Fault diagnosis method for power system based on fuzzy number virus machine model

Abstract

The application discloses a power system fault diagnosis method based on a fuzzy number virus machine model, relates to the technical field of power system fault diagnosis, and improves a virus machine model based on fuzzy numbers to obtain a fuzzy number virus machine model, constructs a bus fault diagnosis model and a line fault diagnosis model for power system fault diagnosis according to the fuzzy number virus machine model, detects buses and lines of a power system, and obtains a power system fault diagnosis result. The application improves a traditional virus machine model based on fuzzy numbers, so that the virus machine model can meet the fault diagnosis requirements of a current power system, solves the problem that a traditional power system fault diagnosis method is insufficient in calculation speed and accuracy and lacks applicability when facing the expansion of the scale of a power system, the complication of the structure of the power system and new energy, leads to errors in the power system fault diagnosis result, and improves the speed and accuracy of power system fault diagnosis.

Description

TECHNICAL FIELD

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

[0002] As a key infrastructure of modern social economy operation and people's daily life, accurately and timely diagnosing power system faults is of great significance to ensure 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, and the large-scale access of new energy has changed the operation characteristics and fault modes of the power system. The traditional power system fault diagnosis method lacks calculation accuracy and speed when facing the current power system. The existing power system fault diagnosis method, including the fault diagnosis method based on convolutional neural network, the tolerance processing in simulation fault diagnosis, the reversible logic circuit technology, the fuzzy set theory, the causal network and the spiking neural P system (SNPSs), although can diagnose the faults of the power system, the precision still needs to be improved, and more methods need to be explored to realize more accurate power system fault diagnosis.

[0003] The virus machine model is a computational model inspired by the biological virus information transmission mechanism, mainly including three components: host, channel and virus transmission. In this model, the process of the virus transmitting its genetic information to the host cell and then controlling the host cell to reproduce is abstracted as the transmission and processing mode of information. The channel parallel virus machine (CPVM) based on the improved channel parallel virus machine of the virus machine is capable of being applied to power system fault diagnosis, but due to the limitation of the data type stored in the virus machine model and the misjudgment of the confidence, the virus machine model still lacks a certain degree of precision and accuracy in some areas of the power system when used for power system fault diagnosis, and needs to be further optimized to adapt to the complex requirements of power system fault diagnosis. SUMMARY

[0004] The present application provides a power system fault diagnosis method based on a fuzzy number virus machine model, which can solve the problem of incorrect power system fault diagnosis results caused by insufficient calculation speed and precision and lack of applicability of traditional power system fault diagnosis methods when facing the expansion of the power system scale, the complication of the structure and new energy, and improve the speed and accuracy of power system fault diagnosis.

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

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

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

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

[0009] According to the bus fault diagnosis model and the line fault diagnosis model, the bus and the line of the power system are detected respectively, and the power system fault diagnosis result is obtained.

[0010] The present application aims at the fact that the existing virus machine model lacks precision and accuracy when used for circuit system diagnosis due to the limitation of stored data type and confidence error judgment, and cannot meet the current demand for power system fault diagnosis. The present application proposes to improve the traditional virus machine model using fuzzy numbers to obtain a fuzzy number virus machine model. Compared with other traditional and existing power system fault diagnosis methods, the power system fault diagnosis method based on the fuzzy number virus machine model proposed by the present application has faster fault diagnosis speed and higher accuracy, and is suitable for the current more complex power system, solving the problem of error in power system fault diagnosis result caused by the lack of calculation speed and precision and applicability of traditional power system fault diagnosis methods.

[0011] Based on fuzzy numbers, the present application changes the natural numbers in the traditional virus machine model to real numbers between 0 and 1, allowing non-integer values to propagate between hosts, improving the versatility of the traditional virus machine model. The fuzzy number virus machine model of the present application represents the confidence of the protection equipment in the power system, and adds different function functions to the hosts in the fuzzy number virus machine, and changes the channel weight of the channel between the hosts, so that the fuzzy number virus machine model can better adapt to the complex requirements of power system fault diagnosis and enhance the versatility in certain power signal processing.

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

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

[0014] The natural numbers associated with the hosts in the virus machine model are changed to fuzzy numbers.

[0015] adjusting a channel in the virus machine model, the first channel weight representing a replication number of the virus, the second channel weight representing a transmission threshold of the virus.

[0016] The application improves the traditional virus machine model by using fuzzy numbers, changes the natural numbers associated with the host in the traditional virus machine model to fuzzy numbers, which can allow non-integer value propagation between hosts in the virus machine model, enable the virus machine model to correspond to the confidence of the protection device in power system fault diagnosis, enhance adaptability, and solve the problem that the traditional virus machine model cannot be applied to non-integer value propagation, 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, the channel weight representing the replication number of the virus is changed to the channel weight representing the transmission threshold of the virus, and the virus transmission threshold abstracts the work required for the virus to move from one host to another, ensuring that non-integer values can be propagated between hosts.

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

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] wherein, is a 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, is is an ordered set of labels of the hosts, is an ordered set of labels of the instructions, is a directed weighted graph, and are both directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, is the initial storage fuzzy number of the i-th host, is the initial confidence of the i-th host, is the activation function of the i-th host, is the initial confidence of the i-th host, is the activation function of the i-th host, is the initial confidence of the i-th host, is the activation function of the i-th host, the initial fuzzy number of the host, the initial confidence of the host, the activation function of the host, the initial fuzzy number of the host, the initial confidence of the host, the activation function of the host, the initial fuzzy number of the host, the initial confidence of the host, the activation function of the host, the label of the first host, the label of the host, the label of the first host, the label of the host, the label of the first host, the label of the first instruction, the label of the first instruction, the label of the first instruction, the output host connected to the environment, the union of the host set and the output host , the directed edge set between the hosts, the directed edge set between the hosts, assign a fuzzy number weight to each edge in the directed edge set , the directed edge set between the instructions, assign a real number weight to each edge in the directed edge set , the environment.

[0024] The present application improves the traditional virus machine model to obtain a fuzzy number virus machine model, which uses to represent the initial confidence and activation function of the host, i.e. the initial confidence and activation function of the protection device in the power system, can process complex power system signals, and solves the problem that the traditional virus machine model cannot be applied to power system fault diagnosis.

[0025] Further, the expression configured by the fuzzy number virus machine model at time is as follows:

[0026]

[0027] The expression configured by the fuzzy number virus machine model at the initial time is as follows:

[0028]

[0029] wherein, is the configuration of the fuzzy number virus machine model at time, The initial configuration for the fuzzy number virus machine model. for Time Host The confidence value, for Time Host The confidence value, For fuzzy number virus machine models in The instructions of the moment, for The output of the time-fuzzy number virus machine model to the environment Host at the initial moment The confidence value, Host at the initial moment The confidence value, This refers to the instructions given to the fuzzy number virus machine model at the initial moment.

[0030] This application sets the initial time configuration of the fuzzy number virus machine model and The system is configured in real time, and the configuration of the fuzzy number virus machine is changed by the activation of the command. Then, the direction of virus propagation between hosts in the fuzzy number virus machine model is determined, reflecting the operation of protection equipment in the power system. Based on the operation of protection equipment, the propagation process when a fault occurs in the power system is determined, and the location of the fault in the power system is confirmed. This solves the problem of inaccuracy and imprecision of traditional virus machine models when used for power system fault diagnosis, and improves the efficiency of power system fault diagnosis.

[0031] Furthermore: the fuzzy number virus machine model in Time instructions The selection methods are as follows:

[0032] when If not connected to any command, then The fuzzy virus machine model is in shutdown configuration, and # represents a null value;

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

[0034] when Connect to instructions and ,and From in a non-deterministic way Select the next instruction;

[0035] when Connect to instructions and ,and , judge Does the virus spread after being applied to the fuzzy number virus machine model? If so, ,otherwise, .

[0036] The fuzzy number virus machine model of this application can reduce unnecessary instruction execution, quickly locate fault propagation paths, and improve fault diagnosis efficiency when performing power system fault diagnosis through its instruction selection rules. When instructions are not connected or only one instruction is connected, the fuzzy number virus machine model can directly determine the next instruction. Faced with uncertain situations where multiple instructions are connected and have equal weights, it simulates the uncertainty of power system faults through nondeterministic selection, adapting to complex fault scenarios. When instruction weights are different, it judges whether virus propagation exists to dynamically select instructions. This application selects instructions based on weights and virus propagation, which can reduce misjudgments caused by blindly executing 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] Furthermore: the fuzzy number virus machine model in The time-based instructions are associated with the channels of the fuzzy number virus machine model, and are used to... The instructions given at specific times are transmitted through the channels of the fuzzy number virus machine model to spread the virus, specifically including:

[0038] Based on the fuzzy number virus machine model The command at a given moment activates the fuzzy number virus machine model and opens the channel of the fuzzy number virus machine model;

[0039] judge Host in the Time-Fuzzy Number Virus Machine Model Is the number of viruses in the channel less than the number of channels? The virus transmission threshold is set; if it is, no virus transmission occurs; otherwise, virus transmission occurs. The virus transmission process is as follows: the virus enters through the channel... of One end spreads to One end;

[0040] Among them, the host Each with the source host Connection, the host With the source host The channels between them are respectively The For natural numbers, the stated .

[0041] In this application, the number of viruses represents the confidence level of the operation of protection equipment in the power system. When the number of viruses is greater than the channel weight value, i.e. the virus transmission threshold, it is determined that virus propagation has occurred, thereby simulating the impact range of power system faults. It can clearly show the propagation path of power system faults between different devices and update the number of viruses in the host in real time. This solves the problem that traditional fault diagnosis methods are difficult to accurately simulate the fault propagation process. It also overcomes the limitations of traditional virus machine models in handling continuous values ​​and uncertain information, and improves the adaptability and diagnostic accuracy of fuzzy number virus machine models for complex fault situations in power systems.

[0042] Furthermore: the aforementioned Time-Fuzzy Number Virus Machine Model The expression for the number of viruses in the virus is as follows:

[0043]

[0044] in, for Host in the Time-Fuzzy Number Virus Machine Model The number of viruses in the body To find the minimum value function, For the host in the fuzzy number virus machine model Activation function, The host in the fuzzy number virus machine model at the initial time. The number of viruses it contains. For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses, For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses, In the fuzzy number virus machine model, the source host Host submitted to the fuzzy number virus machine model The number of viruses, For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses.

[0045] This application proposes a... The calculation expression of the virus quantity in the fuzzy number virus machine model combines the initial virus quantity and the virus quantity submitted by each connected host, takes the minimum value after processing by the activation function, can simulate the dynamic process of fault information accumulation and propagation of bus and line in power system fault diagnosis, ensures that the virus quantity is within a reasonable range, can effectively capture the fault information change characteristics, can adapt to different fault scenarios and protection device configurations, and has applicability and reliability in complex power system fault diagnosis.

[0046] Further, the activation function of the host in the fuzzy number virus machine model includes: , , and ;

[0047] When , the number of viruses generated in the host of the fuzzy number virus machine model is ; When

[0048] , the number of viruses generated in the host of the fuzzy number virus machine model is the product of the virus quantity submitted by the source host of the fuzzy number virus machine model to the host of the fuzzy number virus machine model . When , the number of viruses generated in the host of the fuzzy number virus machine model is the maximum value in the virus quantity submitted by the source host of the fuzzy number virus machine model to the host of the fuzzy number virus machine model .

[0049] When , the number of viruses generated in the host of the fuzzy number virus machine model is .

[0050] When , the number of viruses generated in the host of the fuzzy number virus machine model is , the virus quantity submitted by a source host of the fuzzy number virus machine model to the host of the fuzzy number virus machine model connected thereto.

[0051] ​​​​​​The application designs four activation functions for the fuzzy number virus machine model, which can produce different numbers of viruses according to the input virus number, can flexibly simulate the generation and propagation of faults in different scenarios, improve the adaptability of the fuzzy number virus machine model, accurately reflect the processing and transmission process of fault information in the actual power system, solve the problem that the traditional virus machine model uses a single activation mode and is difficult to flexibly handle different types of faults, leading to inaccurate power system fault diagnosis, improve the diagnosis accuracy and adaptability of the model for complex power system faults, and enhance the effectiveness and reliability of the model in practical application.

[0052] Further, the bus fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model, specifically comprising:

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

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

[0055] The initial confidence of the main protection device, circuit breaker and secondary protection device associated with the bus is set as the virus number in the virus machine model;

[0056] According to the host, channel and virus number, a bus fault diagnosis model for power system fault diagnosis is established.

[0057] The application applies the fuzzy number virus machine model to the bus fault diagnosis of the actual power system, abstracts the protection devices associated with the bus as hosts, channels and virus numbers in the virus machine model, constructs a bus fault diagnosis model suitable for power system fault diagnosis, integrates the protection device information associated with the bus, and fully considers the connection relationship between the devices in the power system and the fault propagation characteristics, which can efficiently identify and locate faults, and has higher efficiency and accuracy compared with the traditional power system diagnosis method.

[0058] Further, the line fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model, specifically comprising:

[0059] The main protection device, circuit breaker and backup protection device 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] Set the initial confidence of the main protection device, circuit breaker and backup protection device associated with the line as the virus quantity in the virus machine model;

[0062] According to the host, channel and virus quantity, a line fault diagnosis model for power system fault diagnosis is established.

[0063] The application applies the fuzzy number virus machine model to the line fault diagnosis of the actual power system, abstracts the protection devices associated with the line as the host, channel and virus quantity in the virus machine model, constructs the line fault diagnosis model suitable for the power system fault diagnosis, integrates the protection device information associated with the line, fully considers the connection relationship between the devices in the power system and the fault propagation characteristics, can efficiently identify and locate the fault, and has higher efficiency and accuracy compared with the traditional power system diagnosis method.

[0064] The technical scheme provided by the application has at least the following technical effects or advantages:

[0065] The application improves the traditional virus machine model based on the fuzzy number, obtains the fuzzy number virus machine model, abstracts the bus and line in the power system and the protection devices associated therewith as the fuzzy number virus machine model, constructs the efficient power system fault diagnosis model, can flexibly simulate the fault propagation process, effectively reduces the misjudgment of the confidence by combining the device action confidence and the virus transmission threshold, improves the fault diagnosis precision, supports the non-integer value propagation, is suitable for processing the complex power system, can accurately reflect the virus generation and propagation law under different fault scenes by designing the diversified activation function and instruction selection mode, improves the calculation efficiency and accuracy of the fuzzy number virus machine model, judges the spreading path of the fault in the power system, improves the efficiency and reliability of the power system fault diagnosis, and guarantees the stable operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0066] The accompanying drawings, which are included to provide a further understanding of the embodiments of the application and constitute a part of the application, do not constitute a limitation on the embodiments of the application;

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

[0068] Figure 2 is the schematic diagram of the bus fault on the bus in embodiment three;

[0069] Figure 3 is the fuzzy number virus machine model structure diagram based on R1, R2 and R3 in embodiment three;

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

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

[0072] Figure 6 is a structural diagram of an IEEE 39 circuit system in Example Five. DETAILED DESCRIPTION

[0073] In order to enable a more clear understanding of the above-mentioned objects, features and advantages of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0074] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can also be practiced in other ways different from those described herein within the scope of the present application, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0075] Example One

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

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

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

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

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

[0081] In the technical solution, the existing virus machine channel number expansion model can be applied to power system fault diagnosis, but lacks precision and accuracy in some power system areas. When a power system fault occurs, the corresponding protection device has a confidence level, and only the action of the protection device is judged, which may lead to fault misjudgment due to the failure of the protection device confidence level. At the same time, the numbers stored and propagated by the host in the traditional virus machine model are natural numbers, but the protection device in the power system usually outputs non-integer data, and different data will lead to inaccurate fault diagnosis results. The present technical solution improves the virus machine model based on fuzzy numbers, so that it can be applied to current power system fault diagnosis.​

[0082] The virus machine model is improved based on fuzzy numbers, specifically including:

[0083] The natural number associated with the host in the virus machine model is changed to a fuzzy number, allowing non-integer values to propagate between hosts, improving the versatility of the virus machine model in power system fault diagnosis tasks, as the numerical range in power system fault diagnosis tasks is usually between 0 and 1, representing the confidence of protection devices in the power system.

[0084] The channel in the virus machine model is adjusted, with the first channel weight adjusted to the second channel weight, the first channel weight representing the number of virus copies, and the second channel weight representing the transmission threshold of the virus, i.e., the channel weight representing the number of virus copies in the traditional virus machine model is changed to the channel weight representing the transmission threshold of the virus, abstracting the work required for the virus to move from one host to another, changing the data transmitted between hosts from a real number to a fuzzy number.

[0085] The present technical solution improves the traditional virus machine model using fuzzy numbers, changes the natural number associated with the host in the traditional virus machine model to a fuzzy number, and changes the channel weight, resulting in a fuzzy number virus machine model that allows non-integer values to propagate between hosts in the virus machine model, enabling the virus machine model to correspond to the confidence of protection devices in power system fault diagnosis, enhancing adaptability and solving the problem of insufficient precision and accuracy when applying the traditional virus machine model to power system fault diagnosis due to its inability to apply to the propagation of non-integer values. At the same time, the channel in the virus machine model is changed, with the channel weight representing the number of virus copies changed to the channel weight representing the transmission threshold of the virus, through the virus transmission threshold, abstracting the work required for the virus to move from one host to another, ensuring that non-integer values can propagate between hosts.

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

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] wherein, is the fuzzy number virus machine model, is the number of hosts in the virus machine model, the number of instructions in the viral machine model, is an ordered set of labels of hosts, is an ordered set of labels of instructions, and are both directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, is the initial storage fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the initial storage fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the initial storage fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the label of the 1st host, is the label of the th host, is the label of the th instruction, is the label of the th instruction, is the output host connected to the environment, the output degree of the output host is 0 in the general state, is the union of the host set and the output host , is the set of directed edges between hosts, is a fuzzy number weight assigned to each edge in the set of directed edges , the fuzzy number is set to a real number between 0 and 1, is the set of directed edges between instructions, is an real number weight assigned to each edge in , is the environment.

[0093] In , such that for each , there is , the output degree of each host and for each edge in G is assigned a positive integer from the set ; in , , is a mapping from to and the out-degree of each node is less than or equal to 2.

[0094] In the technical solution, the expression of the fuzzy number virus machine model can abstract the bus and line in the power system and the associated protection equipment as hosts, channels and virus quantities, and introduce fuzzy numbers to represent the virus quantities, solve the problem of limitations in application of the traditional virus machine model in power system fault diagnosis due to different data types, can more accurately simulate the fault propagation process of the power system, improve the accuracy and reliability of fault diagnosis, and enhance the universality and adaptability of the fuzzy number virus machine model.

[0095] After obtaining the fuzzy number virus machine model, the technical solution can apply the fuzzy number virus machine model in the power system, and according to the fuzzy number virus machine model, a bus fault diagnosis model for power system fault diagnosis can be constructed, which specifically includes:

[0096] The main protection equipment, circuit breaker and secondary protection equipment associated with the bus are abstracted as hosts in the virus machine model;

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

[0098] The initial confidence of the main protection equipment, circuit breaker and secondary protection equipment associated with the bus is set as the virus quantity in the virus machine model;

[0099] According to the hosts, channels and virus quantities, 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 construct a line fault diagnosis model for power system fault diagnosis according to the fuzzy number virus machine model, which specifically includes:

[0101] The main protection equipment, circuit breaker 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 equipment, circuit breaker and backup protection equipment 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] Based on the host, channel, and number of viruses, a line fault diagnosis model for power system fault diagnosis is established.

[0105] This technical solution's fuzzy number virus machine model provides an efficient and accurate solution for power system fault diagnosis. The fuzzy number virus machine model abstracts protection devices and circuit breakers associated with buses and lines as hosts, the connections between hosts as channels, and introduces fuzzy numbers to represent the number of viruses. This allows for more flexible handling of uncertainties and continuous value information in power systems. Simultaneously, each host has an initial confidence level and activation function, which can dynamically adjust the number of viruses according to the fault propagation situation, achieving a refined simulation of the power system fault propagation process. Furthermore, by setting reasonable channel weights, it can effectively avoid misjudgments caused by insufficient confidence in the actions of protection devices. Compared with traditional virus machine models, this technical solution's fuzzy number virus machine model has higher versatility and adaptability in handling complex power system faults, improving the accuracy and efficiency of fault diagnosis. Compared with existing power system fault diagnosis methods, this technical solution's fuzzy number virus machine model, instruction selection method, and activation function design can flexibly cope with different power system fault scenarios, exhibiting stronger fault diagnosis capabilities and reliability.

[0106] Example 2

[0107] This invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. Building upon Example 1, the fuzzy number virus machine model... The expression for the timing configuration is as follows:

[0108]

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

[0110]

[0111] in, For fuzzy number virus machine models in Time configuration, The initial configuration for the fuzzy number virus machine model. for Time Host The confidence value, for Time Host The confidence value, For fuzzy number virus machine models in The instructions of the moment, for The output of the time-fuzzy number virus machine model to the environment Host at the initial moment The confidence value, Host at the initial moment The confidence value, This refers to the instructions given to the fuzzy number virus machine model at the initial moment.

[0112] When the fuzzy number virus machine model is Time instructions At that time, the fuzzy number virus machine model was in shutdown configuration, and the instruction was... Activation will lead to changes in the configuration of the fuzzy number virus machine model; simultaneously, the fuzzy number virus machine model in The instructions at any given time are associated with the channels of the fuzzy number virus machine model, and based on... The instructions at a given time are transmitted through the channels of the fuzzy number virus machine model for virus propagation. In a specific embodiment of the present invention, a host exists within the fuzzy number virus machine model. ,Host Each with the source host Connect, and through the channel Corresponding connections are made, and the channel weights can be set accordingly. Source host Submit to host The number of viruses are respectively The determination of whether virus transmission has occurred is based on a comparison between the number of viruses (i.e., the confidence level of protection devices in the power system) and the channel weight value (i.e., the virus transmission threshold). Specifically, this includes:

[0113] Based on the fuzzy number virus machine model The command at a given moment activates the fuzzy number virus machine model and opens the channel of the fuzzy number virus machine model;

[0114] judge Host in the Time-Fuzzy Number Virus Machine Model Is the number of viruses in the channel less than the number of channels? The virus transmission threshold is set; if it is, no virus transmission occurs; otherwise, virus transmission occurs. The virus transmission process is as follows: the virus enters through the channel... of One end spreads to One end, of which, are natural numbers and That is, the virus enters through the channel. From the host Spread to the source host This simulation model depicts the propagation path of power system faults across different devices. Simultaneously, it requires real-time updates of the virus count on the host to address the difficulty of accurately simulating fault propagation processes in traditional fault diagnosis methods.

[0115] In this technical solution, Host in the Time-Fuzzy Number Virus Machine Model The expression for the number of viruses in the virus is as follows:

[0116]

[0117] in, for Host in the Time-Fuzzy Number Virus Machine Model The number of viruses in the body To find the minimum value function, For the host in the fuzzy number virus machine model Activation function, The host in the fuzzy number virus machine model at the initial time. The number of viruses it contains. For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses, For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses, For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses, For the source host in the fuzzy number virus machine model Host submitted to the fuzzy number virus machine model The number of viruses; this technical solution proposes a method The expression for calculating the number of viruses in the time-fuzzy number virus machine model combines the number of viruses contained in the host itself and the number of viruses submitted by each connected host. After processing by the activation function, the minimum value is taken. This can simulate the dynamic process of the accumulation and propagation of fault information 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 Host in the Time-Fuzzy Number Virus Machine Model The activation function in the expression for the number of viruses in the host during virus transmission. The amount of virus in the virus depends on the host. Activation function and connection to host The virus quantity submitted by the host, therefore, the technical solution puts forward four activation functions, different quantities of viruses are generated according to the input virus quantity, different scene fault generation and propagation can be flexibly simulated, the adaptability of the fuzzy number virus machine model is improved, the processing and transmission process of fault information in the actual power system is accurately reflected, compared with the single activation function of the traditional virus machine model, different types of faults can be flexibly handled, the accuracy of power system fault diagnosis is improved, in the specific embodiment of the application, the activation function of the host in the fuzzy number virus machine model includes: 、 、 、 and ;

[0119] When , the virus quantity generated in the host of the fuzzy number virus machine model is ; When

[0120] , the virus quantity generated in the host of the fuzzy number virus machine model is the product of the virus quantity respectively submitted by the source host in the fuzzy number virus machine model to the host of the fuzzy number virus machine model ; When , the virus quantity generated in the host of the fuzzy number virus machine model is the maximum value in the virus quantity respectively submitted by the source host in the fuzzy number virus machine model to the host of the fuzzy number virus machine model ;

[0121] When , the virus quantity generated in the host of the fuzzy number virus machine model is ; When

[0122] , the virus quantity generated in the host of the fuzzy number virus machine model is the virus quantity submitted by a source host in the fuzzy number virus machine model to the host of the fuzzy number virus machine model connected with it . In the technical solution, the selection mode of the instruction

[0123] of the fuzzy number virus machine model at the time is as follows: When

[0124] is not connected to any instruction, then , the fuzzy virus machine model is in a shutdown configuration, and is a null value;

[0125] When ​​​​​Only connect to one instruction, then ;

[0126] When connect to instruction and , and , the next instruction is selected from in a non-deterministic way;

[0127] When connect to instruction and , and , determine whether there is virus spread after applying to the fuzzy number virus machine model, if yes, , otherwise, ;

[0128] Wherein, 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 configured at time , obtained by applying instruction from , if the fuzzy number virus machine model is in a stop configuration, stop computing.

[0129] The technical scheme can reduce unnecessary instruction execution, quickly locate the fault propagation path, improve the fault diagnosis efficiency, and reduce the fault misjudgment of the power system caused by the execution instruction error, and improve the accuracy of fault diagnosis.

[0130] Embodiment three

[0131] The present application provides a power system fault diagnosis method based on fuzzy number virus machine model, on the basis of embodiment one and embodiment two, a bus fault diagnosis model is constructed, as shown in Figure 2 , it is a schematic diagram of bus fault, in the power system, bus B i is connected with line L1, line L2 and line L3 respectively, the Chinese name of point of failure is fault point, in Figure 2 , it indicates the fault point of bus B i , the following is the specific analysis of the fault of bus B i :

[0132] As shown in Figure 2 , when bus 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, circuit breaker CB1 may fail to disconnect the fault due to lack of response, and the fault will propagate to the outgoing line L1 of the busbar. Furthermore, due to the busbar B... i Without primary backup protection, the secondary backup protection LSR2 of the L1 line will trip the associated circuit breaker CB2 to isolate the fault area and ensure the safety of the circuit.

[0133] like Figure 2 As shown, for line L2, circuit breaker CB3 associated with the main protection device BMRi will trip. If circuit breaker CB3 fails to successfully disconnect the fault, the fault will spread to the outgoing line L2 of the busbar and the busbar B. i Circuit breaker CB4 on connected line L2 will trip under the action of protection device LSR4 on line L2. The operation of line L3 is the same as the previous two lines. The main protection will start and disconnect 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 for line L1 can be represented as R1, the fault diagnosis rule for line L2 can be represented as R2, and the fault diagnosis rule for line L3 can be represented as R3. Figure 2 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, but CB6 does not trip}, then bus B i Fault.

[0138] Based on the fault diagnosis rules of R1, R2, and R3, this paper addresses the fault diagnosis of bus B in power system fault diagnosis. i Construct bus B i Fuzzy number virus machine model, such as Figure 3 As shown, the fuzzy number virus machine model based on R1, R2, and R3 is defined as follows:

[0139]

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

[0141] In the bus fault diagnosis model in the embodiment, each pair of hosts connected to the same host stores the initial state of the relays and circuit breakers used by the corresponding primary protection device and secondary backup protection device, and the initial state is encoded and stored together with the initial value as .

[0142] Embodiment Four

[0143] The present application provides a power system fault diagnosis method based on the fuzzy number virus machine model. On the basis of Embodiment One and Embodiment Two, a line fault diagnosis model is constructed, as shown in Figure 4 , which is a schematic diagram of line fault. In a power system, the line L i has a sending end and a receiving end. The Chinese name of Point of failure is fault point, which Figure 4 represents the fault point of the line L i . The right side of Point of failure is the sending end, and the left side of Point of failure is the receiving end. The following is a specific analysis of the cause of the fault of the line L i .

[0144] As shown in Figure 4 , for the sending end of the line L i , when the line L i fails, the primary protection LMR1 at the sending end of L i acts to trigger the corresponding circuit breaker CB1 to trip. If the primary protection LMR1 does not act, the first backup protection LBR1 is started to trip the corresponding circuit breaker CB1. If the circuit breaker CB1 on the line L i has not tripped, the fault on the line L i will spread to the adjacent line. At this time, the secondary backup protection relay LSR2 will be started, causing the corresponding circuit breaker CB2 to trip. Multiple secondary backup protection relays and corresponding circuit breakers can be set to ensure isolation of the fault.

[0145] As shown in Figure 4 , for the receiving end of the line L i , when the line L i fails, the primary protection LMR3 at the sending end of L i acts to trigger the corresponding circuit breaker CB3 to trip. If the primary protection LMR3 does not act, the first backup protection LBR3 is started to trip the corresponding circuit breaker CB3. If the circuit breaker CB3 on the line L i has not tripped, the fault on the line Li If a fault spreads to an adjacent line, the secondary backup protection relay LSR4 will be activated, causing the corresponding circuit breaker CB4 to trip. 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 at the transmitting end can be represented as R4, and the fault diagnosis rule at the receiving end can be represented as R5. Figure 4 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 is faulty;

[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 is faulty.

[0149] Based on the fault diagnosis rules of R4 and R5, for line L in power system fault diagnosis i Construct line L i Fuzzy number virus machine model, such as Figure 5 The image shows a fuzzy number virus machine model based on R4 and R5, defined as follows:

[0150]

[0151] (6) The first control instruction to be executed, and .

[0152] Example 5

[0153] This invention provides a power system fault diagnosis method based on a fuzzy number virus machine model. Building upon Embodiments 1, 2, 3, and 4, this invention further clarifies the specific application details of the fuzzy number virus machine model in power system fault diagnosis, based on... Figure 6 The rules in the IEEE 39 circuit system shown indicate that Bus refers to the busbar, i.e., busbar B in Example 3. i Line refers to a circuit, specifically line L in Example 4. i, CB is a circuit breaker, i.e. the circuit breaker in Embodiment Three and Embodiment Four, a case study including four cases is designed to verify the effectiveness of the fuzzy number virus machine model; wherein, Case 1, Case 2 and Case 3 are single fault scenarios, and Case 4 is a multiple fault scenario, as shown in Table 1 below, the alarm notification indicating the fault issued by the protection relay and the circuit breaker in Case 1-4 shows the results of the diagnostic evaluation, and clearly distinguishes between single fault scenarios and multiple fault scenarios.

[0154] Table 1

[0155]

[0156] Table 2

[0157]

[0158] Table 1 shows that the power system fault diagnosis method based on the fuzzy number virus machine model provided by the application can accurately diagnose the fault elements in Case 1 to Case 4; Table 2 is the confidence level of the action and inaction of the protection device, which shows the confidence level of the response of the protection device when it is in the running state and the non-running state in the circuit fault diagnosis process, which is the source of the definition data of the channel weight when establishing the model. In order to illustrate the specific steps of the fuzzy virus machine model for power system fault diagnosis using relevant rules, Case 2 is taken as a representative example for further discussion.

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

[0160] According to the basic fault diagnosis method, it is known that line L36 is the most likely element to fail, based on which R6 and R7 are established as follows:

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

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

[0163] As can be seen from R6 and R7, the judgment methods for receiver protection and transmitter 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 faults in power system lines Li. The power system fault diagnosis method based on the fuzzy number virus machine model provided by this invention can be used.

[0164] The following is a fuzzy number virus machine model for example... Figure 6 The process of fault diagnosis at the L36 transmitter end of the IEEE 39 circuit system shown is as follows:

[0165] In the L36 line, after receiving a fault alarm message, the first step is to determine... The value is 0, while the host The values ​​are 0.9023, 0.5249, 0.8949, 0.5249, 0.8842, 0.7683, 0, 0, 0, 0, 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 has not been 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] 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 , which opens the channels from to and from to to transmit the virus; however, since the confidence value is less than the weight of opening the channel, the transmission fails and the instruction activation signal is transmitted along the path of minimum weight from to , thus 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, since the confidence value is less than the weight of opening the channel, the transmission fails and the instruction activation signal is transmitted along the path of minimum weight from to , thus 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, which opens the channels from to and from to to transmit the virus, the number stored in is the one originally stored in and the product of the two numbers in to This maximum weight path transmission, 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, and the channel from to is opened to transmit the virus, and the instruction activation signal is transmitted from to , therefore, C4 = (0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0.6793, 0, i7, 0).

[0178] In the fifth step, the instruction is activated, and the channel from to is opened to transmit the virus, and the number stored in is now a constant multiplier k, which can be 0.95, and the product of the number and the virus transmitted from , the instruction activation signal is transmitted along the maximum weight path from to , therefore, C5 = (0.9023, 0.5249, 0.8949, 0.5249, 0, 0, 0, 0, 0, 0, 0.6453, i fault , 0).

[0179] In the sixth step, the instruction is activated, and the channel from to is opened to transmit the virus, and since there is no further connected 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, which can be used to determine that the line L36 is faulty.

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

[0181] In the line L36, according to the obtained fault alarm information, the initial value is 0, and the host The values of the input variables are 0.7522, 0.8056, 0, 0, 0, 0, 0, 0, 0, 0, respectively, indicating that the main backup protection of the line protection LMR88 acts, causing the circuit breaker CB88 to trip, and the first and second backup protections do not act, thus the circuit breaker CB89 is not operated.

[0182] In this case, the process for computing 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, 0, is outlined 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 , which opens the channels from to and from to to transmit the virus, stored in The number of viruses in the storage is and The product of the number of viruses in the signal, the instruction activation signal along the path from arrive This minimum weighted path transmission, therefore, C1 = (0, 0, 0, 0.9856, 0, 0, 0.9779, 0, 0, 0, 0, i3,0).

[0191] In the second step, the instructions Activated, making from arrive The channel is opened to transmit the virus, and then the instruction activation signal is sent from... Transmit to Therefore, C2 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0, i2,0).

[0192] In the third step, similar to the first step, the instructions... Activated. Makes it from arrive And from arrive The channel was opened to transmit the virus, because If the confidence value is less than the weight of the open channel, transmission fails, and the command activation signal moves along from... arrive This minimum weighted path transmission, therefore, C3 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0,i4, 0).

[0193] In the fourth step, similar to the third step, because arrive The confidence values ​​are all less than the weight of the open channel, resulting in transmission failure. The command activation signal then travels along the path from... arrive This lowest weight path is transmitted, therefore, C4 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0.9779, 0, i7, 0).

[0194] In the fifth step, the instructions Activated, making from arrive The channel was opened to transmit the virus, which is now stored... The number in the equation is a constant multiplier k, which can be 0.95, and... The product of the number of viruses transmitted, the instruction activation signal along the path from arrive This maximum weight path transmission, therefore, C5 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0.9290, i fault , 0).

[0195] In the sixth step, the instruction is activated, and the channel from to is opened to transmit the virus, and since there is no further connected instruction, C6 = (0, 0, 0, 0.9856, 0, 0, 0, 0, 0, 0, 0, 0, #, 0.9290). The output result is 0.9290, according to which it can be judged that the fault of the line L36.

[0196] Through the fault diagnosis of the sending end of the line L36 and the fault diagnosis of the receiving end of the line L36, it is inferred that the faulty component is the line L36, and the output confidence is 0.9290.

[0197] Table 3

[0198]

[0199] Table 3 is a comparison of fault diagnosis results of different diagnosis methods, and a comparison experiment is carried out by using the channel parallel virus machine model and the fuzzy number virus machine model. As shown in Table 3, the fuzzy number virus machine model has the following advantages compared with 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 in the signal receiving and processing process, and obtain accurate judgment values. Especially when there are multiple signal outputs in a small area in the fault diagnosis process, the advantages of the fuzzy number virus machine model of the present application are more obvious, and more accurate fault sources can be identified.

[0202] Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to these embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including all changes and modifications falling within the scope of the present application.

[0203] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A power system fault diagnosis method based on a fuzzy number virus machine model, characterized by, The method comprises the following steps: The virus machine model is improved based on the fuzzy number to obtain a fuzzy number virus machine model; A bus fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model; A line fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model; The bus and the line of the power system are detected respectively according to the bus fault diagnosis model and the line fault diagnosis model to obtain a power system fault diagnosis result; The virus machine model is improved based on the fuzzy number, specifically including: The natural number associated with the host in the virus machine model is changed to a fuzzy number; The channel in the virus machine model is adjusted, and the first channel weight is adjusted to a second channel weight, wherein the first channel weight represents the replication number of the virus, and the second channel weight represents the transmission threshold of the virus; The expression of the fuzzy number virus machine model is as follows: wherein, is a fuzzy automaton model, is the number of hosts in the fuzzy automaton model, is the number of instructions in the fuzzy automaton model, is is an ordered set of labels of hosts, is is an ordered set of labels of instructions, and are both directed weighted graphs, is an undirected bipartite graph representing the correspondence between instructions and channels, is the initial fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the initial fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the initial fuzzy number of the th host, is the initial confidence of the th host, is the activation function of the th host, is the label of the 1st host, is the label of the th host, is the label of the th instruction, is the label of the th instruction, is the output host connected to the environment, is the union of the set of hosts and the output host , is the set of directed edges between hosts, is the set of directed edges between hosts, is the set of directed edges between instructions, is the set of directed edges between instructions, is the environment; The fuzzy number virus machine model is in The expression configured at the moment is as follows: The expression of the fuzzy number virus machine model at the initial time is as follows: wherein, is the configuration of the fuzzy number virus machine model at time , is the initial configuration of the fuzzy number virus machine model, , is the confidence value of the host at time , is the confidence value of the host at time , , is the instruction of the fuzzy number virus machine model at time , is the output of the fuzzy number virus machine model for the environment at time , is the confidence value of the host at initial time , is the confidence value of the host at initial time , is the instruction of the fuzzy number virus machine model at initial time. The fuzzy number virus machine model is in the instruction of the moment The selection mode is as follows: When Not connected to any instruction, then , the fuzzy virus machine model is in a stop configuration, said # is null; When Connected to only one instruction, then ; When connected to the instruction and , and selects the next instruction from in a non-deterministic manner; When connected to the instruction and , and , determine whether there is virus transmission after the application to the fuzzy number virus machine model, if yes, , otherwise, .​ 2. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1, characterized in that, The fuzzy number virus machine model is in The instructions at the time are associated with the channel of the fuzzy number virus machine model, and are used to perform virus propagation through the channel of the fuzzy number virus machine model according to The instructions at the time are associated with the channel of the fuzzy number virus machine model, and are used to perform virus propagation through the channel of the fuzzy number virus machine model according to According to the fuzzy number virus machine model in The fuzzy number virus machine model is activated by the instruction at the moment, and the channel of the fuzzy number virus machine model is opened. judge Host in the Time-Fuzzy Number Virus Machine Model Is the number of viruses in the channel less than the number of channels? The virus transmission threshold is set; if it is, no virus transmission occurs; otherwise, virus transmission occurs. The virus transmission process is as follows: the virus enters through the channel... of One end spreads to One end; Wherein, the host is connected with the source host respectively, the channel between the host and the source host is respectively , and the is a natural number, and the .

3. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 2, characterized in that, The The time fuzzy number virus machine model The expression of the number of viruses in the middle is as follows: wherein is the number of viruses in the fuzzy number virus machine model at time t, is the minimum function, is the activation function of the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t, is the number of viruses in the fuzzy number virus machine model at time t.

4. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 3, characterized in that, The fuzzy number virus machine model in which the host The activation function includes: 、 、 and ; When , in a fuzzy number virus machine model of a host , is generated viruses; When The number of viruses generated within the host of a fuzzy number virus machine model is the product of the number of viruses generated within the host of a fuzzy number virus machine model The number of viruses generated within the host of a fuzzy number virus machine model is the product of the number of viruses generated within the host of a fuzzy number virus machine model The number of viruses generated within the host of a fuzzy number virus machine model is the product of the number of viruses generated within the host of a fuzzy number virus machine model The number of viruses generated within the host of a fuzzy number virus machine model is the product of the number of viruses generated within the host of a fuzzy number When , the number of viruses generated in the host of the fuzzy number viral machine model is the maximum of the number of viruses generated in the source host of the fuzzy number viral machine model; and the maximum of the number of viruses submitted to the fuzzy number viral machine model. When , a host of a fuzzy number virus machine model is generated a virus, is a source host of a fuzzy number virus machine model is submitted to a fuzzy number virus machine model connected to it the number of viruses.

5. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1, characterized in that, The bus fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model, specifically including: The main protection device, the circuit breaker and the secondary protection device associated with the bus are abstracted as the host in the virus machine model; The connection relationship between the main protection device, the circuit breaker and the secondary protection device associated with the bus is abstracted as the channel in the virus machine model; The initial confidence of the main protection device, the circuit breaker and the secondary protection device associated with the bus is set as the virus number in the virus machine model; The bus fault diagnosis model for power system fault diagnosis is established according to the host, the channel and the virus number.

6. The power system fault diagnosis method based on fuzzy number virus machine model according to claim 1, wherein, The line fault diagnosis model for power system fault diagnosis is constructed according to the fuzzy number virus machine model, specifically including: The main protection device, the circuit breaker and the backup protection device associated with the line are abstracted as the host in the virus machine model; The connection relationship between the main protection device, the circuit breaker and the backup protection device associated with the line is abstracted as the channel in the virus machine model; The initial confidence of the main protection device, the circuit breaker and the backup protection device associated with the line is set as the virus number in the virus machine model; The line fault diagnosis model for power system fault diagnosis is established according to the host, the channel and the virus number.

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