Internet-of-things power distribution terminal line alarm state analysis method and system

Abstract

The invention provides an Internet of Things power distribution terminal line alarm state analysis method and system, and the method comprises the steps: obtaining disturbance waveform data, and inputting the disturbance waveform data into a transient information recognition algorithm; obtaining a first alarm result according to a transient information identification algorithm; wherein the first alarm result comprises at least one of an alarm state starting point, an alarm state ending point, an alarm state duration, an alarm state phase voltage initial phase angle and an alarm state phase angle relative threshold value; obtaining first discharge data, and obtaining warning state severity early warning information according to the first discharge data and the first warning result; wherein the first discharge data comprises at least one of the arc discharge frequency, the total discharge duration and the fault occurrence frequency trend. Through the mode, analysis and early warning of the alarm reason of the Internet of Things power distribution terminal are realized, and the safety of the Internet of Things power distribution terminal is improved.

Description

Technical Field

[0001] This application relates to the field of power equipment testing technology, and in particular to a method and system for analyzing alarm states of IoT power distribution terminal lines. Background Technology

[0002] Accurate and reliable early-stage line condition perception in the distribution network is one of the important foundations for power companies to build a "observable, measurable, and controllable" digital panoramic perception system. Among them, IoT distribution terminals are an important component of building smart distribution networks and realizing distribution network automation.

[0003] With the rapid development of artificial intelligence technology in recent years, many intelligent optimization algorithms have strong nonlinear model solving capabilities and have been applied in the power system field. However, the current level of intelligence is low in the face of alarm state early warning scenarios for distribution lines. There is an urgent need for a new alarm state early warning function algorithm based on Internet of Things distribution terminals (XTUs) to ensure economic efficiency while improving the reliability of the distribution network.

[0004] This invention provides a method and system for analyzing the alarm state of IoT distribution terminals (XTUs) for disturbance fault calculation. It belongs to the field of power system automation and is mainly used in new power operation and maintenance systems to build an intelligent operation and maintenance system for early warning of alarm states of distribution lines. It uses IoT distribution terminals (XTUs) to achieve early warning of faults. Summary of the Invention

[0005] This application provides a method and system for analyzing alarm states of IoT power distribution terminals, so as to analyze and warn of alarm causes of IoT power distribution terminals and improve the security of IoT power distribution terminals.

[0006] Firstly, this application provides a method for analyzing the alarm status of IoT power distribution terminal lines. This method is executed by a computing device, which can be understood as a computer or server, etc., and is not specifically limited herein. It includes:

[0007] Acquire disturbance waveform data and input the disturbance waveform data into a transient information identification algorithm; obtain a first alarm result based on the transient information identification algorithm; wherein the first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; acquire first discharge data and obtain alarm state severity warning information based on the first discharge data and the first alarm result; wherein the first discharge data includes at least one of the following: arc discharge count, total discharge duration, and fault occurrence trend.

[0008] Through the above methods, this application first obtains disturbance waveform data, uses a transient information identification algorithm to obtain the first alarm result, and realizes the acquisition of alarm results in the transient information of IoT power distribution terminals to obtain information such as alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; at the same time, based on the first discharge point data and the first alarm result, alarm state severity warning information is obtained, and potential hazards are detected in time before insulation faults occur, realizing the transformation from "periodic maintenance" to "condition-based maintenance", ensuring the safe and reliable operation of the power grid, and avoiding catastrophic accidents and economic losses.

[0009] In the aforementioned IoT power distribution terminal line alarm state analysis method, obtaining disturbance waveform data specifically includes: initializing the parameters of the first transient disturbance fault diagnosis algorithm and constructing the first transient disturbance fault diagnosis algorithm model; wherein, the first transient disturbance fault diagnosis model is used to perform transient disturbance fault diagnosis; inputting transient electrical quantities into the first transient disturbance fault diagnosis model to obtain disturbance waveform data and first discharge data.

[0010] In the above manner, this application first initializes the parameters of the first transient fault disturbance diagnosis algorithm, constructs the first transient disturbance fault diagnosis algorithm model, realizes the diagnosis of transient disturbance faults, and obtains disturbance waveforms and first discharge data by inputting transient electrical quantities into the first transient disturbance fault diagnosis model. It can not only analyze the cause of transient electrical quantities and determine whether the cause of transient electrical quantities is due to faults, but also diagnose the fault type and obtain disturbance waveforms and first discharge data.

[0011] In the aforementioned IoT power distribution terminal line alarm state analysis method, the first transient disturbance fault diagnosis algorithm model is constructed, including: the first transient disturbance fault diagnosis algorithm model includes at least one of the following: algorithm sliding step size, calculation window, sampling frequency, effective value of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero-sequence voltage, effective value of zero-sequence current, sudden change, instantaneous value, and sudden change threshold; wherein, the algorithm sliding step size is less than or equal to one-quarter of a cycle, the calculation window length is equal to one-half a cycle, the sampling frequency is 256 points per cycle, and the effective values ​​of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero-sequence voltage, effective value of zero-sequence current, sudden change, instantaneous value, and sudden change threshold are preset values.

[0012] Through the above methods, this application limits the algorithm's sliding step size, calculation window, sampling frequency, effective value of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero-sequence voltage, effective value of zero-sequence current, mutation amount, instantaneous value, and mutation threshold, thereby realizing the construction of the first transient disturbance fault diagnosis algorithm model. It optimizes the traditional transient fault diagnosis algorithm, making the diagnosis results more consistent with the actual fault content and improving the reliability of the algorithm model.

[0013] In the aforementioned IoT power distribution terminal line alarm state analysis method, the effective values ​​of zero-sequence voltage and zero-sequence current are calculated in the following way:

[0014] ;

[0015] ;

[0016] Where m represents the current sampling point and n represents the starting sampling point of the data window; This represents the effective value of the zero-sequence voltage at sampling point m. This represents the effective value of the zero-sequence current at sampling point m. This indicates the number of sampling points for the window function. Represents the original sampling point of the zero-sequence current. N1 represents the original sampling point of the zero-sequence voltage, and N1 represents the end sampling point of the data window.

[0017] Through the above methods, this application calculates the effective values ​​of zero-sequence voltage and zero-sequence current, thereby improving the precision of the algorithm model and ensuring the accuracy of the output results.

[0018] The aforementioned IoT power distribution terminal line alarm state analysis method also includes: constructing a first transmission mechanism and compression algorithm based on performance parameters such as compression ratio, compression amount, and file compression quality. The first transmission mechanism and compression algorithm are used to compress big data into small-capacity data for remote transmission.

[0019] Through the above methods, this application constructs a first transmission mechanism and compression algorithm based on performance parameters such as compression ratio, compression amount, and file compression quality when transmitting fault recording waveforms. This enables the compression of large data into small-capacity data for long-distance transmission, providing longer transmission distance and capacity support for the transmission of alarm information from IoT power distribution terminals.

[0020] The aforementioned method for analyzing the alarm status of IoT power distribution terminals also includes: obtaining maintenance record information of IoT power distribution terminals; generating an operation and maintenance solution library based on alarm status severity warning information and maintenance record information; performing health rating and graded warning for fault operation and maintenance scenarios based on the operation and maintenance solution library; and generating an IoT power distribution terminal fault inspection plan based on the health rating and graded warning from the operation and maintenance solution library.

[0021] By acquiring and analyzing maintenance records using the methods described above, an operation and maintenance solution library is generated. This library records maintenance methods for different faults, ensuring a rapid response when encountering similar faults in the future. Simultaneously, based on the health ratings and tiered early warnings in the operation and maintenance solution library, an IoT power distribution terminal fault inspection plan is generated, realizing a shift from "periodic maintenance" to "condition-based maintenance," ensuring the safe and reliable operation of the power grid and avoiding catastrophic accidents and economic losses.

[0022] Secondly, this application provides an IoT power distribution terminal line alarm status analysis system, including: an input module, an output module, and an early warning information acquisition module.

[0023] The system includes an input module for acquiring disturbance waveform data and inputting it into a transient information recognition algorithm; an output module for obtaining a first alarm result based on the transient information recognition algorithm; wherein the first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; and a warning information acquisition module for acquiring first discharge data and obtaining alarm state severity warning information based on the first discharge data and the first alarm result; wherein the first discharge data includes at least one of the following: arc discharge count, total discharge duration, and fault occurrence trend.

[0024] Thirdly, this application also provides a computing device, comprising: a memory for storing program instructions; and a processor for calling the program instructions stored in the memory and executing any of the methods described in the first aspect according to the obtained program instructions.

[0025] Fourthly, this application also provides a computer-readable storage medium storing computer-readable instructions that, when read and executed by a computer, implement any of the methods described in the first aspect.

[0026] Fifthly, this application provides a computer program product including a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform any of the methods described in the first aspect.

[0027] Beneficial effects: Through the above method, this application first obtains disturbance waveform data, uses a transient information identification algorithm to obtain the first alarm result, and realizes the acquisition of alarm results in the transient information of IoT power distribution terminals, so as to obtain information such as alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; at the same time, based on the first discharge point data and the first alarm result, the alarm state severity warning information is obtained, and potential hazards are detected in time before insulation faults occur, realizing the transformation from "periodic maintenance" to "condition maintenance", ensuring the safe and reliable operation of the power grid, and avoiding catastrophic accidents and economic losses. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0030] Figure 2 A schematic diagram of the architecture of an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0031] Figure 3 This is a schematic diagram illustrating the influence of system parameters on an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0032] Figure 4 A schematic diagram of the fault recording transmission mechanism of an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0033] Figure 5 This is a schematic diagram of the fault judgment process of an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0034] Figure 6 This is a schematic diagram of a health rating method for an IoT power distribution terminal line alarm state analysis method provided in Embodiment 1 of this application;

[0035] Figure 7 This is a schematic diagram of an IoT power distribution terminal line alarm status analysis system provided in Embodiment 2 of this application;

[0036] Figure 8 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention.

[0038] In the following embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (or more) of the following" or similar expressions refer to any combination of these items, including any combination of single item (or more) or plural items (or more). For example, at least one (or more) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple. The singular expression forms "a", "one kind", "the", "above-mentioned", "this", and "this one" are also intended to include expressions such as "one or more" unless the context clearly indicates otherwise. And, unless otherwise stated, the ordinal numbers such as "first", "second", etc. mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the order, time sequence, priority, or importance degree of multiple objects.

[0039] Describing reference to "one embodiment" or "some embodiments" etc. in the specification of the present application means that in one or more embodiments of the present application, specific features, structures, or characteristics described in combination with that embodiment are included. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments" unless otherwise specifically emphasized in another way. The terms "comprise", "include", "have" and their variants all mean "include but not limited to" unless otherwise specifically emphasized in another way.

[0040] Embodiment 1

[0041] Embodiment 1 of the present application provides a method for analyzing the warning state of the lines of an IoT power distribution terminal, as Figure 1 shown, to analyze and predict the warning reasons of the IoT power distribution terminal and improve the security of the IoT power distribution terminal. This method is for the fault warning framework of the IoT power distribution terminal as Figure 2As shown, the IoT power distribution terminal collects power distribution line data and transmits it to the power distribution automation master station. After analysis and calculation, diagnostic results are derived, and relevant charts are generated. Simultaneously, the assessment results are pushed to relevant maintenance personnel. Users can query line data information by accessing the platform. This method is executed through a computing device, which can be understood as a computer or server, etc. This application does not specifically limit this definition, but includes:

[0042] Step 101: Obtain disturbance waveform data and input the disturbance waveform data into the transient information recognition algorithm.

[0043] This step is performed in Distribution Zone I, where the main station is responsible for production. Disturbance waveform data is obtained based on a transient disturbance fault diagnosis algorithm. The specific process for acquiring disturbance waveform data includes initializing the parameters of the first transient disturbance fault diagnosis algorithm and constructing the first transient disturbance fault diagnosis algorithm model. The first transient disturbance fault diagnosis model is used for transient disturbance fault diagnosis. Transient electrical quantities are input into the first transient disturbance fault diagnosis model to obtain disturbance waveform data and first discharge data. The first transient disturbance fault diagnosis algorithm model is an optimization of the traditional transient fault diagnosis algorithm model. By using a preset parameter model as the fitness function of the improved transient disturbance fault diagnosis algorithm, it completes disturbance fault early warning diagnosis and fault recording. The parameter model is preset according to the needs of the IoT distribution terminal and can be a neural network model; this application does not limit the specific type of parameter model.

[0044] The first transient disturbance fault diagnosis algorithm model includes at least one of the following: algorithm sliding step size, calculation window, sampling frequency, effective value of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero-sequence voltage, effective value of zero-sequence current, abrupt change, instantaneous value, and abrupt change threshold. For example... Figure 3 As shown, the sampling frequency, sliding step size, and calculation window of different waveforms have a significant impact on the algorithm parameters. Therefore, this application limits the sampling frequency, sliding step size, and calculation window.

[0045] The algorithm's sliding step size is less than or equal to one-quarter of a cycle, the calculation window length is equal to one-half a cycle, the sampling frequency is 256 points per cycle, and the effective values ​​of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero-sequence voltage, effective value of zero-sequence current, mutation amount, instantaneous value, and mutation threshold are preset values.

[0046] When the effective values ​​of phase voltage, phase current, sequence voltage, sequence current, zero-sequence voltage, zero-sequence current, sudden changes, instantaneous values, and sudden change thresholds exceed preset values, the process of detecting disturbance waveform data is triggered. The preset values ​​are set according to the specific needs of the IoT power distribution terminal, and this application does not limit them.

[0047] The effective values ​​of zero-sequence voltage and zero-sequence current are calculated as follows:

[0048] ;

[0049] ;

[0050] Where m represents the current sampling point and n represents the starting sampling point of the data window; This represents the effective value of the zero-sequence voltage at sampling point m. This represents the effective value of the zero-sequence current at sampling point m. This indicates the number of sampling points for the window function. Represents the original sampling point of the zero-sequence current. N1 represents the original sampling point of the zero-sequence voltage, and N1 represents the end sampling point of the data window.

[0051] The triggering criteria for zero-sequence voltage and zero-sequence current amplitude are as follows:

[0052]

[0053] ;

[0054] in, This represents the effective value of the zero-sequence voltage at sampling point m. This represents the trigger threshold value of the zero-sequence voltage RMS value; This represents the effective value of the zero-sequence current at sampling point m. This represents the effective value trigger threshold for zero-sequence current. The effective value trigger threshold is used to represent the preset amplitude values ​​of zero-sequence voltage and zero-sequence current. This application does not limit its specific value; it can be determined according to the actual situation.

[0055] The triggering criteria for zero-sequence voltage and zero-sequence current abrupt changes are as follows:

[0056]

[0057] ;

[0058] in, This represents the effective value of the zero-sequence voltage at sampling point m. This represents the effective value of the zero-sequence voltage at sampling point m-1. This represents the threshold value for triggering a sudden change in zero-sequence voltage. This represents the effective value of the zero-sequence current at sampling point m. This represents the effective value of the zero-sequence current at sampling point m-1. This represents the threshold value for triggering a sudden change in zero-sequence current. The threshold value is used to represent the preset value for the sudden change in zero-sequence voltage and zero-sequence current. This application does not limit its specific value, but it can be determined according to the actual situation.

[0059] The triggering criteria for the instantaneous change in zero-sequence current are as follows:

[0060]

[0061] ;

[0062] in, Represents zero-sequence current Sampling points Represents zero-sequence current Sampling points This represents the threshold value for triggering the instantaneous change in the zero-sequence current. The instantaneous trigger threshold value is used to represent the preset value of the instantaneous zero-sequence current. This application does not limit its specific value, but rather allows it to be determined based on the actual situation.

[0063] like Figure 4 As shown, before acquiring the disturbance waveform data, a first transmission mechanism and compression algorithm are constructed based on performance parameters such as compression ratio, compression amount, and file compression quality. This first transmission mechanism and compression algorithm are used to compress large amounts of data into smaller volumes for long-distance transmission. The parameters of the low-flow fault recording transmission mechanism are based on multiple language text libraries, with a compression ratio between 3 and 6; high encoding efficiency for single texts, and decoding efficiency superior to encoding efficiency; CPU utilization can be adjusted by setting algorithm parameters; and file compression quality is based on lossless compression.

[0064] Step 102: Obtain the first alarm result based on the transient information recognition algorithm.

[0065] The first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative to a threshold.

[0066] The alarm state start point is the first time point at which the effective value calculation exceeds the threshold, and the judgment criteria are as follows:

[0067] &&

[0068] in, This represents the threshold value indicating the start and end points of the alarm state. This represents the effective value of the point preceding the starting point of the zero-sequence current. This represents the effective value of the starting point of the zero-sequence current.

[0069] The fault termination point is the first time node when the effective value calculation is less than the threshold, minus the first time node of fault recurrence. The judgment criteria are as follows:

[0070] && ;

[0071] in, This represents the threshold value indicating the start and end points of the fault. This represents the effective value of the point preceding the end of the zero-sequence current. This represents the effective value at the end point of the zero-sequence current.

[0072] The duration of a fault is the time between its start and end points, and it is determined as follows:

[0073]

[0074] in, Indicates the end point time mark, Indicates the starting point time mark. Indicates the duration of the fault.

[0075] The initial phase angle of the fault phase voltage is the initial phase angle calculated based on the voltage waveform data after accurately identifying the fault start time using FFT calculations performed on the previous cycle of the fault. The initial phase angle thresholds are 80° and 100°; an alarm is triggered if these thresholds are exceeded.

[0076] Step 103: Obtain the first discharge data, and obtain alarm severity warning information based on the first discharge data and the first alarm result.

[0077] This step is carried out in Distribution Zone IV, which is the main station responsible for management. The first discharge data includes at least one of the following: number of arc discharges, total discharge duration, and trend of fault occurrences.

[0078] The severity assessment indicators for alarm states include carbonization level, insulation recovery level, insulation breakdown capability, and cumulative energy released during faults. Based on the insulation trend assessment results and information such as maintenance records, typical distribution network line fault operation and maintenance scenarios are extracted, thus forming an operation and maintenance solution library. Health ratings and graded early warnings are conducted based on multiple insulation degradation evolution characteristic indicators. In conjunction with the lean management system for distribution lines, proactive inspection plans are developed.

[0079] The carbonization level is obtained through the following expression.

[0080]

[0081] ;

[0082] The insulation recovery level is obtained using the following expression:

[0083] ;

[0084] The withstand voltage breakdown capability is obtained through the following expression.

[0085] ;

[0086] The cumulative energy released during a fault is obtained using the following expression.

[0087] ;

[0088] in, This represents the current sampling point data. This represents the voltage sampling point data, and N represents the sampling point period for the cumulative released energy.

[0089] The overall process for determining whether a transient electrical quantity may be a latent fault is as follows: Figure 5 As shown, the process includes acquiring relevant data, determining whether the zero-sequence current signal exceeds a preset value, determining the phase of the fault and its duration, determining whether there are changes in the load before and after the fault, and determining whether the fault occurs at the voltage peak. If at least one of these conditions is met, a latent fault is identified in the transient electrical quantities. After calculating the effective, abrupt, and transient values ​​of the three-phase voltage / current, the maximum and minimum values ​​of the three-phase voltage and current are directly compared to determine the phase of the maximum / minimum value of the three-phase voltage and current as the fault phase. Once the fault phase is accurately identified, an FFT calculation is performed using the previous cycle of the fault waveform. The initial phase angle of the fault is calculated based on the voltage waveform data, and the fault duration is used to determine whether the fault is in an alarm state.

[0090] The method also includes obtaining maintenance record information of IoT power distribution terminals, generating an operation and maintenance solution library based on alarm severity warning information and maintenance record information; performing health rating and graded warning for fault operation and maintenance scenarios based on the operation and maintenance solution library; and generating IoT power distribution terminal fault inspection plan based on the health rating and graded warning of the operation and maintenance solution library.

[0091] Specifically, the operation and maintenance decision-making diagrams provided by the health rating and operation and maintenance solution library are as follows: Figure 6As shown. Health ratings include status, evaluation factors, and probability of failure. Status ratings include excellent, good, questionable, abnormal, and dangerous. Evaluation factors include several ranges such as 0-20, 20-40, 40-55, 55-70, and 70-100. Probability of failure includes ratings such as almost never, very low, low, medium, and high. Maintenance decisions include equipment control levels and maintenance cycles. Equipment control levels include Level IV, and maintenance cycles include quarter-cycle, half-cycle, one-cycle, two-cycle, and four-cycle cycles. It should be noted that the above data and their levels are only illustrative and may include different health rating content or maintenance decision content. Specific ratings may also be divided into more detailed or more general content; this application does not limit this.

[0092] Compared with the prior art, this application has the following advantages and beneficial effects:

[0093] (1) Intelligent operation and maintenance: The alarm state early warning function of the power distribution line realizes the modular maintenance of IoT power distribution terminal functions, transforms passive operation and maintenance into active operation and maintenance, and reduces power outage time.

[0094] (2) Precise perception and early warning: IoT power distribution terminal covers the full-state monitoring of switch body, line environment and key modules, and supports edge computing for rapid response.

[0095] (3) Life cycle optimization: IoT power distribution terminals need to reduce long-term operation and maintenance costs through modular design and fault early warning.

[0096] Example 2

[0097] After introducing the IoT power distribution terminal line alarm state analysis method of Embodiment 1 of this application, Embodiment 2 of this application provides an IoT power distribution terminal line alarm state analysis system to implement the content of the IoT power distribution terminal line alarm state analysis method. A schematic diagram of the system is as follows. Figure 7 As shown, it includes: an input module, an output module, and a warning information acquisition module.

[0098] The system includes an input module for acquiring disturbance waveform data and inputting it into a transient information recognition algorithm; an output module for obtaining a first alarm result based on the transient information recognition algorithm; wherein the first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; and a warning information acquisition module for acquiring first discharge data and obtaining alarm state severity warning information based on the first discharge data and the first alarm result; wherein the first discharge data includes at least one of the following: arc discharge count, total discharge duration, and fault occurrence trend.

[0099] Through the above methods, this application first obtains disturbance waveform data, uses a transient information identification algorithm to obtain the first alarm result, and realizes the acquisition of alarm results in the transient information of IoT power distribution terminals to obtain information such as alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; at the same time, based on the first discharge point data and the first alarm result, alarm state severity warning information is obtained, and potential hazards are detected in time before insulation faults occur, realizing the transformation from "periodic maintenance" to "condition-based maintenance", ensuring the safe and reliable operation of the power grid, and avoiding catastrophic accidents and economic losses.

[0100] Example 3

[0101] After introducing the IoT power distribution terminal line alarm state analysis method and system in the exemplary embodiments of this application, the computing device of another exemplary embodiment of this application will be introduced next.

[0102] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0103] In some possible implementations, the computing device according to this application may include at least one processor and at least one memory. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps in the IoT power distribution terminal line alarm state analysis method according to various exemplary embodiments of this application described above.

[0104] The following reference Figure 8 To describe a computing device 130 according to this embodiment of the present application. Figure 8 The computing device 130 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 8 As shown, the computing device 130 is presented in the form of a general-purpose smart terminal (or Bluetooth headset). The components of the computing device 130 may include, but are not limited to: at least one processor 131, at least one memory 132, and a bus 133 connecting different system components (including memory 132 and processor 131).

[0105] Bus 133 represents one or more of several bus architectures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the various bus architectures. Memory 132 may include readable media in the form of volatile memory, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323. Memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0106] The computing device 130 can also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and / or with any device that enables the computing device 130 to communicate with one or more other smart terminals (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 135. Furthermore, the computing device 130 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules used in the computing device 130 via bus 133. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the computing device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0107] In some possible implementations, various aspects of the IoT power distribution terminal line alarm state analysis method provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product is run on a computer device, the computer program is used to cause the computer device to perform the steps in the IoT power distribution terminal line alarm state analysis method according to the various exemplary embodiments of this application described above.

[0108] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0109] The program product for time-domain noise processing according to the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on a smart terminal. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0110] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0112] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable access frequency prediction device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable access frequency prediction device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable access predictive device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable access predictive device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0117] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for analyzing alarm states of IoT power distribution terminal lines, characterized in that, include: Acquire disturbance waveform data and input the disturbance waveform data into the transient information recognition algorithm model; According to the transient information identification algorithm model, a first alarm result is obtained; wherein, the first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold; First discharge data is acquired, and alarm severity warning information is obtained based on the first discharge data and the first alarm result; wherein, the first discharge data includes at least one of arc discharge number, total discharge duration, and fault occurrence trend, and the fault occurrence trend includes the change process and fluctuation trend of the fault occurrence number during the discharge duration.

2. The method according to claim 1, characterized in that, The acquisition of disturbance waveform data specifically includes: Initialize the parameters of the first transient disturbance fault diagnosis algorithm and construct the first transient disturbance fault diagnosis algorithm model; wherein, the first transient disturbance fault diagnosis model is used to perform transient disturbance fault diagnosis; The transient electrical quantities are input into the first transient disturbance fault diagnosis model to obtain disturbance waveform data and first discharge data.

3. The method according to claim 2, characterized in that, The sliding step size of the first transient disturbance fault diagnosis algorithm model of the component is less than or equal to one-quarter of the cycle, the calculation window length is equal to one-half of the cycle, the sampling frequency is 256 points per cycle, and the effective values ​​of phase voltage, effective value of phase current, effective value of sequence voltage, effective value of sequence current, effective value of zero sequence voltage, effective value of zero sequence current, mutation amount, instantaneous value, and mutation threshold are preset values.

4. The method according to claim 3, characterized in that, The first transient disturbance fault diagnosis algorithm model is as follows: Obtain the effective values ​​of zero-sequence current and zero-sequence voltage, including ； ； Wherein, m represents the current sampling point, and n represents the starting sampling point of the data window; The zero-sequence voltage RMS value at sampling point m is represented by the following. The zero-sequence current effective value at sampling point m is represented by the following: The number of sampling points for the window function is indicated. The original sampling point representing the zero-sequence current, the The original sampling point represents the zero-sequence voltage, and N1 represents the end sampling point of the data window; Determine the first trigger detection criterion, including ； Among them, the The effective value of the zero-sequence current at sampling point m, the The effective value of the zero-sequence voltage at sampling point m; The threshold value for triggering the zero-sequence current RMS value is the value of the zero-sequence current. The threshold value for triggering the zero-sequence voltage RMS value; Determine the second trigger detection criterion, including ； Among them, the The zero-sequence current effective value at sampling point m is represented by the following: This represents the effective value of the zero-sequence voltage at sampling point m; the... This represents the effective value of the zero-sequence current at sampling point m-1. This represents the effective value of the zero-sequence voltage at sampling point m-1; the... This represents the threshold value for triggering a sudden change in zero-sequence current. This represents the threshold value for triggering a sudden change in zero-sequence voltage. Determine the third trigger detection criterion, including ； Among them, the Represents the zero-sequence current sampling point The Sampling points representing zero-sequence current The This represents the threshold value for triggering a sudden change in the instantaneous value of the zero-sequence current. When any one of the first trigger detection criterion, the second trigger detection criterion, and the third trigger detection criterion is met, the disturbance waveform data is acquired.

5. The method according to claim 2, characterized in that, The method further includes: Based on compression ratio, compression amount, and file compression quality performance parameters, a first transmission mechanism and compression algorithm are constructed. The first transmission mechanism and compression algorithm are used to compress large data into small-capacity data for long-distance transmission.

6. The method according to claim 1, characterized in that, The method further includes: Obtain maintenance record information of IoT power distribution terminals, and generate an operation and maintenance solution library based on the alarm status severity warning information and the maintenance record information; Based on the aforementioned operation and maintenance solution library, health rating and graded early warning are performed for fault operation and maintenance scenarios; Based on the health rating and graded early warning of the operation and maintenance solution library, an IoT power distribution terminal fault inspection plan is generated.

7. An IoT power distribution terminal line alarm status analysis system, characterized in that, include: The input module is used to acquire disturbance waveform data and input the disturbance waveform data into the transient information recognition algorithm; The output module is used to obtain a first alarm result according to the transient information identification algorithm; wherein the first alarm result includes at least one of the following: alarm state start point, alarm state end point, alarm state duration, alarm state phase voltage initial phase angle, and alarm state phase angle relative threshold. The early warning information acquisition module is used to acquire first discharge data and obtain alarm severity warning information based on the first discharge data and the first alarm result; wherein, the first discharge data includes at least one of the following: number of arc discharges, total discharge duration, and trend of fault occurrences.

8. A computing device, characterized in that, Its features include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method as described in any one of claims 1 to 6 according to the obtained program instructions.

9. A computer-readable storage medium, characterized in that, Includes computer-readable instructions that, when read and executed by a computer, cause the method as described in any one of claims 1 to 6 to be implemented.

10. A computer program product, characterized in that, It includes a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method according to any one of claims 1 to 6.

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