are shown, and the state monitoring device for a relay protection device provided in the embodiment of the present application includes:
the generating module 401 is configured to generate a state monitoring index of the relay protection device based on operation data of the relay protection device;
a first determining module 402, configured to determine, according to a confidence of the state monitoring indicator, a target weight coefficient of the state monitoring indicator by using a preset association rule;
a second determining module 403, configured to determine, by using a preset cloud model, a membership degree of the state monitoring index at each preset state level;
and the analysis module 404 is configured to analyze the operation state grade of the relay protection device according to the target weight coefficient and the membership degree.
In some embodiments, the generating module 401 is specifically configured to:
and classifying the operation data according to the conditions of the equipment body, the secondary circuit and the channel to generate a state monitoring index of the relay protection equipment.
In some embodiments, the first determining module 402 is specifically configured to:
calculating the confidence of the criterion layer index according to the operation data by taking the operation data as an index layer index, taking the state monitoring index as a criterion layer index and taking the operation state grade as a target layer index;
according to the confidence coefficient, calculating a first weight coefficient of the criterion layer index relative to the target layer index and a second weight coefficient of the index layer index relative to the criterion layer index;
and calculating a third weight coefficient of the index layer index relative to the target layer index according to the first weight coefficient and the second weight coefficient, wherein the third weight coefficient is a target weight coefficient of the state monitoring index.
In some embodiments, the first determining module 402 is further configured to:
classifying the operational data into a plurality of sets of data items based on the index layer indicators;
analyzing the confidence coefficient of the criterion layer index according to a plurality of data item sets by using the preset association rule, wherein the preset association rule is as follows:
wherein the content of the first and second substances,
the degree of support is represented by a number of,
and (B) represents the confidence level, wherein theta represents the set of all data item sets, f (A U B) represents the number of times that the data item set A and the data item set B simultaneously appear in theta, and f (A) represents the number of times that the data item set A appears in theta.
In some embodiments, the second determining module 403 is specifically configured to:
calculating cloud digital characteristics of the state monitoring index, wherein the cloud digital characteristics comprise an expected value, an entropy value and a super-entropy value;
generating a state grade cloud model corresponding to each state grade based on the cloud digital features;
on the basis of the cloud model, cloud drop data when the state monitoring indexes are intersected with the state grade cloud models are counted;
and calculating the membership degree of the state monitoring index at each preset state grade according to the cloud droplet data.
In some embodiments, the second determining module 403 is further specifically configured to:
calculating the membership degree of the state monitoring index at each preset state grade by using a preset membership function according to the cloud drop data, wherein the preset membership function is as follows:
wherein r is membership degree, m is cloud droplet data, y i And is the membership value corresponding to the cloud drop.
In some embodiments, the analysis module 404 is specifically configured to:
forming a weight coefficient matrix by the target weight coefficients of all the state monitoring indexes, and forming a membership degree matrix by the membership degrees of all the state monitoring indexes;
and determining the operation state grade of the relay protection equipment according to the weight coefficient matrix and the membership matrix based on a maximum membership principle.
The state monitoring device of the relay protection equipment can implement the state monitoring method of the relay protection equipment in the embodiment of the method. The alternatives in the above-described method embodiments are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the contents of the above method embodiments, and in this embodiment, details are not described again.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 5, the computer device 5 of this embodiment includes: at least one processor 50 (only one shown in fig. 5), a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, the processor 50 implementing the steps of any of the method embodiments described above when executing the computer program 52.
The computer device 5 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server. The computer device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of the computer device 5 and does not constitute a limitation of the computer device 5, and may include more or less components than those shown, or combine some of the components, or different components, such as input output devices, network access devices, etc.
The Processor 50 may be a Central Processing Unit (CPU), and the Processor 50 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may in some embodiments be an internal storage unit of the computer device 5, such as a hard disk or a memory of the computer device 5. The memory 51 may also be an external storage device of the computer device 5 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the computer device 5. The memory 51 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 51 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
The embodiments of the present application provide a computer program product, which when executed on a computer device, enables the computer device to implement the steps in the above method embodiments.
In several embodiments provided herein, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, or portions thereof, which substantially or partially contribute to the prior art, may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present application in detail, and it should be understood that the above-mentioned embodiments are only examples of the present application and are not intended to limit the scope of the present application. It should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the application as defined by the appended claims.
Claims (10)
1. A state monitoring method of relay protection equipment is characterized by comprising the following steps:
generating a state monitoring index of the relay protection equipment based on the operation data of the relay protection equipment;
determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by using a preset association rule;
determining the membership degree of the state monitoring index at each preset state grade by using a preset cloud model;
and analyzing the running state grade of the relay protection equipment according to the target weight coefficient and the membership degree.
2. The method for monitoring the state of a relay protection device according to claim 1, wherein the generating a state monitoring indicator of the relay protection device based on the operation data of the relay protection device comprises:
and classifying the operation data according to the conditions of the equipment body, the secondary circuit and the channel to generate a state monitoring index of the relay protection equipment.
3. The method for monitoring the state of the relay protection device according to claim 1, wherein the determining the target weight coefficient of the state monitoring indicator according to the confidence of the state monitoring indicator by using a preset association rule comprises:
calculating the confidence of the criterion layer index according to the operation data by taking the operation data as an index layer index, taking the state monitoring index as a criterion layer index and taking the operation state grade as a target layer index;
according to the confidence coefficient, calculating a first weight coefficient of the criterion layer index relative to the target layer index and a second weight coefficient of the index layer index relative to the criterion layer index;
and calculating a third weight coefficient of the index layer index relative to the target layer index according to the first weight coefficient and the second weight coefficient, wherein the third weight coefficient is a target weight coefficient of the state monitoring index.
4. The method for monitoring the state of the relay protection device according to claim 3, wherein the calculating the confidence level of the criterion layer indicator according to the operation data comprises:
classifying the operational data into a plurality of sets of data items based on the indicator layer indicators;
analyzing the confidence coefficient of the criterion layer index according to a plurality of data item sets by using the preset association rule, wherein the preset association rule is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the degree of support is expressed in terms of,
and (B) represents the confidence level, wherein theta represents the set of all data item sets, f (A @ B) represents the number of times that the data item set A and the data item set B simultaneously appear in theta, and f (A) represents the number of times that the data item set A appears in theta.
5. The method for monitoring the state of the relay protection device according to claim 1, wherein the determining the membership degree of the state monitoring index at each preset state level by using a preset cloud model comprises:
calculating cloud digital characteristics of the state monitoring indexes, wherein the cloud digital characteristics comprise expected values, entropy values and super-entropy values;
generating a state grade cloud model corresponding to each state grade based on the cloud digital features;
on the basis of the cloud model, cloud drop data when the state monitoring indexes are intersected with the state grade cloud models are counted;
and calculating the membership degree of the state monitoring index at each preset state grade according to the cloud droplet data.
6. The method for monitoring the state of a relay protection device according to claim 5, wherein said calculating the membership of the state monitoring indicator at each preset state level according to the cloud droplet data comprises:
calculating the membership degree of the state monitoring index at each preset state grade according to the cloud drop data by using a preset membership function, wherein the preset membership function is as follows:
wherein r is membership degree, m is cloud droplet data, y i And is the membership value corresponding to the cloud drop.
7. The method for monitoring the state of the relay protection device according to claim 1, wherein the analyzing the operation state grade of the relay protection device according to the weight coefficient and the membership degree comprises:
forming a weight coefficient matrix by using the target weight coefficients of all the state monitoring indexes, and forming a membership degree matrix by using the membership degrees of all the state monitoring indexes;
and determining the operation state grade of the relay protection equipment according to the weight coefficient matrix and the membership matrix based on a maximum membership principle.
8. A state monitoring device of relay protection equipment is characterized by comprising:
the generating module is used for generating a state monitoring index of the relay protection equipment based on the operation data of the relay protection equipment;
the first determining module is used for determining a target weight coefficient of the state monitoring index according to the confidence coefficient of the state monitoring index by utilizing a preset association rule;
the second determining module is used for determining the membership degree of the state monitoring index at each preset state grade by using a preset cloud model;
and the analysis module is used for analyzing the operation state grade of the relay protection equipment according to the target weight coefficient and the membership degree.
9. A computer device, characterized by comprising a processor and a memory for storing a computer program, which when executed by the processor implements the method for monitoring the status of a relay protection device according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program, which when executed by a processor, implements a state monitoring method of a relay protection device according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116760851A (en) * | 2023-08-18 | 2023-09-15 | 绛县祥信盈风力发电有限公司 | Cloud storage method for relay protection fixed value list |
| CN116760851B (en) * | 2023-08-18 | 2023-10-17 | 绛县祥信盈风力发电有限公司 | Cloud storage method for relay protection fixed value list |
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