Power distribution network fault intelligent early warning and rapid positioning method and system

Abstract

The invention relates to the technical field of power distribution network fault detection, in particular to a power distribution network fault intelligent early warning and rapid positioning method and system, and the method comprises the steps: obtaining real-time hardware monitoring data, real-time protection device and relay monitoring data, and real-time hardware operation environment monitoring data of a power distribution network; real-time hardware monitoring data and real-time hardware operation environment monitoring data of the power distribution network are extracted, and a power distribution network operation state abnormal value is generated through quantification; according to the abnormal value of the operation state of the power distribution network and the real-time hardware monitoring data and the real-time hardware operation environment monitoring data of the power distribution network, judging whether to enter an early warning analysis stage; after entering an early warning analysis stage, extracting real-time protection device and relay monitoring data of the power distribution network, performing change analysis on the protection device and relay monitoring data, and evaluating the fitness of the power distribution network to the current hidden danger; and performing risk early warning by integrating the abnormal value of the running state of the power distribution network and the fitness of the power distribution network to the current hidden danger, and checking and positioning the risk by a manager.

Description

Background Art

[0002] The distribution network is an important part of the power system, responsible for transmitting electricity from substations to end users. With the rapid development of smart grids and the widespread application of automation equipment, the operation of distribution networks has become more complex, and the demand for its monitoring, protection and fault diagnosis is also increasing. Traditional distribution network monitoring systems mainly rely on manual inspections, regular testing and basic automation equipment to ensure the normal operation of the system, but with the increase in power demand and the complexity of the power grid operating environment, traditional technologies have been unable to meet the requirements of efficient, real-time, safe and intelligent management.

[0003] Disadvantages of existing technologies: Traditional solutions rely more on electrical parameters such as current and voltage, ignoring the impact of hardware temperature, vibration and operating environment (such as humidity, water immersion, etc.) on faults, resulting in one-sided analysis results; existing technologies usually make abnormal judgments based on fixed thresholds or simple rules, which makes it difficult to dynamically adapt to the complex and changeable distribution network operating environment; in existing technologies, the relationship between the monitoring data of protection devices and relays and the abnormal data of distribution networks is not fully explored, resulting in inaccurate positioning and analysis; existing technologies lack a multi-parameter fusion mechanism for the calculation of risk indexes, and are unable to comprehensively assess the risk level and severity of hidden dangers. Summary of the invention

[0004] The main purpose of the present invention is to provide a distribution network fault intelligent early warning and rapid positioning method, and further to provide a distribution network fault intelligent early warning and rapid positioning system capable of running and implementing the above method, so as to effectively solve the above problems mentioned in the background technology.

[0005] The technical solution of the present invention is as follows: In the first aspect, a method for intelligent early warning and rapid location of distribution network faults is proposed, the method comprising the following steps: S1. Obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; S2, extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating status; S3. Determine whether to enter the early warning analysis stage based on the abnormal values ​​of the distribution network operation status and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; S4, after entering the early warning analysis stage, extract the real-time protection device and relay monitoring data of the distribution network, analyze the changes of the protection device and relay monitoring data, and evaluate the adaptability of the distribution network to the current hidden dangers; S5. Risk warning is provided based on abnormal values ​​of the operating status of the comprehensive distribution network and the adaptability of the distribution network to current hidden dangers, and management personnel investigate and locate risks.

[0006] A further improvement of the present invention is that the real-time hardware monitoring data in S1 includes real-time current monitoring data, real-time voltage monitoring data, real-time power monitoring data, real-time hardware temperature monitoring data and real-time vibration monitoring data; the real-time protection device and relay monitoring data include real-time protection device status monitoring data and relay parameter data; the real-time hardware operating environment monitoring data includes real-time ambient temperature monitoring data, real-time ambient humidity monitoring data, real-time water immersion monitoring data and real-time gas monitoring data.

[0007] A further improvement of the present invention is that S2 comprises the following specific steps: S21: Synchronize and mix hardware monitoring data, hardware operating environment monitoring data and time nodes to output real-time hardware monitoring data , real-time hardware operating environment monitoring data , where t is the time node; S22: extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating state, wherein the calculation formula of the abnormal values ​​of the distribution network operating state is: ;in, is the abnormal value of the distribution network operation status, is the standard value of hardware monitoring data. It is the standard value of the hardware operating environment monitoring data. is the weight factor of the hardware monitoring data, It is the weight factor of the hardware operating environment monitoring data.

[0008] A further improvement of the present invention is that S3 comprises the following specific steps: S31, preset a distribution network operation state abnormal value threshold, compare the distribution network operation state abnormal value with the distribution network operation state abnormal value threshold, when the distribution network operation state abnormal value is less than the distribution network operation state abnormal value threshold, generate a distribution network state normal signal, when the distribution network operation state abnormal value is greater than or equal to the distribution network operation state abnormal value threshold, generate a distribution network state hidden danger signal; S32, preset a hardware monitoring threshold, compare the hardware monitoring data with the hardware monitoring data threshold, and generate a hardware status receivable signal when the hardware monitoring data is greater than the hardware monitoring data threshold; and generate a hardware status hidden danger signal when the hardware monitoring data is less than or equal to the hardware monitoring data threshold; S33, preset a hardware operating environment monitoring data threshold, compare the hardware operating environment monitoring data with the hardware operating environment monitoring data threshold, and generate a hardware operating environment good signal when the hardware operating environment monitoring data is greater than the hardware operating environment monitoring data threshold; and generate a hardware operating environment hidden danger signal when the hardware operating environment monitoring data is less than or equal to the hardware operating environment monitoring data threshold; S34. When the normal distribution network status signal, the hardware status receivable signal and the good hardware operating environment signal are received at the same time, the early warning analysis stage will not be entered, otherwise it will enter the early warning analysis stage.

[0009] A further improvement of the present invention is that S4 comprises the following specific steps: S41. Extract real-time protection device and relay monitoring data of distribution network , the real-time protection device and relay monitoring data include real-time protection device status monitoring data and relay parameter data; S42, analyze the changes of the monitoring data of the protection device and the relay, evaluate the volatility of the monitoring data of the protection device and the relay, and calculate the standard deviation of the monitoring data of the protection device and the relay. The specific formula is: ; Wherein, b is the mean value of the protection device and relay monitoring data within the preset collection time; S43, calculating the correlation between the monitoring data of the protection device and the relay and the abnormal value of the distribution network operation state, the specific calculation formula is: ; Wherein, X is the correlation between the monitoring data of the protection device and relay and the abnormal values ​​of the distribution network operation status, and K is the mean value of the abnormal values ​​of the distribution network operation status within the preset collection time.

[0010] A further improvement of the present invention is that S4 also includes evaluating the adaptability of the distribution network to the current hidden dangers, and the specific formula is: ; Where D is the adaptability of the distribution network to current hidden dangers.

[0011] A further improvement of the present invention is that S5 comprises the following specific steps: S51, abnormal value of the operation status of the integrated distribution network And the adaptability D of the distribution network to the current hidden dangers, calculate the distribution network risk index. The specific calculation formula is: ;in is the weight factor of abnormal value of distribution network operation status, is the weight factor of the adaptability of the distribution network to the current hidden dangers; S52. A risk index threshold is preset, and a warning is issued when and only when F is greater than the risk index threshold, and the management personnel investigate and locate the risk.

[0012] In the second aspect, a distribution network fault intelligent early warning and rapid positioning system is proposed, the system comprising: Data collection module, abnormality assessment module, early warning judgment module, adaptation assessment module, risk early warning module; The data acquisition module is used to obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; The abnormality assessment module is used to extract real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantify and generate abnormal values ​​of the distribution network operating status; The early warning judgment module is used to judge whether to enter the early warning analysis stage according to the abnormal value of the distribution network operation state and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; The adaptability assessment module is used to extract the real-time protection device and relay monitoring data of the distribution network after entering the early warning analysis stage, perform change analysis on the protection device and relay monitoring data, and assess the adaptability of the distribution network to the current hidden dangers; The risk warning module is used to provide risk warning based on abnormal values ​​of the distribution network operation status and the adaptability of the distribution network to current hidden dangers, and management personnel can investigate and locate risks.

[0013] The technical effects of the present invention are as follows: An intelligent early warning and rapid location method for distribution network faults was constructed, which obtained the real-time hardware monitoring data, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data of the distribution network; extracted the real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantified the abnormal values ​​of the distribution network operation status; judged whether to enter the early warning analysis stage according to the abnormal values ​​of the distribution network operation status and the real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network; after entering the early warning analysis stage, extracted the real-time protection device and relay monitoring data of the distribution network, analyzed the changes of the protection device and relay monitoring data, and evaluated the adaptability of the distribution network to the current hidden dangers; comprehensively analyzed the abnormal values ​​of the distribution network operation status and the adaptability of the distribution network to the current hidden dangers to issue risk warnings, and management personnel investigated and located the risks. By acquiring multi-dimensional monitoring data in real time, all-round perception of the distribution network status is achieved; a quantitative model for abnormal values ​​of the distribution network operation status is proposed, and anomalies are accurately described through mathematical formulas, which helps to improve the scientific nature of fault location. In the early warning analysis stage, the adaptability of the distribution network is evaluated through real-time data changes of protection devices and relays, and hidden danger detection is refined; the distribution network abnormal values ​​and hidden danger adaptability are integrated, the risk index is calculated, and early warnings are issued based on thresholds, which improves the accuracy and response speed of early warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 A schematic diagram of a flow chart of a method for intelligent early warning and rapid location of distribution network faults according to Embodiment 1 of the present invention; Figure 2 This is a structural diagram of a distribution network fault intelligent early warning and rapid positioning system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0015] The present embodiment constructs an intelligent early warning and rapid positioning method for distribution network faults, by acquiring real-time hardware monitoring data, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data of the distribution network; extracting the real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operation status; judging whether to enter the early warning analysis stage according to the abnormal values ​​of the distribution network operation status and the real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network; after entering the early warning analysis stage, extracting the real-time protection device and relay monitoring data of the distribution network, performing change analysis on the protection device and relay monitoring data, and evaluating the adaptability of the distribution network to current hidden dangers; conducting risk early warning based on the abnormal values ​​of the distribution network operation status and the adaptability of the distribution network to current hidden dangers, and the management personnel investigate and locate the risks. By acquiring multi-dimensional monitoring data in real time, all-round perception of the distribution network status is achieved; a quantitative model for abnormal values ​​of the distribution network operation status is proposed, and anomalies are accurately described through mathematical formulas, which helps to improve the scientific nature of fault location. In the early warning analysis stage, the adaptability of the distribution network is evaluated through real-time data changes of protection devices and relays, and hidden danger detection is refined; the distribution network abnormal values ​​and hidden danger adaptability are integrated, the risk index is calculated, and early warnings are issued based on thresholds, which improves the accuracy and response speed of early warnings.

[0016] A method for intelligent early warning and rapid location of distribution network faults, such as Figure 1 As shown, the specific steps include: S1. Obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; S2, extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating status; S3. Determine whether to enter the early warning analysis stage based on the abnormal values ​​of the distribution network operation status and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; S4, after entering the early warning analysis stage, extract the real-time protection device and relay monitoring data of the distribution network, analyze the changes of the protection device and relay monitoring data, and evaluate the adaptability of the distribution network to the current hidden dangers; S5. Risk warning is provided based on abnormal values ​​of the operating status of the comprehensive distribution network and the adaptability of the distribution network to current hidden dangers, and management personnel investigate and locate risks.

[0017] In this embodiment, the real-time hardware monitoring data in S1 includes real-time current monitoring data, real-time voltage monitoring data, real-time power monitoring data, real-time hardware temperature monitoring data and real-time vibration monitoring data; the real-time protection device and relay monitoring data includes real-time protection device status monitoring data and relay parameter data; the real-time hardware operating environment monitoring data includes real-time ambient temperature monitoring data, real-time ambient humidity monitoring data, real-time water immersion monitoring data and real-time gas monitoring data.

[0018] In this embodiment, S2 includes the following specific steps: S21: Synchronize and mix hardware monitoring data, hardware operating environment monitoring data and time nodes to output real-time hardware monitoring data , real-time hardware operating environment monitoring data , where t is the time node; S22: extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating state, wherein the calculation formula of the abnormal values ​​of the distribution network operating state is: ;in, is the abnormal value of the distribution network operation status, is the standard value of hardware monitoring data. It is the standard value of the hardware operating environment monitoring data. is the weight factor of the hardware monitoring data, It is the weight factor of the hardware operating environment monitoring data.

[0019] In this embodiment, S3 includes the following specific steps: S31, preset a distribution network operation state abnormal value threshold, compare the distribution network operation state abnormal value with the distribution network operation state abnormal value threshold, when the distribution network operation state abnormal value is less than the distribution network operation state abnormal value threshold, generate a distribution network state normal signal, when the distribution network operation state abnormal value is greater than or equal to the distribution network operation state abnormal value threshold, generate a distribution network state hidden danger signal; S32, preset a hardware monitoring threshold, compare the hardware monitoring data with the hardware monitoring data threshold, and generate a hardware status receivable signal when the hardware monitoring data is greater than the hardware monitoring data threshold; and generate a hardware status hidden danger signal when the hardware monitoring data is less than or equal to the hardware monitoring data threshold; S33, preset a hardware operating environment monitoring data threshold, compare the hardware operating environment monitoring data with the hardware operating environment monitoring data threshold, and generate a hardware operating environment good signal when the hardware operating environment monitoring data is greater than the hardware operating environment monitoring data threshold; and generate a hardware operating environment hidden danger signal when the hardware operating environment monitoring data is less than or equal to the hardware operating environment monitoring data threshold; S34. When the normal distribution network status signal, the hardware status receivable signal and the good hardware operating environment signal are received at the same time, the early warning analysis stage will not be entered, otherwise it will enter the early warning analysis stage.

[0020] In this embodiment, S4 includes the following specific steps: S41. Extract real-time protection device and relay monitoring data of distribution network , the real-time protection device and relay monitoring data include real-time protection device status monitoring data and relay parameter data; S42, analyze the changes of the monitoring data of the protection device and the relay, evaluate the volatility of the monitoring data of the protection device and the relay, and calculate the standard deviation of the monitoring data of the protection device and the relay. The specific formula is: ; Wherein, b is the mean value of the protection device and relay monitoring data within the preset collection time; S43, calculating the correlation between the monitoring data of the protection device and the relay and the abnormal value of the distribution network operation state, the specific calculation formula is: ; Wherein, X is the correlation between the monitoring data of the protection device and relay and the abnormal values ​​of the distribution network operation status, and K is the mean value of the abnormal values ​​of the distribution network operation status within the preset collection time.

[0021] In this embodiment, S4 also includes evaluating the adaptability of the distribution network to the current hidden dangers, and the specific formula is: ; Where D is the adaptability of the distribution network to current hidden dangers.

[0022] In this embodiment, S5 includes the following specific steps: S51, abnormal value of the operation status of the integrated distribution network And the adaptability D of the distribution network to the current hidden dangers, calculate the distribution network risk index. The specific calculation formula is: ;in is the weight factor of abnormal value of distribution network operation status, is the weight factor of the adaptability of the distribution network to the current hidden dangers; S52. A risk index threshold is preset, and a warning is issued when and only when F is greater than the risk index threshold, and the management personnel investigate and locate the risk.

[0023] This embodiment proposes a distribution network fault intelligent early warning and rapid positioning system, such as Figure 2 As shown, it includes: data acquisition module, abnormality assessment module, early warning judgment module, adaptation assessment module, and risk early warning module; The data acquisition module is used to obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; The abnormality assessment module is used to extract real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantify and generate abnormal values ​​of the distribution network operating status; The early warning judgment module is used to judge whether to enter the early warning analysis stage according to the abnormal value of the distribution network operation state and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; The adaptability assessment module is used to extract the real-time protection device and relay monitoring data of the distribution network after entering the early warning analysis stage, perform change analysis on the protection device and relay monitoring data, and assess the adaptability of the distribution network to the current hidden dangers; The risk warning module is used to provide risk warning based on abnormal values ​​of the distribution network operation status and the adaptability of the distribution network to current hidden dangers, and management personnel can investigate and locate risks.

[0024] In this embodiment, real-time hardware monitoring data includes real-time current monitoring data, real-time voltage monitoring data, real-time power monitoring data, real-time hardware temperature monitoring data and real-time vibration monitoring data; real-time protection device and relay monitoring data includes real-time protection device status monitoring data and relay parameter data; real-time hardware operating environment monitoring data includes real-time ambient temperature monitoring data, real-time ambient humidity monitoring data, real-time water immersion monitoring data and real-time gas monitoring data.

[0025] In this embodiment, the implementation of the abnormal assessment module includes the following specific steps: First, the hardware monitoring data, the hardware operating environment monitoring data and the time node are synchronized and mixed to output the real-time hardware monitoring data. , real-time hardware operating environment monitoring data , where t is the time node; then, extract the real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantify and generate the abnormal value of the distribution network operating state. The calculation formula of the abnormal value of the distribution network operating state is: ;in, is the abnormal value of the distribution network operation status, is the standard value of hardware monitoring data. It is the standard value of the hardware operating environment monitoring data. is the weight factor of the hardware monitoring data, It is the weight factor of the hardware operating environment monitoring data.

[0026] In this embodiment, the implementation of the early warning judgment module includes the following specific steps: first, preset the distribution network operation status abnormal value threshold, compare the distribution network operation status abnormal value with the distribution network operation status abnormal value threshold, when the distribution network operation status abnormal value is less than the distribution network operation status abnormal value threshold, generate a distribution network status normal signal, when the distribution network operation status abnormal value is greater than or equal to the distribution network operation status abnormal value threshold, then generate a distribution network status hidden danger signal; then, preset the hardware monitoring threshold, compare the hardware monitoring data with the hardware monitoring data threshold, when the hardware monitoring data is greater than the hardware monitoring data threshold, generate a hardware status receivable signal, when the hardware monitoring When the data is less than or equal to the hardware monitoring data threshold, a hardware status hidden danger signal is generated; further, a hardware operating environment monitoring data threshold is preset, and the hardware operating environment monitoring data is compared with the hardware operating environment monitoring data threshold. When the hardware operating environment monitoring data is greater than the hardware operating environment monitoring data threshold, a hardware operating environment good signal is generated; when the hardware operating environment monitoring data is less than or equal to the hardware operating environment monitoring data threshold, a hardware operating environment hidden danger signal is generated; finally, when the normal distribution network status signal, the hardware status receivable signal and the hardware operating environment good signal are received at the same time, the early warning analysis stage is not entered, otherwise it enters the early warning analysis stage.

[0027] In this embodiment, the implementation of the adaptive assessment module includes the following specific steps: first, extract the real-time protection device and relay monitoring data of the distribution network , the real-time protection device and relay monitoring data include real-time protection device status monitoring data and relay parameter data; then the change analysis of the protection device and relay monitoring data is performed, the volatility of the protection device and relay monitoring data is evaluated, and the standard deviation of the protection device and relay monitoring data is calculated. The specific formula is: ; Wherein, b is the mean value of the monitoring data of the protection device and relay within the preset collection time; Finally, the correlation between the monitoring data of the protection device and relay and the abnormal value of the distribution network operation status is calculated, and the specific calculation formula is: ; Wherein, X is the correlation between the monitoring data of the protection device and relay and the abnormal values ​​of the distribution network operation status, and K is the mean value of the abnormal values ​​of the distribution network operation status within the preset collection time.

[0028] In this embodiment, the implementation of the adaptation assessment module also includes assessing the adaptability of the distribution network to the current hidden dangers. The specific formula is: ; Where D is the adaptability of the distribution network to current hidden dangers.

[0029] In this embodiment, the implementation of the risk warning module includes the following specific steps: And the adaptability D of the distribution network to the current hidden dangers, calculate the distribution network risk index. The specific calculation formula is: ;in is the weight factor of abnormal value of distribution network operation status, is the weight factor of the distribution network's adaptability to current hidden dangers; finally, a risk index threshold is preset, and an early warning is issued when and only when F is greater than the risk index threshold, and the management personnel investigate and locate the risk.

[0030] The above-mentioned parameters and steps for each unit module to realize corresponding functions in a laboratory environment safety monitoring system of the present invention can refer to the parameters and steps in the embodiment of a distribution network fault intelligent early warning and rapid positioning method in the above embodiment 1.

[0031] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned intelligent early warning and rapid positioning method for distribution network faults by calling the computer program stored in the memory.

[0032] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a distribution network fault intelligent early warning and rapid location method provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0033] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as a "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0034] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0035] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0037] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for intelligent early warning and rapid location of distribution network faults, characterized in that: The specific steps include: S1. Obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; S2, extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating status; S3. Determine whether to enter the early warning analysis stage based on the abnormal values ​​of the distribution network operation status and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; S4, after entering the early warning analysis stage, extract the real-time protection device and relay monitoring data of the distribution network, analyze the changes of the protection device and relay monitoring data, and evaluate the adaptability of the distribution network to the current hidden dangers; S5. Risk warnings are issued based on abnormal values ​​of the operating status of the comprehensive distribution network and the adaptability of the distribution network to current hidden dangers, and management personnel investigate and locate risks.

2. A method for intelligent early warning and rapid location of distribution network faults according to claim 1, characterized in that: The real-time hardware monitoring data in S1 includes real-time current monitoring data, real-time voltage monitoring data, real-time power monitoring data, real-time hardware temperature monitoring data and real-time vibration monitoring data; the real-time protection device and relay monitoring data includes real-time protection device status monitoring data and relay parameter data; the real-time hardware operating environment monitoring data includes real-time ambient temperature monitoring data, real-time ambient humidity monitoring data, real-time water immersion monitoring data and real-time gas monitoring data.

3. A method for intelligent early warning and rapid location of distribution network faults according to claim 2, characterized in that: The S2 comprises the following specific steps: S21: Synchronize and mix hardware monitoring data, hardware operating environment monitoring data and time nodes to output real-time hardware monitoring data , real-time hardware operating environment monitoring data , where t is the time node; S22: extracting real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantifying and generating abnormal values ​​of the distribution network operating state, wherein the calculation formula of the abnormal values ​​of the distribution network operating state is: ;in, is the abnormal value of the distribution network operation status, is the standard value of hardware monitoring data. It is the standard value of the hardware operating environment monitoring data. is the weight factor of the hardware monitoring data, It is the weight factor of the hardware operating environment monitoring data.

4. A method for intelligent early warning and rapid location of distribution network faults according to claim 3, characterized in that: The S3 includes the following specific steps: S31, preset a distribution network operation state abnormal value threshold, compare the distribution network operation state abnormal value with the distribution network operation state abnormal value threshold, when the distribution network operation state abnormal value is less than the distribution network operation state abnormal value threshold, generate a distribution network state normal signal, when the distribution network operation state abnormal value is greater than or equal to the distribution network operation state abnormal value threshold, generate a distribution network state hidden danger signal; S32, preset a hardware monitoring threshold, compare the hardware monitoring data with the hardware monitoring data threshold, and generate a hardware status receivable signal when the hardware monitoring data is greater than the hardware monitoring data threshold; and generate a hardware status hidden danger signal when the hardware monitoring data is less than or equal to the hardware monitoring data threshold; S33, preset a hardware operating environment monitoring data threshold, compare the hardware operating environment monitoring data with the hardware operating environment monitoring data threshold, and generate a hardware operating environment good signal when the hardware operating environment monitoring data is greater than the hardware operating environment monitoring data threshold; and generate a hardware operating environment hidden danger signal when the hardware operating environment monitoring data is less than or equal to the hardware operating environment monitoring data threshold; S34. When the normal distribution network status signal, the hardware status receivable signal and the good hardware operating environment signal are received at the same time, the early warning analysis stage will not be entered, otherwise it will enter the early warning analysis stage.

5. A method for intelligent early warning and rapid location of distribution network faults according to claim 4, characterized in that: The S4 comprises the following specific steps: S41. Extract real-time protection device and relay monitoring data of distribution network , the real-time protection device and relay monitoring data include real-time protection device status monitoring data and relay parameter data; S42, analyze the changes of the monitoring data of the protection device and the relay, evaluate the volatility of the monitoring data of the protection device and the relay, and calculate the standard deviation of the monitoring data of the protection device and the relay. The specific formula is: ; Wherein, b is the mean value of the protection device and relay monitoring data within the preset collection time; S43, calculating the correlation between the monitoring data of the protection device and the relay and the abnormal value of the distribution network operation state, the specific calculation formula is: ; Wherein, X is the correlation between the monitoring data of the protection device and relay and the abnormal values ​​of the distribution network operation status, and K is the mean value of the abnormal values ​​of the distribution network operation status within the preset collection time.

6. A method for intelligent early warning and rapid location of distribution network faults according to claim 5, characterized in that: S4 also includes evaluating the adaptability of the distribution network to the current hidden dangers. The specific formula is: ; Where D is the adaptability of the distribution network to current hidden dangers.

7. A method for intelligent early warning and rapid location of distribution network faults according to claim 6, characterized in that: The S5 comprises the following specific steps: S51, abnormal value of the operation status of the integrated distribution network And the adaptability D of the distribution network to the current hidden dangers, calculate the distribution network risk index. The specific calculation formula is: ;in is the weight factor of abnormal value of distribution network operation status, is the weight factor of the adaptability of the distribution network to the current hidden dangers; S52. A risk index threshold is preset, and a warning is issued when and only when F is greater than the risk index threshold, and the management personnel investigate and locate the risk.

8. A distribution network fault intelligent early warning and rapid positioning system, which is implemented based on a distribution network fault intelligent early warning and rapid positioning method according to any one of claims 1 to 7, characterized in that: The system includes: a data acquisition module, an abnormality assessment module, a warning judgment module, an adaptation assessment module, and a risk warning module; The data acquisition module is used to obtain real-time hardware monitoring data of the distribution network, real-time protection device and relay monitoring data, and real-time hardware operating environment monitoring data; The abnormality assessment module is used to extract real-time hardware monitoring data and real-time hardware operating environment monitoring data of the distribution network, and quantify and generate abnormal values ​​of the distribution network operating status; The early warning judgment module is used to judge whether to enter the early warning analysis stage according to the abnormal value of the distribution network operation state and the real-time hardware monitoring data and real-time hardware operation environment monitoring data of the distribution network; The adaptability assessment module is used to extract the real-time protection device and relay monitoring data of the distribution network after entering the early warning analysis stage, perform change analysis on the protection device and relay monitoring data, and assess the adaptability of the distribution network to the current hidden dangers; The risk warning module is used to provide risk warning based on abnormal values ​​of the distribution network operation status and the adaptability of the distribution network to current hidden dangers, and management personnel can investigate and locate risks.

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