CN114866402A - System data dynamic comparison method - Google Patents
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Abstract
The invention relates to power communication system fault diagnosis, in particular to a system data dynamic comparison method, which comprises the steps of obtaining running state data in a power communication system and presetting fault state data; calculating a weight value of the influence of the preset fault state data on fault diagnosis, and calculating the similarity between the operation state data and the preset fault state data with the maximum weight value; coding all system fault alarm transactions into a fault state matrix, and performing model training on a system fault recognition model by using the fault state matrix; acquiring a current fault state matrix of the system, and inputting a trained system fault recognition model; synthesizing a similarity calculation result and a system fault recognition model output result to determine the current fault of the power communication system; the technical scheme provided by the invention can effectively overcome the defect that the system fault of the power communication system can not be diagnosed quickly and accurately through the dynamic comparison of system data in the prior art.
Description
Technical Field
The invention relates to power communication system fault diagnosis, in particular to a system data dynamic comparison method.
Background
The power communication system is an important component of the smart power grid and is an important basis for ensuring normal operation, management and power supply of the power grid. The power communication system is used as an important support network of the power system, and with the rapid development of extra-high voltage, smart grid and the like, higher requirements are put forward for the reliability and stability of the power communication system in order to ensure the safe operation of power production business, and the power communication system is subjected to fault diagnosis in time and is the key content of power communication scheduling work.
At present, the traditional method for diagnosing the fault of the power communication system mainly depends on the experience and subjective judgment of dispatching personnel. When system faults with complex technology and complex hierarchical structure are encountered, the problems of low diagnosis accuracy, poor real-time performance and the like can occur when fault diagnosis is carried out by depending on experience and subjective judgment of scheduling personnel.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a system data dynamic comparison method, which can effectively overcome the defect that the system fault of the power communication system cannot be diagnosed quickly and accurately through the system data dynamic comparison in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a system data dynamic comparison method comprises the following steps:
s1, acquiring operation state data in the power communication system and presetting fault state data;
s2, calculating a weight of the influence of the preset fault state data on fault diagnosis, and calculating the similarity between the operation state data and the preset fault state data with the maximum weight;
s3, coding all system fault alarm transactions into a fault state matrix, and performing model training on a system fault recognition model by using the fault state matrix;
s4, acquiring a current fault state matrix of the system, and inputting a trained system fault recognition model;
and S5, synthesizing the similarity calculation result and outputting the result by the system fault recognition model to determine the current fault of the power communication system.
Preferably, the calculating of the weight of the influence of the preset fault state data on the fault diagnosis in S2 includes:
and performing correlation analysis on all fault state data to obtain the weight of the influence of each fault state data on fault diagnosis.
Preferably, the performing correlation analysis on all fault state data to obtain a weight of the fault diagnosis affected by each fault state data includes:
determining the occurrence frequency of all fault state data, establishing a frequently occurring set of the fault state data according to the occurrence frequency, and determining the weight of the influence of each fault state data in the frequently occurring set on fault diagnosis.
Preferably, the determining a weight of the influence of each fault state data in the frequently occurring set on the fault diagnosis includes:
calculating the weight of the influence of the frequently concentrated fault state data on fault diagnosis by adopting the following formula:
wherein, w k Weight, m, representing fault status data k k Denotes w b Number not equal to 0, b denotes fault type, w b And representing the weight value when the fault state data f is the b-th fault type.
Preferably, the calculating the similarity between the operation state data and the preset fault state data with the maximum weight in S2 includes:
extracting a first feature vector of the running state data, extracting a second feature vector of the preset fault state data with the maximum weight, and calculating the similarity between the first feature vector and the second feature vector by adopting the following formula:
wherein S is similarity, N is fault feature number, x is an indicator variable between the first feature vector and the second feature vector, D is Euclidean distance between the first feature vector and the second feature vector, and D is Euclidean distance threshold.
Preferably, in S3, the coding of all system fault alarm transactions into a fault state matrix includes:
expressing the topological relation among the power communication sites according to the adjacency matrix of the graph theory to obtain the adjacency matrix among the power communication sites;
and acquiring alarm transaction codes corresponding to all system fault alarm transactions by taking the sites as a unit, and acquiring fault state matrixes by combining the adjacency matrixes and the alarm transaction codes among the power communication sites.
Preferably, the expressing the topological relation among the power communication sites by the adjacency matrix according to the graph theory to obtain the adjacency matrix among the power communication sites includes:
if G is a graph with n sites, v (G) is a site set of G, e (G) is an edge set of G, and a (G) is an adjacency matrix of G, the adjacency matrix a (G) is (a) ij ) n*n Wherein a is ij The following were used:
preferably, the obtaining of the alarm transaction code corresponding to all system fault alarm transactions by taking the site as a unit and the obtaining of the fault state matrix by combining the adjacency matrix and the alarm transaction code between the power communication sites includes:
the alarm transaction codes corresponding to all system fault alarm transactions in the n sites are represented by the following formula:
Q(G)＝diag{q 11 ,q 22 ,…,q nn }
wherein Q (G) encodes an alarm transaction, q nn Coding the alarm affairs of each site;
the fault state matrix r (g) ═ a (g) q (g) represents the relationship between the fault occurring at the power communication site and the site topology relationship.
Preferably, in S3, performing model training on the system fault identification model by using the fault state matrix, including:
marking identification labels correspondingly comprising main fault types and fault sites of the system on each fault state matrix, and forming a training set together with the corresponding fault state matrix;
inputting the training set into a system fault recognition model, learning the relation between the system fault type and the site topological relation and the alarm transaction code, comparing the output result with the recognition label, adjusting the network parameters of the system fault recognition model by using a BP algorithm, and finishing the model training until the system fault recognition model reaches the set accuracy.
(III) advantageous effects
Compared with the prior art, the system data dynamic comparison method provided by the invention has the following beneficial effects:
1) the method comprises the steps of obtaining operation state data and preset fault state data in the power communication system, calculating a weight of the preset fault state data on fault diagnosis, and calculating the similarity between the operation state data and the preset fault state data with the maximum weight, so that system faults of the power communication system can be quickly and accurately diagnosed through dynamic comparison of the system data;
2) all system fault warning affairs are coded into a fault state matrix, the fault state matrix is used for conducting model training on a system fault recognition model, a current fault state matrix of the system is obtained, the trained system fault recognition model is input, the system fault recognition model is used for effectively recognizing system faults through the topological relation of power communication sites and warning affair codes, and the limitation that system faults are diagnosed through dynamic comparison of system data is overcome.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic flow chart illustrating a process of calculating similarity between operating state data and preset fault state data with a maximum weight value according to the present invention;
FIG. 3 is a flow chart illustrating the process of encoding all system fault alarm transactions into a fault state matrix according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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 invention.
A system data dynamic comparison method includes that firstly, as shown in figure 1, operation state data in an electric power communication system and preset fault state data are obtained.
Secondly, as shown in fig. 2, a weight value of the preset fault state data influencing the fault diagnosis is calculated, and the similarity between the operation state data and the preset fault state data with the maximum weight value is calculated.
1) Calculating the weight of the influence of the preset fault state data on the fault diagnosis, which comprises the following steps:
and performing correlation analysis on all fault state data to obtain the weight of the influence of each fault state data on fault diagnosis.
Wherein, carry out the correlation analysis to all fault state data, obtain the weight that each fault state data influences fault diagnosis, include:
determining the occurrence frequency of all fault state data, establishing a frequently occurring set of the fault state data according to the occurrence frequency, and determining the weight of the influence of each fault state data in the frequently occurring set on fault diagnosis.
Wherein, determining the weight of the influence of each fault state data in the frequently occurring set on the fault diagnosis comprises:
calculating the weight of the influence of the frequently concentrated fault state data on fault diagnosis by adopting the following formula:
wherein, w k Weight, m, representing fault status data k k Denotes w b Number not equal to 0, b denotes fault type, w b And representing the weight value when the fault state data f is the b-th fault type.
2) Calculating the similarity between the operation state data and the maximum preset fault state data of the weight, wherein the similarity comprises the following steps:
extracting a first feature vector of the running state data, extracting a second feature vector of the preset fault state data with the maximum weight, and calculating the similarity between the first feature vector and the second feature vector by adopting the following formula:
wherein S is similarity, N is fault feature number, x is an indicator variable between the first feature vector and the second feature vector, D is Euclidean distance between the first feature vector and the second feature vector, and D is Euclidean distance threshold.
According to the technical scheme, the operation state data and the preset fault state data in the power communication system are obtained, the weight of the preset fault state data on fault diagnosis is calculated, and the similarity between the operation state data and the preset fault state data with the maximum weight is calculated, so that the system fault of the power communication system can be quickly and accurately diagnosed through dynamic comparison of the system data.
Thirdly, as shown in fig. 1 and fig. 3, all system fault alarm transactions are coded into a fault state matrix, and the fault state matrix is used for carrying out model training on a system fault recognition model.
1) Encoding all system fault alarm transactions into a fault state matrix, comprising:
expressing the topological relation among the power communication sites according to the adjacency matrix of the graph theory to obtain the adjacency matrix among the power communication sites;
and acquiring alarm transaction codes corresponding to all system fault alarm transactions by taking the sites as a unit, and acquiring fault state matrixes by combining the adjacency matrixes and the alarm transaction codes among the power communication sites.
The method for obtaining the adjacency matrix between the power communication sites by expressing the topological relation between the power communication sites according to the adjacency matrix of the graph theory comprises the following steps:
if G is a graph with n sites, v (G) is a site set of G, e (G) is an edge set of G, and a (G) is an adjacency matrix of G, the adjacency matrix a (G) is (a) ij ) n*n Wherein a is ij The following were used:
the method comprises the following steps of obtaining alarm transaction codes corresponding to all system fault alarm transactions by taking sites as a unit, and obtaining a fault state matrix by combining an adjacent matrix and the alarm transaction codes among power communication sites, wherein the method comprises the following steps:
the alarm transaction codes corresponding to all system fault alarm transactions in the n sites are represented by the following formula:
Q(G)＝diag{q 11 ,q 22 ,…,q nn }
wherein Q (G) encodes an alarm transaction, q nn Coding the alarm affairs of each site;
the fault state matrix r (g) ═ a (g) q (g) represents the relationship between the fault occurring at the power communication site and the site topology relationship.
2) Performing model training on the system fault recognition model by using the fault state matrix, wherein the model training comprises the following steps:
marking identification labels correspondingly comprising main fault types and fault sites of the system on each fault state matrix, and forming a training set together with the corresponding fault state matrix;
inputting the training set into a system fault recognition model, learning the relation between the system fault type and the site topological relation and the alarm transaction code, comparing the output result with the recognition label, adjusting the network parameters of the system fault recognition model by using a BP algorithm, and finishing the model training until the system fault recognition model reaches the set accuracy.
And fourthly, as shown in the figure 1, acquiring a current fault state matrix of the system (which can be realized through the step three), and inputting the trained system fault recognition model.
And fifthly, as shown in figure 1, integrating the similarity calculation result and the system fault identification model output result to determine the current fault of the power communication system.
According to the technical scheme, all system fault warning affairs are coded into a fault state matrix, the fault state matrix is used for conducting model training on a system fault recognition model, the current fault state matrix of the system is obtained, the trained system fault recognition model is input, the system fault recognition model can effectively recognize system faults through the topological relation of power communication sites and the warning affair codes, and the limitation that system faults are diagnosed only through dynamic comparison of system data is overcome.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A system data dynamic comparison method is characterized in that: the method comprises the following steps:
s1, acquiring operation state data in the power communication system and presetting fault state data;
s2, calculating a weight of the influence of the preset fault state data on fault diagnosis, and calculating the similarity between the operation state data and the preset fault state data with the maximum weight;
s3, coding all system fault alarm transactions into a fault state matrix, and performing model training on a system fault recognition model by using the fault state matrix;
s4, acquiring a current fault state matrix of the system, and inputting a trained system fault recognition model;
and S5, synthesizing the similarity calculation result and outputting the result by the system fault recognition model to determine the current fault of the power communication system.
2. The system data dynamic comparison method according to claim 1, wherein: in S2, calculating a weight of the influence of the preset fault state data on the fault diagnosis includes:
and performing correlation analysis on all fault state data to obtain the weight of the influence of each fault state data on fault diagnosis.
3. The system data dynamic comparison method according to claim 2, wherein: the performing correlation analysis on all fault state data to obtain a weight of the influence of each fault state data on fault diagnosis includes:
determining the occurrence frequency of all fault state data, establishing a frequently occurring set of the fault state data according to the occurrence frequency, and determining the weight of the influence of each fault state data in the frequently occurring set on fault diagnosis.
4. The system data dynamic comparison method according to claim 3, wherein: the determining the weight of the influence of the frequently occurring concentrated fault state data on the fault diagnosis comprises the following steps:
calculating the weight of the influence of the frequently concentrated fault state data on fault diagnosis by adopting the following formula:
wherein, w k Weight, m, representing fault status data k k Denotes w b Number not equal to 0, b denotes fault type, w b And representing the weight value when the fault state data f is the b-th fault type.
5. The system data dynamic comparison method according to claim 4, wherein: in S2, calculating the similarity between the operating state data and the preset fault state data with the maximum weight value includes:
extracting a first feature vector of the running state data, extracting a second feature vector of the preset fault state data with the maximum weight, and calculating the similarity between the first feature vector and the second feature vector by adopting the following formula:
wherein S is similarity, N is fault feature number, x is an indicator variable between the first feature vector and the second feature vector, D is Euclidean distance between the first feature vector and the second feature vector, and D is Euclidean distance threshold.
6. The system data dynamic comparison method according to claim 1, wherein: in S3, encoding all system fault alarm transactions into a fault state matrix, including:
expressing the topological relation among the power communication sites according to the adjacency matrix of the graph theory to obtain the adjacency matrix among the power communication sites;
and acquiring alarm transaction codes corresponding to all system fault alarm transactions by taking the sites as a unit, and acquiring fault state matrixes by combining the adjacency matrixes and the alarm transaction codes among the power communication sites.
7. The system data dynamic comparison method according to claim 6, wherein: the method for obtaining the adjacency matrix between the power communication stations by expressing the topological relation between the power communication stations according to the adjacency matrix of the graph theory comprises the following steps:
if G is a graph with n sites, v (G) is a site set of G, e (G) is an edge set of G, and a (G) is an adjacency matrix of G, the adjacency matrix a (G) is (a) ij ) n*n Wherein a is ij The following were used:
8. the system data dynamic comparison method according to claim 7, wherein: the method for obtaining the alarm transaction codes corresponding to all system fault alarm transactions by taking the sites as a unit and obtaining the fault state matrix by combining the adjacency matrix and the alarm transaction codes among the power communication sites comprises the following steps:
the alarm transaction codes corresponding to all system fault alarm transactions in the n sites are represented by the following formula:
Q(G)＝diag{q 11 ,q 22 ,…,q nn }
wherein Q (G) encodes an alarm transaction, q nn Coding the alarm affairs of each site;
the fault state matrix r (g) ═ a (g) q (g) represents the relationship between the fault occurring at the power communication site and the site topology relationship.
9. The system data dynamic comparison method according to claim 8, wherein: and S3, performing model training on the system fault recognition model by using the fault state matrix, wherein the model training comprises the following steps:
marking identification labels correspondingly comprising main fault types and fault sites of the system on each fault state matrix, and forming a training set together with the corresponding fault state matrix;
inputting the training set into a system fault recognition model, learning the relation between the system fault type and the site topological relation and the alarm transaction code, comparing the output result with the recognition label, adjusting the network parameters of the system fault recognition model by using a BP algorithm, and finishing the model training until the system fault recognition model reaches the set accuracy.
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| CN114418226A (en) * | 2022-01-21 | 2022-04-29 | 广东电网有限责任公司 | Fault analysis method and device of power communication system |
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| WO2015158198A1 (en) * | 2014-04-17 | 2015-10-22 | 北京泰乐德信息技术有限公司 | Fault recognition method and system based on neural network self-learning |
| CN108090567A (en) * | 2018-01-19 | 2018-05-29 | 国家电网公司 | Power communication system method for diagnosing faults and device |
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Application publication date: 20220805 |