Fault state machine modeling method based on regularization method and fault processing method

Abstract

The invention relates to the technical field of power grid faults, and discloses a fault state machine modeling method and a fault processing method based on a regular method.The modeling method comprises the steps that various fault states possibly existing in a power system are defined, and a self-healing strategy is associated; determining an event that may trigger a transition between the fault states; defining a transfer condition between the fault states; respectively designing regular expressions for the fault state, the event and the transfer condition; representing the regular expression in a graphic form, and constructing a self-healing fault machine state model; and carrying out accuracy verification on the fault machine state model. By using the self-healing fault state machine constructed by the invention, the fault state can be accurately identified, the self-healing process of the system is guided, the stability and reliability of the system are improved, the first-aid repair speed is improved when the low-voltage fault of the transformer area occurs, and the manpower is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid faults, and in particular to a fault state machine modeling method and a fault processing method based on a regularization method. Background Art

[0002] The low-voltage distribution substation is the end of the power distribution network and is connected to a large number of power users. Compared with the 10kV medium-voltage distribution network, the low-voltage distribution network has many points and wide areas, complex environments, many faults, and difficult fault identification. Due to historical reasons, there is no good technical solution to support the collection, analysis, and judgment of low-voltage line fault information in the substation.

[0003] At present, the management of low-voltage lines in distribution substations still lacks scientific and effective means to obtain basic data such as voltage, current, residual current, etc. of low-voltage lines in real time. It can only rely on measurement personnel, which not only causes a waste of manpower, material and other resources, but also directly affects the power supply quality and reliability of the distribution substations, and there are certain safety hazards. As a result, when low-voltage faults occur in the substation area, there are problems such as long repair period and repeated repair process, which seriously affects the quality of power supply service. The low-voltage side distribution lines in the distribution substation area are in a backward state of manual management. Research and development of low-voltage fault isolation and self-healing control systems in the substation area can realize intelligent monitoring of low voltage in the substation area and realize scientific, effective and intelligent management of low-voltage lines in the substation area. Summary of the invention

[0004] The present invention aims to at least solve the technical problems in the prior art that the low-voltage side distribution lines in the distribution station area are in a backward state of manual management, resulting in a long emergency repair period and repeated emergency repair processes when low-voltage faults occur in the station area.

[0005] To this end, an object of the present invention is to propose a fault state machine modeling method based on a regularization method, comprising:

[0006] Defining various possible fault states of the power system and associating a corresponding self-healing strategy with each of the fault states;

[0007] determining events that may trigger transitions between the fault states;

[0008] defining transition conditions between the fault states;

[0009] Design corresponding regular expressions for the fault state, the event and the transfer condition respectively;

[0010] The regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition are expressed in a graphical form to construct a self-healing fault machine state model;

[0011] The accuracy of the faulty machine state model is verified to obtain a final self-healing faulty machine state model.

[0012] Furthermore, the regular expression of the fault state, the regular expression of the event, and the regular expression of the transfer condition are represented in a graphical form to construct a self-healing fault machine state model, including:

[0013] A self-healing fault state machine model in the form of a directed graph is constructed using the regular expression of the fault state, the regular expression of the event, and the regular expression of the transfer condition. Each node in the directed graph represents one of the fault states, and the edges in the directed graph represent the events and the transfer conditions.

[0014] Further, the transition conditions between the fault states are defined, and the transition conditions include:

[0015] Under the conditions of specific fault states and specific events, the transition between the fault states is performed.

[0016] Furthermore, various possible fault states of the power system are defined, and a corresponding self-healing strategy is associated with each fault state, including:

[0017] Conduct an in-depth analysis of the behavioral characteristics of the power system and define various possible fault states of the power system based on the analysis results.

[0018] Furthermore, the behavior characteristics of the power system are deeply analyzed, and various possible fault states of the power system are defined according to the analysis results, including:

[0019] Voltage fluctuations, current imbalances and changes in equipment operating status.

[0020] Furthermore, the behavior characteristics of the power system are deeply analyzed to define various possible fault states of the power system, and a corresponding self-healing strategy is associated with each fault state. The behavior characteristics include:

[0021] Voltage fluctuations, current imbalances and changes in equipment operating status.

[0022] Furthermore, the behavior characteristics of the power system are deeply analyzed, various possible fault states of the power system are defined, and each fault state is associated with a corresponding self-healing strategy. The fault states include:

[0023] Normal operation, warning, minor fault, major fault, and system recovery.

[0024] Furthermore, the behavior characteristics of the power system are deeply analyzed, various possible fault states of the power system are defined, and each fault state is associated with a corresponding self-healing strategy, which includes:

[0025] Adjust power distribution, isolate faulty lines, or activate backup power sources.

[0026] Furthermore, the accuracy of the faulty machine state model is verified to obtain a final self-healing faulty machine state model, including:

[0027] The accuracy of the fault state machine model is verified through actual operation or simulation testing. If the verification result meets the requirements, the final fault machine state model is obtained.

[0028] The present invention provides a fault state machine fault processing method based on a regularization method, and uses any of the above-mentioned fault state machine modeling methods based on a regularization method to implement the construction of the fault state machine, including:

[0029] The power system receives an event, and the fault state machine obtains a transfer condition according to the event and the fault state of the power system;

[0030] The fault state machine determines the fault state to which the power system needs to be transferred according to the transfer condition;

[0031] The fault state machine allocates a corresponding self-healing strategy to the power system according to the fault state to which the power system needs to transfer, so as to realize automatic identification and repair of the fault of the power system.

[0032] The present invention provides a fault state machine modeling method and a fault handling method based on a regularization method, which have the following beneficial effects:

[0033] In the modeling of self-healing fault state machines, the advantages of regular methods are mainly reflected in two aspects: first, it can concisely describe the behavior pattern of the system, reduce the workload of manual coding, and improve the efficiency of model construction. Second, the readability and maintainability of regular expressions make it more intuitive to understand and modify the state machine model. In addition, regular expressions provide rich matching rules and logical operations, and can handle complex patterns, so as to accurately capture the behavioral changes of the system under different fault conditions.

[0034] The present invention combines the simplicity of regular expressions with the intuitiveness of state machines, can effectively establish and simplify self-healing fault models, can effectively describe the behavior patterns of self-healing of low-voltage system faults, and provide a simple and efficient modeling method for the research of system fault diagnosis and fault self-healing strategies.

[0035] The present invention uses a regular method to describe the behavior pattern of the system, and then designs a fault state machine that meets the low-voltage self-healing control requirements. The present invention has advantages in simplifying the fault state machine modeling process and improving the accuracy of the model, and provides a new modeling tool for complex system fault diagnosis and recovery. The self-healing fault state machine constructed using the regular method can accurately identify the fault state, guide the system self-healing process, improve the stability and reliability of the system, increase the repair speed when a low-voltage fault occurs in the substation, and save manpower. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0037] Figure 1 It is a flowchart of a fault state machine modeling method based on a regularization method in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Various aspects and features of the present invention are described herein with reference to the accompanying drawings.

[0039] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of the embodiments. Other modifications within the scope and spirit of the present invention will occur to those skilled in the art.

[0040] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0041] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0042] It should also be understood that, although the invention has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the invention that have the characteristics recited in the claims and are therefore within the scope of protection defined thereby.

[0043] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0044] Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present invention with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present invention in a variety of ways with substantially any suitable detailed structure.

[0045] Example

[0046] like Figure 1 As shown, this embodiment provides a fault state machine modeling method based on a regularization method, including:

[0047] Step S1: Conduct an in-depth analysis of the behavior characteristics of the power system, define various possible fault states of the power system, and associate each fault state with a corresponding self-healing strategy;

[0048] Step S2: determining an event that may trigger a transition between the fault states;

[0049] Step S3: defining the transition conditions between the fault states;

[0050] Step S4: designing corresponding regular expressions for the fault state, the event and the transfer condition respectively;

[0051] Step S5: representing the regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition in a graphical form to construct a self-healing fault machine state model;

[0052] Step S6: verifying the accuracy of the faulty machine state model to obtain a final self-healing faulty machine state model.

[0053] Regarding step S1, the behavior characteristics of the power system are deeply analyzed, various possible fault states of the power system are defined, and each fault state is associated with a corresponding self-healing strategy:

[0054] The behavioral characteristics include:

[0055] Voltage fluctuations, current imbalances, and changes in equipment operating status;

[0056] Defining the various possible fault states of the power system is to identify the various possible fault states of the power system, which usually involves an in-depth understanding of the functions and components of the power system. These fault states can be explicit, that is, obvious fault conditions, or implicit, that is, potential problems or impending failures.

[0057] Based on the analysis of the behavioral characteristics, possible fault states of the system are defined, such as "normal operation", "warning", "minor fault", "serious fault" and "system recovery".

[0058] The fault state is a description of the power system at a specific moment, representing a specific function or state of the system. In the fault state machine, these states can be different situations such as normal system operation, partial failure, serious failure or complete shutdown. Event is the trigger factor that triggers the state transition, which can be external input, changes in internal conditions, or the occurrence of a fault in the system itself. The transition condition defines under what circumstances the system transitions from one state to another, which is usually related to the triggering of the event and the current state of the system.

[0059] According to another specific embodiment of the present invention, based on step S1, the behavior characteristics of the power system are deeply analyzed, various fault states that may exist in the power system are defined, and each fault state is associated with a corresponding self-healing strategy, and the self-healing strategy includes:

[0060] Adjust power distribution, isolate faulty lines, or activate backup power sources.

[0061] In the fault state machine, each fault state is associated with a set of predefined self-healing strategies. When the system is in a certain fault state, the corresponding self-healing strategy will be automatically executed, such as adjusting power distribution, isolating faulty lines, or starting backup power supplies. These strategies are designed based on the physical principles of power system operation and the technicians' understanding of fault modes, aiming to minimize the impact of faults and restore the normal operation of the system. As shown in Table 1 below:

[0062] Table 1 Description of self-healing strategies

[0063]

[0064] According to another specific embodiment of the present invention, based on step S2, events that may trigger transition between the fault states are determined, including external events (such as user operations, environmental changes) and internal events (such as hardware failures, software errors).

[0065] According to another specific embodiment of the present invention, based on the step S3, the transition conditions between the fault states are defined, and the transition conditions include:

[0066] Under the conditions of specific fault states and specific events, the transition between the fault states is performed, which may involve complex logical judgments, such as the severity of the fault, the availability of system resources, etc.

[0067] According to another specific embodiment of the present invention, on the basis of step S4, corresponding regular expressions are designed for the fault state, the event and the transfer condition respectively:

[0068] A corresponding regular expression is designed for the fault state, and the regular expression describes the characteristics of the system entering the state in a concise form. For example, a regular expression may describe that when the voltage fluctuation amplitude exceeds a certain threshold, the system will change from the "normal operation" state to the "warning" state; as shown in Table 2 below:

[0069] Table 2 Fault state regularization

[0070]

[0071] A corresponding regular expression is designed for the event. For example, a regular expression of an event describes when a current fluctuation of a specific frequency is detected.

[0072] A corresponding regular expression is designed for the transfer condition, which describes that under specific conditions, according to specific events and the current fault state of the power system, the power system will transfer to a fault state. For example, when a current fluctuation of a specific frequency is detected, the power system will change from a "warning" fault state to a "minor fault".

[0073] According to another specific embodiment of the present invention, on the basis of step S5, the regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition are represented in a graphical form to construct a self-healing fault machine state model, including:

[0074] Using the regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition, a self-healing fault state machine model in the form of a directed graph is constructed, wherein each node in the directed graph represents one of the fault states, and the edges in the directed graph represent the events and the transfer conditions;

[0075] After receiving a specific event and being triggered, the power system automatically transfers the state according to the matching result of the regular expression, realizing the transfer and rapid identification of the fault state.

[0076] Traditional self-healing models are usually based on theories such as Petri nets and finite state automata, but the construction and maintenance of these models are labor-intensive. When describing the state transition relationship of complex systems, a lot of manual coding is often required, and the readability and maintainability of the models are poor. In order to solve these problems, this study proposes a self-healing fault state machine modeling framework based on regular methods. Regular expressions, as a concise and powerful language, can effectively describe the behavior patterns of the system and provide a new way to build a self-healing fault state machine.

[0077] According to another specific embodiment of the present invention, based on the step S6, the accuracy of the faulty machine state model is verified to obtain a final self-healing faulty machine state model, including:

[0078] The accuracy of the fault state machine model is verified through actual operation or simulation testing. If the verification result meets the requirements, the final fault machine state model is obtained.

[0079] According to another specific embodiment of the present invention, the present invention provides a fault state machine fault processing method based on a regularization method, and the fault state machine is constructed by using any of the above-mentioned fault state machine modeling methods based on a regularization method, including:

[0080] The power system receives an event, and the fault state machine obtains a transfer condition according to the event and the fault state of the power system;

[0081] The fault state machine determines the fault state to which the power system needs to be transferred according to the transfer condition;

[0082] The fault state machine allocates a corresponding self-healing strategy to the power system according to the fault state to which the power system needs to transfer, so as to realize automatic identification and repair of the fault of the power system.

[0083] In the modeling of self-healing fault state machines, the advantages of regular methods are mainly reflected in two aspects: first, it can concisely describe the behavior pattern of the system, reduce the workload of manual coding, and improve the efficiency of model construction. Second, the readability and maintainability of regular expressions make it more intuitive to understand and modify the state machine model. In addition, regular expressions provide rich matching rules and logical operations, and can handle complex patterns, so as to accurately capture the behavioral changes of the system under different fault conditions.

[0084] The present invention combines the simplicity of regular expressions with the intuitiveness of state machines, can effectively establish and simplify self-healing fault models, can effectively describe the behavior patterns of self-healing of low-voltage system faults, and provide a simple and efficient modeling method for the research of system fault diagnosis and fault self-healing strategies.

[0085] The present invention uses a regular method to describe the behavior pattern of the system, and then designs a fault state machine that meets the low-voltage self-healing control requirements. The present invention has advantages in simplifying the fault state machine modeling process and improving the accuracy of the model, and provides a new modeling tool for complex system fault diagnosis and recovery. The self-healing fault state machine constructed using the regular method can accurately identify the fault state, guide the system self-healing process, improve the stability and reliability of the system, increase the repair speed when a low-voltage fault occurs in the substation, and save manpower.

[0086] The embodiment of the present disclosure further provides an electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program in the memory, the method for modeling a fault state machine based on a regularization method is implemented, comprising:

[0087] Step S1: Conduct an in-depth analysis of the behavior characteristics of the power system, define various possible fault states of the power system, and associate each fault state with a corresponding self-healing strategy;

[0088] Step S2: determining an event that may trigger a transition between the fault states;

[0089] Step S3: defining the transition conditions between the fault states;

[0090] Step S4: designing corresponding regular expressions for the fault state, the event and the transfer condition respectively;

[0091] Step S5: representing the regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition in a graphical form to construct a self-healing fault machine state model;

[0092] Step S6: verifying the accuracy of the faulty machine state model to obtain a final self-healing faulty machine state model.

[0093] In some embodiments, the processor executing the computer program may be a processing device including one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0094] The memory may be a read-only memory (ROM), a random access memory (RAM), a phase-change random access memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD) or other optical storage, a cassette or other magnetic storage device, or any other possible non-temporary medium used to store information or instructions that can be accessed by a computer device.

[0095] The electronic devices of the embodiments of the present disclosure may include but are not limited to fixed terminal devices such as MCU controllers, servers, desktop computers, digital TVs, etc., and mobile terminal devices such as vehicle-mounted devices (such as head-up display devices), handheld devices (such as mobile phones, tablet computers, etc.), wearable devices (such as smart watches, smart bracelets, etc.), etc.

[0096] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein the computer-readable medium stores a computer program, and when the computer program is executed by a processor, the method for modeling a fault state machine based on a regularization method is implemented, including:

[0097] Step S1: Conduct an in-depth analysis of the behavior characteristics of the power system, define various possible fault states of the power system, and associate each fault state with a corresponding self-healing strategy;

[0098] Step S2: determining an event that may trigger a transition between the fault states;

[0099] Step S3: defining the transition conditions between the fault states;

[0100] Step S4: designing corresponding regular expressions for the fault state, the event and the transfer condition respectively;

[0101] Step S5: representing the regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition in a graphical form to construct a self-healing fault machine state model;

[0102] Step S6: verifying the accuracy of the faulty machine state model to obtain a final self-healing faulty machine state model.

[0103] The computer-readable storage medium of the embodiments of the present disclosure may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device, for example, the memory described above.

[0104] The computer programs of the embodiments of the present disclosure may be organized into one or more computer executable components or modules. Any number and combination of such components or modules may be used to implement the various aspects of the present disclosure. For example, the various aspects of the present disclosure are not limited to the specific computer executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer executable instructions or components with more or less functions than those shown and described herein.

[0105] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A fault state machine modeling method based on regularization method, characterized in that: include: Defining various possible fault states of the power system and associating a corresponding self-healing strategy with each of the fault states; determining events that may trigger transitions between the fault states; defining transition conditions between the fault states; Design corresponding regular expressions for the fault state, the event and the transfer condition respectively; The regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition are expressed in a graphical form to construct a self-healing fault machine state model; The accuracy of the faulty machine state model is verified to obtain a final self-healing faulty machine state model.

2. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: The regular expression of the fault state, the regular expression of the event and the regular expression of the transfer condition are expressed in a graphical form to construct a self-healing fault machine state model, including: A self-healing fault state machine model in the form of a directed graph is constructed using the regular expression of the fault state, the regular expression of the event, and the regular expression of the transfer condition. Each node in the directed graph represents one of the fault states, and the edges in the directed graph represent the events and the transfer conditions.

3. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: Define transition conditions between the fault states, the transition conditions including: Under the conditions of specific fault states and specific events, the transition between the fault states is performed.

4. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: Defining various possible fault states of the power system and associating a corresponding self-healing strategy with each fault state, including: Conduct an in-depth analysis of the behavioral characteristics of the power system and define various possible fault states of the power system based on the analysis results.

5. The fault state machine modeling method based on the regularization method according to claim 4 is characterized in that: Conduct an in-depth analysis of the behavior characteristics of the power system and define various possible fault states of the power system based on the analysis results, including: Voltage fluctuations, current imbalances and changes in equipment operating status.

6. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: Conduct an in-depth analysis of the behavior characteristics of the power system, define various possible fault states of the power system, and associate each fault state with a corresponding self-healing strategy. The fault states include: Normal operation, warning, minor fault, major fault, and system recovery.

7. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: Conduct an in-depth analysis of the behavior characteristics of the power system, define various possible fault states of the power system, and associate each fault state with a corresponding self-healing strategy, which includes: Adjust power distribution, isolate faulty lines, or activate backup power sources.

8. The fault state machine modeling method based on the regularization method according to claim 1 is characterized in that: The accuracy of the faulty machine state model is verified to obtain a final self-healing faulty machine state model, including: The accuracy of the fault state machine model is verified through actual operation or simulation testing. If the verification result meets the requirements, the final fault machine state model is obtained.

9. A fault state machine fault handling method based on a regularization method, characterized in that: The fault state machine modeling method based on the regularization method as claimed in any one of claims 1 to 8 is used to realize the construction of the fault state machine, comprising: The power system receives an event, and the fault state machine obtains a transfer condition according to the event and the fault state of the power system; The fault state machine determines the fault state to which the power system needs to be transferred according to the transfer condition; The fault state machine allocates a corresponding self-healing strategy to the power system according to the fault state to which the power system needs to transfer, so as to realize automatic identification and repair of the fault of the power system.

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