A power distribution network traveling wave accurate positioning method and system

Abstract

The application provides a power distribution network traveling wave accurate positioning method and system, the method comprises the following steps: an edge intelligent terminal collects a fault traveling wave signal in real time, uses successive variation modal decomposition and continuous wavelet transform to accurately obtain a wave head timestamp, and uses Hilbert transform to calculate an instantaneous frequency, synchronously perceives clock synchronization error, environment parameter error, current sensor response error and time measurement error; a regional coordination node performs initial positioning based on a frequency-variable wave speed model and a line length model considering sag and temperature, quantifies positioning uncertainty through error propagation theory, completes fault research and judgment, and obtains a fault section; and the fault sections of all regional coordination nodes are collected to a cloud intelligent platform for global optimization calculation. The application solves the technical problems of poor real-time performance of fault positioning and isolation and untrustworthy positioning results in complex scenarios.

Description

Technical Field

[0001] This invention relates to the field of distribution network fault location, and specifically to a method and system for accurate location of traveling waves in distribution networks. Background Technology

[0002] The power distribution network is a critical public infrastructure directly serving end users, and its power supply reliability is crucial to economic and social development. However, power distribution networks are characterized by large-scale equipment, complex network structures, and frequent susceptibility to severe weather, leading to frequent faults. Statistics show that over 85% of power outages originate from the distribution network, and fault location and repair are difficult due to limitations in current technology. Furthermore, my country's power distribution network widely employs low-current grounding methods, which, while beneficial for power supply continuity, may increase the risk of accidents if the network remains energized for extended periods after a fault. Therefore, achieving rapid and accurate fault location is a key step in shortening outage time and improving power supply reliability.

[0003] Currently, numerous studies have been conducted on fault location methods for distribution networks. Among them, traveling wave (TW) location technology, due to its theoretical accuracy of up to 100 meters, is considered an ideal solution for precise fault location in distribution networks. However, unlike transmission networks, which have relatively simple structures and uniform parameters, distribution networks are complex, with numerous line branches and the tendency for traveling waves to attenuate and reflect, posing significant challenges to the application of TW location technology. Existing TW location devices mainly rely on single electrical quantity measurements and employ fixed processing algorithms. They lack the ability to synchronously perceive and collaboratively process multidimensional uncertainties such as geographical factors, environmental factors, and the equipment's own state. They often output a single result as a black box, making it impossible to assess its confidence level. In complex distribution network environments, they are prone to providing incorrect locations, leading to low line inspection efficiency. Furthermore, traditional solutions upload massive amounts of TW data to a master station for centralized processing, resulting in high communication pressure and long location delays, making it difficult to meet the requirements for rapid fault isolation.

[0004] The existing invention patent application document CN120669049A, entitled "A Fault Location and Detection System for Distribution Network Lines," describes a method comprising: a hybrid information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer, and a fault location layer. The hybrid signal acquisition layer includes a high-frequency transient waveform recording unit, a power frequency measurement unit, a wireless pulse sensor, and a distributed fiber optic temperature measurement unit. The fault feature extraction layer includes a time-frequency analysis module, a preprocessing module, and a three-dimensional feature vector module. The intelligent diagnosis layer includes a convolutional attention network, a spatiotemporal graph neural network, and a transfer learning module. The fault location layer includes a particle swarm optimization module and a fuzzy inference module. While the aforementioned prior art constructs a multi-source information acquisition and deep neural network diagnostic architecture, its core flaw lies in its over-reliance on a black-box model. It fails to consider the inherent clock synchronization error, response error, and environmental disturbances of the sensors. Furthermore, the fixed traveling wave velocity model ignores the impact of frequency-varying characteristics on location accuracy, ultimately failing to quantify the uncertainty of the output results.

[0005] The existing invention patent application document CN120934186A, entitled "A Distributed FA Collaborative Control Method for Edge Computing Nodes in Distribution Network Terminals," describes a method that includes: synchronously acquiring high-frequency electrical and partial discharge signals through edge computing terminals and performing multi-dimensional processing to form a fault feature vector. Each edge node uses a lightweight algorithm to monitor the fault in real time, enhances the features using Hilbert-Huang transform, and broadcasts them to adjacent nodes. Multi-node cross-validation is used to achieve accurate location, and in complex topologies, it links the edge and cloud for collaborative operation. After fault location, dual verification is initiated, and a tripping command is sent through the encrypted GOOSE protocol. The optimal recovery path is searched based on a mixed-integer linear programming model, prioritizing the use of distributed power sources for reverse power supply. The control strategy is dynamically optimized through federated learning and the MADDPG algorithm, and a digital twin model is trained. The aforementioned existing technology focuses on collaborative control and path optimization of multiple edge nodes, but its underlying location relies on Hilbert-Huang transform for feature enhancement, which is susceptible to mode mixing, leading to inaccurate wavefront calibration. Furthermore, the line length model does not consider dynamic environmental factors such as sag and temperature, and it lacks a mechanism for propagating and fusing the measurement uncertainties of each node in multi-node cross-validation.

[0006] The centralized processing architecture used in existing technologies results in enormous pressure on the uploading of massive amounts of traveling wave data and high communication latency, making it difficult to meet the millisecond-level real-time positioning requirements for rapid fault isolation. At the same time, the fixed algorithms used in existing technologies lack the ability to perceive and dynamically compensate for the multi-dimensional real-time status of the line and the environment. The models are disconnected and the confidence level of the results cannot be quantified, making the positioning results unreliable in complex scenarios.

[0007] In summary, existing technologies suffer from poor real-time performance in fault location and isolation, and unreliable location results in complex scenarios. Summary of the Invention

[0008] The technical problem to be solved by this invention is: how to solve the technical problems of poor real-time performance of fault location and isolation and unreliable location results in complex scenarios in the prior art.

[0009] This invention solves the above-mentioned technical problems by employing the following technical solution: A method for accurate positioning of traveling waves in a distribution network includes:

[0010] S1. Real-time acquisition of fault traveling wave signals, successive variational mode decomposition and continuous wavelet transform (CWT) of the fault traveling wave signals, obtaining wavefront timestamps, and using Hilbert transform to obtain analytical signals. Based on the analytical signals, the instantaneous frequency corresponding to the wavefront timestamps is obtained, and error data is synchronously sensed based on the instantaneous frequency.

[0011] S2. Based on the frequency-varying wave velocity model, time alignment and correction operations are performed on the error data to obtain the corrected error. The traveling wave velocity data is obtained through Karrenbauer transformation, environmental parameter error calculation, Carson grounding impedance correction, and temperature and humidity environment correction. Based on the line length model considering sag and temperature, the line length data is calculated using the oblique parabolic mathematical model. According to the wave velocity data and line length data, a double-end traveling wave positioning formula is constructed for initial positioning to obtain the front-end traveling wave positioning result. According to the front-end traveling wave positioning result, multi-factor error quantitative analysis is performed through error propagation theory. The error section is quantified and processed according to the positioning uncertainty.

[0012] S3. Collect the fault sections of all regional collaborative nodes, perform global optimization calculations, and obtain the distribution network traveling wave location results.

[0013] This invention eliminates the reliance of traditional technologies on single electrical quantity measurements. It adopts a non-fixed processing algorithm that can synchronously perceive and collaboratively process multi-dimensional uncertainties such as geographical factors, environmental factors, and the equipment's own state. It outputs fault sections of multiple regional collaborative nodes, assesses the confidence level of fault sections, and can provide accurate fault location in complex distribution network environments, thus improving line inspection efficiency.

[0014] This invention employs a cloud-based intelligent platform to globally optimize the assessment results of collaborative nodes in various regions, alleviating the problems of high communication pressure and long positioning latency, and meeting the requirements for rapid fault isolation. This invention avoids the problems of massive uploading pressure and high communication latency caused by traditional centralized processing architectures, and can meet the real-time positioning requirements for rapid fault isolation.

[0015] In a more specific technical solution, in S1, when a distribution network fault occurs, the edge intelligent terminal triggers waveform recording to collect the fault traveling wave signal.

[0016] The sequential variational mode decomposition (SVMD) is used to decompose the traveling wave current data in the fault traveling wave signal to obtain the corresponding mode functions. IMFn ;

[0017] Based on the intrinsic mode function IMFn Calculate the envelope entropy of each mode; n Indicates the first n One mode;

[0018] Select the dominant mode with the largest envelope entropy;

[0019] Continuous wavelet transform (CWT) is performed on the dominant mode, and the wavefront timestamp is detected; the wavefront timestamp is determined by detecting the modulus maxima of the wavelet coefficients.

[0020] Perform Hilbert transform on the dominant mode to obtain the analytic signal, and calculate the instantaneous frequency;

[0021] Based on instantaneous frequency, local error data is sensed and statistically analyzed;

[0022] Error data is encapsulated and uploaded to the regional collaborative node at the edge intelligent terminal.

[0023] This invention explicitly senses and quantifies multiple types of measurement errors at the signal acquisition front end, introduces a wavefront identification and frequency-wave velocity accurate mapping relationship model based on successive variational mode decomposition and continuous wavelet transform, and combines error propagation theory to output positioning results with confidence intervals, thus elevating positioning from qualitative diagnosis to quantitative analysis with physical interpretability and controllable accuracy.

[0024] This invention employs successive variational mode decomposition and continuous wavelet transform to accurately extract wavefronts, constructs a dynamic line length model and a frequency-varying wave velocity model that integrates environmental parameters, and quantifies the positioning uncertainty of each node based on error propagation theory. It also achieves multi-source information fusion based on confidence weights in the cloud, significantly improving the robustness and accuracy of collaborative positioning in complex environments.

[0025] In more specific technical solutions, local error data includes: clock synchronization error, environmental parameter error, current sensor response error, and time measurement error.

[0026] In a more specific technical solution, the edge intelligent terminal receives the time synchronization signal sent by the regional collaborative node, and estimates the local clock deviation and standard deviation based on the time synchronization signal to obtain the clock synchronization error.

[0027] By using built-in environmental sensing sensors, temperature, humidity, and soil resistivity are collected, and environmental parameter errors are estimated in real time, including temperature detection error, humidity detection error, and soil resistivity detection error.

[0028] The frequency response characteristics of the current sensor channel are detected, and the group delay error at the instantaneous frequency is calculated based on the frequency response characteristics as the current sensor response error.

[0029] In statistical signal processing, the theoretical calculation time for parameter estimation and the estimation error of the wavefront arrival time are used as time measurement errors.

[0030] In a more specific technical solution, in S2, error data is received at the regional collaborative node; based on the time protocol, the observation vector of the error data is time-aligned, and the timestamp of the error data is corrected using the local clock deviation;

[0031] In the frequency-varying wave speed model, the series impedance and parallel admittance of the power distribution line in the frequency domain are transformed into the modal domain state through the Karrenbauer transformation.

[0032] Based on the traveling wave dispersion effect, the mode domain impedance and traveling wave transmission coefficient of the mode domain state are calculated, and the traveling wave attenuation constant and traveling wave phase constant of the mode domain state are obtained. Based on the traveling wave attenuation constant and traveling wave phase constant, the traveling wave velocity relationship function is obtained.

[0033] The average frequency is calculated based on the instantaneous frequency, and the line wave velocity estimation data is obtained by processing the traveling wave velocity relationship function and the average frequency.

[0034] The series impedance in the modal domain is corrected by Carson grounding impedance correction, based on the Carson grounding impedance formula.

[0035] The traveling wave velocity data is obtained and calculated based on the temperature and humidity environmental data and the line wave velocity estimation data.

[0036] This invention focuses on physical feature mining in the one-dimensional signal domain. It achieves precise fault location through accurate wavefront arrival time calibration, frequency-varying wave velocity correction, and dynamic line length model. Furthermore, it constructs a full-link analysis from sensor error perception and model parameter correction to the quantification of uncertainty in the location results, enabling the location results to have traceable physical confidence.

[0037] In a more specific technical solution, in S2, the route length data is obtained by processing the parabolic equation in the parabolic model, summing, and temperature thermal expansion and contraction correction.

[0038] This invention uses a frequency-varying wave speed model and a line length model that considers sag and temperature for initial positioning. Through error propagation theory, it quantifies the positioning uncertainty. By data aggregation, time alignment, initial error judgment based on spatial geometric constraints, and coarse positioning of fault sections, it solves the problem that the fixed algorithms used in traditional technologies lack multi-dimensional real-time perception and dynamic compensation capabilities for line and environmental conditions. The model is consistent and can quantify the confidence of the results, ensuring the reliability of the positioning results in complex scenarios.

[0039] In a more specific technical solution, S2, the fault point is calculated using a double-ended traveling wave localization formula. a The distance to the side is used to obtain the positioning result of the traveling wave at the front end.

[0040] In a more specific technical solution, in S2, during the process of multi-factor error quantitative analysis, an error propagation equation is constructed; wherein, based on the double-ended traveling wave positioning formula, error analysis is performed on the front-end traveling wave positioning result to establish an error propagation model; partial derivatives are calculated, and the partial derivatives are substituted into the double-ended traveling wave positioning formula to obtain the error propagation equation.

[0041] In a more specific technical solution, the variances of various errors are calculated; the error variances are synthesized to obtain the traveling wave error; the traveling wave error is solved and quantified to obtain the uncertainty; based on the uncertainty, the front-end traveling wave positioning results are integrated and processed to obtain the fault section.

[0042] In a more specific technical solution, a power distribution network traveling wave precise positioning system includes:

[0043] The edge intelligent terminal is used to collect fault traveling wave signals in real time, perform successive variational mode decomposition and continuous wavelet transform (CWT) on the fault traveling wave signals, obtain wavefront timestamps, and obtain analytical signals using Hilbert transform. Based on the analytical signals, the instantaneous frequency corresponding to the wavefront timestamps is obtained, and error data is synchronously sensed based on the instantaneous frequency.

[0044] The regional collaborative node is used to perform time alignment and correction operations on error data based on the frequency-varying wave velocity model to obtain the corrected error. Through Karrenbauer transformation, environmental parameter error calculation, Carson grounding impedance correction, and temperature and humidity environment correction, the traveling wave velocity data is obtained. Based on the line length model that considers sag and temperature, the line length data is calculated using the oblique parabolic mathematical model. Based on the wave velocity data and the line length data, a double-end traveling wave positioning formula is constructed for initial positioning to obtain the front-end traveling wave positioning result. Based on the front-end traveling wave positioning result, multi-factor error quantitative analysis is performed through error propagation theory. The quantification and processing based on the positioning uncertainty are used to obtain the fault section. The regional collaborative node is connected to the edge intelligent terminal.

[0045] The cloud-based intelligent platform is used to collect fault sections from all regional collaborative nodes, perform global optimization calculations, and obtain the distribution network traveling wave location results. The cloud-based intelligent platform is connected to the regional collaborative nodes.

[0046] The present invention has the following advantages over the prior art:

[0047] This invention eliminates the reliance of traditional technologies on single electrical quantity measurements. It adopts a non-fixed processing algorithm that can synchronously perceive and collaboratively process multi-dimensional uncertainties such as geographical factors, environmental factors, and the equipment's own state. It outputs fault sections of multiple regional collaborative nodes, assesses the confidence level of fault sections, and can provide accurate fault location in complex distribution network environments, thus improving line inspection efficiency.

[0048] This invention employs a cloud-based intelligent platform to globally optimize the assessment results of collaborative nodes in various regions, alleviating the problems of high communication pressure and long positioning latency, and meeting the requirements for rapid fault isolation. This invention avoids the problems of massive uploading pressure and high communication latency caused by traditional centralized processing architectures, and can meet the real-time positioning requirements for rapid fault isolation.

[0049] This invention explicitly senses and quantifies multiple types of measurement errors at the signal acquisition front end, introduces a wavefront identification and frequency-wave velocity accurate mapping relationship model based on successive variational mode decomposition and continuous wavelet transform, and combines error propagation theory to output positioning results with confidence intervals, thus elevating positioning from qualitative diagnosis to quantitative analysis with physical interpretability and controllable accuracy.

[0050] This invention employs successive variational mode decomposition and continuous wavelet transform to accurately extract wavefronts, constructs a dynamic line length model and a frequency-varying wave velocity model that integrates environmental parameters, and quantifies the positioning uncertainty of each node based on error propagation theory. It also achieves multi-source information fusion based on confidence weights in the cloud, significantly improving the robustness and accuracy of collaborative positioning in complex environments.

[0051] This invention focuses on physical feature mining in the one-dimensional signal domain. It achieves precise fault location through accurate wavefront arrival time calibration, frequency-varying wave velocity correction, and dynamic line length model. Furthermore, it constructs a full-link analysis from sensor error perception and model parameter correction to the quantification of uncertainty in the location results, enabling the location results to have traceable physical confidence.

[0052] This invention uses a frequency-varying wave speed model and a line length model that considers sag and temperature for initial positioning. Through error propagation theory, it quantifies the positioning uncertainty. By data aggregation, time alignment, initial error judgment based on spatial geometric constraints, and coarse positioning of fault sections, it solves the problem that the fixed algorithms used in traditional technologies lack multi-dimensional real-time perception and dynamic compensation capabilities for line and environmental conditions. The model is consistent and can quantify the confidence of the results, ensuring the reliability of the positioning results in complex scenarios.

[0053] This invention solves the technical problems of poor real-time performance of fault location and isolation, and unreliable location results in complex scenarios in the prior art. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the basic steps of a method for accurate positioning of traveling waves in a power distribution network according to Embodiment 1 of the present invention;

[0055] Figure 2 This is a schematic diagram of data stream processing for a power distribution network traveling wave precise positioning system according to Embodiment 2 of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] like Figure 1 As shown, the present invention provides a method for accurate positioning of traveling waves in a power distribution network, comprising the following basic steps:

[0059] S1. Real-time acquisition of fault traveling wave signals, successive variational mode decomposition and continuous wavelet transform (CWT) of the fault traveling wave signals, obtaining wavefront timestamps, and using Hilbert transform to obtain analytical signals. Based on the analytical signals, the instantaneous frequency corresponding to the wavefront timestamps is obtained, and error data is synchronously sensed based on the instantaneous frequency.

[0060] In this embodiment, the edge intelligent terminal is deployed at key monitoring points of the power distribution network to perform high-frequency traveling wave signal acquisition, local error sensing, signal preprocessing, and wavefront feature extraction.

[0061] In this embodiment, fault triggering and edge perception are performed at the edge intelligent terminal;

[0062] In S1, when a distribution network fault occurs, the edge intelligent terminal triggers waveform recording to collect the fault traveling wave signal;

[0063] Successive variational mode decomposition (SVMD) is used to decompose the traveling wave current data in the fault traveling wave signal to obtain the mode function corresponding to the nth mode. IMFn ;

[0064] Based on mode function IMFn Calculate the envelope entropy of each mode;

[0065] Select the dominant mode with the largest envelope entropy;

[0066] Specifically, the envelope entropy of each mode is calculated. E k The mode with the largest envelope entropy is selected as the dominant mode. The envelope entropy is calculated as follows:

[0067]

[0068] In the formula, k For modal index, aj It is the first k The mode in the th ... j The envelope amplitude of each sampling point; N s The number of sampling points. i Indicates the first i One sampling point, t Indicates time, house Indicates dominance;

[0069] Select the dominant mode with the largest envelope entropy;

[0070] Continuous wavelet transform (CWT) is performed on the dominant mode, and the wavefront timestamp is detected; the wavefront timestamp is determined by detecting the modulus maxima of the wavelet coefficients.

[0071] Specifically, regarding the dominant mode Continuous wavelet transform (CWT) is performed to detect wavefronts and determine wavefront timestamps. The continuous wavelet transform process is as follows:

[0072]

[0073] In the formula: a The scaling parameter controls the scaling of the wavelet function; b The translation parameter controls the position of the wavelet function on the time axis; P ( t ) represents the Morlet mother wavelet basis function; P This represents the complex conjugate of the wavelet function. The local timestamp of the wavefront arrival is determined by detecting the modulus maxima of the wavelet coefficients. , Represents the wavelet transform function;

[0074] Perform Hilbert transform on the dominant mode to obtain the analytic signal, and calculate the instantaneous frequency; specifically, for the dominant mode... Perform Hilbert transform to obtain the analytic signal. The instantaneous frequency is calculated as follows:

[0075]

[0076] In the formula: H For Hilbert transform operators; A i ( t () represents the instantaneous amplitude. ; F i ( t () represents the instantaneous phase. Take the instantaneous frequency at the wavefront. , Indicates the firsti Wavefront time corresponding to each sampling point Indicates the first i The instantaneous frequency corresponding to each sampling point;

[0077] Based on the instantaneous frequency, local error data is sensed and statistically analyzed; local error data includes: clock synchronization error, environmental parameter error, current sensor response error, and time measurement error.

[0078] In this embodiment, clock synchronization error is calculated. Specifically, the edge intelligent terminal receives a time synchronization signal from the regional collaborative node and estimates the local clock deviation in real time based on the time synchronization signal. and its standard deviation The clock synchronization error is obtained. east Indicates local, Indicates clock skew;

[0079] Calculate environmental parameter errors; specifically, obtain temperature through a built-in environmental sensing sensor. T ,humidity H Soil resistivity r Real-time estimation of temperature detection error Humidity detection error and soil resistivity detection error ;

[0080] The frequency response characteristics of the current sensor channel were detected. H i ( f According to frequency response characteristics H i ( f ) Calculate the instantaneous frequency The group delay error is used as the response error of the current sensor; specifically, the frequency response characteristics of the current sensor channel are obtained through periodic self-testing. H i ( f ), calculate the instantaneous frequency Group delay error :

[0081] ;

[0082] Based on parameter estimation theory, the time measurement error is calculated; specifically, the parameter estimation theory in statistical signal processing is used to calculate the estimation error of the wavefront arrival time. d ( t This is used as the time measurement error. d This is the equivalent distance between the conductor and the ground.

[0083] In this embodiment, the selected dominant mode is determined. Effective root mean square bandwidth β rms :

[0084]

[0085] In the formula: f To select the mode function corresponding to the maximum value of the envelope entropy IMFt The intermediate frequency; S ( f ) as the dominant mode Fourier transform, rms This represents the root mean square.

[0086] Calculate the signal-to-noise ratio of a traveling wave signal. :

[0087]

[0088] In the formula: A i dominant mode The signal amplitude; The variance of the remaining modal functions, oh It represents angular frequency.

[0089] Based on the parameter estimation of the Cramer-Rhodes lower bound, the time measurement error is evaluated. d ( t ):

[0090]

[0091] In the formula: f s The sampling frequency;

[0092] Error data is encapsulated and uploaded to the regional collaborative node at the edge intelligent terminal.

[0093] In this embodiment, the edge intelligent terminal encapsulates the preprocessed error data into an observation vector and uploads it, along with fragments of the dominant mode, to the corresponding regional collaborative node.

[0094] S2. Based on the frequency-varying wave velocity model, time alignment and correction operations are performed on the error data to obtain the corrected error. The traveling wave velocity data is obtained through Karrenbauer transformation, environmental parameter error calculation, Carson grounding impedance correction, and temperature and humidity environment correction. Based on the line length model considering sag and temperature, the line length data is calculated using the oblique parabolic mathematical model. According to the wave velocity data and line length data, a double-end traveling wave positioning formula is constructed for initial positioning to obtain the front-end traveling wave positioning result. According to the front-end traveling wave positioning result, multi-factor error quantitative analysis is performed through error propagation theory. The error section is quantified and processed according to the positioning uncertainty.

[0095] In this embodiment, the regional collaborative node is deployed in a substation or important aggregation point, manages no less than two edge intelligent terminals in a physical and electrical area, and is responsible for data aggregation, time alignment, initial error judgment based on spatial geometric constraints, coarse location of fault sections, and uploading the results to the cloud;

[0096] In S2, error data is received at the regional collaborative node; specifically, the regional collaborative node receives the observation vectors of all edge intelligent terminals within its jurisdiction.

[0097] Based on a time protocol, the observation vectors of the error data are time-aligned, and the local clock offset is utilized. Correct the timestamps of the error data;

[0098] Specifically, utilizing the local clock deviation reported by the edge intelligent terminal timestamps of error data Preliminary revisions made:

[0099]

[0100] For each line within the jurisdiction, the edge smart terminals at both ends are denoted as follows: a and b The arrival time of the traveling waves at both ends is denoted as . , ; Indicates the arrival time of the traveling wave.

[0101] Perform precise wave calculations; use Karrenbauer transformation to convert the series impedance and parallel admittance of the power distribution line in the frequency domain into the mode domain.

[0102] Specifically, in the frequency-varying wave speed model, the series impedance and parallel admittance of the power distribution line in the frequency domain are transformed into the modal domain state through the Karrenbauer transformation, as shown in the following equation:

[0103]

[0104]

[0105] In the formula: m = 0, 1, 2; Z abc The three-phase impedance in the frequency domain is... Y abc The three-phase admittance in the frequency domain; Z 1( oh ), Z 2( oh )and Y 1( oh ), Y 2( oh ) represent the line-mode impedance and admittance, respectively. Z 0( oh )and Y 0( oh () represent the zero-mode impedance and admittance, respectively; R m ( oh ), L m ( oh ), G m ( oh )and C m ( oh The values ​​of modulus resistance, inductance, conductance, and capacitance per unit length of the transmission line are all functions of frequency. Z m For the impedance matrix, Y m Admittance matrix.

[0106] Calculate the mode-domain impedance of the mode-domain state based on the traveling wave dispersion effect. Z cm Traveling wave transmission coefficient c m The traveling wave attenuation constant and traveling wave phase constant in the mode domain state are obtained by processing them, and the traveling wave velocity relationship function is obtained by processing the traveling wave attenuation constant and traveling wave phase constant.

[0107] Specifically, considering the traveling wave dispersion effect, its mode domain impedance Z cm and traveling wave transmission coefficient c m The calculation is as follows:

[0108]

[0109]

[0110] In the formula α m ( oh)and β m ( oh Let be the traveling wave attenuation constant and the traveling wave phase constant in the mode domain, respectively, and we have:

[0111]

[0112] The traveling wave velocity relationship function is obtained by processing the traveling wave attenuation constant and traveling wave phase constant:

[0113]

[0114] The average frequency is calculated based on the instantaneous frequency, and the line wave velocity estimation data is obtained by processing the traveling wave velocity relationship function and the average frequency. A Indicates the signal amplitude.

[0115] Specifically, substituting the instantaneous frequency reported by the edge intelligent terminals at both ends. and Calculate the average frequency The wave velocity estimate of the line is obtained. ; This represents the wave velocity corresponding to the mode domain state.

[0116] The series impedance in the modal domain is corrected by Carson grounding impedance correction, based on the Carson grounding impedance formula.

[0117] In this embodiment, Carson grounding impedance correction is performed. Taking full account of the influence of geographical factors on the traveling wave velocity, the series impedance of the distribution line in the modal domain state is corrected according to the Carson grounding impedance formula. The basic formula is as follows:

[0118]

[0119] In the formula: The earth loop impedance; c The earth propagation constant, ; r Soil resistivity; m 0 represents the permeability of free space; d This is the equivalent distance between the conductor and the ground.

[0120] The approximate relationship between wave velocity and soil resistivity can be obtained through Taylor expansion:

[0121]

[0122] In the formula: β The fitting parameters represent the maximum possible velocity decay rate;

[0123] Acquire and calculate the traveling wave velocity data based on temperature and humidity environmental data and line wave velocity estimation data;

[0124] Perform temperature and humidity environment correction;

[0125] Specifically, taking environmental factors into full consideration, the resulting traveling wave velocity... v for:

[0126]

[0127] In the formula: T 0、 H 0 represents the reference value for temperature and humidity under standard conditions; T , H Temperature and humidity during the traveling wave acquisition process; k T , k H These are correction coefficients obtained through experiments or precise electromagnetic calculations.

[0128] In the line length model that takes into account sag and temperature, the line length data is obtained by processing the parabolic equation in the parabolic model, summing, and temperature thermal expansion and contraction correction.

[0129] The length of the line is calculated using the parabolic model; the length of the line is determined using the mathematical equation of the parabolic curve.

[0130] Perform a summation of multiple spans; specifically, for one span of conductor, the span distance is... l 1. The angle between the line connecting the left and right suspension points and the horizontal direction is... f The stress per unit area at the origin is s 0, the conductor load per unit area is g The line length is approximately 1. :

[0131]

[0132] In this embodiment, thermal expansion and contraction correction is performed;

[0133] Specifically, because the overhead conductors of the distribution network themselves expand and contract with temperature, the total length of the line is corrected to... L :

[0134]

[0135] In the formula: α It is the coefficient of linear expansion of the conductor, from which the route length data is obtained;

[0136] In this embodiment, a two-end traveling wave positioning formula is constructed;

[0137] Specifically, considering the entire line within the jurisdiction of the edge smart terminals at both ends, z Given the number of towers on this line, the approximate total length of the line is:

[0138]

[0139] In this embodiment, multi-factor error quantitative analysis and error propagation equation are performed.

[0140] Construct and calculate the fault point using the double-ended traveling wave localization formula. a Side distance d Fa The front-end traveling wave positioning result is obtained;

[0141] In the process of multi-factor error quantitative analysis, an error propagation equation is constructed. Specifically, based on the double-ended traveling wave positioning formula, error analysis is performed on the front-end traveling wave positioning results to establish an error propagation model. The partial derivatives are calculated and substituted into the double-ended traveling wave positioning formula to obtain the error propagation equation.

[0142] Specifically, based on the double-ended traveling wave positioning formula, error analysis is performed on the front-end traveling wave positioning results, and an error propagation model is established:

[0143]

[0144] Calculate the partial derivatives:

[0145]

[0146] Substituting the partial derivatives into the two-ended traveling wave positioning formula, we obtain the error propagation equation as follows:

[0147]

[0148] In the formula: δL For line length error, dv For wave velocity error, d (Δ t ) represents the time measurement error, expressed as Δ. t Measurement interval;

[0149] Calculate the variance of each error; perform a synthesis operation on the error variance to obtain the traveling wave error; solve for the traveling wave error and quantify it to obtain the uncertainty; based on the uncertainty, integrate and calculate the front-end traveling wave positioning results, and process them to obtain the fault section.

[0150] In this embodiment, variance calculation and synthesis are performed to determine the confidence interval; specifically, the variance of each error is calculated:

[0151] The variance of the line length error is:

[0152]

[0153] Where: Var ( δL ) represents variance; δL sag This is the error in modeling sag.

[0154] The variance of wave velocity error is:

[0155]

[0156] The variance of the time measurement error is:

[0157]

[0158] Traveling wave error solution based on variance synthesis: Based on the traveling wave data at both ends, the variances of line length error, wave velocity error, and time measurement error are input and linearly propagated to the variance of traveling wave error through the model equation.

[0159] Due to various input error terms δL , dv , d (Δ t Since the values ​​of each group are statistically independent, their expected values ​​are all zero. .

[0160] Taking the variance of both sides of the error propagation equation, we get:

[0161]

[0162] Then, based on the properties of variance, we can obtain:

[0163]

[0164] The standard deviation of the traveling wave error is obtained by taking the square root of the variance. s x :

[0165]

[0166] In this embodiment, at a 95% confidence level, the positioning error is taken as... .

[0167] In this embodiment, the faulty section is determined by integrating and calculating the front-end traveling wave location results.

[0168]

[0169] S3. Collect the fault sections of all regional collaborative nodes, perform global optimization calculations, and obtain the distribution network traveling wave location results.

[0170] In this embodiment, the cloud-based intelligent platform is deployed on a cloud server and is responsible for the fusion of data across the entire network. The cloud-based intelligent platform gathers fault segments from no less than two regional collaborative nodes and performs global optimization calculations.

[0171] In summary, this invention eliminates the reliance of traditional technologies on single electrical quantity measurements. By employing a non-fixed processing algorithm, it can simultaneously perceive and collaboratively process multi-dimensional uncertainties such as geographical factors, environmental factors, and the equipment's own state. It outputs fault sections from multiple regional collaborative nodes, assesses the confidence level of the fault sections, and can provide accurate fault location in complex distribution network environments, thereby improving line inspection efficiency.

[0172] This invention employs a cloud-based intelligent platform to globally optimize the assessment results of collaborative nodes in various regions, alleviating the problems of high communication pressure and long positioning latency, and meeting the requirements for rapid fault isolation. This invention avoids the problems of massive uploading pressure and high communication latency caused by traditional centralized processing architectures, and can meet the real-time positioning requirements for rapid fault isolation.

[0173] This invention explicitly senses and quantifies multiple types of measurement errors at the signal acquisition front end, introduces a wavefront identification and frequency-wave velocity accurate mapping relationship model based on successive variational mode decomposition and continuous wavelet transform, and combines error propagation theory to output positioning results with confidence intervals, thus elevating positioning from qualitative diagnosis to quantitative analysis with physical interpretability and controllable accuracy.

[0174] This invention employs successive variational mode decomposition and continuous wavelet transform to accurately extract wavefronts, constructs a dynamic line length model and a frequency-varying wave velocity model that integrates environmental parameters, and quantifies the positioning uncertainty of each node based on error propagation theory. It also achieves multi-source information fusion based on confidence weights in the cloud, significantly improving the robustness and accuracy of collaborative positioning in complex environments.

[0175] This invention focuses on physical feature mining in the one-dimensional signal domain. It achieves precise fault location through accurate wavefront arrival time calibration, frequency-varying wave velocity correction, and dynamic line length model. Furthermore, it constructs a full-link analysis from sensor error perception and model parameter correction to the quantification of uncertainty in the location results, enabling the location results to have traceable physical confidence.

[0176] This invention uses a frequency-varying wave speed model and a line length model that considers sag and temperature for initial positioning. Through error propagation theory, it quantifies the positioning uncertainty. By data aggregation, time alignment, initial error judgment based on spatial geometric constraints, and coarse positioning of fault sections, it solves the problem that the fixed algorithms used in traditional technologies lack multi-dimensional real-time perception and dynamic compensation capabilities for line and environmental conditions. The model is consistent and can quantify the confidence of the results, ensuring the reliability of the positioning results in complex scenarios.

[0177] This invention solves the technical problems of poor real-time performance of fault location and isolation, and unreliable location results in complex scenarios in the prior art.

[0178] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for accurate location of traveling wave in power distribution network characterized by, The method includes: S1. Real-time acquisition of fault traveling wave signals, successive variational mode decomposition and continuous wavelet transform (CWT) of the fault traveling wave signals to obtain wavefront timestamps, and Hilbert transform to obtain analytical signals. Based on the analytical signals, the instantaneous frequency corresponding to the wavefront timestamps is obtained, and error data is synchronously sensed based on the instantaneous frequency. S2. Based on the frequency-varying wave velocity model, time alignment and correction operations are performed on the error data to obtain the corrected error. The traveling wave velocity data is obtained through Karrenbauer transformation, environmental parameter error calculation, Carson grounding impedance correction, and temperature and humidity environment correction. Based on the line length model considering sag and temperature, the line length data is calculated using a parabolic mathematical model. According to the wave velocity data and line length data, a double-end traveling wave positioning formula is constructed for initial positioning to obtain the front-end traveling wave positioning result. Based on the front-end traveling wave positioning result, multi-factor error quantitative analysis is performed through error propagation theory. The error section is quantified and processed according to the positioning uncertainty. S3. Collect the fault sections of all regional collaborative nodes, perform global optimization calculations, and obtain the distribution network traveling wave location results.

2. The method of claim 1, wherein, In S1, when a power distribution network fault occurs, the edge intelligent terminal triggers waveform recording to collect the fault traveling wave signal; Successive variational mode decomposition (SVMD) is used to decompose the traveling wave current data in the fault traveling wave signal to obtain the corresponding mode functions. IMFn ; Based on mode function IMFn Calculate the envelope entropy of each mode; n Indicates the first n One mode; Select the dominant mode with the largest envelope entropy; The dominant mode is subjected to the continuous wavelet transform (CWT), and the wavefront timestamp is detected; wherein, the wavefront timestamp is determined by detecting the modulus maxima of the wavelet coefficients. The dominant mode is subjected to the Hilbert transform to obtain the analytical signal, and the instantaneous frequency is calculated. Based on the instantaneous frequency, the local error data is sensed and statistically analyzed; The error data is encapsulated and uploaded to the regional collaborative node at the edge intelligent terminal.

3. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, The error data includes: clock synchronization error, environmental parameter error, current sensor response error, and time measurement error.

4. The method for precise positioning of traveling waves in a distribution network according to claim 3, characterized in that, The edge intelligent terminal receives the time synchronization signal sent by the regional collaborative node, and estimates the local clock deviation and standard deviation based on the time synchronization signal to obtain the clock synchronization error. Temperature is collected through a built-in environmental sensing sensor. T ,humidity H Soil resistivity ρ Real-time estimation of the environmental parameter errors, including: temperature detection error, humidity detection error and soil resistivity detection error; The frequency response characteristics of the current sensor channel are detected, and the group delay error at the instantaneous frequency is calculated based on the frequency response characteristics, which is used as the response error of the current sensor. The time estimation error in the parameter estimation theory calculation during statistical signal processing, and the estimation error of the wavefront arrival time, are used as the time measurement error.

5. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, In step S2, the error data is received at the regional coordination node; based on the time protocol, the observation vector of the error data is time-aligned, and the timestamp of the error data is corrected using the local clock offset; In the frequency-varying wave speed model, the series impedance and parallel admittance of the power distribution line in the frequency domain are transformed into the modal domain state through the Karrenbauer transformation. Based on the traveling wave dispersion effect, the mode domain impedance and traveling wave transmission coefficient of the mode domain state are calculated, and the traveling wave attenuation constant and traveling wave phase constant of the mode domain state are obtained. The traveling wave velocity relationship function is obtained based on the traveling wave attenuation constant and the traveling wave phase constant. The average frequency is calculated based on the instantaneous frequency, and the line wave speed estimation data is obtained by processing the traveling wave velocity relationship function and the average frequency. The series impedance in the modal domain state is corrected by the Carson grounding impedance correction, based on the Carson grounding impedance formula. The traveling wave velocity data is obtained and calculated based on the temperature and humidity environmental data and the line wave velocity estimation data.

6. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, In S2, In the route length model that considers sag and temperature, the route length data is obtained by processing the parabolic equation in the parabolic model, summing, and temperature expansion and contraction correction.

7. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, In S2, Construct and calculate the fault point to the location using the double-ended traveling wave localization formula. a The distance to the side is used to obtain the positioning result of the traveling wave at the front end.

8. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, In S2, In the process of the multi-factor error quantitative analysis, an error propagation equation is constructed; wherein, based on the double-ended traveling wave positioning formula, error analysis is performed on the front-end traveling wave positioning result to establish an error propagation model; partial derivatives are calculated, and the partial derivatives are substituted into the double-ended traveling wave positioning formula to obtain the error propagation equation.

9. The method for precise positioning of traveling waves in a distribution network according to claim 1, characterized in that, Calculate the variance of each error; perform a synthesis operation on the error variance to obtain the traveling wave error; solve the traveling wave error and quantify it to obtain the uncertainty; based on the uncertainty, integrate and calculate the front-end traveling wave positioning results, and process them to obtain the fault section.

10. A precise positioning system for traveling waves in a power distribution network, characterized in that, The system includes: An edge intelligent terminal is used to collect fault traveling wave signals in real time, perform successive variational mode decomposition and continuous wavelet transform (CWT) on the fault traveling wave signals, obtain wavefront timestamps, and obtain analytical signals using Hilbert transform. Based on the analytical signals, the instantaneous frequency corresponding to the wavefront timestamps is obtained, and error data is synchronously sensed based on the instantaneous frequency. The regional collaborative node is used to perform time alignment and correction operations on error data based on a frequency-varying wave velocity model to obtain the corrected error. Through Karrenbauer transformation, environmental parameter error calculation, Carson grounding impedance correction, and temperature and humidity environment correction, the traveling wave velocity data is obtained. Based on a line length model considering sag and temperature, a parabolic mathematical model is used to calculate the line length data. Based on the wave velocity data and line length data, a double-end traveling wave positioning formula is constructed for initial positioning to obtain the front-end traveling wave positioning result. Based on the front-end traveling wave positioning result, multi-factor error quantitative analysis is performed using error propagation theory. The quantified error and the position uncertainty are processed to obtain the fault section. The regional collaborative node is connected to the edge intelligent terminal. A cloud-based intelligent platform is used to collect fault sections from all regional collaborative nodes, perform global optimization calculations, and obtain the distribution network traveling wave location results. The cloud-based intelligent platform is connected to the regional collaborative nodes.

Images