Fault detection method and system based on fuzzy reasoning and storage medium
By adopting a fuzzy inference method in power system fault detection, using Petri network to decompose complex rules and build a fuzzy inference rule database, the problem of large and complicated fault detection in the existing technology is solved, and more efficient and accurate fault detection is achieved.
Patent Information
- Application Number
- CN202411632114.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-03-28
AI Technical Summary
The fault detection work of the existing power system is large and complicated, and it is difficult to meet the fault detection needs. The knowledge base maintenance is difficult and the fault tolerance capability is poor, and there are problems such as combination explosion and slow inference speed.
The fault detection method based on fuzzy inference is adopted, and the inference rules are sorted out through the Petri network, complex rules are decomposed into atomic rules, a fuzzy inference rule database is constructed, and the failure risk is determined based on the event Token value, improving the efficiency and accuracy of fault detection.
It improves the efficiency and accuracy of power system fault detection, simplifies the maintenance of the knowledge base, facilitates update and expansion, provides a variety of processing methods, and improves the pertinence and effectiveness of fault processing.
Smart Images
Figure CN119721265A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of power system protection, and in particular to a fault detection method, system and storage medium based on fuzzy reasoning. Background Art
[0002] In recent years, high voltage direct current (HVDC) technology has achieved breakthrough development and has been widely used in the operation of power systems. As a very novel transmission technology, HVDC technology occupies an extremely important position in the new power system because of its unique advantages. Compared with traditional high voltage AC transmission technology, high voltage direct current transmission technology has many advantages such as low line cost, large transmission capacity, low loss, ability to complete asynchronous networking and limit short-circuit current of power system. Therefore, it is widely used in long-distance, high-power, asynchronous networking transmission occasions. The HVDC system is a very large and complex system. Various faults are inevitable during operation. When the system fails, it is necessary to identify the fault in time and organize personnel to repair it to avoid the bad situation of the entire system being shut down.
[0003] In the prior art, expert systems are often used for fault detection, and computer models of expert reasoning methods are used to judge faults, which makes it difficult to maintain the knowledge base. For example, when an accident occurs, a large amount of time series information is generated, from which alarm information and accident information are extracted to prompt substation staff to check and process, but the amount of information is too large, and the workload will increase exponentially. Two faults with different credibility correspond to the same processing method. At this time, it is necessary to determine whether to choose this processing method. In some cases, there are multiple reasons for the generation of a fault signal, resulting in a variety of processing methods, and the specific selection also needs to be determined. Therefore, it is necessary to optimize the existing power system faults. Summary of the invention
[0004] In view of the defects existing in the prior art, the present application provides a fault detection method, system and storage medium based on fuzzy reasoning to improve the existing power system fault detection workload, which is large and complicated and difficult to meet the fault detection needs.
[0005] To achieve the above objectives, the present application provides a fault detection method based on fuzzy reasoning, comprising the following steps:
[0006] S1. Obtain historical fault data of substation equipment, wherein the historical fault data includes alarm information, fault information and execution action information;
[0007] S2. Constructing fuzzy reasoning rules based on the historical fault data of the substation equipment, wherein the fuzzy reasoning rules include: P is the fault premise of the substation equipment, Q is the fault conclusion of the substation equipment, RV is the rule value, and the inference rule set A is found through Petri net combing, and each rule in the set A is decomposed to obtain a set B containing only atomic rules; the premise proposition of the set B is assigned a value and marked as P, and the conclusion proposition of the set B is assigned a value and marked as Q;
[0008] S3. Based on the fuzzy inference rules, obtain the current data information of the substation equipment and determine the fault condition of the substation equipment.
[0009] Furthermore, the fuzzy reasoning rules include:
[0010]
[0011] Wherein, n is the number of faults of the substation equipment, and Qn is the nth fault conclusion of the substation equipment.
[0012] Furthermore, the fuzzy reasoning rules include:
[0013]
[0014] Wherein, n is the number of faults of the substation equipment, and Pn is the nth fault premise of the substation equipment.
[0015] Furthermore, the fuzzy reasoning rules, the failure premise of the substation equipment, and the failure conclusion of the substation equipment are all determined through technical specifications of the substation equipment and failure case analysis.
[0016] Furthermore, after the step S2 of constructing fuzzy reasoning rules based on the historical fault data of the substation equipment, the fault detection method based on fuzzy reasoning further includes:
[0017] Based on the historical fault data of the substation equipment, determining an event token value of the substation equipment, wherein the event token value represents an abnormal situation of the substation equipment;
[0018] Determining a failure risk of the substation equipment based on an event token value of the substation equipment;
[0019] If the event token value is greater than a preset rule activation threshold, the value of the RV is calculated and the event token value is adjusted.
[0020] Furthermore, the event token value is the product of the token value of the substation equipment before the risk transition and the directed arc weight.
[0021] The present application also provides a fault detection system based on fuzzy reasoning, and the fault detection system based on fuzzy reasoning includes:
[0022] An acquisition module, used to acquire a timing signal of the substation equipment, wherein the timing signal includes alarm information, accident and displacement information;
[0023] A fault discrimination rule module is used to construct a fault discrimination rule base and set antecedent weights, rule activation thresholds and consequent credibility based on the timing signals of the substation equipment;
[0024] A model building module, used to build a network topology model of the substation equipment based on the Petri net and the antecedent weight, the rule activation threshold and the consequent credibility, wherein the network topology model of the substation equipment includes a fault intelligent judgment rule base;
[0025] The fault judgment module is used to obtain the current timing signal of the substation equipment based on the network topology model of the substation equipment and judge the fault condition of the substation equipment.
[0026] Furthermore, the fault detection system based on fuzzy reasoning includes:
[0027] The token value determination module is used to determine the event token value of the substation equipment based on the historical fault data of the substation equipment, wherein the event token value represents the abnormal situation of the substation equipment.
[0028] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fuzzy reasoning-based fault detection method as described in any one of the above items is implemented.
[0029] Compared with the prior art, the advantages of the present invention are:
[0030] (1) The present invention improves the efficiency and accuracy of power system fault detection by combining Petri nets with fuzzy reasoning rules.
[0031] (2) By combing the reasoning rules through Petri nets, complex rules can be decomposed into atomic rules, which simplifies the maintenance of the knowledge base and facilitates updating and expansion. It provides a variety of processing methods for different causes of fault signals, thereby improving the pertinence and effectiveness of fault processing. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0033] Figure 1 A flowchart of a fault detection method based on fuzzy reasoning according to an embodiment of the present application;
[0034] Figure 2 Schematic diagram of fault detection in an existing power system. DETAILED DESCRIPTION
[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0036] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.
[0037] In the description of the present application, the terms "first / second" involved are merely used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.
[0038] It should be understood that the orientation or position relationship is based on the orientation or position relationship shown in the drawings. These orientation terms are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application.
[0039] Power system fault judgment refers to the use of knowledge of the power system and its protection devices and information such as relay protection to accurately identify the location, type and malfunction of faulty components, and lay the foundation for taking measures to repair and improve existing deficiencies and improve the operation level of the power system. In the process of power system fault judgment, fault component identification is the focus and key content. At the same time, in order to improve the accuracy of fault judgment, effectively grasp the actual situation of the power system, grasp the key points of operation, and improve the level of fault judgment, it is necessary.
[0040] At present, with the continuous summary and development of technology, the fault judgment method of power system is gradually improved and perfected. The main methods and research status are reflected in the following aspects: expert system, artificial neural network, optimization technology, and fuzzy set theory. Among them, the expert system uses the computer model of the expert reasoning method to judge the fault. At present, this method is widely used and has obvious effects. It is a relatively successful method. According to the different fault diagnosis knowledge representation and the reasoning strategy used, the expert system can be divided into a heuristic rule-based reasoning system and a combined positive and negative reasoning system. At present, most fault judgments use the first method. The expert system diagnosis method can use rules to represent the action logic of the protector and circuit breaker and the diagnostic experience of the operator, allowing the addition or deletion of rules to improve the real-time and effectiveness of the diagnosis system. This method is suitable for small and medium-sized power systems and substation fault diagnosis. However, this method also has defects. For example, it is difficult to establish a knowledge base, the fault tolerance is poor, the knowledge base maintenance is difficult, and there will be problems such as combinatorial explosion and slow reasoning speed in fault diagnosis. It is necessary to improve and perfect it.
[0041] The fault judgment method based on artificial neural network has strong fault tolerance and learning ability. Common methods include forward neural network based on BP algorithm and neural network based on radial basis function. Through the application of these methods, diagnostic information can be effectively mastered and accurate diagnostic results can be obtained. This method can avoid the problem of difficult knowledge base construction and does not require the construction of inference engine. It is suitable for fault diagnosis of small and medium-sized power systems. The main problems faced in the application include: it is not easy to obtain complete sample data of the power system, the interaction function with symbolic data is weak, the heuristic knowledge processing is difficult, and the ability to explain its own behavior and output results is lacking. These defects restrict the promotion and application of this method and need to be further studied and analyzed.
[0042] The fault judgment method based on optimization technology is a solution method based on mathematical models. It describes the fault diagnosis problem as a 0-1 integer programming problem, constructs an analytical mathematical model, and uses optimization technology to find the optimal solution. This method is relatively strict in both diagnostic theory and diagnostic model, and has good results in specific applications. It does not require the introduction of heuristic knowledge, and conventional algorithms can accurately obtain detection results. It is suitable for power system fault diagnosis with relatively complete required information. The content that needs to be improved includes: establishing a reasonable mathematical model for power grid fault diagnosis, considering that multi-level backup protection is difficult, there are random factors, and the optimal solution may be lost, and the speed may be relatively slow. It is necessary to increase the running speed of the model.
[0043] The fault judgment method based on fuzzy set theory is to describe the possibility of fault existence with fuzzy membership, or to give fault diagnosis results by expert system or artificial neural network according to the system network topology and the action protection caused by the fault, the credibility of alarm information, etc. This method can handle the uncertainty of information and is usually used in combination with expert system and artificial neural network. It is difficult to use effective methods to describe uncertainty problems and establish large-scale fuzzy models. Therefore, it is necessary to propose an effective fault judgment method.
[0044] Figure 1 : is a flow chart of a fault detection method based on fuzzy reasoning according to an embodiment of the present application. Figure 2 This is a schematic diagram of the existing power system fault detection. Figure 1 and Figure 2 As shown, the fault detection method based on fuzzy reasoning includes the following steps:
[0045] S1. Obtain historical fault data of substation equipment, wherein the historical fault data includes alarm information, fault information and execution action information;
[0046] S2. Constructing fuzzy reasoning rules based on the historical fault data of the substation equipment, wherein the fuzzy reasoning rules include: P is the fault premise of the substation equipment, Q is the fault conclusion of the substation equipment, RV is the rule value, and the inference rule set A is found through Petri net combing, and each rule in the set A is decomposed to obtain a set B containing only atomic rules; the premise proposition of the set B is assigned a value and marked as P, and the conclusion proposition of the set B is assigned a value and marked as Q;
[0047] S3. Based on the fuzzy inference rules, obtain the current data information of the substation equipment and determine the fault condition of the substation equipment.
[0048] Specifically, fault data from the past five years are extracted from the maintenance record database of the substation, including all alarm information, fault records, and corresponding execution action information. These data cover different types of fault events, such as equipment overheating, insulator damage, line short circuit, etc. Using the extracted historical fault data, fuzzy reasoning rules are constructed through Petri net technology. First, the fault premise P and fault conclusion Q are defined. For example, P can be "the current rises abnormally and the temperature exceeds the threshold", while Q can be "the equipment may have a short circuit fault". Through Petri net analysis, a series of reasoning rule sets A are identified, such as "A1: If the current rises abnormally and the temperature exceeds the threshold, the equipment may have a short circuit fault". Each rule in set A is decomposed to obtain a set B containing only atomic rules. For example, rule A1 is decomposed into atomic rule B1: "abnormal current rises" and atomic rule B2: "temperature exceeds the threshold". Assign specific values to each atomic rule in set B, for example, assign B1 to "current increases by 30%", assign B2 to "temperature reaches 80°C", and mark these assignments as P, assign values to the conclusion propositions of the atomic rules, for example, assign Q to "the probability of equipment short circuit failure is 80%". Real-time monitoring of current data information of substation equipment, including key parameters such as current, voltage, and temperature, when abnormal data is detected, it is input into the fuzzy reasoning system, and the system fuzzifies the input data according to the fuzzy reasoning rules constructed in step S2, and applies fuzzy logic for reasoning. For example, if the current monitored currently increases by 35% and the temperature reaches 85°C, the system matches these two data with P in the fuzzy reasoning rules and draws conclusion Q: "The probability of equipment short circuit failure is 85%". According to the conclusion Q, the system will issue a corresponding alarm.
[0049] It should be noted that the fuzzy reasoning rules constructed by combining Petri nets can improve the efficiency and accuracy of power system fault detection; the reasoning rules are sorted out by Petri nets, and the complex rules are decomposed into atomic rules, which simplifies the maintenance of the knowledge base and facilitates updating and expansion. It provides a variety of processing methods for the causes of different fault signals, thereby improving the pertinence and effectiveness of fault processing.
[0050] In some embodiments, the fuzzy inference rules include:
[0051]
[0052] Wherein, n is the number of faults of the substation equipment, and Qn is the nth fault conclusion of the substation equipment.
[0053] In some embodiments, the fuzzy inference rules include:
[0054]
[0055] Wherein, n is the number of faults of the substation equipment, and Pn is the nth fault premise of the substation equipment.
[0056] Specifically, the complex fuzzy inference rule set can be decomposed layer by layer according to the above four forms, and finally only This form of reasoning is composed of a set of atomic rules that cannot be decomposed any further.
[0057] Step 1: Find the inference rule set A, decompose each rule, and obtain a set A' containing only atomic rules; if there is a third composite rule in the rule set, mark all its premise propositions as p i , mark the conclusion proposition as q;
[0058] Step 2: Find all the propositions involved in A', obtain the proposition set B, and its corresponding initial truth value vector U;
[0059] Step 3: Create TR matrix R according to the rule set;
[0060] Step 4: Calculation
[0061] Step 5: If U'=U, then U' is the final inference result; otherwise, if p in U' i If the corresponding truth values are all 1, then change the truth value corresponding to q to 1, U=U', and go to Step 4.
[0062] It should be noted that the fault detection method in the embodiment of the present application is compared with the expert system: the expert system has difficulty in establishing a knowledge base, has poor fault tolerance, is difficult to maintain the knowledge base, and has problems such as combinatorial explosion and slow reasoning speed in fault detection; while the present application combines fuzzy reasoning rules with Petri nets to perform rapid calculation and reasoning to draw conclusions, which greatly improves the reasoning speed compared to the expert system.
[0063] In some embodiments, the fuzzy reasoning rules, the failure premise of the substation equipment, and the failure conclusion of the substation equipment are all determined through technical specifications of the substation equipment and failure case analysis.
[0064] Specifically, the technical specifications of substation equipment, including the design manual, operation manual, maintenance guide, etc. of the equipment, as well as past fault case analysis reports, are used to establish the basis of fuzzy reasoning rules. The technical specifications provide key information such as parameter range, safety threshold, and equipment response characteristics during normal operation of the equipment, while the fault case analysis report provides various fault conditions and their causes encountered in actual operation. According to the technical specifications, the fault premise P is determined. For example, the technical specifications may indicate that when the current exceeds 110% of the rated value and lasts for more than 5 minutes, there may be a risk of equipment overload. Therefore, P can be defined as "the current exceeds 110% of the rated value and lasts for more than 5 minutes." By analyzing the fault case, the fault conclusion Q is determined. For example, historical fault cases may show that when the current is overloaded and not handled in time, the probability of equipment failure is 90%. Therefore, Q can be defined as "the probability of equipment failure is 90%." Determine the rule value RV, which may be based on the frequency and severity of the fault in the fault case. For example, RV can be a value between 0 and 1, indicating the credibility of the fault occurrence.
[0065] For example, (1) determine the fault premise P1: "The current exceeds 110% of the rated value and lasts for more than 5 minutes"; (2) analyze historical fault cases and determine the fault conclusion Q1: "The probability of equipment failure is 90%"; (3) based on the severity and frequency of the fault case, determine the rule value RV1 to be 0.9.
[0066] When the monitoring system detects that the current exceeds 110% of the rated value and lasts for more than 5 minutes, the system will activate the fuzzy reasoning rules. The system will perform fuzzy reasoning based on the determined fault premise P1 and fault conclusion Q1, as well as the rule value RV1. For example, the system may output: "According to the technical specifications and fault case analysis, the current current state may cause equipment failure with a failure probability of 90%". Based on this reasoning result, the system will recommend taking corresponding preventive measures, such as reducing the load, checking the equipment, or preparing for maintenance.
[0067] In some embodiments, after the step of constructing fuzzy reasoning rules based on the historical fault data of the substation equipment in S2, the fault detection method based on fuzzy reasoning further includes:
[0068] Based on the historical fault data of the substation equipment, determining an event token value of the substation equipment, wherein the event token value represents an abnormal situation of the substation equipment;
[0069] Determining a failure risk of the substation equipment based on an event token value of the substation equipment;
[0070] If the event token value is greater than a preset rule activation threshold, the value of the RV is calculated and the event token value is adjusted.
[0071] Specifically, relevant events are extracted from the historical fault data of substation equipment, such as equipment overheating, current anomaly, voltage fluctuation, etc., and a token value is assigned to each event, which represents the degree of abnormality of the event. The event token value can be determined by an expert scoring system, in which experts score according to the degree of impact of the event on the normal operation of the equipment. For example, the event token value of equipment overheating may be set to 0.8, while the event token value of current anomaly may be set to 0.6. The token values of all events are accumulated to obtain a total event token value, which is used to evaluate the overall failure risk of substation equipment and set a rule activation threshold. For example, the rule activation threshold is 0.5. When the total event token value exceeds this threshold, it is considered that the equipment has a high failure risk and the rule value RV needs to be further calculated. Three events are extracted from the historical fault data: equipment overheating (token value 0.8), current abnormality (token value 0.6) and voltage fluctuation (token value 0.4), and the total event token value is calculated: 0.8+0.6+0.4=1.8. The total event token value is compared with the preset rule activation threshold (for example, 0.5). Since 1.8>0.5, it is necessary to further calculate the rule value RV. According to the fuzzy inference rule, the rule value RV is calculated. For example, if the event token value of equipment overheating is the highest, then the calculation of RV may focus more on this event. Adjust the event token value according to the value of RV. For example, if RV indicates that equipment overheating is the main risk, then the token values of other events can be reduced to more accurately reflect the current fault risk. In particular, the event token value is the token value of the substation equipment before the risk transition multiplied by the directed arc weight.
[0072] The present application also provides a fault detection system based on fuzzy reasoning, and the fault detection system based on fuzzy reasoning includes:
[0073] An acquisition module, used to acquire a timing signal of the substation equipment, wherein the timing signal includes alarm information, accident and displacement information;
[0074] A fault discrimination rule module is used to construct a fault discrimination rule base and set antecedent weights, rule activation thresholds and consequent credibility based on the timing signals of the substation equipment;
[0075] A model building module, used to build a network topology model of the substation equipment by combining the Petri net and the antecedent weight, the rule activation threshold and the consequent credibility, wherein the network topology model of the substation equipment includes a fault intelligent judgment rule base;
[0076] The fault judgment module is used to obtain the current timing signal of the substation equipment based on the network topology model of the substation equipment and judge the fault condition of the substation equipment.
[0077] In some embodiments, the fuzzy reasoning-based fault detection system includes:
[0078] The token value determination module is used to determine the event token value of the substation equipment based on the historical fault data of the substation equipment, wherein the event token value represents the abnormal situation of the substation equipment.
[0079] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fuzzy reasoning-based fault detection method as described in any one of the above items is implemented.
[0080] It should be noted that the storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk.
[0081] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer-readable storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).
[0082] As is known to those of ordinary skill in the art, the term computer-readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.
[0083] Exemplarily, the computer-readable storage medium may be an internal storage unit of the electronic device of the aforementioned embodiment, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device.
[0084] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.
Claims
1. A fault detection method based on fuzzy reasoning, characterized in that: The steps include: S1. Obtain historical fault data of substation equipment, wherein the historical fault data includes alarm information, fault information and execution action information; S2. Constructing fuzzy reasoning rules based on the historical fault data of the substation equipment, wherein the fuzzy reasoning rules include: P is the fault premise of the substation equipment, Q is the fault conclusion of the substation equipment, RV is the rule value, and the inference rule set A is found through Petri net combing, and each rule in the set A is decomposed to obtain a set B containing only atomic rules; the premise proposition of the set B is assigned a value and marked as P, and the conclusion proposition of the set B is assigned a value and marked as Q; S3. Based on the fuzzy inference rules, obtain the current data information of the substation equipment and determine the fault condition of the substation equipment.
2. The fault detection method based on fuzzy reasoning according to claim 1 is characterized in that: The fuzzy inference rules include: Wherein, n is the number of faults of the substation equipment, and Qn is the nth fault conclusion of the substation equipment.
3. The fault detection method based on fuzzy reasoning according to claim 2 is characterized in that: The fuzzy inference rules include: Wherein, n is the number of faults of the substation equipment, and Pn is the nth fault premise of the substation equipment.
4. The fault detection method based on fuzzy reasoning according to claim 1 is characterized in that: The fuzzy reasoning rules, the failure premise of the substation equipment, and the failure conclusion of the substation equipment are all determined through technical specifications of the substation equipment and failure case analysis.
5. The fault detection method based on fuzzy reasoning according to claim 4 is characterized in that: After the step of constructing fuzzy reasoning rules based on the historical fault data of the substation equipment in step S2, the fault detection method based on fuzzy reasoning further includes: Based on the historical fault data of the substation equipment, determining an event token value of the substation equipment, wherein the event token value represents an abnormal situation of the substation equipment; Determining a failure risk of the substation equipment based on an event token value of the substation equipment; If the event token value is greater than a preset rule activation threshold, the value of the RV is calculated and the event token value is adjusted.
6. The fault detection method based on fuzzy reasoning according to claim 5 is characterized in that: The event token value is the product of the token value of the substation equipment before the risk transition and the directed arc weight.
7. A fault detection system based on fuzzy reasoning, characterized in that: The fault detection system based on fuzzy reasoning comprises: An acquisition module, used to acquire a timing signal of the substation equipment, wherein the timing signal includes alarm information, accident and displacement information; A fault discrimination rule module is used to construct a fault discrimination rule base and set antecedent weights, rule activation thresholds and consequent credibility based on the timing signals of the substation equipment; A model building module, used to build a network topology model of the substation equipment by combining the Petri net and the antecedent weight, the rule activation threshold and the consequent credibility, wherein the network topology model of the substation equipment includes a fault intelligent judgment rule base; The fault judgment module is used to obtain the current timing signal of the substation equipment based on the network topology model of the substation equipment and judge the fault condition of the substation equipment.
8. The fuzzy reasoning-based fault detection system according to claim 7, characterized in that: The fault detection system based on fuzzy reasoning comprises: The token value determination module is used to determine the event token value of the substation equipment based on the historical fault data of the substation equipment, wherein the event token value represents the abnormal situation of the substation equipment.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fault detection method based on fuzzy reasoning according to any one of claims 1 to 6 is implemented.