Distribution network recording fault accurate positioning method based on big data analysis

Abstract

The invention discloses a distribution network recording fault accurate positioning method based on big data analysis, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: deploying a fault indicator; setting a trigger threshold value, and triggering high-precision transient recording to generate recording data when the current or the electric field suddenly changes; carrying out time alignment on the recording data and synchronously acquired current intensity, electric field intensity, cable temperature and environment data, extracting transient characteristics and environment characteristics after wavelet transform denoising, and combining the transient characteristics with the environment characteristics to generate a comprehensive characteristic matrix; binding the comprehensive characteristic matrix and the line topology information, and uploading the information to a monitoring center through encryption communication; formulating a dynamic threshold association rule, and judging a fault type; the monitoring center matches the current transient waveform with a historical database and calculates a fault signal propagation time difference; and calculating a fault distance by combining line topology information, and fusing the fault signal propagation time difference and the fault distance to obtain a fault coordinate.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and particularly to a precise fault location method for distribution network waveform recording based on big data analysis. Background Art

[0002] The distribution network fault location technology is the core link to ensure the reliable operation of the power system. Early fault location mainly relied on steady-state electrical quantity analysis and manual inspection. However, due to the complex structure, numerous branches, and serious signal attenuation of the distribution network, the positioning accuracy was generally not high. With the development of intelligent sensors and communication technologies, the traveling wave location technology has gradually become the mainstream due to its high-precision characteristics. It captures the transient traveling wave signals generated by faults and realizes location by combining the double-end ranging principle, and the error can be controlled within one hundred meters. In recent years, the fault diagnosis method based on transient characteristics has further improved the location efficiency. For example, the wavelet transform technology effectively filters out noise interference by decomposing the frequency domain characteristics of signals; Although the existing traveling wave location technology performs excellently in the transmission network, it faces significant challenges in the distribution network: First, the traveling wave signals attenuate rapidly in the short lines of the distribution network and are easily interfered by the reflected waves at branch nodes, resulting in the accumulation of wavefront detection errors. For example, a high-humidity environment will reduce the sensitivity of electric field sensors, and the threshold needs to be dynamically corrected, while the traditional method lacks a real-time compensation mechanism. Second, the existing fault indicators usually independently collect electrical quantities and do not synchronously record environmental parameters such as temperature and humidity, resulting in an incomplete feature matrix and making it difficult to distinguish similar faults. Finally, the data of distribution network terminal equipment is mostly transmitted through unencrypted protocols, which poses a risk of being tampered with, and the topology information is updated laggingly, affecting the real-time performance of the impedance inversion algorithm. In addition, the traditional threshold rules (such as the IEEE 519 harmonic standard) are designed based on static operating conditions and cannot adapt to the dynamic characteristics of transient processes. For example, the instantaneous increase in the starting current of a motor will trigger misrecording of waveforms. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a precise fault location method for distribution network waveform recording based on big data analysis to solve the problems that the traveling wave signals attenuate rapidly in the short lines of the distribution network and are easily interfered by the reflected waves at branch nodes, resulting in the accumulation of wavefront detection errors.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a precise fault location method for distribution network waveform recording based on big data analysis, which includes, Deploying fault indicators to monitor the current intensity and electric field intensity in real time, setting a trigger threshold, and triggering high-precision transient waveform recording when the current or electric field changes suddenly, generating waveform recording data including a fault timestamp, a transient waveform, and trigger parameters; Synchronously collect the current intensity, electric field intensity, cable temperature and environmental data, align the time of the recorded wave data and the synchronously collected current intensity, electric field intensity, cable temperature and environmental data, extract the transient characteristics and environmental characteristics after denoising by wavelet transform, and combine the transient characteristics and environmental characteristics to generate a comprehensive feature matrix; Bind the comprehensive feature matrix with the line topology information and upload it to the monitoring center through encrypted communication; The monitoring center formulates dynamic threshold association rules according to the comprehensive feature matrix to judge the fault type; The monitoring center matches the current transient waveform with the historical database, calculates the propagation time difference of the fault signal, combines the line topology information, calculates the fault distance through the impedance-distance inversion algorithm, and fuses the propagation time difference of the fault signal and the fault distance to obtain the fault coordinates.

[0006] As a preferred scheme of the method for accurately locating the fault of the distribution network recorded wave based on big data analysis according to the present invention, wherein: the specific steps for triggering high-precision transient recording when the current mutation or electric field mutation occurs are as follows: Install dual-channel fault indicators at each branch node of the distribution network line, with high-precision current sensors and electric field sensors built in; Set the current mutation and electric field mutation thresholds and recording parameters; When the change amount of the current intensity exceeds the current mutation threshold, immediately start recording. When the change amount of the current intensity does not exceed the current mutation threshold, and when the change amount of the electric field intensity exceeds the electric field mutation threshold, also start recording. The current mutation triggers preferentially, and the electric field mutation triggers secondarily; After triggering the recording, record the fault timestamp, start dual-channel synchronous recording, save the current waveform and the electric field waveform, and store the triggered current mutation threshold and electric field mutation threshold at the same time.

[0007] As a preferred scheme of the method for accurately locating the fault of the distribution network recorded wave based on big data analysis according to the present invention, wherein: the synchronous collection of the current intensity, electric field intensity, cable temperature and environmental data, and the time alignment of the recorded wave data and the synchronously collected current intensity, electric field intensity, cable temperature and environmental data are specifically as follows: Integrate current sensors, electric field sensors, cable temperature sensors and environmental temperature and humidity sensors in the fault indicator to collect the current intensity, electric field intensity, cable temperature, environmental temperature and environmental humidity; When triggering the recording, use the interpolation alignment method for time alignment.

[0008] As a preferred solution of the method for accurately locating the fault of the distribution network recording wave based on big data analysis described in the present invention, wherein: after denoising by wavelet transform, transient features and environmental features are extracted, and a comprehensive feature matrix is generated by combining the transient features and environmental features. The specific steps are as follows: Select the Daubechies 4 wavelet basis, perform wavelet decomposition on the current waveform and the electric field waveform, and eliminate high-frequency noise by the threshold filtering method; Calculate the current change rate within a period of time window before and after the fault starting point to obtain the current mutation slope; Perform Fourier transform on the denoised current waveform and calculate the proportion of high-frequency harmonic energy in the frequency band; Calculate the cable temperature gradient for a period of time before and after the fault; Directly obtain the environmental temperature and environmental humidity at the moment of fault triggering; Align the current mutation slope, the proportion of high-frequency harmonic energy, the cable temperature gradient, the environmental temperature and environmental humidity at the moment of fault triggering according to the time stamp and merge them into a matrix to obtain the comprehensive feature matrix.

[0009] As a preferred solution of the method for accurately locating the fault of the distribution network recording wave based on big data analysis described in the present invention, wherein: the comprehensive feature matrix is bound to the line topology information and uploaded to the monitoring center through encrypted communication. Specifically: Take the electrical connection points in the distribution network as nodes, and obtain the line topology information in real time from the distribution network GIS; Gradually superimpose the branch resistance and branch reactance in the order of branches to generate a node impedance matrix; Use topological information coding to encode the impedance matrix, node coordinates and hierarchical relationship into a topological matrix, Assign a unique device ID to each fault indicator, make the device ID in the comprehensive feature matrix correspond to the nodes in the topological matrix one by one, and merge the feature matrix data and topological information within the same time window to form a spatio-temporal correlation data packet; Upload the spatio-temporal correlation data packet to the monitoring center through the MQTT protocol.

[0010] As a preferred solution of the method for accurately locating the fault of the distribution network recording wave based on big data analysis described in the present invention, wherein: the dynamic threshold correlation rule is used to judge the fault type. Specifically: Judge the fault type according to the current mutation slope, the proportion of high-frequency harmonic energy, the cable temperature gradient and the environmental humidity; Based on the requirements of the short-circuit current instantaneous change rate in the IEEE C37.118 standard, set the short-circuit fault threshold; Based on the requirements of the total harmonic voltage distortion rate in the IEEE 519-2022 standard, set the normal condition threshold, the general fault threshold and the lightning strike fault threshold; Based on the Joule heat formula in the IEC 60287-2-1 thermodynamic model, set the overload fault threshold and the normal temperature rise threshold. According to the short-circuit fault threshold, normal operating condition threshold, general fault threshold, lightning fault threshold, overload fault threshold and normal temperature rise threshold, perform dynamic combination to determine the fault types of short-circuit faults, small current grounding faults, lightning overvoltage faults and other faults.

[0011] As a preferred solution of the method for accurately locating faults in distribution network wave recording based on big data analysis according to the present invention, wherein: the matching of the current transient waveform with the historical database and the calculation of the fault signal propagation time difference are specifically as follows: Classify the historical fault waveforms by fault type, and integrate the current waveform, high-frequency harmonic energy distribution and corresponding fault coordinates as transient waveform templates for storage. Calculate the overlap degree of the current transient waveform template, select the ones with high overlap degree as candidate templates, perform dynamic time warping on the candidate templates, calculate the cumulative distance, set a normalization threshold for matching, and output the historical fault type and historical fault coordinates if the matching is successful. Calculate the fault signal propagation time difference according to the historical fault coordinates, the traveling wave propagation speed and the distance difference between the fault point and the fault indicators at both ends.

[0012] As a preferred solution of the method for accurately locating faults in distribution network wave recording based on big data analysis according to the present invention, wherein: calculating the fault distance through the impedance-distance inversion algorithm, and fusing the fault signal propagation time difference and the fault distance to obtain the fault coordinates, specifically as follows: Obtain the real-time branch impedance from the distribution network GIS, generate a node impedance matrix by the branch addition method, set an abnormal area threshold, and mark it as a candidate fault section when the branch impedance mutation exceeds the abnormal area threshold. Calculate the fault distance for the candidate fault section by using the impedance-distance inversion formula. Fuse the traveling wave propagation speed and the fault signal propagation time difference to obtain the final fault coordinates.

[0013] In a second aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the method for accurately locating faults in distribution network wave recording based on big data analysis as described in the first aspect of the present invention is implemented.

[0014] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, any step of the method for accurately locating faults in distribution network wave recording based on big data analysis as described in the first aspect of the present invention is implemented.

[0015] The beneficial effects of the present invention are as follows: By deploying dual-channel fault indicators to monitor the current intensity and electric field intensity in real time, setting intelligent trigger thresholds, and preferentially starting high-precision transient recording, the present invention solves the defect that traditional detection cannot capture high-frequency transient signals, reduces the misjudgment rate, synchronizes low-frequency environmental data with high-frequency recording time series through the interpolation alignment method, combines wavelet decomposition and threshold filtering to eliminate noise interference, extracts the current mutation slope and the proportion of high-frequency harmonic energy, constructs a multi-dimensional feature matrix, improves the accuracy of feature extraction, uses an encrypted communication protocol to bind the GIS topological node impedance matrix, dynamically associates with international standard thresholds, realizes the accurate discrimination of fault types, and outputs high-precision fault coordinates through the rapid matching of historical waveform templates and the weighted fusion of traveling wave propagation and impedance inversion data, significantly improving the fault tracing efficiency of complex distribution networks and providing a reliable guarantee for the stable operation of the power system. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the method for accurately locating distribution network recording faults based on big data analysis in Embodiment 1.

[0018] Figure 2 It is a flowchart of feature extraction and data processing in Embodiment 1.

[0019] Figure 3 It is a flowchart of fault type discrimination and location in Embodiment 1. Detailed Embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0021] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Second, the "one embodiment" or "embodiment" mentioned herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or selectively mutually exclusive embodiment with other embodiments.

[0023] Embodiment 1, referring to Figures 1 to 3 , is the first embodiment of the present invention. This embodiment provides a precise fault location method for distribution network waveform recording based on big data analysis, including the following steps: S1. Deploy fault indicators to monitor the current intensity and electric field intensity in real time, set a trigger threshold, and when the current or electric field mutates, trigger high-precision transient waveform recording to generate waveform recording data including a fault timestamp, transient waveform, and trigger parameters; Install dual-channel fault indicators at each branch node of the distribution network line, with built-in high-precision current sensors (accuracy ±0.5% FS) and electric field sensors (frequency response range 0.1Hz - 10kHz); Set the current mutation and electric field mutation thresholds and waveform recording parameters, specifically: the current mutation threshold is set to ±20% of the rated current of the line (for example, when the rated current is 100A, the trigger threshold is 80A or 120A). According to the IEEE 1159 standard, the short-circuit fault current usually exceeds 50% of the rated value, but to avoid misjudgment during normal large-load startup (such as the motor startup current rising instantaneously by 30%), ±20% is selected as the balance point; The electric field mutation threshold is set to ±15% of the rated value of the phase electric field intensity (for example, when the rated phase electric field intensity is 10kV / m, the trigger threshold is 8.5kV / m or 11.5kV / m). The phase electric field intensity is directly related to the line voltage. During normal operation, the fluctuation of the phase electric field intensity is usually less than ±5%. During a fault (such as a single-phase ground fault), the electric field intensity drops suddenly (when it is a metallic ground, E approaches 0) or rises suddenly (such as lightning overvoltage). The ±15% electric field mutation threshold can cover typical fault scenarios and at the same time avoid environmental interference (such as the ±5% fluctuation caused by humidity change); The sampling frequency ≥10kHz to ensure capturing the details of the transient waveform (such as high-frequency harmonics), the waveform recording duration ≥200ms (covering the whole process of the fault occurrence). The setting basis of the sampling frequency ≥10kHz is that the frequency range of transient fault signals (such as high-frequency harmonics, traveling waves) is usually 0.1kHz - 5kHz. According to the Nyquist sampling theorem, the sampling frequency needs to be at least twice the highest frequency of the signal (i.e., ≥10kHz) to completely capture the waveform details. According to the CIGRE research report, a waveform recording duration of 200ms can cover more than 90% of the complete transient process of faults; When the change in current intensity exceeds the current mutation threshold, waveform recording is immediately initiated. When the change in current intensity does not exceed the current mutation threshold, and when the change in electric field intensity exceeds the electric field mutation threshold, waveform recording is also initiated. Current mutation takes precedence in triggering, and electric field mutation takes secondary precedence in triggering; After triggering the recording, record the fault timestamp, initiate dual-channel synchronous waveform recording, save the current waveform and the electric field waveform, and simultaneously store the triggered current mutation threshold and the electric field mutation threshold.

[0024] S2. Synchronously collect the phase current intensity, electric field intensity, cable temperature, and environmental data. Align the waveform recording data and the synchronously collected current intensity, electric field intensity, cable temperature, and environmental data in terms of time. After denoising through wavelet transform, extract the transient features and environmental features, and combine the transient features and environmental features to generate a comprehensive feature matrix; The transient features include the current mutation slope and the proportion of high-frequency harmonic energy, and the environmental features include the cable temperature gradient and the environmental temperature and humidity; Integrate a current sensor, an electric field sensor, a cable temperature sensor, and an environmental temperature and humidity sensor in the fault indicator; When triggering waveform recording, synchronously record the cable temperature, environmental temperature, and environmental humidity; Adopt the interpolation alignment method to align the non-waveform data (environmental temperature, environmental humidity) in terms of time; The sampling frequency of the environmental temperature and humidity sensor is 1 Hz. Align the cable temperature, environmental temperature, and environmental humidity to the waveform recording timestamp (10 kHz sampling rate) through linear interpolation. All sensors are unified in terms of timestamp by the GPS clock to ensure that the sampling time bases of different devices are consistent. Specifically: For each waveform recording timestamp (such as t = 0 ms, 0.1 ms, 0.2 ms,..., 200 ms), calculate the interpolation of the current point according to the time ratio based on the values of two adjacent low-frequency data points (such as t = 0 s and t = 1 s); Convert the 1 Hz low-frequency data to a 10 kHz sampling rate through interpolation (i.e., one interpolation point every 0.1 ms) to generate a temperature and humidity sequence consistent with the time axis of the waveform recording data. The waveform recording data (current waveform, electric field waveform) itself is already at a 10 kHz sampling rate, directly retain the original timestamp without interpolation processing, and then merge the waveform recording data, cable temperature, environmental temperature, and environmental humidity according to the timestamp; Select the Daubechies 4 wavelet basis (suitable for power transient signals) and perform 3-layer wavelet decomposition on the current waveform and the electric field waveform; Eliminate high-frequency noise through the threshold filtering method; Calculate the current change rate within a 1 ms window before and after the fault starting point to obtain the current mutation slope , and the expression is: ; Among them, the current mutation slope with the unit of amperes per millisecond (A / ms) represents the instantaneous change rate of the current when a fault occurs, and its value range is 0 to 1000 A / ms. For a typical short-circuit fault ≥500 A / ms, and for a small-current grounding fault ≤200 A / ms. represents the time point 1 ms before the fault trigger, represents the time point 1 ms after the trigger, represents the instantaneous value of the current at 1 ms after the fault trigger, with the unit of amperes (A), represents the instantaneous value of the current at 1 ms before the fault trigger, with the unit of amperes (A), is a unit conversion factor that converts the milliseconds (ms) in the denominator to seconds (s) to ensure that the slope unit is A / ms instead of A / s. The current mutation slope formula: improved based on the differential definition, referring to the IEEE C37.118 standard; Perform a Fourier transform on the denoised current waveform and calculate the proportion of the high-frequency harmonic energy in the 2 kHz - 5 kHz frequency band , and the expression is: ; Among them, the proportion of the high-frequency harmonic energy with the unit of dimensionless percentage (%) represents the proportion of the total energy in the 2 kHz - 5 kHz frequency band, and its value range is 0% to 100%. When ≤5%, it is in normal operation. When ≥15%, it is a fault. represents the result of the Fourier transform of the current intensity, with the unit of amperes per hertz (A / Hz), representing the energy distribution of the signal in the frequency domain, represents the integration of the current intensity energy in the frequency band from 2 kHz to 5 kHz, with the unit of amperes squared hertz (A²·Hz), reflecting the total energy of the high-frequency harmonic components, represents the integration of the current intensity energy in the frequency band from 0 Hz to 5 kHz, with the unit of amperes squared hertz, represents the frequency differential variable. The high-frequency harmonic energy formula: based on the Fourier energy conservation theorem, cited from the paper "IEEE Transactions on Power Delivery" (DOI: 10.1109 / TPWRD.2015.2405933); The environmental characteristics include the cable temperature gradient, ambient temperature, and ambient humidity; Calculate the cable temperature gradient within 1 minute before and after the fault , and the expression is: ; Among them, represents the average cable temperature within 1 minute after the fault occurs, with the unit of degree Celsius (°C), represents the average cable temperature within 1 minute before the fault occurs, represents seconds; directly obtain the ambient temperature and humidity at the moment of fault triggering; Align the current mutation slope, high-frequency harmonic energy ratio, cable temperature gradient, ambient temperature and humidity at the moment of fault triggering according to the time stamp and merge them into a matrix to obtain a comprehensive feature matrix.

[0025] S3. Bind the comprehensive feature matrix and the line topology information, and upload them to the monitoring center through encrypted communication; Take the electrical connection points in the distribution network as nodes, and obtain the line topology information in real time from the distribution network GIS, including node coordinates (latitude and longitude), branch impedance and hierarchical relationship. The hierarchical relationship includes root nodes, branch nodes and leaf nodes; The root node refers to the starting point of the distribution network, the branch node refers to the intermediate node connecting two or more sub-nodes, and the leaf node refers to the terminal node without sub-nodes; Gradually superimpose the branch resistance and branch reactance in the order of branches to generate a node impedance matrix; Encode the impedance matrix, node coordinates and hierarchical relationship into a topology matrix by using topology information coding, Assign a unique device ID to each fault indicator, make the device ID in the comprehensive feature matrix correspond to the nodes in the topology matrix one by one, and merge the feature matrix data and topology information within the same time window (such as 200 ms before and after the fault occurs) to form a spatio-temporal correlation data packet; Upload the spatio-temporal correlation data packet to the monitoring center through the MQTT protocol.

[0026] S4. The monitoring center formulates dynamic threshold correlation rules according to the comprehensive feature matrix to judge the fault type; After receiving the encrypted spatio-temporal correlation data packet, the monitoring center decrypts it through the TLS1.3 protocol, verifies the integrity of the device ID data and the data signature. If the spatio-temporal correlation data packet is damaged or the signature is invalid, trigger an alarm and discard the data; Extract the current mutation slope, high-frequency harmonic energy ratio, cable temperature gradient and ambient humidity of the comprehensive feature matrix from the spatio-temporal correlation data packet; Judge the fault type according to the current mutation slope, high-frequency harmonic energy ratio, cable temperature gradient and ambient humidity. The fault types include short-circuit faults, small current grounding faults, lightning overvoltage faults and other faults; Based on the requirement of the instantaneous change rate of short-circuit current in IEEE C37.118 standard, when the short-circuit current is 3 to 5 times the rated value, set the short-circuit fault threshold; Based on the requirement that the total harmonic voltage distortion rate THD ≤ 5% in IEEE 519-2022 standard, set the normal operating condition threshold, general fault threshold and lightning fault threshold; Based on the Joule heat formula in the IEC 60287-2-1 thermodynamic model, set the overload fault threshold and normal temperature rise threshold. The expression of the Joule heat formula is: ; Among them, represents the change rate of temperature with time, with the unit of degrees Celsius per second (℃ / s), represents the current, with the unit of ampere (A), represents the resistance, with the unit of ohm (Ω), represents the specific heat capacity of the conductor, with the unit of joules per kilogram per degree Celsius (J / (kg·℃)), represents the mass of the conductor, with the unit of kilogram (kg); According to the sensor error compensation requirement in high humidity environment in IEC 62351-6, when H ≥ 85%RH, where RH represents relative humidity, the electric field mutation threshold needs to be dynamically adjusted by ±5%. Set the humidity correction trigger threshold and the relaxation amount of the electric field threshold. When the environmental humidity ≥ 85%RH, the electric field threshold is relaxed by ±5% to avoid misjudging as a ground fault; When the current mutation slope ≥ 500 A / ms and the high-frequency harmonic energy ratio ≥ 15% and the cable temperature gradient ≥ 0.5℃ / s, judge that the fault type is a short-circuit fault; When the current mutation slope ≤ 200 A / ms and the high-frequency harmonic energy ratio ≥ 20% and the environmental humidity ≥ 70%RH, judge that the fault type is a small current grounding fault; When the high-frequency harmonic energy ratio ≥ 30% and the current mutation slope ≥ 300 A / ms and the environmental humidity ≥ 85%RH, judge that the fault type is a lightning overvoltage fault; When the fault type does not belong to any of the short-circuit fault, small current grounding fault and lightning overvoltage fault, it is judged as other faults, and immediately enable the standby line or tie switch to restore power supply to the affected area.

[0027] S5. The monitoring center matches the current transient waveform with the historical database, calculates the time difference of the fault signal propagation, combines the line topology information, calculates the fault distance through the impedance-distance inversion algorithm, and fuses the time difference of the fault signal propagation and the fault distance to obtain the fault coordinates; Classify the historical fault waveforms (current waveform and electric field waveform) according to the fault type, and integrate the current waveform, high-frequency harmonic energy distribution and corresponding fault coordinates as the transient waveform template for storage; Generate the minimum bounding rectangle (MBR) for each transient waveform template. Specifically, use the rotating calipers algorithm to dynamically adjust the rotation angle of the rectangle boundary, calculate the minimum area rectangle covering all convex hull vertices, record the corresponding rectangle area for each rotation angle, and finally select the rectangle with the minimum area as the MBR. Record the waveform extreme points (maximum and minimum values) and energy envelope (integral value in the frequency band of 2 kHz - 5 kHz). Compare the current current waveform and electric field waveform with the historical templates one by one, and use the K-D tree structure to store the minimum bounding rectangle MBR to reduce the matching complexity; Extract the high-frequency harmonic energy ratio and current mutation slope from the current current waveform and electric field waveform as the transient electrical feature vector. Screen the candidate templates through the K-D tree index. Starting from the root node of the K-D tree, calculate the overlap degree between the current waveform feature points and the MBR of each node; Overlap degree = Area of intersection region / Area of current waveform MBR; Overlap degree = Area of current waveform MBR / Area of intersection region; If the overlap degree ≤ 90%, directly prune the subtree and thus eliminate it. Take the subtree with an overlap degree > 90% as the candidate template, perform dynamic time warping on the candidate template, calculate the cumulative distance. If the minimum distance ≤ 0.2 (normalized threshold), it is determined that the match is successful, and then output the historical fault type and historical fault coordinates; Calculate the propagation time difference of the fault signal The expression of the traveling wave time difference formula is: ; Among them, represents time, represents the distance difference between the fault point and the fault indicators at both ends, represents the traveling wave propagation speed; Obtain the real-time branch impedance from the distribution network GIS, generate the node impedance matrix by the branch addition method, set the abnormal area threshold. When the branch impedance mutation exceeds the abnormal area threshold, it is marked as the candidate fault section. Specifically: Obtain the reference impedance of the line under normal operating conditions from the distribution network GIS , obtain the average value through statistical analysis of historical operation data. According to the IEEE 141 - 1993 and IEC 60909 standards, when the line impedance mutation exceeds ±15% - ±20% of the reference value, it is determined as abnormal, and thus obtain the abnormal area threshold; For the candidate fault section, calculate the fault distance using the impedance-distance inversion formula. The impedance inversion formula is: ; Among them, represents the fault distance, in kilometers (km), specifically the distance from the fault point to the nearest fault indicator. represents the measured impedance of the fault point. represents the reference impedance, corresponding to the impedance value of the line under normal operating conditions, which is obtained in real time by the distribution network GIS. represents the length impedance, which is directly obtained by a loop resistance tester. The final fault coordinates are obtained by weighting the traveling wave time difference formula (weight 70%) and the impedance inversion formula (weight 30%). The expression is: ; Among them, represents the final fault distance, in kilometers (km).

[0028] This embodiment also provides a computer device applicable to the case of the precise fault location method for distribution network waveform recording based on big data analysis, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the precise fault location method for distribution network waveform recording based on big data analysis as proposed in the above embodiment.

[0029] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad set on the outer shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0030] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for accurately locating the fault of the distribution network recording wave based on big data analysis proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0031] In summary, the present invention: deploys dual-channel fault indicators to monitor the current intensity and electric field intensity in real time, sets intelligent trigger thresholds, and preferentially starts high-precision transient recording waves, solves the defect that traditional detection cannot capture high-frequency transient signals, reduces the misjudgment rate, synchronizes low-frequency environmental data with high-frequency recording wave timings through the interpolation alignment method, combines wavelet decomposition and threshold filtering to eliminate noise interference, extracts the current mutation slope and the proportion of high-frequency harmonic energy, constructs a multi-dimensional feature matrix, improves the accuracy of feature extraction, uses an encrypted communication protocol to bind the GIS topological node impedance matrix, dynamically associates international standard thresholds, realizes the accurate discrimination of fault types, and outputs high-precision fault coordinates through the rapid matching of historical waveform templates and the weighted fusion of traveling wave propagation and impedance inversion data, significantly improving the fault tracing efficiency of complex distribution networks and providing a reliable guarantee for the stable operation of the power system.

[0032] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A distribution network recording fault accurate positioning method based on big data analysis, characterized by: include: Deploy fault indicators, monitor current intensity and electric field intensity in real time, set trigger thresholds, and trigger high-precision transient recording when current or electric field changes suddenly, generating recording data containing fault timestamps, transient waveforms, and trigger parameters; The current intensity, electric field intensity, cable temperature and environmental data are collected synchronously, and the recorded data and the synchronously collected current intensity, electric field intensity, cable temperature and environmental data are time-aligned. After de-noising through wavelet transform, transient characteristics and environmental characteristics are extracted, and the transient characteristics and environmental characteristics are combined to generate a comprehensive feature matrix; Bind the comprehensive feature matrix and line topology information and upload them to the monitoring center through encrypted communication; The monitoring center formulates dynamic threshold association rules based on the comprehensive feature matrix to determine the fault type; The monitoring center matches the current transient waveform with the historical database, calculates the fault signal propagation time difference, combines the line topology information, calculates the fault distance through the impedance-distance inversion algorithm, and fuses the fault signal propagation time difference and the fault distance to obtain the fault coordinates.

2. The distribution network recording fault accurate positioning method based on big data analysis according to claim 1 is characterized by: The specific steps of triggering high-precision transient recording when the current or electric field suddenly changes are: Install a dual-channel fault indicator at each branch node of the distribution network line, with built-in high-precision current sensor and electric field sensor; Set the current mutation and electric field mutation thresholds and recording parameters; When the change in current intensity exceeds the current mutation threshold, the wave recording is started immediately. When the change in current intensity does not exceed the current mutation threshold, and when the change in electric field intensity exceeds the electric field mutation threshold, the wave recording is also started. The current mutation is triggered first, and the electric field mutation is triggered second. After triggering the recording, the fault timestamp is recorded, dual-channel synchronous recording is started, the current waveform and electric field waveform are saved, and the triggered current mutation threshold and electric field mutation threshold are stored at the same time.

3. The distribution network recording fault accurate positioning method based on big data analysis as claimed in claim 2 is characterized by: The synchronous collection of current intensity, electric field intensity, cable temperature and environmental data, and the time alignment of the recorded data and the synchronously collected current intensity, electric field intensity, cable temperature and environmental data are specifically as follows: The fault indicator integrates a current sensor, an electric field sensor, a cable temperature sensor, and an ambient temperature and humidity sensor to collect current intensity, electric field intensity, cable temperature, ambient temperature, and ambient humidity; When triggering the recording, the interpolation alignment method is used for time alignment.

4. The distribution network recording fault accurate positioning method based on big data analysis as claimed in claim 3 is characterized by: After the denoising by wavelet transform, the transient features and environmental features are extracted, and the transient features and environmental features are combined to generate a comprehensive feature matrix. The specific steps are as follows: The Daubechies 4 wavelet basis is selected to perform wavelet decomposition on the current waveform and the electric field waveform, and the high-frequency noise is eliminated by the threshold filtering method; Calculate the current change rate in a window before and after the fault starting point to obtain the current mutation slope; Perform Fourier transform on the denoised current waveform and calculate the proportion of high-frequency harmonic energy in the frequency band; Calculate the cable temperature gradient before and after the fault; Directly obtain the ambient temperature and humidity at the moment of fault triggering; The current mutation slope, high-frequency harmonic energy ratio, cable temperature gradient, ambient temperature and humidity at the moment of fault triggering are aligned by timestamps and merged into a matrix to obtain a comprehensive feature matrix.

5. The distribution network recording fault accurate positioning method based on big data analysis as claimed in claim 4 is characterized by: The comprehensive feature matrix and line topology information are bound and uploaded to the monitoring center through encrypted communication, specifically: The electrical connection points in the distribution network are taken as nodes, and the line topology information is obtained from the distribution network GIS in real time; The branch resistance and branch reactance are gradually added up in branch order to generate a node impedance matrix; The impedance matrix, node coordinates and hierarchical relationships are encoded into a topological matrix using topological information encoding. Assign a unique device ID to each fault indicator, correspond the device ID in the comprehensive feature matrix to the nodes in the topology matrix one by one, merge the feature matrix data and topology information in the same time window to form a time-space correlation data packet; The time-space correlation data packets are uploaded to the monitoring center via the MQTT protocol.

6. The distribution network recording fault accurate positioning method based on big data analysis as claimed in claim 5 is characterized by: The dynamic threshold association rule is used to determine the fault type, specifically: Identify the fault type based on the current mutation slope, high-frequency harmonic energy ratio, cable temperature gradient and ambient humidity; The short-circuit fault threshold is set based on the short-circuit current instantaneous change rate requirements in the IEEE C37.118 standard; Based on the requirements of total harmonic voltage distortion rate in IEEE 519-2022 standard, set the normal operating condition threshold, general fault threshold and lightning fault threshold; Based on the Joule heat formula in the IEC 60287-2-1 thermodynamic model, set the overload fault threshold and normal temperature rise threshold; According to the dynamic combination of short circuit fault threshold, normal operating condition threshold, general fault threshold, lightning fault threshold, overload fault threshold and normal temperature rise threshold, the fault types of short circuit fault, small current grounding fault, lightning overvoltage fault and other faults are determined.

7. The distribution network recording fault accurate positioning method based on big data analysis according to claim 6 is characterized by: The current transient waveform is matched with the historical database to calculate the fault signal propagation time difference, specifically: Classify historical fault waveforms by fault type, and integrate current waveforms, high-frequency harmonic energy distribution, and corresponding fault coordinates as transient waveform templates for storage; Calculate the overlap of the current transient waveform template, select the template with high overlap as the candidate template, perform dynamic time warping on the candidate template, calculate the cumulative distance, set the normalized threshold for matching, and output the historical fault type and historical fault coordinates if the match is successful; The fault signal propagation time difference is calculated based on the historical fault coordinates, the traveling wave propagation speed and the distance difference between the fault point and the fault indicators at both ends.

8. The distribution network recording fault accurate positioning method based on big data analysis as claimed in claim 7 is characterized by: The fault distance is calculated by the impedance-distance inversion algorithm, and the fault signal propagation time difference and the fault distance are merged to obtain the fault coordinates, which are specifically: Obtain real-time branch impedance from the distribution network GIS, generate a node impedance matrix using the branch addition method, set an abnormal area threshold, and mark the branch impedance as a candidate fault section when it suddenly changes beyond the abnormal area threshold. For the candidate fault section, the fault distance is calculated using the impedance-distance inversion formula; The traveling wave propagation velocity and the fault signal propagation time difference are fused to obtain the final fault coordinates.

9. 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 for accurately locating distribution network recording faults based on big data analysis as described in any one of claims 1 to 8 are implemented.

10. 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 for accurately locating distribution network recording faults based on big data analysis as described in any one of claims 1 to 8 are implemented.

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