Phase modifier fault early warning method and related device

Abstract

The invention discloses a phase modifier fault early warning method, a phase modifier fault early warning system, fault early warning equipment and a computer readable storage medium, and the method comprises the steps that an early warning device collects monitoring data of each piece of electrical equipment, carries out the alarm preprocessing of the monitoring data according to a reference threshold grading rule, and obtains alarm information; analyzing and processing the alarm information according to an entity model to obtain a basic alarm instance; performing fault correlation analysis processing on the basic alarm instance according to a fault reasoning reference model to obtain an intelligent alarm instance; performing scheme analysis processing on the intelligent alarm instance by adopting a phase modifier expert knowledge decision tree to obtain a target intelligent alarm instance; and performing publishing processing on the target intelligent alarm instance according to a comprehensive publishing model. The efficiency and effectiveness of fault analysis of the phase modifier are improved, and analysis errors caused by artificial experience are avoided.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and more specifically, relates to a phase regulator fault warning method, a phase regulator fault warning system, a fault warning device, and a computer-readable storage medium. Background Art

[0002] In modern power systems, phase regulators, as a key reactive power compensation device, play a vital role in maintaining the voltage level and power factor of the power grid. However, phase regulators may encounter various faults during operation. If these faults are not discovered and handled in time, they will seriously affect the stability of the power grid and the reliability of power supply. Therefore, effective fault warning and diagnosis of phase regulators is the key to ensuring the safe operation of the power grid.

[0003] In related technologies, the operation of fault warning mostly relies on professionals to analyze and process the signals collected on site. This mode has the following limitations: Real-time limitation: The real-time nature of fault discovery and disposal is limited by the speed and efficiency of manual analysis, and rapid response cannot be achieved. Information omission: In complex system signals, important fault signals may be missed, resulting in the failure to be discovered in time. Dependence on manual experience: Fault diagnosis is highly dependent on the experience and knowledge of professionals. For new fault modes or uncommon faults, the accuracy and efficiency of diagnosis may be limited.

[0004] Therefore, how to improve the efficiency and effectiveness of phase regulator fault analysis is a key issue that technical personnel in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide a phase regulator fault warning method, a phase regulator fault warning system, a fault warning device, and a computer-readable storage medium to improve the efficiency and effectiveness of phase regulator fault analysis and avoid analysis errors caused by human experience.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a phase regulator fault warning method, comprising:

[0007] The early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to a reference threshold classification rule, and obtains alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data;

[0008] Analyzing and processing the alarm information according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0009] Performing fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model;

[0010] Using the phase-shifting expert knowledge decision tree to perform solution analysis on the intelligent alarm instance, and obtain a target intelligent alarm instance;

[0011] The target intelligent alarm instance is published and processed according to the comprehensive publishing model.

[0012] Optionally, the early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to the benchmark threshold classification rule, and obtains alarm information, including:

[0013] The early warning device collects monitoring data of each electrical device and extracts monitoring feature quantities from the monitoring data;

[0014] Determine whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rule and the monitoring characteristic quantity;

[0015] If so, a normalized logical operation is performed based on the monitoring feature quantity to obtain the alarm information.

[0016] Optionally, performing fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance includes:

[0017] Perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on the basic alarm instance to obtain multiple analysis results;

[0018] The multiple analysis results are analyzed and calculated, and the obtained calculation results are merged to obtain the intelligent alarm instance; wherein the analysis and calculation include summary processing, comparison processing, screening processing, and derivation processing.

[0019] Optionally, a phase-shifting expert knowledge decision tree is used to perform solution analysis and processing on the intelligent alarm instance to obtain a target intelligent alarm instance, including:

[0020] Using a phase-shifting expert knowledge decision tree to perform knowledge decision processing on the intelligent alarm instance to obtain an expert processing solution;

[0021] The expert processing solution is added to the intelligent alarm instance to obtain the target intelligent alarm instance.

[0022] Optionally, publishing the target intelligent alarm instance according to the comprehensive publishing model includes:

[0023] Performing data processing on the target intelligent alarm instance based on the early warning configuration information to obtain intelligent alarm information;

[0024] The intelligent alarm information is published and processed through multiple view and multiple window display paths.

[0025] The present application also provides a phase regulator fault warning system, comprising:

[0026] A signal monitoring and alarm preprocessing module, used to collect monitoring data of each electrical device, perform alarm preprocessing on the monitoring data according to a reference threshold classification rule, and obtain alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data;

[0027] A phase-shifting analysis module, used for analyzing and processing the alarm information according to a physical model to obtain a basic alarm instance; wherein the physical model is model data obtained by configuring according to site condition data; wherein the site condition data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0028] An alarm fault reasoning module is used to perform fault correlation analysis on the basic alarm instance according to a fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model;

[0029] An expert intelligent decision module is used to perform solution analysis and processing on the intelligent alarm instance by using a phase-shifting expert knowledge decision tree to obtain a target intelligent alarm instance;

[0030] The comprehensive alarm publishing module is used to publish the target intelligent alarm instance according to the comprehensive publishing model.

[0031] Optionally, the signal monitoring and alarm preprocessing module includes:

[0032] A monitoring unit, used to collect monitoring data of each electrical device and extract monitoring feature quantities from the monitoring data;

[0033] An alarm judgment unit, used to judge whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rule and the monitoring characteristic quantity;

[0034] The data processing unit is used to perform normalized logic operations based on the monitoring characteristic quantity to obtain the alarm information when the electrical equipment generates the alarm information.

[0035] Optionally, the alarm fault reasoning module includes:

[0036] A correlation analysis unit, used to perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on the basic alarm instance to obtain multiple analysis results;

[0037] A merging processing unit is used to analyze and calculate the multiple analysis results, merge the obtained calculation results, and obtain the intelligent alarm instance; wherein the analysis and calculation includes summary processing, comparison processing, screening processing, and derivation processing.

[0038] The present application also provides a fault warning device, comprising:

[0039] Memory for storing computer programs;

[0040] A processor is used to implement the steps of the phase regulator failure warning method as described above when executing the computer program.

[0041] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the phase modulator failure warning method described above are implemented.

[0042] The present application provides a phase shifter fault warning method, comprising: a warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to a benchmark threshold classification rule, and obtains alarm information; wherein the benchmark threshold classification rule is a benchmark threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data; the alarm information is analyzed and processed according to a physical model to obtain a basic alarm instance; wherein the physical model is model data obtained by configuring according to site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data; according to a fault reasoning benchmark model, a fault correlation analysis is performed on the basic alarm instance to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model; a phase shifter expert knowledge decision tree is used to perform solution analysis on the intelligent alarm instance to obtain a target intelligent alarm instance; and the target intelligent alarm instance is released and processed according to a comprehensive release model.

[0043] It has the following beneficial effects:

[0044] Improve fault diagnosis efficiency: Through real-time monitoring and automated analysis, the system can quickly identify fault signals of phase regulators, significantly shorten fault diagnosis time, and improve response speed.

[0045] Enhanced fault identification accuracy: Using advanced data mining and machine learning algorithms, the system can accurately identify fault modes from large amounts of monitoring data, reducing misjudgments and missed judgments caused by human factors.

[0046] Reduce operation and maintenance costs: Automated fault warning and diagnosis reduce dependence on professionals, thereby reducing labor costs and training costs and improving operation and maintenance efficiency.

[0047] Improve system reliability: The system can provide timely warnings and assist operation and maintenance personnel in troubleshooting, reducing downtime caused by faults and enhancing the stability and reliability of the power grid.

[0048] Optimized decision support: The fault handling suggestions and solutions provided by the system are based on a large amount of historical data and intelligent analysis, providing scientific decision support for operation and maintenance personnel and improving the quality and efficiency of fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application 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 embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0050] Figure 1 A flow chart of a phase regulator failure warning method provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the structure of a phase regulator failure warning system provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of the structure of the fault warning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The core of this application is to provide a phase regulator fault warning method, a phase regulator fault warning system, a fault warning device, and a computer-readable storage medium to improve the efficiency and effectiveness of phase regulator fault analysis and avoid analysis errors caused by human experience.

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] A phase modulator fault warning method provided by the present application is described below through an embodiment.

[0056] Please refer to Figure 1 , Figure 1 A flow chart of a phase regulator failure warning method provided in an embodiment of the present application.

[0057] In this embodiment, the method may include:

[0058] S101, the early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to the reference threshold classification rule, and obtains alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data;

[0059] This step aims to enable the early warning device to collect monitoring data of various electrical equipment, perform alarm preprocessing on the monitoring data according to the benchmark threshold classification rule, and obtain alarm information; wherein the benchmark threshold classification rule is a benchmark threshold corresponding to each fault of each electrical equipment under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data.

[0060] Specifically, the basic data sources of the early warning device are divided into source access and alarm access. Source access refers to collecting monitoring data from various electrical equipment, extracting monitoring feature quantities, calculating the benchmark threshold corresponding to each fault of each electrical equipment under different working conditions, and obtaining and using the benchmark threshold classification rules to determine whether the current electrical equipment has generated an alarm and include it in the alarm preprocessing link.

[0061] Among them, alarm access refers to accessing manufacturer alarms, implementing normalized logical operations through alarm preprocessing, converting access information into basic model input and generating basic alarm instances.

[0062] Optionally, this step may include:

[0063] Step 1: The early warning device collects monitoring data of each electrical device and extracts monitoring feature quantities from the monitoring data;

[0064] Step 2: judging whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rules and the monitoring characteristic quantity;

[0065] Step 3: If yes, perform normalized logic operation based on the monitored feature quantity to obtain alarm information.

[0066] It can be seen that this optional solution mainly explains how to obtain alarm information. In this optional solution, the early warning device collects monitoring data of each electrical device and extracts monitoring feature quantities from the monitoring data; determines whether the corresponding electrical device generates alarm information based on the benchmark threshold classification rules and the monitoring feature quantities; if so, performs normalized logical operations based on the monitoring feature quantities to obtain alarm information.

[0067] S102, analyzing and processing the alarm information according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0068] Based on S101, this step aims to analyze and process the alarm information according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0069] Specifically, in this step, the basic model of the phase-shifting machine is defined according to the site conditions. This includes entity models of the unit, functional system, system sub-functions, monitoring data, etc., as well as models such as the weight of operating conditions between entities, fault reasoning benchmarks, and expert decision weightings, which are the basis for early warning analysis.

[0070] S103, performing fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model;

[0071] Based on S102, this step aims to perform fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model.

[0072] Specifically, based on the fault reasoning benchmark model, the basic alarm instances formed by monitoring and alarm information are subjected to fault root cause reasoning, single event and multi-event correlation analysis. After aggregation, comparison, screening, derivation and other calculations, they are merged to generate intelligent alarm instances.

[0073] Furthermore, this step may include:

[0074] Step 1: Perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on basic alarm instances to obtain multiple analysis results;

[0075] Step 2, analyze and calculate multiple analysis results, merge the obtained calculation results, and obtain an intelligent alarm instance; wherein the analysis and calculation include summary processing, comparison processing, screening processing, and derivative processing.

[0076] S104, using the phase-shifting expert knowledge decision tree to perform solution analysis on the intelligent alarm instance to obtain a target intelligent alarm instance;

[0077] Based on S103, this step aims to use the phase adjustment expert knowledge decision tree to perform solution analysis and processing on the intelligent alarm instance to obtain the target intelligent alarm instance;

[0078] Specifically, we build a knowledge base of condenser experts to form domain knowledge of condenser operation, testing, maintenance, design structure and manufacturing process, and analyze various possible failure mechanisms, causes, development and adverse consequences of condensers, and assign weights to decision rules. Based on the knowledge decision tree, the intelligent decision module analyzes the category of intelligent alarm instances and the weighting of knowledge points, makes mixed inference decision disposal suggestions, and supplements the corresponding expert processing solutions to the intelligent alarm instances.

[0079] Furthermore, this step may include:

[0080] Step 1: Use the phase-shifting expert knowledge decision tree to perform knowledge decision processing on the intelligent alarm instance to obtain an expert processing solution;

[0081] Step 2: Add the expert processing solution to the intelligent alarm instance to obtain the target intelligent alarm instance.

[0082] S105: Publish the target intelligent alarm instance according to the comprehensive publishing model.

[0083] Based on S104, this step aims to publish the target intelligent alarm instance according to the comprehensive publishing model.

[0084] Furthermore, this step may include:

[0085] Step 1: Process the target intelligent alarm instance based on the warning configuration information to obtain intelligent alarm information;

[0086] Step 2: Publish and process the intelligent alarm information through multiple view and multiple window display paths.

[0087] In summary, this embodiment has the following beneficial effects:

[0088] Improve fault diagnosis efficiency: Through real-time monitoring and automated analysis, the system can quickly identify fault signals of phase regulators, significantly shorten fault diagnosis time, and improve response speed.

[0089] Enhanced fault identification accuracy: Using advanced data mining and machine learning algorithms, the system can accurately identify fault modes from large amounts of monitoring data, reducing misjudgments and missed judgments caused by human factors.

[0090] Reduce operation and maintenance costs: Automated fault warning and diagnosis reduce dependence on professionals, thereby reducing labor costs and training costs and improving operation and maintenance efficiency.

[0091] Improve system reliability: The system can provide timely warnings and assist operation and maintenance personnel in troubleshooting, reducing downtime caused by faults and enhancing the stability and reliability of the power grid.

[0092] Optimized decision support: The fault handling suggestions and solutions provided by the system are based on a large amount of historical data and intelligent analysis, providing scientific decision support for operation and maintenance personnel and improving the quality and efficiency of fault handling.

[0093] The phase modulator fault warning system provided in the embodiment of the present application is introduced below. The phase modulator fault warning system and the phase modulator fault warning method described below can be referred to each other.

[0094] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a phase modulator fault warning system provided in an embodiment of the present application.

[0095] In this embodiment, the system may include:

[0096] The signal monitoring and alarm preprocessing module 100 is used to collect monitoring data of each electrical device, perform alarm preprocessing on the monitoring data according to the reference threshold classification rule, and obtain alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data;

[0097] The phase-converter analysis module 200 is used to analyze and process the alarm information according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0098] The alarm fault reasoning module 300 is used to perform fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model;

[0099] The expert intelligent decision module 400 is used to perform solution analysis and processing on the intelligent alarm instance by using the phase adjustment expert knowledge decision tree to obtain the target intelligent alarm instance;

[0100] The comprehensive alarm publishing module 500 is used to publish the target intelligent alarm instance according to the comprehensive publishing model.

[0101] Optionally, the signal monitoring and alarm preprocessing module 100 includes:

[0102] A monitoring unit, used to collect monitoring data of each electrical device and extract monitoring feature quantities from the monitoring data;

[0103] An alarm judgment unit, used to judge whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rules and the monitoring characteristic quantity;

[0104] The data processing unit is used to perform normalized logic operations based on monitoring characteristic quantities to obtain alarm information when the electrical equipment generates alarm information.

[0105] Optionally, the alarm fault reasoning module 300 includes:

[0106] The correlation analysis unit is used to perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on basic alarm instances to obtain multiple analysis results;

[0107] The merging processing unit is used to analyze and calculate multiple analysis results, merge the obtained calculation results, and obtain an intelligent alarm instance; wherein the analysis and calculation include summary processing, comparison processing, screening processing, and derivative processing.

[0108] This application also provides a fault warning device, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a fault warning device provided in an embodiment of the present application. The fault warning device may include:

[0109] Memory for storing computer programs;

[0110] The processor is used to implement the steps of any one of the above-mentioned phase regulator failure warning methods when executing a computer program.

[0111] like Figure 3, which is a schematic diagram of the structure of a fault warning device, the fault warning device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 communicate with each other through the communication bus 13.

[0112] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0113] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0114] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0115] The early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to the benchmark threshold classification rule, and obtains alarm information; wherein the benchmark threshold classification rule is a benchmark threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data;

[0116] The alarm information is analyzed and processed according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data;

[0117] Perform fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model;

[0118] The phase-shifting expert knowledge decision tree is used to analyze and process the intelligent alarm instance to obtain the target intelligent alarm instance;

[0119] The target intelligent alarm instance is released and processed according to the comprehensive release model.

[0120] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0121] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0122] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0123] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the fault warning device in the embodiment of the present application. In actual applications, the fault warning device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0124] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned phase regulator failure warning methods can be implemented.

[0125] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0126] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0127] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0128] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0130] The above is a detailed introduction to a phase regulator fault warning method, phase regulator fault warning system, fault warning device, and computer-readable storage medium provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A phase regulator failure early warning method, characterized in that: include: The early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to a reference threshold classification rule, and obtains alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data; Analyzing and processing the alarm information according to the entity model to obtain a basic alarm instance; wherein the entity model is model data obtained by configuring according to the site situation data; wherein the site situation data includes unit data, functional system data, system sub-function data, and historical monitoring data; Performing fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model; Using the phase-shifting expert knowledge decision tree to perform solution analysis on the intelligent alarm instance, and obtain a target intelligent alarm instance; The target intelligent alarm instance is published and processed according to the comprehensive publishing model.

2. The phase regulator failure early warning method according to claim 1 is characterized in that: The early warning device collects monitoring data of each electrical device, performs alarm preprocessing on the monitoring data according to the benchmark threshold classification rules, and obtains alarm information, including: The early warning device collects monitoring data of each electrical device and extracts monitoring feature quantities from the monitoring data; Determine whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rule and the monitoring characteristic quantity; If so, a normalized logical operation is performed based on the monitoring feature quantity to obtain the alarm information.

3. The phase regulator failure early warning method according to claim 1 is characterized in that: Performing fault correlation analysis on the basic alarm instance according to the fault reasoning benchmark model to obtain an intelligent alarm instance, including: Perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on the basic alarm instance to obtain multiple analysis results; The multiple analysis results are analyzed and calculated, and the obtained calculation results are merged to obtain the intelligent alarm instance; wherein the analysis and calculation include summary processing, comparison processing, screening processing, and derivation processing.

4. The phase regulator failure early warning method according to claim 1 is characterized in that: The intelligent alarm instance is analyzed and processed by using the phase-shifting expert knowledge decision tree to obtain the target intelligent alarm instance, including: Using a phase-shifting expert knowledge decision tree to perform knowledge decision processing on the intelligent alarm instance to obtain an expert processing solution; The expert processing solution is added to the intelligent alarm instance to obtain the target intelligent alarm instance.

5. The phase regulator failure early warning method according to claim 1 is characterized in that: The target intelligent alarm instance is released and processed according to the comprehensive release model, including: Performing data processing on the target intelligent alarm instance based on the early warning configuration information to obtain intelligent alarm information; The intelligent alarm information is published and processed through multiple view and multiple window display paths.

6. A phase regulator fault warning system, characterized in that: include: A signal monitoring and alarm preprocessing module, used to collect monitoring data of each electrical device, perform alarm preprocessing on the monitoring data according to a reference threshold classification rule, and obtain alarm information; wherein the reference threshold classification rule is a reference threshold corresponding to each fault of each electrical device under different working conditions calculated based on the monitoring feature quantity extracted from the monitoring data; A phase-shifting analysis module, used for analyzing and processing the alarm information according to a physical model to obtain a basic alarm instance; wherein the physical model is model data obtained by configuring according to site condition data; wherein the site condition data includes unit data, functional system data, system sub-function data, and historical monitoring data; An alarm fault reasoning module is used to perform fault correlation analysis on the basic alarm instance according to a fault reasoning benchmark model to obtain an intelligent alarm instance; wherein the fault reasoning benchmark model includes a fault root cause reasoning model, a single event analysis model, and a multi-event correlation analysis model; An expert intelligent decision module is used to perform solution analysis and processing on the intelligent alarm instance by using a phase-shifting expert knowledge decision tree to obtain a target intelligent alarm instance; The comprehensive alarm publishing module is used to publish the target intelligent alarm instance according to the comprehensive publishing model.

7. The phase modulator failure warning system according to claim 6 is characterized in that: The signal monitoring and alarm preprocessing module includes: A monitoring unit, used to collect monitoring data of each electrical device and extract monitoring feature quantities from the monitoring data; An alarm judgment unit, used to judge whether the corresponding electrical equipment generates alarm information according to the reference threshold classification rule and the monitoring characteristic quantity; The data processing unit is used to perform normalized logic operations based on the monitoring characteristic quantity to obtain the alarm information when the electrical equipment generates the alarm information.

8. The phase modulator failure warning system according to claim 6, characterized in that: The alarm fault reasoning module comprises: A correlation analysis unit, used to perform fault root cause reasoning, single event analysis, and multi-event correlation analysis on the basic alarm instance to obtain multiple analysis results; A merging processing unit is used to analyze and calculate the multiple analysis results, merge the obtained calculation results, and obtain the intelligent alarm instance; wherein the analysis and calculation includes summary processing, comparison processing, screening processing, and derivation processing.

9. A fault warning device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the phase modulator failure warning method as claimed in any one of claims 1 to 5 when executing the computer program.

10. 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 steps of the phase regulator failure warning method as described in any one of claims 1 to 5 are implemented.

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