Power distribution network fault detection and positioning method and device, computer equipment and storage medium

Abstract

The invention relates to a power distribution network fault detection and positioning method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring power grid data of at least one power grid device on a power distribution network; determining the fault condition of the power distribution network according to the power grid data of each power grid device; under the condition that the power distribution network has a fault, obtaining fault data of at least one power grid device on the power distribution network; and according to the fault data of each power grid device, determining the fault position of the fault on the power distribution network. By adopting the method, the utilization degree of power grid data can be improved, and the fault identification and positioning accuracy and efficiency are further improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network technology, and in particular to a method, device, computer equipment and storage medium for detecting and locating distribution network faults. Background Art

[0002] In the power system, the distribution network is one of the indispensable and important components. Distribution network automation specifically refers to the monitoring of the operating status of the distribution system based on the primary grid and related equipment, with the distribution automation system as the core, and with the help of various communication methods. It also integrates information with other systems to carry out scientific and standardized management of the distribution system. However, the terminal recording files of existing line faults and disturbances do not have remote transmission functions or cannot be transmitted reliably. The master station's pre-warning, post-inversion and fault location lack on-site data, resulting in low distribution network operation and maintenance efficiency and insufficient operation and maintenance technical support capabilities. In addition, the current utilization of the distribution network synchronous measurement big data is not high, which makes it difficult to identify faults and inaccurately locate them. Summary of the Invention

[0003] Based on this, it is necessary to provide a distribution network fault detection and positioning method, device, computer equipment and storage medium that can accurately locate faults in order to address the above technical problems.

[0004] In a first aspect, the present application provides a method for detecting and locating a distribution network fault, comprising:

[0005] Obtaining grid data of at least one grid device on the distribution network;

[0006] Determine the fault condition of the distribution network based on the grid data of each grid device;

[0007] In the event of a fault in the distribution network, obtaining fault data of at least one grid device on the distribution network;

[0008] According to the fault data of each power grid equipment, the fault location of the fault on the distribution network is determined.

[0009] In one embodiment, determining a fault condition of a distribution network based on grid data of each grid device includes:

[0010] For each power grid device, determining the operating characteristics of the power grid device based on the power grid data of the power grid device;

[0011] Determine the fault reference value of the distribution network based on the operating characteristics of each power grid equipment;

[0012] When the fault reference value of the distribution network is greater than a preset reference threshold, it is determined that a fault occurs in the distribution network.

[0013] In one embodiment, determining a fault reference value of the distribution network based on operating characteristics of each grid device includes:

[0014] For each grid device, determining an initial reference value of the grid device according to operating characteristics of the grid device and operating characteristics of adjacent grid devices of the grid device;

[0015] The fault reference value of the distribution network is determined based on the initial reference value of each grid device and the preset value of the topology type to which the distribution network belongs.

[0016] In one embodiment, determining an initial reference value of a power grid device based on operating characteristics of the power grid device and operating characteristics of adjacent power grid devices of the power grid device includes:

[0017] Determining a first parameter value of the power grid device according to the operating characteristics of the power grid device; and, for each adjacent power grid device, determining the first parameter value of the adjacent power grid device according to the operating characteristics of the adjacent power grid device;

[0018] An initial reference value of the power grid device is determined according to the first parameter value of the power grid device, the preset power grid weight, the first parameter value of the adjacent power grid device, and the preset adjacent weight.

[0019] In one embodiment, determining a fault location of a fault on a distribution network based on fault data of each power grid device includes:

[0020] The power grid device corresponding to the fault data is regarded as the fault device;

[0021] For each faulty device, determine the target location corresponding to the faulty device based on the location of the faulty device on the distribution network and the traveling wave signal of at least one measurement point within a preset range of the location of the faulty device;

[0022] The target location corresponding to the faulty device is used as the fault location of the fault on the distribution network;

[0023] Among them, the fault device is used to receive the power grid data measured by each measurement point connected to the fault device; the traveling wave signal is the high-frequency signal output when the measurement point detects an abnormality in the power grid data; the target location is the location where the fault occurs within the preset range of the location of the fault device.

[0024] In one embodiment, the method further comprises:

[0025] Input the fault data of each faulty device into a pre-trained fault classification model to obtain the fault type of the faulty device at the corresponding target location;

[0026] When the fault type is a preset type, the operating state of the faulty device is adjusted according to the operating state of the adjacent power grid devices of the faulty device.

[0027] In a second aspect, the present application further provides a distribution network fault detection and positioning device, comprising:

[0028] A data acquisition module, configured to acquire grid data of at least one grid device on the distribution network;

[0029] A fault determination module is used to determine the fault condition of the distribution network based on the grid data of each grid device;

[0030] A fault acquisition module is used to obtain fault data of at least one power grid device on the distribution network when a fault occurs in the distribution network;

[0031] The location determination module is used to determine the fault location of the fault on the distribution network based on the fault data of each power grid device.

[0032] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Obtaining grid data of at least one grid device on the distribution network;

[0034] Determine the fault condition of the distribution network based on the grid data of each grid device;

[0035] In the event of a fault in the distribution network, obtaining fault data of at least one grid device on the distribution network;

[0036] According to the fault data of each power grid equipment, the fault location of the fault on the distribution network is determined.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtaining grid data of at least one grid device on the distribution network;

[0039] Determine the fault condition of the distribution network based on the grid data of each grid device;

[0040] In the event of a fault in the distribution network, obtaining fault data of at least one grid device on the distribution network;

[0041] According to the fault data of each power grid equipment, the fault location of the fault on the distribution network is determined.

[0042] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0043] Obtaining grid data of at least one grid device on the distribution network;

[0044] Determine the fault condition of the distribution network based on the grid data of each grid device;

[0045] In the event of a fault in the distribution network, obtaining fault data of at least one grid device on the distribution network;

[0046] According to the fault data of each power grid equipment, the fault location of the fault on the distribution network is determined.

[0047] The above-described distribution network fault detection and location method, apparatus, computer device, and storage medium obtain grid data from at least one grid device on the distribution network; determine the distribution network fault condition based on the grid data of each grid device; and, in the event of a distribution network fault, obtain fault data from at least one grid device on the distribution network; and determine the fault location of the distribution network fault based on the fault data of each grid device. This embodiment can improve the utilization of synchronized grid data, thereby enhancing the accuracy and efficiency of fault identification and location. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A diagram illustrating an application environment of a distribution network fault detection and location method provided in this embodiment;

[0050] Figure 2A A schematic diagram of a flow chart of a method for detecting and locating faults in a distribution network provided in this embodiment;

[0051] Figure 2B A chip structure diagram provided for this embodiment;

[0052] Figure 3 A schematic diagram of a flow chart of steps for determining a fault reference value provided in this embodiment;

[0053] Figure 4 A flowchart of a fault adjustment method provided in this embodiment;

[0054] Figure 5A structural block diagram of a distribution network fault detection and locating device provided in this embodiment;

[0055] Figure 6 This is a diagram of the internal structure of a computer device provided in this embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] The distribution network fault detection and positioning method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The computer device obtains grid data of at least one grid device on the distribution network; determines the fault status of the distribution network based on the grid data of each grid device; in the event of a fault in the distribution network, obtains fault data of at least one grid device on the distribution network; and determines the fault location of the fault on the distribution network based on the fault data of each grid device. The computer device can be either a terminal or a server. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0058] In an exemplary embodiment, Figure 2A As shown, a method for detecting and locating faults in a distribution network is provided. Figure 1 The computer device in the embodiment is used as an example to illustrate the method, including the following steps S201 to S204.

[0059] S201 obtains grid data of at least one grid device on the distribution network.

[0060] The grid data may include at least one of voltage, current, frequency, phase and harmonics.

[0061] In some embodiments, a topology type corresponding to the distribution network is obtained, and based on the topology type corresponding to the distribution network, a topology of the distribution network is determined (such as determined based on design drawings or field surveys, etc.); based on the topology of the distribution network, the grid equipment and lines in the distribution network are determined; based on the number of grid equipment in the distribution network, the number of grid equipment on the distribution network is determined; grid data of each grid equipment is obtained in real time based on FPGA, and a PPS signal output by GPS is obtained; in response to the PPS signal being in a normal state, the soft clock is synchronized using the PPS signal; in response to the synchronization being completed, a sampling time stamp for obtaining the grid data of each grid equipment is determined based on an interpolation mechanism, so as to time synchronize the grid data corresponding to each grid equipment to obtain the synchronized grid data.

[0062] Distribution network topologies include radial, ring, mesh, loop, and multi-terminal topologies. The topology type of a particular area can be determined based on design drawings or field surveys. Grid equipment describes the location of grid equipment within the distribution network, while lines describe the connections between grid equipment within the network. Grid equipment information also includes its location information, which is its coordinate information. Grid equipment typically refers to access points for distribution equipment or loads, such as cable terminals, distribution boxes, and switchgear.

[0063] Exemplarily, the interpolation mechanism includes: when the local clock is latched into the interpolation RAM buffer, a pulse is output for latching a synchronous clock count, and the latching time is defined as T1; a fixed time delay between the latching time and the actual sampling time is defined as Td; in the interpolation module, when the interpolation module uses the interpolation time, a pulse for latching and the interpolation time are output as T2; based on the latching time T1, the fixed time delay Td and the interpolation time T2, the sampling synchronization time of the interpolated telemetry value is calculated as: Tsmp = T1-Td+T2, where Tsmp is the sampling time mark.

[0064] For example, Figure 2BAs shown, FPGA (Field Programmable Gate Array) is a chip whose internal structure can be re-edited to achieve different functions. It is a dedicated hardware chip used to collect voltage and current sampling data. Code needs to be written internally to run it. Compared with the CPU's own collection, the collection efficiency is higher. After data collection, it is transmitted to the CPU (main chip) through the external communication interface for further processing. GPS / BD is a GPS module. The module will output the year, month, day, hour, minute, and second, as well as the PPS signal to the CPU and FPGA. The PPS signal is a level signal that is accurately output once every 1 second. The FPGA counts the soft clock within 1 second. The soft clock resolution is 1μs. It is used to time-stamp the sampled data. The soft clock is defined as a 32-bit number. The counting range is 0-999999, and it will automatically return to zero after the count overflows. Among them, the soft clock is a timing value maintained by the software code independently of the hardware. The sampling time register is used to latch the sampling time of the first sampling in each set of sampled data. The sampling time is read synchronously when the main chip reads the sampling value. The value of the register is updated before the FPGA sends the exchange interrupt. Among them, every time the FPGA collects some data, it needs to notify the CPU to take it away. The notification method is interruption. The sampling time register is a storage unit that stores the value of the soft clock. The CPU will take this value at the same time when taking the sampled data from the FPGA. In this way, the data reaching the CPU has a time stamp synchronized with the satellite, that is, the sampling time stamp.

[0065] Furthermore, the soft clock implementation method includes: after power-on, before the FPGA obtains the PPS signal, the soft clock does not count and maintains an all-zero state. When the PPS signal is captured for the first time, the quality of the PPS signal is first judged. If the PPS signal is normal, the soft clock is synchronized with the PPS signal for time synchronization; otherwise, it is not synchronized until the PPS signal is normal; after successful time synchronization, if the PPS signal is lost or the quality is not satisfactory, the soft time is no longer synchronized with the PPS and enters a punctual state until the second pulse returns to normal. The time synchronization state and the PPS state are written into register addresses respectively and can be read by the CPU.

[0066] Furthermore, the calculation method of the sampling time stamp includes: in the FPGA, a 32-bit local relative clock counter (hereinafter referred to as "local clock") is set for interpolation synchronization calculation; when the local clock is latched into the interpolation RAM buffer, a pulse is output to latch a synchronous clock count, and this time is recorded as T1; there is a fixed time delay between the latching moment and the actual sampling time, which is recorded as Td; in the interpolation module, when the interpolation module uses the interpolation time (the default value is 7812), a pulse (for latching) and the interpolation time are output, which is recorded as T2, wherein the interpolation time is the sampling frequency calculated by the CPU based on the voltage, which can be changed at any time and latched to prevent the interpolation time from introducing calculation errors. From the above, the sampling synchronization time of the telemetry value after interpolation can be calculated as: Tsmp = T1-Td+T2, Tsmp is the synchronization time of writing to the register, which is the sampling time stamp.

[0067] S202 determines the fault condition of the distribution network based on the grid data of each grid device.

[0068] In an optional embodiment, fault characteristics are extracted from the synchronized data corresponding to each terminal using a short-time Fourier transform. The fault characteristics from the synchronized data corresponding to all terminals are then fused. Based on the fused fault characteristics, abnormal fluctuations are detected to determine whether a distribution network fault has occurred. Fault characteristics include voltage sag, instantaneous current peaks, and harmonic distortion.

[0069] In another optional embodiment, for each power grid device, the operating characteristics of the power grid device are determined based on the power grid data of the power grid device; the fault reference value of the distribution network is determined based on the operating characteristics of each power grid device; when the fault reference value of the distribution network is greater than a preset reference threshold, it is determined that a fault has occurred in the distribution network.

[0070] S203: When a fault occurs in the distribution network, fault data of at least one power grid device on the distribution network is obtained.

[0071] In some embodiments, when a fault occurs in the power distribution network, the computer device obtains fault data of at least one power grid device on the power distribution network.

[0072] S204 determines the fault location of the distribution network based on the fault data of each power grid device.

[0073] In an optional embodiment, based on the fault data, the location information of the power grid equipment that collects the fault data is determined; the traveling wave signals of multiple measurement points within a preset range corresponding to the power grid equipment location information and the time difference between the traveling wave signals reaching the collection points are obtained, and a group of linear equations is constructed, wherein the traveling wave method is based on the traveling wave theory, that is, when a fault occurs in the distribution network, the fault point will produce sudden changes in voltage and current, forming traveling waves. These traveling waves propagate along the transmission line at a speed close to the speed of light. By installing traveling wave detection devices at different locations, these traveling wave signals can be captured to locate the fault. For example, assuming that there are n measurement points, each measurement point records the arrival time of the traveling wave signal, and the signal propagation speed is v, then the linear equation is, d is the distance from the fault point to the i-th measurement point; the group of linear equations is solved to obtain the location information of the fault point, wherein the constructed group of linear equations can be solved by methods such as Gaussian elimination to obtain the specific location of the fault point, such as the distance from the starting point of the line.

[0074] In another optional embodiment, the power grid device corresponding to the fault data is regarded as the fault device; for each fault device, the target position corresponding to the fault device is determined according to the position of the fault device on the distribution network and the traveling wave signal of at least one measuring point within a preset range of the position of the fault device; the target position corresponding to the fault device is regarded as the fault position where the fault occurs on the distribution network; wherein the fault device is used to receive the power grid data measured by each measuring point connected to the fault device; the traveling wave signal is a high-frequency signal output when the measuring point detects an abnormality in the power grid data; the target position is the position where the fault occurs within the preset range of the position of the fault device.

[0075] The above-described distribution network fault detection and location method, apparatus, computer device, and storage medium obtain grid data from at least one grid device on the distribution network; determine the distribution network fault condition based on the grid data of each grid device; and, in the event of a distribution network fault, obtain fault data from at least one grid device on the distribution network; and determine the fault location of the distribution network fault based on the fault data of each grid device. This embodiment can improve the utilization of synchronized grid data, thereby enhancing the accuracy and efficiency of fault identification and location.

[0076] Figure 3 This is a flow chart of the steps for determining a fault reference value in one embodiment. This embodiment refines the steps for determining a fault reference value for the distribution network based on the operating characteristics of each power grid device in the above example. This embodiment provides an optional method for determining a fault reference value, including the following steps:

[0077] S301 determines, for each grid device, an initial reference value of the grid device according to the operating characteristics of the grid device and the operating characteristics of adjacent grid devices of the grid device.

[0078] The operating characteristics may include at least one of voltage drop, instantaneous current peak, and harmonic distortion. It should be noted that voltage drop refers to a rapid drop in the effective value of the supply voltage to 90%-10% of the rated value, and harmonic distortion refers to the inclusion of additional harmonic components in the output signal due to system incomplete linearity. Harmonic distortion is the ratio of the root mean square (RMS) value of the target harmonics (second-order, third-order, etc.) to the RMS value of the signal level.

[0079] In some optional embodiments, a first parameter value of the power grid device is determined based on the operating characteristics of the power grid device; and, for each adjacent power grid device, a first parameter value of the adjacent power grid device is determined based on the operating characteristics of the adjacent power grid device; and an initial reference value of the power grid device is determined based on the first parameter value of the power grid device, a preset power grid weight, the first parameter value of the adjacent power grid device, and the preset adjacent weight.

[0080] In another optional embodiment, the synchronization data of each terminal is obtained, and the synchronization data of each terminal is preprocessed, wherein the preprocessing includes steps such as data cleaning, which is a common method and its specific process is not repeated here; determining the target window function and the window length, wherein the target window function and the window length can be selected according to actual needs, such as the target window function is a Hanning window or a Hamming window, etc., to balance the requirements of time resolution and frequency resolution, and determine the window length. The shorter the window length, the higher the time resolution, but the frequency resolution may be reduced, and vice versa; according to the target window function and the window length, the preprocessed synchronization data of each terminal is divided into multiple time Window, and perform short-time Fourier transform on each time window to obtain the frequency component corresponding to the synchronous data in the target window, wherein the specific scheme of short-time Fourier transform is a commonly used method, and its process is not repeated here. Frequency component refers to the components of different frequencies in a signal or system; the Fourier transform results of all windows are combined to obtain the target time-frequency diagram, that is, a two-dimensional array is obtained, which is the time-frequency representation of the signal; based on the time-frequency diagram, the features related to the fault are extracted, including at least voltage drop, instantaneous current peak and harmonic distortion, that is, in the time-frequency diagram, the changes of the signal with time and frequency are observed and analyzed to extract the features related to the fault.

[0081] S302 determines a fault reference value of the distribution network according to an initial reference value of each grid device and a preset value of a topology type to which the distribution network belongs.

[0082] In some embodiments, grid device location information of a topological structure of a distribution network, i.e., grid device coordinate information, is obtained; based on the grid device location information of the topological structure of the distribution network, a first fluctuation rate of a line between a target grid device and adjacent grid devices is determined, and a sum of the first fluctuation rates corresponding to the target grid device is calculated, wherein the first fluctuation rate is calculated as follows: Wherein, (x1, y1, z1) and (x2, y2, z2) represent the coordinate values of the target power grid device and the adjacent power grid device, respectively. The sum of the first fluctuation rates refers to the sum of the first fluctuation rates corresponding to the target power grid device and multiple adjacent power grid devices. According to the sum of the first fluctuation rates corresponding to multiple target power grid devices, the second fluctuation rate corresponding to all power grid devices is determined, wherein the second fluctuation rate refers to the sum of the sum of the first fluctuation rates corresponding to multiple target power grid devices. The fault characteristics of the target power grid device are obtained, and based on the first fusion function, the first fusion result of the target power grid device is determined. The first fusion function includes the following formula (1-1):

[0083] N1=a1T+a2U+a3P (1-1)

[0084] Wherein, N1 represents the first fusion result, a1, a2, and a3 represent weight coefficients, T represents the voltage drop amplitude, U represents the instantaneous current peak value, and P represents the ratio of the root mean square value of the target harmonic corresponding to the harmonic distortion to the root mean square value of the signal level. The relevant parameters in the above formula and the following formulas are all normalized parameter values to eliminate the dimension between the parameters, improve the calculation speed, and thus enhance the fault location efficiency.

[0085] Based on the second fusion function, a second fusion result between the target power grid device and the adjacent power grid devices adjacent to the target power grid device is determined. The second fusion function includes the following formula (1-2):

[0086] N2=a4N 11 +a5N 12 (1-2)

[0087] Among them, N2 represents the second fusion result, a4 and a5 both represent weight coefficients, N1 represents the first fusion result of the target power grid equipment, N 12 The second fusion result of the adjacent power grid devices is represented; the third fusion result corresponding to all the power grid devices is determined according to the sum of the second fusion results corresponding to the multiple target power grid devices; the fault feature fusion result is determined based on the second fluctuation rate and the third fusion result, and the third fusion function, and the third fusion function includes the following formula (1-3):

[0088]

[0089] Among them, N4 represents the fault feature fusion result, a6 represents the adjustment coefficient, and Y irepresents the value assigned to the distribution network of the i-th topology type, which is assigned by experts based on experience. K represents the second volatility, and N3 represents the third fusion result. In this embodiment, an initial reference value is determined for each grid device based on its operating characteristics and the operating characteristics of its neighboring devices. Based on the initial reference value of each grid device and the preset value assigned for the distribution network topology type, a more accurate fault reference value for the distribution network is determined.

[0090] Figure 4 FIG. 1 is a flow chart of a fault adjustment method in an embodiment. This embodiment provides an optional method for the fault adjustment method, including the following steps:

[0091] S401 inputs the fault data of each faulty device into a pre-trained fault classification model to obtain the fault type of the faulty device at the corresponding target location.

[0092] In some embodiments, the fault data of each faulty device is obtained; a target clustering algorithm is determined, wherein the clustering algorithm includes K-means and DBSCAN, etc., and the corresponding target clustering algorithm can be selected according to actual needs; based on the target clustering algorithm, the fault data in the synchronized data is summarized and counted, wherein the method of summarizing and counting the data by the clustering algorithm is a common method, and its specific process is not repeated here; historical fault data is obtained, and the historical fault data is divided into a training set and a test set, wherein the division ratio can be set to 7:3, etc.; based on the machine learning algorithm, an initial classification model is constructed, and the initial target classification model is trained and verified using the training set and the test set, wherein the machine learning algorithm can be a decision tree and a neural network, and the basic neural network model can be selected according to actual needs; in response to the verification result meeting the preset standard, the target classification model is output, wherein the preset standard can be that the training accuracy meets the requirement or the number of training times reaches the requirement, and the expression of the target classification model includes the following formula (1-4):

[0093]

[0094] Among them, μ represents the number of time grid devices corresponding to the fault data, W represents the output value of the target classification model, h represents the number of location grid devices corresponding to the fault data, and B m represents the logical distribution probability value corresponding to the mth fault data, n represents the amount of fault data, and C represents the fusion feature value of the fault data. The fault data and the fusion result of the fault feature are input into the target classification model to obtain the output value. The corresponding fault type is determined based on the output value and the preset classification standard. The classification standard includes the following formula (1-5):

[0095]

[0096] Wherein, x1 represents the second preset threshold, G represents the classification result; in response to G=0, the fault type is judged to be the first fault type, wherein the first fault type is the common fault type mentioned above; in response to G=1, the fault type is judged to be the second fault type, wherein the second fault type is the uncommon fault type mentioned above.

[0097] In step 402 , when the fault type is a preset type, the operating state of the faulty device is adjusted according to the operating state of the adjacent power grid devices of the faulty device.

[0098] In some embodiments, in response to the fault type of the fault point being a first fault type, the grid data of the fault point is not adjusted, wherein, if the fault type is a common fault, the operating status of the adjacent grid equipment is obtained to determine whether it is affected, and the grid data of the adjacent grid equipment is adjusted according to the degree of impact, such as adjusting the current and other data; in response to the fault type of the fault point being a second fault type, the operating status of the adjacent grid equipment connected to the fault point is obtained, and the grid data of the fault point is adjusted according to the operating status, that is, if the fault type is an uncommon fault, the operating status of the adjacent grid equipment is obtained to determine whether it is affected, and the grid data of the faulty grid equipment is adjusted according to the degree of impact, such as adjusting the output voltage and other data.

[0099] In this embodiment, the fault data of each faulty device is input into a pre-trained fault classification model to obtain the fault type of the faulty device at the corresponding target location; when the fault type is a preset type, the operating status of the faulty device is accurately adjusted according to the operating status of the adjacent power grid equipment of the faulty device to repair the fault location.

[0100] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0101] Based on the same inventive concept, embodiments of the present application also provide a distribution network fault detection and locating device for implementing the distribution network fault detection and locating method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more distribution network fault detection and locating device embodiments provided below can be found in the limitations of the distribution network fault detection and locating method described above and will not be repeated here.

[0102] In an exemplary embodiment, Figure 5 As shown, a distribution network fault detection and positioning device is provided, comprising: a data acquisition module 10, a fault determination module 20, a fault acquisition module 30 and a location determination module 40, wherein:

[0103] A data acquisition module 10 is used to acquire grid data of at least one grid device on the distribution network;

[0104] A fault determination module 20 is used to determine the fault condition of the distribution network based on the grid data of each grid device;

[0105] A fault acquisition module 30 is configured to acquire fault data of at least one power grid device on the distribution network when a fault occurs in the distribution network;

[0106] The location determination module 40 is configured to determine the fault location of a fault occurring on the distribution network based on the fault data of each power grid device.

[0107] In some embodiments, the fault determination module 20 is further used to determine, for each power grid device, the operating characteristics of the power grid device based on the power grid data of the power grid device; determine the fault reference value of the distribution network based on the operating characteristics of each power grid device; and determine that a fault has occurred in the distribution network when the fault reference value of the distribution network is greater than a preset reference threshold.

[0108] In some embodiments, the fault determination module 20 is also used to determine, for each power grid device, an initial reference value of the power grid device based on the operating characteristics of the power grid device and the operating characteristics of adjacent power grid devices of the power grid device; and determine the fault reference value of the distribution network based on the initial reference value of each power grid device and the preset value of the topology type to which the distribution network belongs.

[0109] In some embodiments, the fault determination module 20 is further used to determine a first parameter value of the power grid device based on the operating characteristics of the power grid device; and, for each adjacent power grid device, determine the first parameter value of the adjacent power grid device based on the operating characteristics of the adjacent power grid device; and determine an initial reference value of the power grid device based on the first parameter value of the power grid device, a preset power grid weight, the first parameter value of the adjacent power grid device, and the preset adjacent weight.

[0110] In some embodiments, the location determination module 40 is further used to treat the power grid equipment corresponding to the fault data as the fault equipment; for each fault equipment, the target location corresponding to the fault equipment is determined based on the location of the fault equipment on the distribution network and the traveling wave signal of at least one measuring point within a preset range of the location of the fault equipment; the target location corresponding to the fault equipment is used as the fault location of the fault on the distribution network; wherein the fault equipment is used to receive the power grid data measured by each measuring point connected to the fault equipment; the traveling wave signal is a high-frequency signal output when the measuring point detects an abnormality in the power grid data; the target location is the location where the fault occurs within the preset range of the location of the fault equipment.

[0111] In some embodiments, the location determination module 40 is also used to input the fault data of each faulty device into a pre-trained fault classification model to obtain the fault type of the faulty device at the corresponding target location; when the fault type is a preset type, the operating status of the faulty device is adjusted according to the operating status of the adjacent power grid equipment of the faulty device.

[0112] Each module in the above-mentioned distribution network fault detection and location device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0113] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting and locating distribution network faults is implemented.

[0114] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0115] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0116] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0117] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0118] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0119] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0120] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for detecting and locating faults in a distribution network, characterized in that: The method comprises: Obtaining grid data of at least one grid device on the distribution network; determining a fault condition of the distribution network based on the grid data of each of the grid devices; When a fault occurs in the distribution network, obtaining fault data of at least one grid device on the distribution network; The fault location of the fault on the distribution network is determined according to the fault data of each of the power grid devices.

2. The method according to claim 1, characterized in that The determining of the fault condition of the distribution network according to the grid data of each grid device includes: For each power grid device, determining an operating characteristic of the power grid device based on the power grid data of the power grid device; Determining a fault reference value of the distribution network according to the operating characteristics of each of the power grid devices; When the fault reference value of the power distribution network is greater than a preset reference threshold, it is determined that a fault occurs in the power distribution network.

3. The method according to claim 2, characterized in that The determining of the fault reference value of the distribution network according to the operating characteristics of each of the power grid devices includes: For each grid device, determining an initial reference value of the grid device according to operating characteristics of the grid device and operating characteristics of adjacent grid devices of the grid device; The fault reference value of the distribution network is determined according to the initial reference value of each of the power grid devices and the preset value of the topology type to which the distribution network belongs.

4. The method according to claim 3, characterized in that Determining an initial reference value of the power grid device according to operating characteristics of the power grid device and operating characteristics of adjacent power grid devices of the power grid device includes: Determining a first parameter value of the power grid device according to the operating characteristics of the power grid device; and determining, for each adjacent power grid device, a first parameter value of the adjacent power grid device according to the operating characteristics of the adjacent power grid device; An initial reference value of the grid device is determined according to the first parameter value of the grid device, the preset grid weight, the first parameter value of the adjacent grid device, and the preset adjacent weight.

5. The method according to claim 1, wherein Determining the fault location of the distribution network according to the fault data of each of the power grid devices includes: The power grid device corresponding to the fault data is regarded as the fault device; For each faulty device, determine the target location corresponding to the faulty device based on the location of the faulty device on the distribution network and the traveling wave signal of at least one measurement point within a preset range of the location of the faulty device; Using the target location corresponding to the faulty device as the fault location of the fault on the distribution network; Among them, the fault device is used to receive the power grid data measured by each measuring point connected to the fault device; the traveling wave signal is a high-frequency signal output when the measuring point detects an abnormality in the power grid data; the target position is the location where the fault occurs within a preset range of the location of the fault device.

6. The method according to claim 5, characterized in that The method further comprises: Inputting the fault data of each faulty device into a pre-trained fault classification model to obtain the fault type of the faulty device at the corresponding target location; In the case that the fault type is a preset type, the operating state of the faulty device is adjusted according to the operating state of the adjacent power grid devices of the faulty device.

7. A distribution network fault detection and positioning device, characterized in that: The device comprises: A data acquisition module, configured to acquire grid data of at least one grid device on the distribution network; A fault determination module, configured to determine a fault condition of the distribution network based on the grid data of each of the grid devices; A fault acquisition module is used to obtain fault data of at least one power grid device on the distribution network when a fault occurs in the distribution network; The location determination module is used to determine the fault location of the fault on the distribution network based on the fault data of each of the power grid devices.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images