Power system fault diagnosis method based on fuzzy number virus machine model

Abstract

The invention discloses a power system fault diagnosis method based on a fuzzy number virus machine model, and relates to the technical field of power system fault diagnosis, the method comprises the following steps: improving a virus machine model based on a fuzzy number to obtain the fuzzy number virus machine model, and according to the fuzzy number virus machine model, obtaining the fuzzy number virus machine model; and constructing a bus fault diagnosis model and a line fault diagnosis model for fault diagnosis of the power system, and detecting a bus and a line of the power system to obtain a fault diagnosis result of the power system. The traditional virus machine model is improved based on the fuzzy number, so that the traditional virus machine model can meet the fault diagnosis requirement of the current power system, and the problem that the traditional power system fault diagnosis method is poor in calculation speed and precision and lacks applicability when facing scale expansion, structure complication and new energy of the power system is solved. Therefore, the fault diagnosis speed and the fault diagnosis accuracy of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault diagnosis, and in particular to a power system fault diagnosis method based on a fuzzy number virus machine model. Background Art

[0002] As a key infrastructure for the operation of modern social economy and people's daily lives, accurate and timely diagnosis of power system faults is extremely important for ensuring the safe and stable operation of the system. At present, the scale of the power system continues to expand and the structure becomes more complex. At the same time, the large-scale access of new energy sources has changed the operating characteristics and failure modes of the power system. Traditional power system fault diagnosis methods lack computational accuracy and speed when facing the current power system. Existing power system fault diagnosis methods, including fault diagnosis methods based on convolutional neural networks, tolerance processing in analog fault diagnosis, reversible logic circuit technology, fuzzy set theory, causal networks, and spiking neural P systems (SNPSs), can diagnose power system faults, but the accuracy still needs to be improved. More methods need to be explored to achieve more accurate power system fault diagnosis.

[0003] The virus machine model is a computational model inspired by the information propagation mechanisms of biological viruses. It consists of three main components: host, channel, and viral propagation. In this model, the process by which a virus transmits its genetic information to a host cell and subsequently controls its reproduction is abstracted as a means of information transmission and processing. An extended model of the virus machine with multiple channels, the Channel Parallel Virus Machine (CPVM), derived from an improved spiking neural P system, can be applied to power system fault diagnosis. However, due to limitations in the data type stored in the virus machine model and misjudgments of confidence, it still lacks a certain degree of precision and accuracy in some power system regions when used for power system fault diagnosis. Further optimization is needed to adapt it to the complex requirements of power system fault diagnosis. Summary of the Invention

[0004] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model, which can solve the problem that traditional power system fault diagnosis methods, when faced with the expansion of power system scale, complex structure and new energy, are insufficient in calculation speed and accuracy and lack applicability, resulting in erroneous power system fault diagnosis results, thereby improving the speed and accuracy of power system fault diagnosis.

[0005] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model, comprising the following steps:

[0006] The virus machine model is improved based on fuzzy numbers to obtain the fuzzy number virus machine model;

[0007] Based on the fuzzy number virus machine model, a bus fault diagnosis model for power system fault diagnosis is constructed;

[0008] Based on the fuzzy number virus machine model, a line fault diagnosis model for power system fault diagnosis is constructed;

[0009] According to the busbar fault diagnosis model and the line fault diagnosis model, the busbar and line of the power system are detected respectively to obtain the power system fault diagnosis results.

[0010] This application addresses the problem that the existing virus machine model lacks precision and accuracy when used for circuit system diagnosis due to storage data type limitations and confidence misjudgment, and cannot meet the current needs of power system fault diagnosis. This application proposes to use fuzzy numbers to improve the traditional virus machine model to obtain a fuzzy number virus machine model; in the face of the current power system with expanded scale, complex structure and access to new energy, compared with other traditional and existing power system fault diagnosis methods, this application proposes a power system fault diagnosis method based on the fuzzy number virus machine model, which has faster fault diagnosis speed and higher accuracy, and is suitable for the increasingly complex power systems. It solves the problem that the traditional power system fault diagnosis method has insufficient calculation speed and accuracy and lacks applicability, resulting in errors in the power system fault diagnosis results.

[0011] This application is based on fuzzy numbers, changing the natural numbers in the traditional virus machine model into real numbers from 0 to 1, allowing non-integer values to propagate between hosts, and improving the versatility of the traditional virus machine model, that is, the fuzzy number virus machine model of this application, the fuzzy number represents the confidence of the protection equipment in the power system, and at the same time, adding different function functions to the hosts in the fuzzy number virus machine, changing the channel weights of the channels between hosts, so that the fuzzy number virus machine model can better adapt to the complex needs of power system fault diagnosis and enhance the versatility in certain power signal processing.

[0012] This application uses a fuzzy number virus machine model to construct a power system diagnostic model for the bus and a power system diagnostic model for the line. It can classify the hosts corresponding to the action status of the main protection equipment and the backup protection equipment when a power system fault occurs, improve information processing efficiency, and can judge the action status of the main protection equipment and the backup protection equipment through the activation status of the instructions in the fuzzy number virus machine model, and compare the action status of each protection device, and obtain accurate fault judgment results based on the confidence of each host.

[0013] Furthermore, the improvement of the virus machine model based on fuzzy numbers specifically includes:

[0014] Change the natural numbers associated with the host in the virus machine model to fuzzy numbers;

[0015] The channels in the virus machine model are adjusted, and the first channel weight is adjusted to the second channel weight, where the first channel weight represents the number of virus replications and the second channel weight represents the transmission threshold of the virus.

[0016] This application uses fuzzy numbers to improve the traditional virus machine model, changing the natural numbers associated with the host in the traditional virus machine model into fuzzy numbers, which can allow non-integer values to propagate between hosts in the virus machine model, so that the virus machine model can correspond to the confidence of the protection equipment in the power system fault diagnosis, enhance adaptability, and solve the problem that the traditional virus machine model is not suitable for the propagation of non-integer values, resulting in insufficient precision and accuracy when applied to power system fault diagnosis; at the same time, the channel in the virus machine model is changed, and the channel weight representing the number of virus replications is changed to the channel weight representing the virus transmission threshold. Through the virus transmission threshold, the work required for the virus to move from one host to another is abstracted, ensuring that non-integer values can be propagated between hosts.

[0017] Furthermore: the expression of the fuzzy number virus machine model is as follows:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] in, is the fuzzy number virus machine model, is the number of hosts in the virus machine model, is the number of instructions in the virus machine model, for An ordered set of labels for each host. for An ordered set of labels for instructions, and are all directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, is the label of the first host, For the The host's label, For the The label of the instruction, For the The label of the instruction, To connect to the output host of the environment, Host collection and output host The union of is the set of directed edges between hosts, is a set of directed edges Each edge in is assigned a fuzzy number weight, is the set of directed edges between instructions, For Each edge in is assigned a real weight, For the environment.

[0024] This application improves the traditional virus machine model to obtain a fuzzy number virus machine model. It represents the initial confidence and activation function of the host, that is, the initial confidence and activation function of the protection equipment in the power system. It can process complex power system signals and solve the problem that the traditional virus machine model cannot be applied to power system fault diagnosis.

[0025] Further: the fuzzy number virus machine model is The expression for moment configuration is as follows:

[0026]

[0027] The expression of the initial configuration of the fuzzy number virus machine model is as follows:

[0028]

[0029] in, For the fuzzy number virus machine model Configuration of the moment, is the initial configuration of the fuzzy virus machine model, for Moment Host The confidence value of for Moment Host The confidence value of For the fuzzy number virus machine model The instructions of the moment, for The output of the fuzzy virus machine model to the environment at each moment, Initial host The confidence value of Initial host The confidence value of is the instruction of the fuzzy virus machine model at the initial moment.

[0030] This application sets the initial configuration of the fuzzy virus machine model and The system can change the configuration of the fuzzy number virus machine by activating the command, and then determine the direction of virus propagation between hosts in the fuzzy number virus machine model, reflecting the action of the protection equipment in the power system. According to the action of the protection equipment, the propagation process of the fault in the power system can be determined, and the location of the fault in the power system can be confirmed. This solves the problem of inaccuracy of the traditional virus machine model when used for power system fault diagnosis, and improves the efficiency of power system fault diagnosis.

[0031] Further: the fuzzy number virus machine model is Instructions at the moment The selection is as follows:

[0032] when Not connected to any instruction, then , the fuzzy virus machine model is in a shutdown configuration, and the # is a null value;

[0033] when is connected to only one instruction, then ;

[0034] when Connect to the command and ,and , in a non-deterministic way from Select the next command;

[0035] when Connect to the command and ,and , judgement will After applying the fuzzy number virus machine model, is there virus propagation? If so, ,otherwise, .

[0036] When the fuzzy number virus machine model of the present application is used to diagnose power system faults, the instruction selection rules can reduce unnecessary instruction execution, quickly locate the fault propagation path, and improve the efficiency of fault diagnosis. When the instruction is not connected or only one instruction is connected, the fuzzy number virus machine model can directly determine the next instruction. Faced with the uncertain situation of multiple instructions connected and equal weights, the uncertainty of power system faults is simulated through non-deterministic selection, and it adapts to complex fault scenarios. When the instruction weights are different, it judges whether virus propagation exists to dynamically select instructions. The present application selects instructions based on weights and virus propagation, which can reduce misjudgments caused by blind execution of instructions, improve the accuracy of fault diagnosis, and solve the problem of low availability of traditional virus machine models in actual power system fault diagnosis due to limitations in instruction selection.

[0037] Further: the fuzzy number virus machine model is The instruction at the moment is associated with the channel of the fuzzy number virus machine model, which is used to The instructions at each moment spread the virus through the channel of the fuzzy virus machine model, specifically including:

[0038] According to the fuzzy number virus machine model The instruction at the moment activates the fuzzy number virus machine model and opens the channel of the fuzzy number virus machine model;

[0039] judge Time fuzzy number virus machine model host Is the number of viruses in the channel smaller than the If the virus transmission threshold is , no virus transmission occurs. Otherwise, virus transmission occurs. The process of virus transmission is: the virus from the channel of One end propagates to one end;

[0040] Among them, the host Source host Connect, the host With the source host The channels between , is a natural number, .

[0041] In this application, the number of viruses represents the confidence level of the action of the protection equipment in the power system. When the number of viruses is greater than the channel weight value, that is, the virus transmission threshold, it is judged that virus propagation has occurred, and the simulation of the impact range of the power system fault is realized. It can clearly show the propagation path of the power system fault between different devices and update the number of viruses in the host in real time, solving the problem that it is difficult to accurately simulate the fault propagation process in traditional fault diagnosis methods, and overcoming the limitations of traditional virus machine models in processing continuous values and uncertain information, and improving the adaptability and diagnostic accuracy of the fuzzy number virus machine model to complex fault conditions in the power system.

[0042] Further: Time fuzzy number virus machine model The expression for the number of viruses in the virus is as follows:

[0043]

[0044] in, for Time fuzzy number virus machine model host The number of viruses, To obtain the minimum function, is the host in the fuzzy virus machine model The activation function, is the fuzzy number of hosts in the virus machine model at the initial moment The number of viruses it contains, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses.

[0045] This application proposes a The calculation expression of the number of viruses in the moment fuzzy number virus machine model combines the initial number of viruses and the number of viruses submitted by each connected host, and takes the minimum value after being processed by the activation function. It can simulate the dynamic process of fault information accumulation and propagation of buses and lines in power system fault diagnosis, ensuring that the number of viruses is within a reasonable range, and can effectively capture the changing characteristics of fault information. It can adapt to different fault scenarios and protection equipment configurations, and has applicability and reliability in complex power system fault diagnosis.

[0046] Further: the host in the fuzzy virus machine model The activation functions include: 、 、 as well as ;

[0047] when , in the host of the fuzzy virus machine model Internal Generation viruses;

[0048] when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submit to the host of the fuzzy virus machine model respectively The product of the number of viruses;

[0049] when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submitted to the fuzzy number virus machine model The maximum value among the number of viruses;

[0050] when , in the host of the fuzzy virus machine model Internal Generation A virus, is a source host in the fuzzy virus machine model Submitted to a fuzzy virus machine model connected to it The number of viruses.

[0051] This application designs four activation functions for the fuzzy number virus machine model. The activation function generates different numbers of viruses according to the number of input viruses, which can flexibly simulate the generation and propagation of faults in different scenarios, improve the adaptability of the fuzzy number virus machine model, and accurately reflect the processing and transmission process of fault information in the actual power system. It solves the problem that the traditional virus machine model adopts a single activation method and is difficult to flexibly handle different types of faults, resulting in inaccurate power system fault diagnosis. It improves the model's diagnostic accuracy and adaptability to complex power system faults, and enhances the model's effectiveness and reliability in practical applications.

[0052] Furthermore, the busbar fault diagnosis model for power system fault diagnosis is constructed based on the fuzzy number virus machine model, specifically including:

[0053] The primary protection device, circuit breaker and secondary protection device associated with the busbar are abstracted as hosts in the virus machine model;

[0054] The connection relationship between the primary protection device, circuit breaker and secondary protection device associated with the busbar is abstracted as a channel in the virus machine model;

[0055] The initial confidence levels of the primary protection devices, circuit breakers, and secondary protection devices associated with the busbar are set to the number of viruses in the virus machine model;

[0056] According to the number of hosts, channels and viruses, a bus fault diagnosis model for power system fault diagnosis is established.

[0057] This application applies the fuzzy number virus machine model to the bus fault diagnosis of actual power systems. By abstracting the bus-related protection devices as hosts, channels and virus numbers in the virus machine model, a bus fault diagnosis model suitable for power system fault diagnosis is constructed. It integrates the protection device information associated with the bus and fully considers the connection relationship and fault propagation characteristics between devices in the power system. It can efficiently identify and locate faults and has higher efficiency and accuracy than traditional power system diagnosis methods.

[0058] Furthermore, the line fault diagnosis model for power system fault diagnosis is constructed based on the fuzzy number virus machine model, specifically including:

[0059] The main protection equipment, circuit breakers and backup protection equipment associated with the line are abstracted as hosts in the virus machine model;

[0060] The connection relationship between the main protection device, circuit breaker and backup protection device associated with the line is abstracted as a channel in the virus machine model;

[0061] The initial confidence levels of the main protection devices, circuit breakers, and backup protection devices associated with the line are set to the number of viruses in the virus machine model;

[0062] According to the number of hosts, channels and viruses, a line fault diagnosis model for power system fault diagnosis is established.

[0063] This application applies the fuzzy number virus machine model to the line fault diagnosis of actual power systems. By abstracting the line-associated protection devices as hosts, channels and virus numbers in the virus machine model, a line fault diagnosis model suitable for power system fault diagnosis is constructed. It integrates the protection device information associated with the line and fully considers the connection relationship and fault propagation characteristics between devices in the power system. It can efficiently identify and locate faults and has higher efficiency and accuracy than traditional power system diagnosis methods.

[0064] The technical solution provided by the present invention has at least the following technical effects or advantages:

[0065] The present invention improves the traditional virus machine model based on fuzzy numbers to obtain a fuzzy number virus machine model, abstracts the busbars and lines in the power system and their associated protection equipment into a fuzzy number virus machine model, and constructs an efficient power system fault diagnosis model, which can flexibly simulate the fault propagation process. At the same time, combined with the judgment of equipment action confidence and virus transmission threshold, it effectively reduces the misjudgment of confidence and improves the fault diagnosis accuracy; at the same time, the fuzzy number virus machine model supports non-integer value propagation and is suitable for processing complex power systems. By designing diverse activation functions and instruction selection methods, it can accurately reflect the virus generation and propagation laws under different fault scenarios, improve the calculation efficiency and accuracy of the fuzzy number virus machine model, judge the propagation path of the fault in the power system, improve the efficiency and reliability of power system fault diagnosis, and ensure the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention;

[0067] Figure 1 It is a flow chart of the power system fault diagnosis method based on the fuzzy number virus machine model in the present invention;

[0068] Figure 2 is a schematic diagram of a bus fault on a bus in Example 3;

[0069] Figure 3 This is a structural diagram of the fuzzy number virus machine model based on R1, R2 and R3 in Example 3;

[0070] Figure 4is a schematic diagram of a line fault in Example 4;

[0071] Figure 5 This is a structural diagram of the fuzzy number virus machine model based on R4 and R5 in Example 4;

[0072] Figure 6 This is a structural diagram of the IEEE 39 circuit system in Example 5. DETAILED DESCRIPTION

[0073] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0074] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0075] Example 1

[0076] The present invention provides a power system fault diagnosis method based on fuzzy number virus machine model, such as Figure 1 As shown, the following steps are included:

[0077] The virus machine model is improved based on fuzzy numbers to obtain the fuzzy number virus machine model;

[0078] Based on the fuzzy number virus machine model, a bus fault diagnosis model for power system fault diagnosis is constructed;

[0079] Based on the fuzzy number virus machine model, a line fault diagnosis model for power system fault diagnosis is constructed;

[0080] According to the busbar fault diagnosis model and the line fault diagnosis model, the busbar and line of the power system are detected respectively to obtain the power system fault diagnosis results.

[0081] In this technical solution, the expansion model of the existing number of virus machine channels can be applied to power system fault diagnosis, but it lacks precision and accuracy in some power system areas. When a power system fault occurs, the corresponding protection equipment has confidence. Only judging the action of the protection equipment may lead to fault misjudgment due to the failure to reach the confidence level of the protection equipment. At the same time, the numbers stored and transmitted by the host in the traditional virus machine model are natural numbers, but the protection equipment in the power system usually outputs non-integer data. Different data will lead to inaccurate fault diagnosis results. This technical solution improves the virus machine model based on fuzzy numbers so that it can be applied to the current power system fault diagnosis.

[0082] Among them, the virus machine model is improved based on fuzzy numbers, including:

[0083] Changing the natural numbers associated with hosts in the virus machine model to fuzzy numbers allows non-integer values to propagate between hosts, improving the versatility of the virus machine model in power system fault diagnosis tasks. The numerical values in power system fault diagnosis tasks usually range from 0 to 1, representing the confidence level of protection devices in the power system.

[0084] The channels in the virus machine model are adjusted, and the first channel weight is adjusted to the second channel weight. The first channel weight represents the number of virus replications, and the second channel weight represents the virus transmission threshold. That is, the channel weights of the traditional virus machine model are changed, and the channel weight representing the number of virus replications is changed to the channel weight representing the virus transmission threshold. This abstracts the work required for the virus to move from one host to another, and changes the data transmitted between hosts from real numbers to fuzzy numbers.

[0085] This technical solution uses fuzzy numbers to improve the traditional virus machine model, changes the natural numbers associated with the host in the traditional virus machine model into fuzzy numbers, and changes the channel weights. The resulting fuzzy number virus machine model allows non-integer values to propagate between hosts in the virus machine model, so that the virus machine model can correspond to the confidence of the protection equipment in the power system fault diagnosis, enhance adaptability, and solve the problem that the traditional virus machine model is not suitable for the propagation of non-integer values, resulting in insufficient precision and accuracy when applied to power system fault diagnosis; at the same time, the channels in the virus machine model are changed, and the channel weight representing the number of virus replications is changed to the channel weight representing the virus transmission threshold. Through the virus transmission threshold, the work required for the virus to move from one host to another is abstracted, ensuring that non-integer values can be propagated between hosts.

[0086] The expression of the fuzzy number virus machine model in this technical solution is as follows:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] in, is the fuzzy number virus machine model, is the number of hosts in the virus machine model, is the number of instructions in the virus machine model, for An ordered set of labels for each host. for An ordered set of labels for instructions, and are all directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, is the label of the first host, For the The host's label, For the The label of the instruction, For the The label of the instruction, The output host connected to the environment, the output degree of the output host is 0 in general. Host collection and output host The union of is the set of directed edges between hosts, is a set of directed edges Each edge in is assigned a fuzzy weight, which is set to a real number between 0 and 1. is the set of directed edges between instructions, For Each edge in is assigned a real weight, For the environment.

[0093] exist middle, So that for each , both , the output degree of each host ,and for Each edge in is assigned a A positive integer; middle, , It is from arrive The output degree of each node is less than or equal to 2.

[0094] In this technical solution, the expression of the fuzzy number virus machine model can abstract the busbars and lines in the power system and their associated protection devices into hosts, channels and virus numbers, and introduce fuzzy numbers to represent the number of viruses, which solves the problem that the traditional virus machine model has limitations in the application of power system fault diagnosis due to different data types. It can more accurately simulate the power system fault propagation process, improve the accuracy and reliability of fault diagnosis, and enhance the versatility and adaptability of the fuzzy number virus machine model.

[0095] After obtaining the fuzzy number virus machine model, this technical solution applies the fuzzy number virus machine model to the power system. Based on the fuzzy number virus machine model, a bus fault diagnosis model for power system fault diagnosis can be constructed, specifically including:

[0096] The primary protection device, circuit breaker and secondary protection device associated with the busbar are abstracted as hosts in the virus machine model;

[0097] The connection relationship between the primary protection device, circuit breaker and secondary protection device associated with the busbar is abstracted as a channel in the virus machine model;

[0098] The initial confidence levels of the primary protection devices, circuit breakers, and secondary protection devices associated with the busbar are set to the number of viruses in the virus machine model;

[0099] According to the number of hosts, channels and viruses, a bus fault diagnosis model for power system fault diagnosis is established.

[0100] The fuzzy number virus machine model of the technical solution can also be used to construct a line fault diagnosis model for power system fault diagnosis based on the fuzzy number virus machine model, specifically including:

[0101] The main protection equipment, circuit breakers and backup protection equipment associated with the line are abstracted as hosts in the virus machine model;

[0102] The connection relationship between the main protection device, circuit breaker and backup protection device associated with the line is abstracted as a channel in the virus machine model;

[0103] The initial confidence levels of the main protection devices, circuit breakers, and backup protection devices associated with the line are set to the number of viruses in the virus machine model;

[0104] According to the number of hosts, channels and viruses, a line fault diagnosis model for power system fault diagnosis is established.

[0105] The fuzzy number virus machine model of the present technical solution provides an efficient and accurate solution for power system fault diagnosis. The fuzzy number virus machine model abstracts the protection devices and circuit breakers associated with the bus and line as hosts, abstracts the connection relationship between the hosts as channels, and introduces fuzzy numbers to represent the number of viruses, so that it can more flexibly handle uncertainty and continuous value information in the power system; at the same time, each host has an initial confidence and activation function, and can dynamically adjust the number of viruses according to the fault propagation situation, realizing a refined simulation of the power system fault propagation process, and by setting reasonable channel weights, it can effectively avoid misjudgments caused by insufficient confidence in the protection device action. Compared with the traditional virus machine model, the fuzzy number virus machine model of the present technical solution has higher versatility and adaptability in handling complex power system faults, and improves the accuracy and efficiency of fault diagnosis. Compared with the existing power system fault diagnosis method, the fuzzy number virus machine model, instruction selection method and activation function design of the present technical solution can flexibly respond to different power system fault scenarios and have stronger fault diagnosis capabilities and reliability.

[0106] Example 2

[0107] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. On the basis of the first embodiment, the fuzzy number virus machine model is used in The expression for moment configuration is as follows:

[0108]

[0109] The expression of the initial configuration of the fuzzy number virus machine model is as follows:

[0110]

[0111] in, For the fuzzy number virus machine model Configuration of the moment, is the initial configuration of the fuzzy virus machine model, for Moment Host The confidence value of for Moment Host The confidence value of For the fuzzy number virus machine model The instructions of the moment, for The output of the fuzzy virus machine model to the environment at each moment, Initial host The confidence value of Initial host The confidence value of is the instruction of the fuzzy virus machine model at the initial moment.

[0112] When the fuzzy number virus machine model is Instructions at the moment When the fuzzy virus machine model is in the shutdown configuration, the instruction The activation of will lead to the change of the configuration of the fuzzy number virus machine model; at the same time, the fuzzy number virus machine model The instructions at the moment are associated with the channel of the fuzzy virus machine model, and according to The instruction at the moment of time is used to spread the virus through the channel of the fuzzy number virus machine model. In the specific embodiment of the present invention, there is a host in the fuzzy number virus machine model. ,Host Source host Connected and through the channel The corresponding connections are made, and the channel weights can be set to , source host Submit to host The number of viruses is ,By comparing the number of viruses, i.e., the confidence level of the protection equipment in the power system, and the channel weight value, i.e., the virus transmission threshold, it is determined whether virus propagation occurs, specifically including:

[0113] According to the fuzzy number virus machine model The instruction at the moment activates the fuzzy number virus machine model and opens the channel of the fuzzy number virus machine model;

[0114] judge Time fuzzy number virus machine model host Is the number of viruses in the channel smaller than the If the virus transmission threshold is , no virus transmission occurs. Otherwise, virus transmission occurs. The process of virus transmission is: the virus from the channel of One end propagates to One end, where is a natural number and ; That is, the virus passes through the channel From the host Spread to the source host ,The propagation path of power system faults between different devices is simulated. At the same time, the number of viruses in the host needs to be updated in real time to solve the problem that it is difficult to accurately simulate the fault propagation process in traditional fault diagnosis methods.

[0115] In this technical solution, Time fuzzy number virus machine model host The expression for the number of viruses in the virus is as follows:

[0116]

[0117] in, for Time fuzzy number virus machine model host The number of viruses, To obtain the minimum function, is the host in the fuzzy virus machine model The activation function, is the fuzzy number of hosts in the virus machine model at the initial moment The number of viruses it contains, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses; This technical solution proposes a The calculation expression of the number of viruses in the moment fuzzy virus machine model combines the number of viruses contained in the host itself and the number of viruses submitted by each connected host, and takes the minimum value after being processed by the activation function. It can simulate the dynamic process of fault information accumulation and propagation of buses and lines in power system fault diagnosis. At the same time, it ensures that the number of viruses is within a reasonable range and can effectively capture the changing characteristics of fault information, adapting to different fault scenarios and protection equipment configurations.

[0118] pass Time fuzzy number virus machine model host The activation function in the expression of the number of viruses in the virus transmission process, the host The amount of virus in the host depends on the The activation function and connection to the host Therefore, the present technical solution proposes four activation functions, which generate different numbers of viruses according to the number of input viruses, and can flexibly simulate the generation and propagation of faults in different scenarios, improve the adaptability of the fuzzy number virus machine model, and accurately reflect the processing and transmission process of fault information in the actual power system. Compared with the traditional virus machine model that uses a single activation function, it can flexibly handle different types of faults and improve the accuracy of power system fault diagnosis. In the specific embodiment of the present invention, the host in the fuzzy number virus machine model The activation functions include: 、 、 as well as ;

[0119] when , in the host of the fuzzy virus machine model Internal Generation viruses;

[0120] when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submit to the host of the fuzzy virus machine model respectively The product of the number of viruses;

[0121] when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submitted to the fuzzy number virus machine model The maximum value among the number of viruses;

[0122] when , in the host of the fuzzy virus machine model Internal Generation A virus, is a source host in the fuzzy virus machine model Submitted to a fuzzy virus machine model connected to it The number of viruses.

[0123] In this technical solution, the fuzzy virus machine model is Instructions at the moment The selection is as follows:

[0124] when Not connected to any instruction, then ,The fuzzy virus machine model is in the shutdown configuration, # is a null value;

[0125] when is connected to only one instruction, then ;

[0126] when Connect to the command and ,and , in a non-deterministic way from Select the next command;

[0127] when Connect to the command and ,and , judgement will After applying the fuzzy number virus machine model, is there virus propagation? If so, ,otherwise, ;

[0128] in, and are weights; through instruction selection, the configuration of the fuzzy number virus machine model can be updated, such as the fuzzy number virus machine model in Time Configuration , by applying instructions from If the fuzzy number virus machine model is in the shutdown configuration, the calculation is stopped.

[0129] When diagnosing power system faults, this technical solution can reduce unnecessary instruction execution through instruction selection rules, quickly locate the fault propagation path, and improve fault diagnosis efficiency. At the same time, it can reduce misjudgment of power system faults caused by incorrect instruction execution and improve the accuracy of fault diagnosis.

[0130] Example 3

[0131] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. On the basis of the first and second embodiments, a busbar fault diagnosis model is constructed. Figure 2 The figure shows a schematic diagram of a busbar fault. In the power system, busbar B i Connected to line L1, line L2 and line L3 respectively. Point of failure is the fault point in Chinese. Figure 2 The middle represents busbar B i The following is the fault point that causes busbar B i Detailed analysis of the failure:

[0132] like Figure 2 As shown, when busbar B iWhen a fault occurs, the corresponding main protection device BMRi is activated. For line L1, the circuit breaker CB1 associated with the main protection device BMRi will trip. However, the circuit breaker CB1 may fail to successfully cut off the fault due to no response. The fault will spread to the outgoing line L1 of the busbar, and due to the busbar B i Without primary backup protection, the secondary backup protection LSR2 of line L1 will trip the associated circuit breaker CB2, thereby isolating the fault area and ensuring the safety of the circuit;

[0133] like Figure 2 As shown, for line L2, the circuit breaker CB3 associated with the main protection device BMRi will trip. If the circuit breaker CB3 fails to successfully cut off the fault, the fault will spread to the outgoing line L2 of the busbar and the busbar B i Circuit breaker CB4 on the connected line L2 will trip under the action of line L2's protection device LSR4. The operation of line L3 is the same as the previous two lines. The primary protection will start and open circuit breaker CB5. If circuit breaker CB5 fails to operate, its associated secondary backup protection LSR6 will trip circuit breaker CB6.

[0134] When busbar B i When a fault occurs, the fault diagnosis rule of line L1 can be expressed as R1, the fault diagnosis rule of line L2 can be expressed as R2, and the fault diagnosis rule of line L3 can be expressed as R3. Figure 2 It can be seen that the fault diagnosis rules are as follows:

[0135] R1: If {BMRi operates and CB1 trips} or {LSR2 operates and CB2 trips, CB1 does not trip}, then bus B i Fault;

[0136] R2: If {BMRi operates and CB3 trips} or {LSR4 operates and CB4 trips, CB3 does not trip}, then bus B i Fault;

[0137] R3: If {BMRi operates and CB5 trips} or {LSR6 operates and CB6 trips, CB6 does not trip}, then bus B i Fault.

[0138] According to the fault diagnosis rules of R1, R2 and R3, the fault diagnosis of bus B in the power system is carried out. i , construct busbar B i Fuzzy number virus machine model, such as Figure 3 As shown in the figure, it is a fuzzy number virus machine model based on R1, R2 and R3, which is defined as:

[0139]

[0140] (6) is the first control instruction executed, and .

[0141] In the bus fault diagnosis model of this embodiment, each pair of hosts connected to the same host will store the initial states of the relays and circuit breakers used by the corresponding primary protection device and secondary backup protection device, and the initial states together with the initial values will be encoded and stored together, recorded as the fuzzy number virus machine model. .

[0142] Example 4

[0143] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. On the basis of the first and second embodiments, a line fault diagnosis model is constructed, such as Figure 4 The figure shows a schematic diagram of a line fault. In the power system, line L i With a sending end and a receiving end, the Chinese word for Point of failure is the failure point. Figure 4 Indicates line L i The right side of the Point of failure is the sending end, and the left side of the Point of failure is the receiving end. The following is the failure point of line L i Detailed analysis of the failure:

[0144] like Figure 4 As shown, for line L i At the sending end, when line L i When a fault occurs, the i The main protection LMR1 at the sending end is activated, triggering the relevant circuit breaker CB1 to trip; if the main protection LMR1 does not operate, its first-level backup protection LBR1 is activated, tripping the corresponding circuit breaker CB1. i The circuit breaker CB1 on line L has not tripped yet. i The fault on will spread to the adjacent line. At this time, the secondary backup protection relay LSR2 will start, causing the corresponding circuit breaker CB2 to trip. At the same time, multiple secondary backup protection relays and corresponding circuit breakers can be set to ensure fault isolation.

[0145] like Figure 4 As shown, for line L i At the receiving end, when line L i When a fault occurs, the i The main protection LMR3 at the sending end is activated, triggering the relevant circuit breaker CB3 to trip; if the main protection LMR3 does not operate, its first-level backup protection LBR3 is activated, tripping the corresponding circuit breaker CB3. i The circuit breaker CB3 on line L has not tripped yet.i The fault on the line will spread to the adjacent line. At this time, the secondary backup protection relay LSR4 will start, causing the corresponding circuit breaker CB4 to trip. At the same time, multiple secondary backup protection relays and corresponding circuit breakers can be set to ensure fault isolation.

[0146] When line L i When a fault occurs, the fault diagnosis rule of the sending end can be expressed as R4, and the fault diagnosis rule of the receiving end can be expressed as R5. Figure 4 It can be seen that the fault diagnosis rules are as follows:

[0147] R4: If {LMR1 operates and CB1 trips} or {LBR1 operates and CB1 trips} or {LSR2 operates and CB2 trips, CB1 does not trip}, then Li fault occurs;

[0148] R5: If {LMR3 operates and CB3 trips} or {LBR3 operates and CB3 trips} or {LSR4 operates and CB4 trips, CB3 does not trip}, then Li faults.

[0149] According to the fault diagnosis rules of R4 and R5, for the power system fault diagnosis of line L i , construct line L i Fuzzy number virus machine model, such as Figure 5 As shown in the figure, it is a fuzzy number virus machine model based on R4 and R5, which is defined as:

[0150]

[0151] (6) is the first control instruction executed, and .

[0152] Example 5

[0153] The present invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. Based on the first, second, third and fourth embodiments, in order to illustrate the specific application details of the fuzzy number virus machine model in power system fault diagnosis, based on the following example: Figure 6 The rules of the IEEE 39 circuit system shown in FIG. 1 are bus, i.e., bus B in Example 3. i , Line is a line, i.e., line L in the fourth embodiment iCB is a circuit breaker, i.e., the circuit breaker in Examples 3 and 4. A case study consisting of four cases is designed to verify the effectiveness of the fuzzy number virus machine model. Among them, Case 1, Case 2, and Case 3 are single fault scenarios, and Case 4 is a multiple fault scenario. Table 1 below shows the alarm notifications indicating faults issued by the protection relay and circuit breaker in Cases 1-4, showing the results of the diagnostic evaluation and clearly distinguishing between single fault scenarios and multiple fault scenarios.

[0154] Table 1

[0155]

[0156] Table 2

[0157]

[0158] Table 1 demonstrates that the power system fault diagnosis method based on the fuzzy virus machine model provided by the present invention can accurately diagnose the faulty components in Cases 1 to 4. Table 2 shows the confidence levels for protective device action and inaction, demonstrating the confidence levels of protective device responses when the devices are in operation and inoperation during circuit fault diagnosis. This serves as the data source for defining channel weights when establishing the model. To illustrate the specific steps employed in the fuzzy virus machine model for power system fault diagnosis using relevant rules, Case 2 will be further discussed as a representative example.

[0159] As shown in Table 1 and Figure 6 As shown in the figure, case 2 is: the fault alarm information LMR55, LBR55, LMR83 and LSR54 are triggered, and CB54, CB55 and CB83 are tripped. The fault diagnosis process is as follows:

[0160] According to basic fault diagnosis methods, it is known that line L36 is the component most likely to fail. Based on this, R6 and R7 are established as follows:

[0161] R6: If {LMR55 operates and CB55 trips} or {LBR55 operates and CB55 trips} or {LSR54 operates and CB54 trips and CB55 does not trip}, then L36 is faulted;

[0162] R7: If {LMR88 operates and CB88 trips} or {LBR88 operates and CB88 trips} or {LSR89 operates and CB89 trips and CB88 does not trip}, then L36 is faulty.

[0163] It can be seen from R6 and R7 that the judgment methods for receiving-end protection and sending-end protection are the same, therefore, it can be inferred that the corresponding fuzzy number virus machine models are the same; Case 2 corresponds to both R6 and R7, therefore, it is reasonable to use the fuzzy virus machine model customized for diagnosing power system line Li faults, and the power system fault diagnosis method based on the fuzzy number virus machine model provided by the present invention can be used.

[0164] The following is the fuzzy number virus machine model Figure 6 The following is the process of fault diagnosis at the sending end of line L36 in the IEEE 39 circuit system:

[0165] In L36 line, after receiving the fault alarm information, first determine The value is 0, and the host The values are 0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, respectively, indicating that the secondary backup protection of line protection LMR54 has been activated and circuit breaker CB54 has tripped, while the main protection and primary backup protection LMR55 and LBR55 have not been activated. Therefore, circuit breaker CB55 is not activated.

[0166] In this case, the process of calculating the fuzzy virus machine model for power system fault diagnosis using production rules with input values of 0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0 is summarized as follows:

[0167] C0=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0, i1,0);

[0168] C1=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0, i2,0);

[0169] C2=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0,i4,0);

[0170] C3=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0, i6,0);

[0171] C4=(0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0.6793, 0, i7, 0);

[0172] C5=(0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0, 0.6453, i fault ,0);

[0173] C6=(0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0, 0, #, 0.6453).

[0174] The initial configuration C0=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0, i1, 0). In the first step, the first trigger instruction is , so that arrive and from arrive The channels are open to transmit the virus; however, due to The confidence value is less than the weight of the open channel, the transmission fails, and the instruction activation signal is transmitted along the arrive This minimum weight path transmits, therefore, C1=(0.9023, 0.5249, 0.8949, 0.5249, 0.8842,0.7683, 0, 0, 0, 0, 0, i2, 0).

[0175] In the second step, similar to the first step, due to The confidence value is less than the weight of the open channel, the transmission fails, and the instruction activation signal is transmitted along the arrive This minimum weight path transmits, therefore, C2=(0.9023, 0.5249, 0.8949,0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 0,i4, 0).

[0176] In the third step, the instruction is activated, so that arrive and from arrive The channel is opened to transmit the virus, stored in The numbers in are originally stored in and The product of the two numbers in the instruction activation signal is along the arrive This maximum weight path transmits, therefore, C3 = (0.9023, 0.5249, 0.8949, 0.5249, 0.8842,0.7683, 0, 0, 0, 0, 0, i6, 0).

[0177] In the fourth step, the instruction is activated, so that arrive The channel is opened to transmit the virus, and the activation signal is sent from Transfer to , therefore, C4 = (0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0.6793,0, i7, 0).

[0178] In the fifth step, the instruction is activated, so that arrive The channel is open to transmit the virus, now stored in The number in is a constant multiplier k, which can be 0.95, which is the same as The product of the number of viruses transmitted, the instruction activation signal along the arrive This maximum weight path transmits, therefore, C5 = (0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0.6453, i fault , 0).

[0179] In step 6, the instruction is activated, so that arrive The channel is opened to transmit the virus. Since there is no further connection instruction, C6 = (0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0, 0,#, 0.6453), and the output result is 0.6453. Based on this, it can be judged that line L36 is faulty.

[0180] The following is the fuzzy number virus machine model Figure 6 The following is the process of fault diagnosis at the receiving end of line L36 in the IEEE 39 circuit system:

[0181] In line L36, according to the acquired fault warning information, The initial value is 0, the host The values are 0.7522, 0.8056, 0, 0, 0, 0, 0, 0, 0, 0, respectively, indicating that the primary backup protection of line protection LMR88 is activated, causing circuit breaker CB88 to trip, the first backup protection and the second backup protection are not activated, therefore, circuit breaker CB89 is not operated.

[0182] In this case, the process of calculating the fuzzy virus machine model for power system fault diagnosis using production rules with input values of 0.7522, 0.8056, 0, 0, 0, 0, 0, 0, 0, 0 is summarized as follows:

[0183] C0 = (0.9922, 0.9856, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0, i1, 0),

[0184] C1 = (0, 0, 0, 0.9856, 0, 0, 0.9779, 0, 0, 0, 0, i3, 0);

[0185] C2 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0, i2, 0);

[0186] C3 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0, i4, 0);

[0187] C4 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0, i7, 0);

[0188] C5 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0.9290, i fault , 0);

[0189] C6 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0, 0, #, 0.9290).

[0190] The initial configuration is C0 = (0.9922, 0.9856, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0, i1,0). In the first step, the first trigger instruction is , so that arrive and from arrive The channel is opened to transmit the virus, stored in The number of viruses in the and The product of the number of viruses in the instruction activation signal along the arrive This minimum weight path transmits, therefore, C1 = (0, 0, 0, 0.9856, 0, 0, 0.9779, 0, 0, 0, 0, i3,0).

[0191] In the second step, the instruction is activated, so that arrive The channel is opened to transmit the virus, and then the command activation signal is sent from Transfer to , therefore, C2 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0.9779, 0, i2,0).

[0192] In the third step, similar to the first step, the instruction is activated. arrive and from arrive The channel is opened to transmit the virus, because The confidence value is less than the weight of the open channel, the transmission fails, and the instruction activation signal is transmitted along the arrive This minimum weight path transmits, therefore, C3 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0.9779, 0,i4, 0).

[0193] In the fourth step, similar to the third step, due to arrive The confidence values of the channels are all less than the weights of the open channels, and the transmission fails. The command activation signal is transmitted along the arrive This lowest weight path transmits, therefore, C4 = (0, 0, 0,0.9856, 0, 0, 0, 0, 0.9779, 0, i7, 0).

[0194] In the fifth step, the instruction is activated, so that arrive The channel is open to transmit the virus, now stored in The number in is a constant multiplier k, which can be 0.95, which is the same as The product of the number of viruses transmitted, the instruction activation signal along the arrive This maximum weight path is transmitted, so C5 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0,0, 0.9290, i fault , 0).

[0195] In step 6, the instruction is activated, so that arrive The channel is open to transmit the virus. Since there are no further connection instructions, C6 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0, #, 0.9290). The output result is 0.9290, which indicates that line L36 is faulty.

[0196] By performing fault diagnosis on the transmitting end of line L36 and the receiving end of line L36, it is inferred that the faulty component is line L36, and the output confidence is 0.9290.

[0197] Table 3

[0198]

[0199] Table 3 compares the fault diagnosis results of different diagnostic methods. The channel parallel virus machine model and the fuzzy number virus machine model are used for comparative experiments. As shown in Table 3, the fuzzy number virus machine model has the following advantages over the traditional channel parallel virus machine model:

[0200] The integration of fuzzy numbers and related functions, combined with reasonable subject grouping, makes the fault handling process more direct and easy to understand;

[0201] The fuzzy number virus machine model can process more accurate data and obtain accurate judgment values during the signal reception and processing process. Especially when there are multiple signal outputs in a small area during the fault diagnosis process, the advantages of the fuzzy number virus machine model of the present invention are more obvious and can identify more accurate fault sources.

[0202] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0203] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A power system fault diagnosis method based on fuzzy number virus machine model, characterized in that: The following steps are involved: The virus machine model is improved based on fuzzy numbers to obtain the fuzzy number virus machine model; Based on the fuzzy number virus machine model, a bus fault diagnosis model for power system fault diagnosis is constructed; Based on the fuzzy number virus machine model, a line fault diagnosis model for power system fault diagnosis is constructed; According to the busbar fault diagnosis model and the line fault diagnosis model, the busbar and line of the power system are detected respectively to obtain the power system fault diagnosis results.

2. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1 is characterized in that: The improvement of the virus machine model based on fuzzy numbers specifically includes: Change the natural numbers associated with the host in the virus machine model to fuzzy numbers; The channels in the virus machine model are adjusted, and the first channel weight is adjusted to the second channel weight, where the first channel weight represents the number of virus replications and the second channel weight represents the transmission threshold of the virus.

3. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 2 is characterized in that: The expression of the fuzzy number virus machine model is as follows: in, is the fuzzy number virus machine model, is the number of hosts in the virus machine model, is the number of instructions in the virus machine model, for An ordered set of labels for each host. for An ordered set of labels for instructions, and are all directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, For the The initial storage fuzzy number of hosts, For the The initial confidence of each host, For the The activation function of each host, is the label of the first host, For the The host's label, For the The label of the instruction, For the The label of the instruction, To connect to the output host of the environment, Host collection and output host The union of is the set of directed edges between hosts, is a set of directed edges Each edge in is assigned a fuzzy number weight, is the set of directed edges between instructions, For Each edge in is assigned a real weight, For the environment.

4. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 3 is characterized in that: The fuzzy number virus machine model is The expression for moment configuration is as follows: The expression of the initial configuration of the fuzzy number virus machine model is as follows: in, For the fuzzy number virus machine model Configuration of the moment, is the initial configuration of the fuzzy virus machine model, for Moment Host The confidence value of for Moment Host The confidence value of For the fuzzy number virus machine model The instructions of the moment, for The output of the fuzzy virus machine model to the environment at each moment, Initial host The confidence value of Initial host The confidence value of is the instruction of the fuzzy virus machine model at the initial moment.

5. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 4 is characterized in that: The fuzzy number virus machine model is Instructions at the moment The selection is as follows: when Not connected to any instruction, then , the fuzzy virus machine model is in a shutdown configuration, and the # is a null value; when is connected to only one instruction, then ; when Connect to the command and ,and , in a non-deterministic way from Select the next command; when Connect to the command and ,and , judgement will After applying the fuzzy number virus machine model, is there virus propagation? If so, ,otherwise, .

6. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 4 is characterized in that: The fuzzy number virus machine model is The instruction at the moment is associated with the channel of the fuzzy number virus machine model, which is used to The instructions at each moment spread the virus through the channel of the fuzzy virus machine model, specifically including: According to the fuzzy number virus machine model The instruction at the moment activates the fuzzy number virus machine model and opens the channel of the fuzzy number virus machine model; judge Time fuzzy number virus machine model host Is the number of viruses in the channel smaller than the If the virus transmission threshold is , no virus transmission occurs. Otherwise, virus transmission occurs. The process of virus transmission is: the virus from the channel of One end propagates to one end; Among them, the host Source host Connect, the host With the source host The channels between , is a natural number, .

7. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 6 is characterized in that: described Time fuzzy number virus machine model The expression for the number of viruses in the virus is as follows: in, for Time fuzzy number virus machine model host The number of viruses, To obtain the minimum function, is the host in the fuzzy virus machine model The activation function, is the fuzzy number of hosts in the virus machine model at the initial moment The number of viruses it contains, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses, is the source host in the fuzzy virus machine model Submit to the host in the fuzzy virus machine model The number of viruses.

8. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 7 is characterized in that: The host in the fuzzy virus machine model The activation functions include: 、 、 as well as ; when , in the host of the fuzzy virus machine model Internal Generation viruses; when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submit to the host of the fuzzy virus machine model respectively The product of the number of viruses; when , in the host of the fuzzy virus machine model The number of viruses generated in the source host is the fuzzy number virus machine model Submitted to the fuzzy number virus machine model The maximum value among the number of viruses; when , in the host of the fuzzy virus machine model Internal Generation A virus, is a source host in the fuzzy virus machine model Submitted to a fuzzy virus machine model connected to it The number of viruses.

9. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1 is characterized in that: The busbar fault diagnosis model for power system fault diagnosis is constructed based on the fuzzy number virus machine model, which specifically includes: The primary protection device, circuit breaker and secondary protection device associated with the busbar are abstracted as hosts in the virus machine model; The connection relationship between the primary protection device, circuit breaker and secondary protection device associated with the busbar is abstracted as a channel in the virus machine model; The initial confidence levels of the primary protection devices, circuit breakers, and secondary protection devices associated with the busbar are set to the number of viruses in the virus machine model; According to the number of hosts, channels and viruses, a bus fault diagnosis model for power system fault diagnosis is established.

10. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1 is characterized in that: The line fault diagnosis model for power system fault diagnosis is constructed based on the fuzzy number virus machine model, which specifically includes: The main protection equipment, circuit breakers and backup protection equipment associated with the line are abstracted as hosts in the virus machine model; The connection relationship between the main protection device, circuit breaker and backup protection device associated with the line is abstracted as a channel in the virus machine model; The initial confidence levels of the main protection devices, circuit breakers, and backup protection devices associated with the line are set to the number of viruses in the virus machine model; According to the number of hosts, channels and viruses, a line fault diagnosis model for power system fault diagnosis is established.

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