A Fault State Machine Modeling Method and Fault Handling Method Based on Regularization

Abstract

This invention relates to the field of power grid fault technology, and discloses a fault state machine modeling method and fault handling method based on regularization. The modeling method includes defining various possible fault states in a power system and associating them with self-healing strategies; identifying events that may trigger transitions between the fault states; defining transition conditions between the fault states; designing regular expressions for the fault states, the events, and the transition conditions; representing the regular expressions graphically to construct a self-healing fault machine state model; and verifying the accuracy of the fault machine state model. The self-healing fault state machine constructed using this invention can accurately identify fault states, guide the system's self-healing process, improve system stability and reliability, increase repair speed when low-voltage faults occur in distribution areas, and save manpower.

Description

Technical Field

[0001] This invention relates to the field of power grid fault technology, and in particular to a fault state machine modeling method and fault handling method based on regularization. Background Technology

[0002] Low-voltage distribution substations are the end points of the power distribution network, connecting a large number of electricity users. Compared with 10kV medium-voltage distribution networks, low-voltage distribution networks have more points, wider coverage, more complex environments, more faults, and are more difficult to identify. Due to historical reasons, there are no good technical solutions to support the collection, analysis, and judgment of fault information on low-voltage lines in distribution substations.

[0003] Currently, the management of low-voltage lines in distribution substations lacks scientific and effective methods to obtain basic data such as voltage, current, and residual current in real time. Relying solely on measurement personnel wastes human and material resources, directly impacting the power supply quality and reliability of the distribution substation, and posing certain safety hazards. This leads to excessively long repair times and repeated repair processes when low-voltage faults occur, severely affecting the quality of power supply services. The low-voltage side of distribution substations is currently under outdated manual management. Researching and developing a low-voltage fault isolation and self-healing control system for distribution substations would enable intelligent monitoring of low-voltage lines, achieving scientific, effective, and intelligent management. Summary of the Invention

[0004] The present invention aims to at least solve the technical problem that the low-voltage side power distribution lines of the distribution substation are in a backward state of manual management in the prior art, which leads to excessively long repair time and repeated repair processes when low-voltage faults occur in the substation.

[0005] Therefore, one objective of this invention is to propose a fault state machine modeling method based on regularization, comprising:

[0006] Define various possible fault states in a power system and associate each fault state with its corresponding self-healing strategy;

[0007] Identify events that may trigger a transition between the aforementioned fault states;

[0008] Define the transition conditions between the aforementioned fault states;

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

[0010] The regular expressions for the fault states, events, and transition conditions are represented graphically to construct a self-healing fault machine state model.

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

[0012] Furthermore, the regular expressions for the fault states, the events, and the transition conditions are represented graphically 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 expressions for the fault states, the events, and the transition conditions. Each node in the directed graph represents a fault state, and the edges in the directed graph represent the events and the transition conditions.

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

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

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

[0017] A thorough analysis of the behavioral characteristics of the power system is conducted, and various possible fault states of the power system are defined based on the analysis results.

[0018] Furthermore, the behavioral characteristics of the power system are analyzed in depth, and various possible fault states of the power system are defined based on the analysis results, including:

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

[0020] Furthermore, a deeper analysis of the behavioral characteristics of the power system is conducted, defining various possible fault states of the power system, and associating each fault state with its corresponding self-healing strategy. These behavioral characteristics include:

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

[0022] Furthermore, a deeper analysis of the behavioral characteristics of the power system is conducted, defining various possible fault states of the power system, and associating each fault state with its corresponding self-healing strategy. The fault states include:

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

[0024] Furthermore, the behavioral characteristics of the power system are analyzed in depth, 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.

[0026] Furthermore, the accuracy of the faulty machine state model is verified to obtain the 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 results meet the requirements, the final fault state machine model is obtained.

[0028] This invention provides a fault state machine-based fault handling method based on regularization, which utilizes the fault state machine modeling method based on regularization described above to construct the fault state machine, including:

[0029] When the power system receives an event, the fault state machine derives the transition conditions based on the event and the fault state of the power system.

[0030] The fault state machine determines the fault state that the power system needs to transition to based on the transition conditions.

[0031] The fault state machine assigns a corresponding self-healing strategy to the power system based on the fault state that the power system needs to transition to, thereby realizing the automatic identification and maintenance of power system faults.

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

[0033] In self-healing fault state machine modeling, the advantages of regular expressions are mainly reflected in two aspects: First, they can concisely describe the system's behavioral patterns, reducing the workload of manual coding and improving the efficiency of model construction. Second, the readability and maintainability of regular expressions make understanding and modifying the state machine model more intuitive. Furthermore, regular expressions provide rich matching rules and logical operations, capable of handling complex patterns, thus accurately capturing the behavioral changes of the system under different fault conditions.

[0034] This invention combines the simplicity of regular expressions with the intuitiveness of state machines, effectively establishing and simplifying self-healing fault models. It can effectively describe the self-healing behavior patterns of low-voltage system faults, providing a concise and efficient modeling method for system fault diagnosis and research on fault self-healing strategies.

[0035] This invention utilizes regularization methods to describe the behavioral patterns of a system, thereby designing a fault state machine that meets the requirements of low-voltage self-healing control. This invention has advantages in simplifying the fault state machine modeling process and improving model accuracy, providing a new modeling tool for fault diagnosis and recovery of complex systems. The self-healing fault state machine constructed using regularization methods can accurately identify fault states, guide the system's self-healing process, improve system stability and reliability, and increase repair speed and save manpower when low-voltage faults occur in the distribution area. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[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 can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of the invention will be apparent to those skilled in the art.

[0040] The accompanying drawings, which are included in and form part of this 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 features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, 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 can certainly implement many other equivalent forms of the invention, which have the features described in the claims and are therefore all within the scope of protection defined herein.

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

[0044] Specific embodiments of the invention are described below with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of the invention, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the invention. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use the invention in various 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 regularization, including:

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

[0048] Step S2: Identify events that may trigger a transition between the fault states;

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

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

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

[0052] Step S6: Verify the accuracy of the fault machine state model to obtain the final self-healing fault machine state model.

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

[0054] The behavioral characteristics include:

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

[0056] Defining the various fault states that may exist in the power system means identifying the various fault states that may exist in the power system. This usually involves a deep 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 faults.

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

[0058] A fault state is a description of a power system at a specific moment, representing a particular function or state of the system. In a fault state machine, these states can be different situations such as normal system operation, partial fault, severe fault, or complete shutdown. An event is a triggering factor that initiates a state transition; it can be an external input, a change in internal conditions, or the occurrence of a fault within the system itself. A transition condition defines the conditions under which the system transitions from one state to another; this is typically related to the triggering of an event and the current state of the system.

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

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

[0061] In a fault state machine, each fault state is associated with a set of predefined self-healing strategies. When the system is in a fault state, the corresponding self-healing strategy is automatically executed, such as adjusting power distribution, isolating faulty lines, or starting backup power. 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 the fault and restore the system to normal operation. See Table 1 below:

[0062] Table 1. Explanation of Self-Healing Strategies

[0063]

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

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

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

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

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

[0069] Table 2 Fault State Regularization

[0070]

[0071] Design corresponding regular expressions for the events. For example, a regular expression for an event describes what happens when a current fluctuation of a specific frequency is detected.

[0072] Design a corresponding regular expression for the transition conditions. This regular expression describes how, under specific conditions, the power system will transition from a fault state to a fault state based on a specific event and the current fault state of the power system. 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, based on step S5, the regular expression of the fault state, the regular expression of the event, and the regular expression of the transition condition are represented graphically to construct a self-healing fault machine state model, including:

[0074] Using the regular expressions for the fault states, the events, and the transition conditions, a self-healing fault state machine model in the form of a directed graph is constructed. Each node in the directed graph represents a fault state, and the edges in the directed graph represent the events and the transition conditions.

[0075] When a power system receives a specific event that triggers it, it automatically performs state transitions based on the matching results of regular expressions, thereby enabling the transfer and rapid identification of fault states.

[0076] Traditional self-healing models are typically based on Petri nets, finite state automata, and other theories. However, these models are labor-intensive to construct and maintain, often requiring extensive manual coding to describe the state transition relationships of complex systems, and suffer from poor readability and maintainability. To address these issues, this study proposes a self-healing fault state machine modeling framework based on regular expressions. Regular expressions, as a concise yet powerful language, can effectively describe the behavioral patterns of systems, providing a new approach for constructing self-healing fault state machines.

[0077] According to another specific embodiment of the present invention, based on 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 results meet the requirements, the final fault state machine model is obtained.

[0079] According to another specific embodiment of the present invention, the present invention provides a fault state machine fault handling method based on regularization methods, which utilizes the fault state machine modeling method based on regularization methods described above to construct a fault state machine, including:

[0080] When the power system receives an event, the fault state machine derives the transition conditions based on the event and the fault state of the power system.

[0081] The fault state machine determines the fault state that the power system needs to transition to based on the transition conditions.

[0082] The fault state machine assigns a corresponding self-healing strategy to the power system based on the fault state that the power system needs to transition to, thereby realizing the automatic identification and maintenance of power system faults.

[0083] In self-healing fault state machine modeling, the advantages of regular expressions are mainly reflected in two aspects: First, they can concisely describe the system's behavioral patterns, reducing the workload of manual coding and improving the efficiency of model construction. Second, the readability and maintainability of regular expressions make understanding and modifying the state machine model more intuitive. Furthermore, regular expressions provide rich matching rules and logical operations, capable of handling complex patterns, thus accurately capturing the behavioral changes of the system under different fault conditions.

[0084] This invention combines the simplicity of regular expressions with the intuitiveness of state machines, effectively establishing and simplifying self-healing fault models. It can effectively describe the self-healing behavior patterns of low-voltage system faults, providing a concise and efficient modeling method for system fault diagnosis and research on fault self-healing strategies.

[0085] This invention utilizes regularization methods to describe the behavioral patterns of a system, thereby designing a fault state machine that meets the requirements of low-voltage self-healing control. This invention has advantages in simplifying the fault state machine modeling process and improving model accuracy, providing a new modeling tool for fault diagnosis and recovery of complex systems. The self-healing fault state machine constructed using regularization methods can accurately identify fault states, guide the system's self-healing process, improve system stability and reliability, and increase repair speed and save manpower when low-voltage faults occur in the distribution area.

[0086] This disclosure also provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the aforementioned fault state machine modeling method based on a regularization method, including:

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

[0088] Step S2: Identify events that may trigger a transition between the fault states;

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

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

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

[0092] Step S6: Verify the accuracy of the fault machine state model to obtain the final self-healing fault machine state model.

[0093] In some embodiments, the processor executing a computer program may be a processing device that includes 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 that runs other instruction sets, or a processor that runs 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), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash drives or other forms of flash memory, cache, registers, static memory, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape cassette or other magnetic storage device, or any other possible non-transitory medium used to store information or instructions that can be accessed by computer equipment.

[0095] The electronic devices disclosed herein may include, but are not limited to, fixed terminal devices such as MCU controllers, servers, desktop computers, and digital TVs, as well as mobile terminal devices such as in-vehicle devices (e.g., head-up displays), handheld devices (e.g., mobile phones, tablets, etc.), and wearable devices (e.g., smartwatches, smart bracelets, etc.).

[0096] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described fault state machine modeling method based on a regularization method, including:

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

[0098] Step S2: Identify events that may trigger a transition between the fault states;

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

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

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

[0102] Step S6: Verify the accuracy of the fault machine state model to obtain the final self-healing fault machine state model.

[0103] The computer-readable storage medium of this disclosure can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device; for example, it can be the memory described above.

[0104] The computer programs of embodiments of this disclosure can be organized into one or more computer-executable components or modules. Various aspects of this disclosure can be implemented with any number and combination of such components or modules. For example, aspects of this disclosure are not limited to the specific computer-executable instructions or particular components or modules shown in the drawings and described herein. Other embodiments may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0105] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A fault state machine modeling method based on regularization, characterized in that, include: Define various possible fault states in a power system and associate each fault state with its corresponding self-healing strategy; Identify events that may trigger a transition between the aforementioned fault states; Define the transition conditions between the aforementioned fault states; Design corresponding regular expressions for the fault state, the event, and the transition condition, respectively; The regular expressions for the fault states, events, and transition conditions are represented graphically to construct a self-healing fault machine state model. The accuracy of the fault machine state model is verified to obtain the final self-healing fault machine state model.

2. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, The regular expressions for the fault states, events, and transition conditions are represented graphically 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 expressions for the fault states, the events, and the transition conditions. Each node in the directed graph represents a fault state, and the edges in the directed graph represent the events and the transition conditions.

3. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, Define the transition conditions between the fault states, the transition conditions including: Under specific fault conditions and specific events, the transition between the fault states is carried out.

4. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, Define various possible fault states of the power system, and associate each fault state with its corresponding self-healing strategy, including: A thorough analysis of the behavioral characteristics of the power system is conducted, and various possible fault states of the power system are defined based on the analysis results.

5. The fault state machine modeling method based on regularization as described in claim 4, characterized in that, A thorough analysis of the behavioral characteristics of the power system is conducted, and based on the analysis results, various possible fault states of the power system are defined, including: Voltage fluctuations, current imbalances, and changes in equipment operating status.

6. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, A thorough analysis of the behavioral characteristics of the power system is conducted, defining various possible fault states of the power system, and associating each fault state with its corresponding self-healing strategy. The fault states include: Normal operation, warning, minor fault, serious fault, and system recovery.

7. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, A thorough analysis of the behavioral characteristics of the power system is conducted, defining various possible fault states of the power system, and associating each fault state with a corresponding self-healing strategy, the self-healing strategy including: Adjust power distribution, isolate faulty lines, or activate backup power.

8. The fault state machine modeling method based on regularization as described in claim 1, characterized in that, The accuracy of the faulty machine state model is verified to obtain the 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 results meet the requirements, the final fault state machine model is obtained.

9. A fault handling method for a fault state machine based on regularization, characterized in that, The fault state machine is constructed using the fault state machine modeling method based on regularization as described in any one of claims 1-8, comprising: When the power system receives an event, the fault state machine derives the transition conditions based on the event and the fault state of the power system. The fault state machine determines the fault state that the power system needs to transition to based on the transition conditions. The fault state machine assigns a corresponding self-healing strategy to the power system based on the fault state that the power system needs to transition to, thereby realizing the automatic identification and maintenance of power system faults.

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