CN114021925A - Safety evaluation method and device for power system, computer equipment and storage medium - Google Patents

Safety evaluation method and device for power system, computer equipment and storage medium Download PDF

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CN114021925A
CN114021925A CN202111259615.2A CN202111259615A CN114021925A CN 114021925 A CN114021925 A CN 114021925A CN 202111259615 A CN202111259615 A CN 202111259615A CN 114021925 A CN114021925 A CN 114021925A
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severity
power system
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刘涛
陈晓伟
马越
孙文龙
梁洪浩
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Shenzhen Power Supply Co ltd
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Abstract

The application relates to a safety assessment method and device for a power system, computer equipment and a storage medium. The method comprises the following steps: and acquiring safety evaluation indexes of the power system, wherein the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time. And calculating a target vector corresponding to each safety assessment index, wherein the target vector is used for representing the safety assessment result of the safety assessment index. And calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index. By adopting the method, the power system can be comprehensively and accurately evaluated.

Description

Safety evaluation method and device for power system, computer equipment and storage medium
Technical Field
The present application relates to the field of power system technologies, and in particular, to a method and an apparatus for security assessment of a power system, a computer device, and a storage medium.
Background
With the development of power systems, the structure of the power grid becomes more and more complex. In recent years, a large number of large-area power failure accidents worldwide cause huge economic losses to society, so research on the safety problem of a power grid should be strengthened.
The research on the safety problem of the power grid is mainly to rapidly and accurately give the safety level of the power grid in the current operation state by performing safety evaluation on the power grid, and to make a corresponding control strategy according to the safety level obtained by the evaluation so as to ensure the safe operation of the power system. Therefore, the safety assessment has great significance for the research of the safety problem of the power grid.
The safety assessment method for the power system in the prior art is mainly used for assessing through a single target, so that the assessment method for the power system in the prior art is single, and the power system cannot be accurately assessed.
Disclosure of Invention
In view of the above, it is necessary to provide a safety evaluation method, apparatus, computer device, and storage medium for a power system capable of accurately evaluating the power system in view of the above technical problems.
A method of safety assessment of a power system, the method comprising:
acquiring a safety evaluation index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
In one embodiment, if the safety evaluation indicators include a fault severity, a fault occurrence probability, and an abnormal detection time, the calculating, for each safety evaluation indicator, a target vector corresponding to the safety evaluation indicator includes:
calculating a first target vector corresponding to the fault severity for the fault severity;
calculating a second target vector corresponding to the fault occurrence probability aiming at the fault occurrence probability;
and calculating a third target vector corresponding to the abnormality detection time aiming at the abnormality detection time.
In one embodiment, said calculating, for said fault severity, a first target vector corresponding to said fault severity comprises:
acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity;
determining a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm;
and determining a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
In one of the embodiments, the fault severity comprises software failure severity, hardware paralysis severity, and data tampering or failure severity; the acquiring of the fault proportion corresponding to the fault severity comprises:
acquiring a first fault proportion corresponding to the software failure severity; the first fault proportion is used for representing the proportion of the fault process number of the software to the total process number of the software;
acquiring a second fault proportion corresponding to the severity of the hardware paralysis; the second fault proportion is used for representing the proportion of the difference value of the data processing quantity before the fault and the data quantity after the fault of the hardware to the total data quantity of the hardware;
acquiring a third fault proportion corresponding to the proportion of the data tampering or failure severity; the third fault proportion is used for representing the proportion of the data volume of the power system which is not influenced after the fault occurs to the total data volume of the power system in normal operation.
In one embodiment, said calculating a second target vector corresponding to the probability of occurrence of the fault with respect to the probability of occurrence of the fault includes:
acquiring a sample set of the fault occurrence probability;
inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set;
and performing sharpening processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of sharpening processing.
In one embodiment, the calculating, for the anomaly detection time, a third target vector corresponding to the anomaly detection time includes:
acquiring a sample set of the anomaly detection time;
inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set;
and performing sharpening processing on the grading result of each abnormality detection time, and generating a third target vector corresponding to the abnormality detection time based on the grading result of sharpening processing.
In one embodiment, the calculating a target safety assessment result of the power system according to the target vector corresponding to each safety assessment index includes:
acquiring the weight corresponding to each safety evaluation index;
and calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
A safety assessment device for an electrical power system, the device comprising:
the system comprises an acquisition module, a detection module and a processing module, wherein the acquisition module is used for acquiring safety evaluation indexes of the power system, and the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time;
the first calculation module is used for calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and the second calculation module is used for calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
the method comprises the steps of obtaining safety evaluation indexes of the power system, wherein the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
the method comprises the steps of obtaining safety evaluation indexes of the power system, wherein the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
The safety evaluation method, the safety evaluation device, the computer equipment and the storage medium of the power system are used for obtaining the safety evaluation indexes of the power system, wherein the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time. And calculating a target vector corresponding to each safety assessment index, wherein the target vector is used for representing the safety assessment result of the safety assessment index. And finally, calculating a target safety evaluation result of the power system according to the target vectors corresponding to the safety evaluation indexes. By integrating at least two safety assessment indexes, the safety of the power system is assessed from multiple angles, and therefore a more comprehensive safety assessment result of the power system can be obtained.
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FIG. 1 is a diagram of an exemplary embodiment of a security assessment method for an electrical power system;
FIG. 2 is a schematic flow chart diagram of a method for security assessment of a power system in one embodiment;
FIG. 3 is a schematic flow chart of a safety assessment method for an electric power system according to another embodiment;
FIG. 4 is a schematic flow chart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 5 is a graph illustrating membership function curves in one embodiment;
FIG. 6 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 7 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 8 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 9 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 10 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 11 is a flowchart illustrating a safety assessment method for an electric power system according to another embodiment;
FIG. 12 is a block diagram showing a safety evaluation device of an electric power system according to one embodiment;
fig. 13 is a block diagram showing a safety evaluation device of an electric power system in another embodiment;
fig. 14 is a block diagram showing a safety evaluation device of an electric power system in another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The safety evaluation method of the power system provided by the application can be applied to the application environment shown in fig. 1. The internal structure of the server may be as shown in fig. 1. The server includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the server is configured to provide computing and control capabilities. The memory of the server comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the server is used for storing project case test data. The network interface of the server is used for communicating with an external terminal through network connection. The server acquires the safety assessment indexes of the power system, calculates target vectors corresponding to the safety assessment indexes aiming at the safety assessment indexes, and calculates target safety assessment results of the power system according to the target vectors corresponding to the safety assessment indexes. The server may be implemented by an independent server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a safety evaluation method for a power system is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
s201, obtaining safety evaluation indexes of the power system, wherein the safety evaluation indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time.
The safety evaluation indexes are indexes representing the running state of the power system in the running process of the power system, and can be fault severity, fault occurrence probability, abnormal detection time and the like. The failure severity refers to the severity of a failure of the power system, the failure occurrence probability refers to the probability of failure of each device of the power system, and the anomaly detection time refers to the time required for detecting an anomaly after the power system is damaged.
Specifically, a safety evaluation index commonly used in the history evaluation process may be used as the safety evaluation index of the power system, and a safety evaluation index having the greatest influence on the power system may also be selected as the safety evaluation index of the power system. The safety evaluation index includes at least two of a fault severity, a fault occurrence probability, and an abnormality detection time, for example, the safety evaluation index may be the fault severity and the fault occurrence probability, the safety evaluation index may be the fault severity and the abnormality detection time, the safety evaluation index may be the fault occurrence probability and the abnormality detection time, and the safety evaluation index may be the fault severity, the fault occurrence probability, and the abnormality detection time.
S202, aiming at each safety assessment index, calculating a target vector corresponding to the safety assessment index. The target vector is used for representing a safety evaluation result of the safety evaluation index.
The target vector is an evaluation vector corresponding to each safety evaluation index and is used for evaluating the severity level corresponding to each safety evaluation index, wherein the target vector comprises an evaluation vector corresponding to the severity of the fault, an evaluation vector corresponding to the probability of the fault occurrence and an evaluation vector corresponding to the abnormal detection time.
Specifically, each safety assessment index is subjected to severity grade division, and a corresponding target vector is obtained according to the severity grade of each safety assessment index. The target vectors comprise a first target vector corresponding to the severity of the fault, a second target vector corresponding to the probability of the fault occurrence and a third target vector corresponding to the abnormal detection time. For example, the severity level of each safety assessment indicator is divided into four levels: the system comprises a first level, a second level, a third level and a fourth level, wherein the severity level corresponding to the first level is normal, the severity level corresponding to the second level is attention, the severity level corresponding to the third level is severe, and the severity level corresponding to the fourth level is dangerous. When the fault severity is taken as one of the safety assessment indexes, a first target vector corresponding to the fault severity can be represented as:
V1＝[v11,v12,v13,v14]
wherein, V1Representing a first target vector, v11、v12、v13、v14Respectively corresponding to a level I, a level II, a level III and a level IV, and when the fault severity is level II, the corresponding first target vector can be expressed as:
V1＝[0,1,0,0]
and S203, calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
The target safety evaluation result of the power system is obtained by integrating the severity levels corresponding to the safety evaluation indexes, performing weighted summation on target vectors corresponding to the safety evaluation indexes, and averaging the target vectors corresponding to the safety evaluation indexes to obtain an integrated evaluation level, which is the target safety evaluation result of the power system.
Specifically, the first target vector corresponding to the severity of the fault, the second target vector corresponding to the probability of occurrence of the fault, and the third target vector corresponding to the abnormal detection time may be obtained through step S202, corresponding weights may be set for the target vectors corresponding to the safety assessment indicators according to expert experience, the target vectors corresponding to the safety assessment indicators and the target vectors corresponding to the safety assessment indicators are multiplied by each other, and the obtained products are added to obtain a target safety assessment result of the power system. Optionally, the target vectors corresponding to the safety assessment indexes may be subjected to weighted average calculation to obtain a target safety assessment result of the power system. In this embodiment, it is not limited in what manner the calculation is performed according to the target vector corresponding to each safety assessment index, and it is only necessary to obtain the target safety assessment result of the power system obtained by the method.
In the safety assessment method of the power system, safety assessment indexes of the power system are obtained, wherein the safety assessment indexes comprise at least two of fault severity, fault occurrence probability and abnormal detection time. And calculating a target vector corresponding to each safety assessment index, wherein the target vector is used for representing the safety assessment result of the safety assessment index. And finally, calculating a target safety evaluation result of the power system according to the target vectors corresponding to the safety evaluation indexes. By integrating at least two safety assessment indexes, the safety of the power system is assessed from multiple angles, and therefore a more comprehensive safety assessment result of the power system can be obtained.
Based on the embodiment shown in fig. 2, in an embodiment, as shown in fig. 3, a detailed implementation step of calculating a target vector corresponding to a safety evaluation index for each safety evaluation index if the safety evaluation index includes a fault severity, a fault occurrence probability, and an abnormality detection time is described, including:
s301, a first target vector corresponding to the severity of the fault is calculated for the severity of the fault.
Wherein the fault severity comprises: severity of software failure, severity of hardware paralysis, and severity of data tampering or failure. The severity of software failure refers to the damage degree of a software system of the power system after being attacked, the severity of hardware paralysis refers to the damage degree of a hardware system of the power system after being attacked, and the severity of data tampering or failure refers to the damage degree of a storage system of the power system after being attacked.
Specifically, the severity levels corresponding to the software failure severity, the hardware paralysis severity and the data tampering or failure severity are respectively calculated, the fault severity level is comprehensively obtained according to the severity levels corresponding to the software failure severity, the hardware paralysis severity and the data tampering or failure severity, and the first target vector corresponding to the fault severity is calculated according to the fault severity level.
S302, a second target vector corresponding to the failure occurrence probability is calculated for the failure occurrence probability.
Specifically, the data of the failure occurrence probability is obtained, optionally, the server may classify the data of the failure occurrence probability through a naive bayes classification algorithm, and the server may also classify the data of the failure occurrence probability through a support vector machine method. And carrying out severity grade division on the classified data of the fault occurrence probability according to the classified data of the fault occurrence probability, and calculating a second target vector corresponding to the fault occurrence probability according to the severity grade of the fault occurrence probability.
S303, a third target vector corresponding to the abnormality detection time is calculated for the abnormality detection time.
Specifically, the data of the anomaly detection time is obtained, optionally, the server may classify the data of the anomaly detection time through a naive bayes classification algorithm, and the server may classify the data of the anomaly detection time through a support vector machine method. And carrying out severity grade division on the classified data of the abnormal detection time according to the classified data of the abnormal detection time, and calculating a third target vector corresponding to the abnormal detection time according to the severity grade of the abnormal detection time.
According to the safety evaluation method of the power system, the first target vector corresponding to the fault severity, the second target vector corresponding to the fault occurrence probability and the third target vector corresponding to the abnormal detection time are calculated, and the power system is evaluated respectively according to the three evaluation indexes of the fault severity, the fault occurrence probability and the abnormal detection time, so that the evaluation process of the power system is more accurate and reasonable, and the stable operation of the power system is guaranteed.
Based on the embodiment shown in fig. 3, in an embodiment, as shown in fig. 4, a detailed implementation step of calculating a first target vector corresponding to a fault severity for the fault severity is described, which includes:
s401, acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity.
The fault proportion corresponding to the fault severity refers to a ratio of a loss value caused by a fault to a data value in normal operation, wherein the fault proportion corresponding to the fault severity comprises: the system comprises a first fault proportion corresponding to the severity of software failure, a second fault proportion corresponding to the severity of hardware paralysis and a third fault proportion corresponding to the severity of data tampering or failure.
Specifically, the obtaining of the fault proportion corresponding to the fault severity is to obtain a first fault proportion corresponding to the software failure severity, a second fault proportion corresponding to the hardware paralysis severity, and a third fault proportion corresponding to the data tampering or failure severity. The fault proportion corresponding to the fault severity refers to a proportion of a difference value between a value of normal operation of the equipment and a loss value of a fault operation state and a value of normal operation of the equipment after the power system fails, and the fault proportion corresponding to the fault severity can be expressed as:
Figure BDA0003325045220000091
wherein X represents the fault proportion corresponding to the severity of the fault, and n0Value n representing the equipment in the normal operating state of the power systemiA value representing the operation of the equipment in the event of a fault in the power system.
Further, a preset fault severity model corresponding to the fault severity is constructed by using the risk preference type utility function, and the fault severity model can be expressed as:
Figure BDA0003325045220000092
S'(x)>0
S”(x)>0
wherein, S (x) represents a fault severity model, and a, b and c are positive numbers.
And S402, determining a membership function curve of the fault severity according to the fault severity model and the trapezoidal fuzzy distribution algorithm.
Specifically, a membership function curve corresponding to the severity of the fault can be obtained through a model corresponding to the severity of the fault and a trapezoidal fuzzy distribution algorithm, the membership function curve corresponding to the severity of the fault is shown in fig. 5, the abscissa of the membership function curve is a fault proportion corresponding to the severity of the fault, and the ordinate of the membership function curve is membership degrees of different grades.
And S403, determining a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
Specifically, the grade corresponding to the fault severity can be obtained from the membership function curve of the fault severity through the fault proportion corresponding to the fault severity, and the grade corresponding to the fault severity is combined with the fuzzy rule to obtain the first target vector corresponding to the fault severity.
According to the safety evaluation method of the power system, firstly, a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity are obtained, then a membership function curve of the fault severity is determined according to the fault severity model and a trapezoidal fuzzy distribution algorithm, and a first target vector corresponding to the fault severity can be determined more accurately according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
The fault proportion corresponding to the fault severity comprises a first fault proportion corresponding to the software failure severity, a second fault proportion corresponding to the hardware paralysis severity and a third fault proportion corresponding to the data tampering or failure severity. Therefore, in an embodiment, as shown in fig. 6, a detailed implementation step of obtaining a fault ratio corresponding to the severity of the fault is described, which includes:
s601, acquiring a first fault proportion corresponding to the software failure severity; the first fault proportion is used for representing the proportion of the fault process number of the software to the total process number of the software.
Specifically, the first fault proportion corresponding to the software failure severity refers to a proportion of the number of processes that the sub-software system needs to be stopped after the occurrence of the fault to the original number of processes, and the first fault proportion corresponding to the software failure severity can be represented as:
Figure BDA0003325045220000101
wherein, X1Showing the first fault proportion, w, corresponding to the severity of software failureiIndicating the number of processes, w, that the subsystem needs to be disabled after the failure occurs0Representing the original process number of the sub-software system.
Further, a first model corresponding to the software failure severity is constructed by using the risk preference type utility function, and the model corresponding to the software failure severity can be expressed as:
Figure BDA0003325045220000102
wherein S isev1The first model corresponding to the severity of software failure generally has different failure consequences in consideration of different software importance degrees. Therefore, a software importance factor alpha is introduced on the basis of the model corresponding to the software failure severityiIf the software failure severity corresponds to the first model, the first model is as follows:
Figure BDA0003325045220000103
the invention gives a quantitative classification of the importance level. In order to clearly see the influence of important grade factors of a software system, the value range is a dimensionless value between (1-10). The larger the score, the more important the representation, and the greater the resulting loss value, the quantitative division of the software importance factor is shown in table 1.
TABLE 1 quantitative partition of software importance factor
Figure BDA0003325045220000104
S602, acquiring a second fault proportion corresponding to the severity of hardware paralysis; the second fault proportion is used for representing the proportion of the difference value of the data processing quantity before the fault and the data quantity after the fault of the hardware to the total data quantity of the hardware.
Specifically, the second failure proportion corresponding to the severity of hardware paralysis refers to a proportion of a difference between a data processing amount of the hardware before failure and a data amount after failure and a total data amount of the hardware, and the second failure proportion corresponding to the severity of hardware paralysis can be expressed as:
Figure BDA0003325045220000111
wherein, X2Showing the first fault proportion, w, corresponding to the severity of software failureiIndicating the amount of data after a sub-hardware failure, L0Indicating the data throughput before the sub-hardware failure.
Further, a second model corresponding to the severity of hardware paralysis is constructed by using the risk preference type utility function, and the model corresponding to the severity of software hardware paralysis can be expressed as:
Figure BDA0003325045220000112
wherein S isev2The second model corresponding to the severity of hardware paralysis considers that the different importance degrees of the sub-equipment are different, and the consequence loss of the sub-equipment caused by the fault is different. Therefore, a hardware importance factor beta is introduced on the basis of the model corresponding to the severity of the hardware paralysisiThen, the first model corresponding to the severity of the hardware paralysis is:
Figure BDA0003325045220000113
the influencing factors of the hardware importance factor include the use frequency of the equipment, the importance of the line topology structure and the like.
S603, acquiring a third fault proportion corresponding to the data tampering or failure severity; the third fault proportion is used for representing the proportion of the data volume of the power system which is not influenced after the fault occurs to the total data volume of the power system in normal operation.
Specifically, the third failure proportion of the severity of data tampering or failure refers to a proportion of an unaffected data amount of the power system after a failure occurs to a total data amount of the power system during normal operation, and the third failure proportion of the severity of data tampering or failure can be represented as:
Figure BDA0003325045220000114
wherein, X3Third proportion of failures, V, indicating the severity of data tampering or failureiIndicating the amount of data, V, affected after a fault in the power system0Indicating the amount of stored data in the case of normal operation of the power system.
Further, a second model corresponding to the severity of hardware paralysis is constructed by using the risk preference type utility function, and the model corresponding to the severity of software hardware paralysis can be expressed as:
Figure BDA0003325045220000121
wherein S isev3The second model corresponding to the severity of hardware paralysis considers that the different importance degrees of the sub-equipment are different, and the consequence loss of the sub-equipment caused by the fault is different. Therefore, a data importance degree factor gamma is introduced on the basis of the model corresponding to the severity of the hardware paralysisiThen, the first model corresponding to the severity of the hardware paralysis is:
Figure BDA0003325045220000122
wherein the data importance factor gammaiThe influence factors include the data damage degree, the data loss degree and the like, and in the actual evaluation work, gamma isiCan be determined according to the following formula:
γi＝γai*γbi
in the formula, gammaai、γbiCorresponding to the importance factors of data damage and data lossWhen not counting the influence of the above factors, gammaai、γbiAll take the value of 1.
Further, determining a first severity grade of the software failure according to a first fault proportion corresponding to the software failure severity and a model corresponding to the software failure severity; determining a second severity grade of the hardware paralysis according to a second fault proportion corresponding to the severity of the hardware paralysis and a model corresponding to the severity of the hardware paralysis; determining a third severity grade of data tampering or failure according to a third fault proportion corresponding to the severity of data tampering or failure and a model corresponding to the severity of data tampering or failure, and obtaining the severity grade corresponding to the severity of failure by combining a fuzzy rule according to the first severity grade of software failure, the second severity grade of hardware paralysis and the third severity grade of data tampering or failure, as shown in table 2 exemplarily.
TABLE 2 severity level for fault severity
Figure BDA0003325045220000123
Figure BDA0003325045220000131
According to the fuzzy rule, the first target vector corresponding to the fault severity level may be represented as:
V1＝[v11,v12,v13,v14]
in the formula, v11、v12、v13、v14The degree of membership of the index to the grades I to IV is evaluated for the severity of the comprehensive consequence.
According to the safety evaluation method of the power system, the grade division of the fault severity is more reasonable through the first fault proportion corresponding to the software failure severity, the second fault proportion corresponding to the hardware paralysis severity, the third fault proportion corresponding to the data tampering or failure severity, the first model corresponding to the software failure severity, the second model corresponding to the hardware paralysis severity and the third model corresponding to the data tampering or failure severity, and the first target vector corresponding to the more reasonable fault severity is obtained according to the grade division of the fault severity.
On the basis of the embodiment shown in fig. 3, in an embodiment, as shown in fig. 7, a detailed implementation step of obtaining a fault ratio corresponding to a fault severity is described, including:
s701, acquiring a sample set of the fault occurrence probability.
Specifically, the server may obtain a sample set of the fault occurrence probability from a memory of the power system at preset time intervals, for example, the sample set of the fault occurrence probability may be p ═ { p ═1,p2,...,pn}。
S702, inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set.
Specifically, a mathematical model corresponding to the fault occurrence probability is obtained according to a sample set of the fault occurrence probability and a definition function of fuzzy C-means algorithm (FCM), and an objective function corresponding to the mathematical model may be expressed as:
Figure BDA0003325045220000132
the constraint conditions corresponding to the mathematical model are as follows:
Figure BDA0003325045220000133
Figure BDA0003325045220000134
Figure BDA0003325045220000135
in the formula, T1Objective function, v, corresponding to mathematical model of probability of occurrence of faultijIs the cluster center.
And S703, carrying out clarification processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of the clarification processing.
Specifically, in step S702, a fuzzy classification result is obtained from the sample set of the fault occurrence probability by using a fuzzy C-means clustering method, and since the final classification result needs to be clearly divided, the fuzzy classification result obtained by using the fuzzy C-means clustering method needs to be clarified. For example, for the sample p of the probability of occurrence of a faultiThe classification type which can not be clearly divided in the fuzzy classification process, if the sample p of the fault occurrence probabilityiSatisfy the requirement of
Figure BDA0003325045220000141
Then the sample p of the probability of failure occurrenceiAnd classifying the results of the fuzzy partition clarification and the fuzzy C-means clustering according to the range of the clustering center, wherein the results of the fuzzy partition clarification and the fuzzy C-means clustering are classified into the jth class, and the classification result of the fault occurrence probability is shown in a table 3.
TABLE 3 Fault probability ranking results
Figure BDA0003325045220000142
As can be seen from the above classification result of the fault occurrence probability, the fault occurrence probability may be divided into 4 types of class centers, and the membership degree of the probability of the same class is weighted to obtain a second target vector corresponding to the fuzzy fault occurrence probability, that is, the second target vector corresponding to the fault occurrence probability may be represented as:
V2＝[v21,v22,v23,v24]
wherein v is21、v22、v23、v24Respectively representing the fault occurrence probability evaluation indexes corresponding to the I level,Membership degree of class II, class III and class IV. For example, when the probability of the fault occurring in the first stage is 0.2, the probability of the fault occurring in the second stage is 0.4, the probability of the fault occurring in the third stage is 0.3, and the probability of the fault occurring in the fourth stage is 0.1, the second target vector corresponding to the fault occurring probability can be represented as:
V2＝[0.2,0.4,0.3,0.1]
according to the safety evaluation method of the power system, the sample set of the fault occurrence probability is obtained, the sample set of the fault occurrence probability is input into the fuzzy C-means clustering model for grading, the grading result of each fault occurrence probability in the sample set is obtained, the grading result of each fault occurrence probability is subjected to clarification processing, and the generated second target vector corresponding to the fault occurrence probability is more accurate based on the grading results of the fuzzy C-means clustering and the clarification processing.
On the basis of the embodiment shown in fig. 3, in an embodiment, as shown in fig. 8, a detailed implementation step of obtaining a fault ratio corresponding to a fault severity is described, including:
s801, a sample set of the abnormality detection time is acquired.
Specifically, the server may obtain a sample set of the abnormality detection time from a memory of the power system at preset time intervals, for example, the sample set of the abnormality detection time may be t ═ { t ═ t%1,t2,...,tn}。
S802, inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain grading results of each abnormal detection time in the sample set.
Specifically, a mathematical model corresponding to the abnormal detection time is obtained according to a sample set of the abnormal detection time and a definition function of the fuzzy C-means cluster, and an objective function corresponding to the mathematical model can be expressed as:
Figure BDA0003325045220000151
the constraint conditions corresponding to the mathematical model are as follows:
Figure BDA0003325045220000152
Figure BDA0003325045220000153
Figure BDA0003325045220000154
in the formula, T2Objective function, v, corresponding to mathematical model of probability of occurrence of faultijIs the cluster center.
And S803, performing sharpening processing on the grading result of each abnormal detection time, and generating a third target vector corresponding to the abnormal detection time based on the grading result of sharpening processing.
Specifically, in step S802, a fuzzy classification result is obtained from the abnormal detection time sample set by the fuzzy C-means clustering method, and since the final classification result needs to be clearly divided, the fuzzy classification result obtained by the fuzzy C-means clustering method needs to be clarified. For example, for the sample t of the abnormality detection timeiThe classification type which cannot be clearly divided in the fuzzy classification process is determined if the sample t of the abnormal detection time meets the requirement
Figure BDA0003325045220000155
Then the sample p of the probability of failure occurrenceiClassifying the results of the fuzzy partition clarification and the fuzzy C-means clustering according to the fuzzy partition clarification and the fuzzy C-means clustering results and classifying the results of the fuzzy partition clarification and the fuzzy C-means clustering according to the range of the clustering center, wherein the abnormal detection time classification results are shown in a table 4.
TABLE 4 anomaly detection time-ranking results
Figure BDA0003325045220000161
As can be seen from the above classification result of the anomaly detection time, the anomaly detection time can be divided into 4 classes of centers, and the membership degree of the probability at the same class is weighted to obtain a third target vector corresponding to the fuzzy anomaly detection time, that is, the third target vector corresponding to the anomaly detection time can be represented as:
V3＝[v31,v32,v33,v34]
wherein v is21、v22、v23、v24And respectively representing the membership degrees of the failure occurrence probability evaluation indexes corresponding to I level, II level, III level and IV level. For example, when the probability of the abnormality detection time classified into the i class is 0.2, the probability of the abnormality detection time classified into the ii class is 0.4, the probability of the abnormality detection time classified into the iii class is 0.3, and the probability of the abnormality detection time classified into the iv class is 0.1, the third target vector corresponding to the abnormality detection time can be represented as:
V3＝[0.2,0.4,0.3,0.1]
according to the safety evaluation method of the power system, the sample set of the abnormal detection time is obtained, the sample set of the abnormal detection time is input into the fuzzy C-means clustering model for grading, grading results of all the abnormal detection time in the sample set are obtained, the grading results of all the abnormal detection time are subjected to clarification processing, and the generated third target vector corresponding to the abnormal detection time is more accurate based on the grading results of the fuzzy C-means clustering and the clarification processing.
On the basis of the embodiment shown in fig. 2, in an embodiment, as shown in fig. 9, a detailed implementation step of obtaining a fault ratio corresponding to a fault severity is described, including:
and S901, acquiring weights corresponding to the safety assessment indexes.
Specifically, an initial weight is determined for each safety assessment index according to expert experience, and a weight corresponding to each safety assessment index is determined according to the initial weight of each safety assessment index. Optionally, the initial weight of each safety assessment index may be calculated through a corresponding algorithm, so as to obtain the weight of each safety assessment index. For example, the weight corresponding to the severity of the fault may be 0.4, the weight corresponding to the probability of occurrence of the fault may be 0.3, and the weight corresponding to the abnormality detection time may be 0.3.
And S902, calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
Specifically, a safety assessment hierarchical model is constructed by using fuzzy operation in fuzzy mathematics to weight corresponding to each safety assessment index and a target vector of each safety assessment index, and the safety assessment hierarchical model can be expressed as follows:
Figure BDA0003325045220000171
wherein W represents a weight vector; w is a1、w2、w3Weight values representing the severity of a fault, the probability of occurrence of a fault, and the time of detection of an abnormality, and w1+w2+w3＝1，V1、V2、V3And the first target vector corresponding to the severity of the fault, the second target vector corresponding to the probability of the fault occurrence and the target vector corresponding to the abnormal detection time are represented.
Furthermore, according to the risk assessment theory, the influence of the fault probability and the consequence severity on the system risk can be known, so that generally w is taken1＝0.4，w2＝0.4，w3And (3) judging a target safety evaluation result after the power system fails according to the maximum membership degree principle in the fuzzy inference, wherein the target safety evaluation result is exemplarily shown in table 5, and the specific data is determined according to the calculation result.
TABLE 5 results of target Security assessment
Figure BDA0003325045220000172
According to the safety assessment method of the power system, the weight corresponding to each safety assessment index is obtained, and the target safety assessment result of the power system is calculated according to the weight corresponding to each safety assessment index and the target vector of each safety assessment index, so that the target safety assessment result of the power system is more reasonable.
In one embodiment, as shown in fig. 10, to facilitate understanding of those skilled in the art, the following detailed description is provided for a safety assessment method of a power system, which may include:
s1001, acquiring a safety evaluation index of the power system;
s1002, acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity;
s1003, determining a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm;
s1004, determining a first target vector corresponding to the severity of the fault according to the fault proportion corresponding to the severity of the fault and a membership function curve of the severity of the fault;
s1005, acquiring a sample set of the fault occurrence probability;
s1006, inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set;
s1007, performing clarification processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of the clarification processing;
s1008, obtaining a sample set of abnormal detection time;
s1009, inputting the sample set of the abnormal detection time into the fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set;
s1010, carrying out clarification processing on the grading result of each abnormal detection time, and generating a third target vector corresponding to the abnormal detection time based on the grading result of the clarification processing;
s1011, obtaining the weight corresponding to each safety evaluation index;
and S1012, calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
It should be noted that, for the descriptions in S1001 to S1012, reference may be made to the descriptions related to the foregoing embodiments, and the effects are similar, and the description of this embodiment is not repeated herein.
Further, the safety evaluation method for the power system provided by the application is described in detail with reference to the flowchart shown in fig. 11, and includes defining 3 input variable membership functions according to the data of the severity of the fault, obtaining a software severity level, a hardware severity level and a data tampering or failure severity level according to the membership functions, respectively, specifying a fuzzy rule base of the severity of the fault, and integrating the software severity level, the hardware severity level and the data tampering or failure severity level based on the fuzzy inference operation of max-min composite operation to obtain an integrated severity level, and obtaining an integrated severity fuzzy evaluation vector V according to the integrated severity level1. Then, classifying the fault probability sample set by using fuzzy C-means algorithm (FCM) to obtain a classified fault probability grade, and obtaining a probability fuzzy evaluation vector V according to the fault probability grade2. Classifying the abnormal detection time sample set by using a fuzzy C-means clustering algorithm to obtain the classified abnormal detection time grade, and obtaining an abnormal detection time evaluation vector V according to the abnormal detection time grade3. Fuzzy evaluation vector V according to comprehensive severity1Probability fuzzy evaluation vector V2And abnormality detection time evaluation vector V3Obtaining a risk fuzzy evaluation vector R, and finally determining a safety evaluation early warning grade according to the risk fuzzy evaluation vector R, wherein the safety evaluation early warning grade can perform safety evaluation on the power system more comprehensively and reasonablyAnd (6) estimating.
The safety evaluation method of the power system comprises the steps of obtaining a safety evaluation index of the power system, firstly obtaining a fault proportion corresponding to the severity of a fault and a preset fault severity model corresponding to the severity of the fault, determining a membership function curve of the severity of the fault according to the fault severity model and a trapezoidal fuzzy distribution algorithm, determining a first target vector corresponding to the severity of the fault according to the fault proportion corresponding to the severity of the fault and the membership function curve of the severity of the fault, then obtaining a sample set of the probability of the fault, inputting the sample set of the probability of the fault into a fuzzy C mean value clustering model for grading to obtain a grading result of the probability of the fault in the sample set, carrying out clarification processing on the grading result of the probability of the fault, and generating a second target vector corresponding to the probability of the fault based on the grading result of the clarification processing, and then obtaining a sample set of abnormal detection time, inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set, carrying out sharpening processing on the grading result of each abnormal detection time, generating a third target vector corresponding to the abnormal detection time based on the sharpening processed grading result, finally obtaining weights corresponding to each safety evaluation index, and calculating a target safety evaluation result of the power system according to the weights corresponding to each safety evaluation index and the target vectors of each safety evaluation index. According to the embodiment, the respective evaluation results of the three indexes, namely the fault severity, the fault occurrence probability and the abnormal detection time, are firstly calculated, and then comprehensive safety evaluation is performed on the three evaluation results, so that the safety evaluation can be comprehensively and reasonably performed on the power system.
It should be understood that although the various steps in the flowcharts of fig. 2-4 and 6-11 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 and 6-11 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or at least partially with other steps or with at least some of the other steps.
In one embodiment, as shown in fig. 12, there is provided a safety evaluation device of a power system, including: a first obtaining module 11, a first calculating module 12 and a second calculating module 13, wherein:
the first obtaining module 11 is configured to obtain a safety assessment index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
the first calculation module 12 is configured to calculate, for each safety assessment indicator, a target vector corresponding to the safety assessment indicator; the target vector is used for representing the safety evaluation result of the safety evaluation index;
and a second calculating module 13, configured to calculate a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, as shown in fig. 13, the second calculating module 13 includes:
a first calculation unit 131 configured to calculate, for a fault severity, a first target vector corresponding to the fault severity;
a second calculation unit 132 configured to calculate, for the failure occurrence probability, a second target vector corresponding to the failure occurrence probability;
a third calculating unit 133 for calculating a third target vector corresponding to the abnormality detection time with respect to the abnormality detection time.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the first calculating unit 131 is specifically configured to obtain a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity, determine a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm, and determine a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the first calculating unit 131 is specifically configured to obtain a first fault proportion corresponding to the severity of software failure, obtain a second fault proportion corresponding to the severity of hardware paralysis, and obtain a third fault proportion corresponding to the proportion of the severity of data tampering or failure.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the second calculating unit 132 is specifically configured to obtain a sample set of the fault occurrence probability, input the sample set of the fault occurrence probability into a fuzzy C-means clustering model for classification, obtain a classification result of each fault occurrence probability in the sample set, perform sharpening processing on the classification result of each fault occurrence probability, and generate a second target vector corresponding to the fault occurrence probability based on the classification result of the sharpening processing.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
On the basis of the foregoing embodiment, optionally, the third calculating unit 133 is specifically configured to obtain a sample set of the abnormal detection time, input the sample set of the abnormal detection time into a fuzzy C-means clustering model for classification, obtain a classification result of each abnormal detection time in the sample set, perform sharpening processing on the classification result of each abnormal detection time, and generate a third target vector corresponding to the abnormal detection time based on the classification result of the sharpening processing.
In one embodiment, as shown in fig. 14, the above apparatus further comprises: the second acquisition module 14 is configured to acquire,
and the second obtaining module 14 is configured to obtain weights corresponding to the security assessment indicators.
The safety evaluation device for an electrical power system provided in this embodiment may implement the above method embodiments, and its implementation principle and technical effect are similar, which are not described herein again.
For specific limitations of the safety evaluation device of the power system, reference may be made to the above limitations of the safety evaluation method of the power system, which are not described herein again. The various modules in the safety evaluation device of the power system may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the servers to which the subject application applies, as a particular server may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a server comprising a memory and a processor, the memory having a computer program stored therein, the processor when executing the computer program implementing the steps of:
acquiring a safety evaluation index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
In one embodiment, the processor, when executing the computer program, further performs the steps of: if the safety assessment indexes include fault severity, fault occurrence probability and abnormal detection time, calculating a target vector corresponding to each safety assessment index for each safety assessment index, including:
calculating a first target vector corresponding to the fault severity for the fault severity;
calculating a second target vector corresponding to the fault occurrence probability aiming at the fault occurrence probability;
and calculating a third target vector corresponding to the abnormality detection time aiming at the abnormality detection time.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the calculating, for the fault severity, a first target vector corresponding to the fault severity, comprising:
acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity;
determining a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm;
and determining a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the fault severity comprises software failure severity, hardware paralysis severity and data tampering or failure severity; the acquiring of the fault proportion corresponding to the fault severity comprises:
acquiring a first fault proportion corresponding to the software failure severity; the first fault proportion is used for representing the proportion of the fault process number of the software to the total process number of the software;
acquiring a second fault proportion corresponding to the severity of the hardware paralysis; the second fault proportion is used for representing the proportion of the difference value of the data processing quantity before the fault and the data quantity after the fault of the hardware to the total data quantity of the hardware;
acquiring a third fault proportion corresponding to the proportion of the data tampering or failure severity; the third fault proportion is used for representing the proportion of the data volume of the power system which is not influenced after the fault occurs to the total data volume of the power system in normal operation.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the calculating, for the fault occurrence probability, a second target vector corresponding to the fault occurrence probability includes:
acquiring a sample set of the fault occurrence probability;
inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set;
and performing sharpening processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of sharpening processing.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the calculating, for the anomaly detection time, a third target vector corresponding to the anomaly detection time includes:
acquiring a sample set of the anomaly detection time;
inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set;
and performing sharpening processing on the grading result of each abnormality detection time, and generating a third target vector corresponding to the abnormality detection time based on the grading result of sharpening processing.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the calculating a target safety assessment result of the power system according to the target vector corresponding to each safety assessment index includes:
acquiring the weight corresponding to each safety evaluation index;
and calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a safety evaluation index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the safety assessment indexes include fault severity, fault occurrence probability and abnormal detection time, calculating a target vector corresponding to each safety assessment index for each safety assessment index, including:
calculating a first target vector corresponding to the fault severity for the fault severity;
calculating a second target vector corresponding to the fault occurrence probability aiming at the fault occurrence probability;
and calculating a third target vector corresponding to the abnormality detection time aiming at the abnormality detection time.
In one embodiment, the computer program when executed by the processor further performs the steps of: the calculating, for the fault severity, a first target vector corresponding to the fault severity, comprising:
acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity;
determining a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm;
and determining a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
In one embodiment, the computer program when executed by the processor further performs the steps of: the fault severity comprises software failure severity, hardware paralysis severity and data tampering or failure severity; the acquiring of the fault proportion corresponding to the fault severity comprises:
acquiring a first fault proportion corresponding to the software failure severity; the first fault proportion is used for representing the proportion of the fault process number of the software to the total process number of the software;
acquiring a second fault proportion corresponding to the severity of the hardware paralysis; the second fault proportion is used for representing the proportion of the difference value of the data processing quantity before the fault and the data quantity after the fault of the hardware to the total data quantity of the hardware;
acquiring a third fault proportion corresponding to the proportion of the data tampering or failure severity; the third fault proportion is used for representing the proportion of the data volume of the power system which is not influenced after the fault occurs to the total data volume of the power system in normal operation.
In one embodiment, the computer program when executed by the processor further performs the steps of: the calculating, for the fault occurrence probability, a second target vector corresponding to the fault occurrence probability includes:
acquiring a sample set of the fault occurrence probability;
inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set;
and performing sharpening processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of sharpening processing.
In one embodiment, the computer program when executed by the processor further performs the steps of: the calculating, for the anomaly detection time, a third target vector corresponding to the anomaly detection time includes:
acquiring a sample set of the anomaly detection time;
inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set;
and performing sharpening processing on the grading result of each abnormality detection time, and generating a third target vector corresponding to the abnormality detection time based on the grading result of sharpening processing.
In one embodiment, the computer program when executed by the processor further performs the steps of: the calculating a target safety assessment result of the power system according to the target vector corresponding to each safety assessment index includes:
acquiring the weight corresponding to each safety evaluation index;
and calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for safety assessment of an electrical power system, the method comprising:
acquiring a safety evaluation index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
2. The method according to claim 1, wherein if the safety assessment indicators include a fault severity, a fault occurrence probability, and an anomaly detection time, the calculating a target vector corresponding to the safety assessment indicators for each safety assessment indicator includes:
calculating a first target vector corresponding to the fault severity for the fault severity;
calculating a second target vector corresponding to the fault occurrence probability aiming at the fault occurrence probability;
and calculating a third target vector corresponding to the abnormality detection time aiming at the abnormality detection time.
3. The method of claim 2, wherein said calculating, for the fault severity, a first target vector corresponding to the fault severity comprises:
acquiring a fault proportion corresponding to the fault severity and a preset fault severity model corresponding to the fault severity;
determining a membership function curve of the fault severity according to the fault severity model and a trapezoidal fuzzy distribution algorithm;
and determining a first target vector corresponding to the fault severity according to the fault proportion corresponding to the fault severity and the membership function curve of the fault severity.
4. The method of claim 3, wherein the severity of the failure comprises a severity of software failure, a severity of hardware paralysis, and a severity of data tampering or failure; the acquiring of the fault proportion corresponding to the fault severity comprises:
acquiring a first fault proportion corresponding to the software failure severity; the first fault proportion is used for representing the proportion of the fault process number of the software to the total process number of the software;
acquiring a second fault proportion corresponding to the severity of the hardware paralysis; the second fault proportion is used for representing the proportion of the difference value of the data processing quantity before the fault and the data quantity after the fault of the hardware to the total data quantity of the hardware;
acquiring a third fault proportion corresponding to the proportion of the data tampering or failure severity; the third fault proportion is used for representing the proportion of the data volume of the power system which is not influenced after the fault occurs to the total data volume of the power system in normal operation.
5. The method of claim 2, wherein said calculating a second target vector corresponding to the probability of occurrence of the fault with respect to the probability of occurrence of the fault comprises:
acquiring a sample set of the fault occurrence probability;
inputting the sample set of the fault occurrence probability into a fuzzy C-means clustering model for grading to obtain a grading result of each fault occurrence probability in the sample set;
and performing sharpening processing on the grading result of each fault occurrence probability, and generating a second target vector corresponding to the fault occurrence probability based on the grading result of sharpening processing.
6. The method of claim 2, wherein said calculating, for the anomaly detection time, a third target vector corresponding to the anomaly detection time comprises:
acquiring a sample set of the anomaly detection time;
inputting the sample set of the abnormal detection time into a fuzzy C-means clustering model for grading to obtain a grading result of each abnormal detection time in the sample set;
and performing sharpening processing on the grading result of each abnormality detection time, and generating a third target vector corresponding to the abnormality detection time based on the grading result of sharpening processing.
7. The method of claim 1, wherein calculating the target safety assessment results of the power system according to the target vectors corresponding to the safety assessment indicators comprises:
acquiring the weight corresponding to each safety evaluation index;
and calculating a target safety evaluation result of the power system according to the weight corresponding to each safety evaluation index and the target vector of each safety evaluation index.
8. A safety evaluation device for an electric power system, the device comprising:
the first acquisition module is used for acquiring the safety evaluation index of the power system; the safety evaluation index comprises at least two of fault severity, fault occurrence probability and abnormal detection time;
the first calculation module is used for calculating a target vector corresponding to each safety assessment index; the target vector is used for representing a safety evaluation result of the safety evaluation index;
and the second calculation module is used for calculating a target safety evaluation result of the power system according to the target vector corresponding to each safety evaluation index.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN202111259615.2A 2021-10-28 2021-10-28 Safety evaluation method and device for power system, computer equipment and storage medium Pending CN114021925A (en)

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