Method for positioning short-circuit fault of molded case circuit breaker

Abstract

The invention discloses a molded case circuit breaker short-circuit fault positioning method, and the method comprises the steps: obtaining the current differential response data of all branches of a molded case circuit breaker control loop when a short-circuit fault occurs based on a magnetoresistive sensor, obtaining a first data set, analyzing the first data set, and extracting the wavefront features of each branch; wavefront arrival time is obtained based on wavefront features, the first data set is input into a wave velocity calibration model to obtain frequency-dependent wave velocities of all branches, and the wavefront arrival time and the frequency-dependent wave velocities are input into a short-circuit fault positioning model to obtain space coordinates of fault points; and carrying out confidence evaluation on the fault point space coordinates to obtain an evaluation result, and judging whether to send the fault point space coordinates to a terminal user based on the evaluation result. The current differential response data is obtained based on the magnetoresistive sensor, the wavefront features are extracted, the frequency-varying wave velocity calibration and path compensation optimization model is combined, the problems of insufficient signal capture, wave velocity modeling deviation and low time difference detection precision in a traditional method are solved, and the short-circuit fault positioning method has the advantage of improving the short-circuit fault positioning precision and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breaker fault detection, and in particular to a method for locating a short-circuit fault of a molded case circuit breaker. Background Art

[0002] In the field of molded case circuit breaker (MCCB) fault diagnosis, accurate spatial location of short-circuit faults has always been a technical pain point in the industry. Traditional location methods rely primarily on current amplitude comparison or calculation of the time difference of traveling waves with a fixed propagation speed. However, the internal structure of a molded case circuit breaker is highly compact, and the control circuit includes a multi-branch parallel topology of opening and closing coils, auxiliary contacts, relays, etc. Existing technologies face the following problems: (1) Insufficient signal sensitivity. During the microsecond-level establishment of short-circuit current, conventional current sensors such as Hall elements are limited by bandwidth and magnetic saturation effects, making it difficult to capture key wavefront features.

[0003] (2) Deviation in the modeling of electromagnetic wave propagation. The conductors of the control loop are not ideal transmission lines. The electromagnetic wave velocity is affected by the conductor material, insulation layer thickness, adjacent metal components, etc., resulting in significant dispersion effects. The existing method uses a fixed wave velocity, which leads to large positioning errors.

[0004] (3) The accuracy of time difference detection is limited. Due to the lack of precise time synchronization, it is impossible to analyze the nanosecond propagation time difference of electromagnetic waves between branches. Multipath reflection further disrupts the determination of wavefront arrival time. Summary of the Invention

[0005] (1) Technical issues to be resolved To solve the above problems, the present invention proposes a method for locating short-circuit faults of molded case circuit breakers, aiming to solve the problems of insufficient signal sensitivity, deviation in electromagnetic wave propagation modeling and limited time difference detection accuracy in the prior art.

[0006] (2) Technical solution A method for locating a short-circuit fault of a molded case circuit breaker according to the present invention comprises: obtaining current differential response data of all branches of the molded case circuit breaker control loop during a short circuit fault using a magnetoresistive sensor to obtain a first data set, and analyzing the first data set to extract wavefront features of each branch; Based on the wavefront characteristics, a wavefront arrival time is obtained, the first data set is input into a wave velocity calibration model to obtain a frequency-varying wave velocity of each branch, and the wavefront arrival time and the frequency-varying wave velocity are input into a short-circuit fault location model to obtain the spatial coordinates of the fault point; A confidence evaluation is performed on the spatial coordinates of the fault point to obtain an evaluation result, and based on the evaluation result, it is determined whether to send the spatial coordinates of the fault point to a terminal user.

[0007] In the present invention, when the molded case circuit breaker determines that a short circuit fault has occurred, a rectangular pulse voltage is injected into the control loop to trigger the magnetoresistive sensor to perform data sampling.

[0008] In the present invention, the pulse width of the rectangular pulse voltage is greater than or equal to 50ms and less than or equal to 100ms, the pulse amplitude is greater than or equal to 180V and less than or equal to 250V, and the duration of the rectangular pulse voltage is configured to allow the fault characteristics to be fully established and avoid equipment damage.

[0009] In the present invention, the magnetoresistive sensor network achieves full network clock alignment through a precise time synchronization unit, the time synchronization accuracy of which meets the requirements of electromagnetic wave propagation time difference detection, and the sensor sampling frequency is configured to a rate that can resolve the transient characteristics of the current wavefront.

[0010] In the present invention, the precision time synchronization unit includes an atomic clock module and an optical fiber bus. The wavefront feature extraction is achieved by processing the current differential response data using a time-frequency joint analysis algorithm. The time-frequency joint analysis algorithm decouples the signal energy distribution in the time-frequency plane and locates the energy focusing point that characterizes the fault electromagnetic disturbance. The signal spectrum characteristics are obtained by decoupling the current differential response data through a time-frequency joint analysis algorithm.

[0011] In the present invention, the first data set includes current parameters, sensor position parameters, and signal spectrum parameters. The wave velocity calibration model receives a set of conductor physical property parameters and signal spectrum characteristics as input, and outputs an electromagnetic propagation velocity function that varies with frequency. This function quantifies the influence mechanism of conductor structure and material properties on propagation velocity.

[0012] In the present invention, the short-circuit fault location model is constructed as an optimization problem based on spatial distance constraints. The optimization problem takes the wavefront arrival time difference and the sensor spatial topology as input, and solves the spatial coordinates of the fault point by minimizing the weighted residual of the observed time difference and the theoretical time difference; In the process of solving the optimization problem, a propagation path compensation term related to the device structure is introduced, which corrects the deviation of the original time difference observation value caused by the reflection path of the electromagnetic wave in the closed metal structure.

[0013] In the present invention, the confidence assessment constructs a spatial error covariance model through the statistical distribution characteristics of the positioning residual, and determines the coordinate output conditions based on the confidence index derived from the model. When the confidence does not meet the predetermined threshold, the fault re-detection mechanism is triggered.

[0014] Another computing device of the present invention comprises: at least one processor; and The memory stores instructions, and when the instructions are executed by the at least one processor, the at least one processor executes the molded case circuit breaker short circuit fault locating method as described in any one of the above technical solutions.

[0015] Another non-transitory machine-readable storage medium of the present invention stores executable instructions, which, when executed, enable a machine to execute the molded case circuit breaker short circuit fault locating method as described in any one of the above technical solutions.

[0016] (3) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention obtains current differential response data and extracts wavefront features based on a magnetoresistive sensor, and combines frequency-varying wave velocity calibration with a path compensation optimization model to solve the problems of insufficient signal capture, wave velocity modeling deviation, and low time difference detection accuracy in traditional methods. This method has the advantage of improving the accuracy and reliability of short-circuit fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of the logic framework of the method for locating short-circuit faults of molded case circuit breakers; Figure 2 This is a schematic diagram of the electromagnetic wave speed frequency variation characteristic curve of the molded case circuit breaker control circuit; Figure 3 Schematic diagram of the framework structure of the execution equipment.

[0019] 1. Processor, 2. Memory, 3. Communication interface, 4. Communication bus. DETAILED DESCRIPTION

[0020] In the existing technology, the location of short-circuit faults in molded case circuit breakers has long faced three major challenges: insufficient signal capture accuracy, deviations in the electromagnetic wave propagation model, and time difference detection errors. Traditional methods rely on current amplitude comparisons or fixed wave speed traveling wave time difference calculations, which cannot adapt to the compact internal structure and multi-branch parallel topology characteristics of molded case circuit breakers. Conventional current sensors are limited by bandwidth and magnetic saturation effects, making it difficult to capture the wavefront transient characteristics of microsecond short-circuit currents; the dispersion effect generated when electromagnetic waves propagate in non-ideal transmission lines is not effectively compensated; multipath reflection interference and insufficient time synchronization accuracy lead to the accumulation of time difference detection errors, ultimately causing the positioning results to deviate from the actual fault point.

[0021] To address these issues, the inventors first observed that traditional current sensors are unable to capture high-frequency transient signals. They then investigated the differential response characteristics of magnetoresistive sensors, discovering their advantage in directly measuring the rate of change of current. To address propagation errors caused by fixed wave velocity, they proposed a method for dynamically calibrating wave velocity based on signal spectral characteristics. To address multipath interference, they introduced a propagation path compensation mechanism, combining pre-stored information about the device structure to correct for the effects of reflected waves. Finally, they constructed a confidence assessment model and used statistical residual analysis to ensure the reliability of positioning results.

[0022] Example 1

[0023] like Figure 1-Figure 2 A method for locating a short-circuit fault of a molded case circuit breaker is shown, comprising the following steps: S100: Obtain current differential response data of all branches of a molded case circuit breaker control loop during a short circuit fault using a magnetoresistive sensor to obtain a first data set, and analyze the first data set to extract wavefront features of each branch.

[0024] S200 , obtaining the wavefront arrival time based on the wavefront characteristics, inputting the first data set into the wave velocity calibration model to obtain the frequency-variable wave velocity of each branch, and inputting the wavefront arrival time and the frequency-variable wave velocity into the short-circuit fault location model to obtain the spatial coordinates of the fault point.

[0025] S300: Perform confidence evaluation on the spatial coordinates of the fault point to obtain an evaluation result, and determine whether to send the spatial coordinates of the fault point to the terminal user based on the evaluation result.

[0026] Specifically, a magnetoresistive sensor is a device that measures the differential current signal based on the tunnel magnetoresistive effect. It can be implemented using a tunnel junction magnetoresistive element combined with a differential amplifier circuit. This device directly captures the current rate of change signal, effectively improving the sensitivity of detecting transient wavefront features. The wavefront feature refers to the abrupt change in the current differential signal during electromagnetic wave propagation. Specifically, a time-frequency joint analysis algorithm can be used to extract the focal point of the signal energy. This feature is used to determine the precise time reference of the electromagnetic wave's arrival at each sensor. The frequency-dependent wave velocity calibration model is a functional model that calculates the electromagnetic wave propagation velocity based on the physical properties of the conductor and the signal's spectral characteristics. Specifically, it can be implemented using a multi-band transmission line equation combined with a material parameter database. This model dynamically corrects the propagation velocity corresponding to different frequency components to eliminate cumulative errors caused by dispersion effects. Confidence assessment involves constructing a spatial error distribution model using statistical positioning residuals. This can be achieved using covariance matrix analysis combined with a preset threshold judgment mechanism. This assessment mechanism ensures the reliability of the output coordinates and avoids erroneous operations caused by incorrect positioning.

[0027] When a short circuit occurs in the molded case circuit breaker, the magnetoresistive sensor network synchronously collects the current differential signals of each branch. The current differential response data is processed by the time-frequency joint analysis algorithm. , the energy focusing point representing the fault electromagnetic disturbance is identified on the time-frequency plane, thereby extracting the wavefront arrival time of each branch. At the same time, the spectral characteristics of the current differential response data are input into the wave velocity calibration model, and the frequency-related propagation velocity is calculated in combination with the conductor material parameters. In the short-circuit fault location model, a weighted residual minimization optimization problem is constructed with the wavefront arrival time difference and the sensor spatial topology as constraints to solve the spatial coordinates of the fault point. The propagation path compensation term is automatically introduced during the positioning process to correct the time difference observation deviation caused by the reflection of the metal structure. Finally, the credibility of the positioning result is evaluated through the error covariance model, and the re-detection process is triggered when the residual exceeds the preset threshold.

[0028] While traditional methods use Hall sensors to detect current amplitude, this solution utilizes magnetoresistive sensors to directly measure the current differential signal, significantly improving wavefront time detection accuracy. Existing technologies use a fixed wave velocity to calculate propagation time differences. This solution dynamically corrects the propagation velocities of different frequency components using a frequency-variable wave velocity model, effectively eliminating dispersion effect errors. Conventional positioning algorithms ignore multipath reflection interference, but this solution compensates for the effects of reflection paths by pre-storing device structure information, improving positioning accuracy in complex structures. Furthermore, the introduction of a confidence assessment mechanism addresses the traditional method's inability to determine the reliability of positioning results.

[0029] This application achieves millimeter-level precision in locating internal short-circuit faults in molded case circuit breakers, addressing positioning errors caused by insufficient high-frequency signal capture, lack of compensation for dispersion effects, and multipath interference. Dynamic wave velocity calibration and propagation path compensation significantly reduce electromagnetic wave modeling errors. Based on differential signal time-frequency analysis and confidence assessment, the reliability and anti-interference capabilities of fault location are effectively improved, meeting the technical requirements of smart grids for rapid and accurate fault isolation.

[0030] Furthermore, the present application further proposes a technical solution of injecting a rectangular pulse voltage into a control loop to trigger a magnetoresistive sensor to perform data sampling when a molded case circuit breaker determines that a short circuit fault has occurred.

[0031] The rectangular pulse voltage refers to a voltage signal with a specific amplitude and duration applied to the control loop after a short-circuit fault is determined. This can be achieved by combining a DC power supply module with a high-speed switching circuit. Its function is to reconstruct the current path at the short-circuit point and stimulate detectable electromagnetic transient processes. A magnetoresistive sensor is a sensing device that measures current differential parameters based on the tunnel magnetoresistive effect. This can be achieved using a multi-axis tunnel magnetoresistive chip array. Its function is to capture the displacement current signal caused by a sudden voltage change and generate high-resolution current differential response data.

[0032] After a short-circuit fault causes a control loop to lose power, a rectangular pulse voltage is injected to restore the loop to its rated operating voltage, forcing the short-circuit point to re-establish a conductive path consistent with the moment of fault occurrence. The steep rising edge of this pulse excites the distributed parameters of the control loop to generate a transient electromagnetic wave. The magnetoresistive sensor captures the propagation characteristics of the electromagnetic wave in the branch by measuring the current differential signal. The optimized amplitude and duration of the pulse voltage ensure sufficient propagation of the electromagnetic wave in the loop while avoiding secondary damage to the equipment, providing a complete data foundation for subsequent wavefront feature extraction.

[0033] Traditional methods rely on passive detection of current signals naturally generated by faults. However, due to the loss of power after a short circuit, the signal amplitude is insufficient and cannot reproduce the actual electromagnetic environment. This solution actively injects standardized pulse voltages to physically reconstruct the fault transient process. This solves the problems of unreliable natural signals and inaccurate sensor triggering timing, while also eliminating interference from post-fault state changes such as relay contact welding.

[0034] Through the above technical solution, the present application realizes the triggering of high-precision data acquisition at the moment of short circuit of the molded case circuit breaker, ensuring that the magnetoresistive sensor can timely capture the key wavefront characteristics in the current differential response, providing stable and reliable raw data for fault location, and overcoming the positioning failure problem caused by signal delay or distortion in traditional passive detection mechanisms.

[0035] The present application further proposes that the pulse width of the rectangular pulse voltage is greater than or equal to 50ms and less than or equal to 100ms, the pulse amplitude is greater than or equal to 180V and less than or equal to 250V, and the duration of the rectangular pulse voltage is configured to allow the fault characteristics to be fully established and avoid equipment damage.

[0036] The pulse width refers to the duration of the rectangular pulse voltage. This can be achieved using a programmable pulse generator, with the pulse duration adjusted by setting a time control module. This parameter range ensures that the electromagnetic wave completes its round trip through the longest branch of the control loop and is captured by the sensor, while also preventing the coil from overheating due to prolonged power supply.

[0037] The pulse amplitude refers to the peak voltage of the rectangular pulse voltage. This can be achieved using a DC boost circuit in conjunction with a current-limiting resistor, with output stability maintained via a voltage feedback module. This parameter range ensures the electric field strength required to break through the carbonized layer at the short-circuit point while remaining below the secondary circuit insulation withstand threshold to prevent breakdown.

[0038] The duration interval refers to the dynamic range of rectangular pulse voltage application time, which can be implemented using an adaptive control algorithm based on fault current feedback. This interval balances the buildup time of the fault electromagnetic disturbance signal with the critical value of equipment heat accumulation, ensuring that signal strength meets requirements and temperature rise remains within a safe range.

[0039] Specifically, after a short-circuit fault is triggered, a rectangular pulse voltage with specific parameters is injected to stimulate the transient current in the control loop. The pulse width is set to 50-100ms, allowing the electromagnetic wave sufficient time to propagate to the farthest branch and reflect back to the detection point, while covering the ionization process at the fault point. The pulse amplitude is controlled within the range of 180-250V, which not only provides sufficient electric field strength to break through the high-resistance carbonization path to form an effective current wavefront, but also avoids exceeding the tolerance limit of the insulation material. The duration is dynamically adjusted according to the real-time monitored temperature rise rate, and the pulse output is immediately cut off after the signal acquisition is completed to prevent thermal damage.

[0040] Traditional methods typically use a fixed pulse width or a single voltage amplitude, such as a 1s pulse width or a 380V high-voltage pulse, which can lead to coil overheating or insulation degradation. This solution, however, precisely matches electromagnetic wave propagation time with material tolerances to limit pulse energy to within the device's safety threshold while ensuring signal integrity. Existing technologies fail to consider the impact of conductor distribution parameters on pulse propagation. This solution, however, derives parameter boundaries through physical constraints, resolving the conflict between missed high-resistance fault detection and device damage.

[0041] Through the above-mentioned technical solution, this application achieves the coordinated optimization of effective stimulation of short-circuit fault characteristics and equipment safety protection. The design of the pulse parameter range ensures that the electromagnetic disturbance signal generated by the fault point has sufficient amplitude and signal-to-noise ratio, enabling the sensor to accurately capture the wavefront characteristics. At the same time, the energy control mechanism suppresses the risk of coil temperature rise and insulation degradation, avoiding secondary faults caused by pulse injection. This solution significantly improves the reliability of the short-circuit positioning system, especially showing stronger adaptability when dealing with high-resistance faults and complex topology circuits.

[0042] Furthermore, the present application further proposes that the magnetoresistive sensor network achieves full network clock alignment through a precision time synchronization unit, the time synchronization accuracy of which meets the requirements of electromagnetic wave propagation time difference detection, and the sensor sampling frequency is configured to a rate that can resolve the transient characteristics of the current wavefront.

[0043] A precision time synchronization unit is a device that aligns the clock phases of each node within a sensor network. This can be achieved by combining an atomic clock module with a fiber optic bus. The atomic clock module provides a highly stable frequency reference, while the fiber optic bus transmits the clock signal and compensates for transmission delays, thereby eliminating clock deviations between network nodes. Sensor sampling frequency configuration dynamically adjusts the data acquisition rate based on the upper limit of the current wavefront signal spectrum. This is achieved using equivalent time sampling technology. By triggering acquisition multiple times and reconstructing the equivalent high-frequency sampling waveform using time synchronization accuracy, the high-frequency components of transient signals can be captured with low hardware cost.

[0044] The magnetoresistive sensor network generates a unified clock signal using an atomic clock module, which is distributed to each sensor node via a fiber optic bus. Delays in the fiber optic transmission path are corrected in real time using a temperature compensation algorithm, ensuring sub-nanosecond clock deviation across the entire network. The sensor initiates data acquisition upon the arrival of the current wavefront. Using equivalent time sampling, the signals generated by multiple pulse triggers are superimposed to reconstruct waveform data with high temporal resolution, enabling accurate extraction of the wavefront's arrival time. The coordinated design of time synchronization accuracy and sampling frequency reduces the error in detecting the time difference in electromagnetic wave propagation to below the threshold required for millimeter-level positioning.

[0045] Traditional methods rely on GPS or NTP protocols for time synchronization, but their synchronization errors exceed 100 nanoseconds, failing to meet the precision requirements for time difference detection in electromagnetic wave propagation. This solution, however, achieves sub-nanosecond synchronization through atomic clocks and fiber-optic transmission, addressing clock signal distortion in strong electromagnetic environments. Existing sampling systems typically use a fixed sampling rate, which, due to limitations in ADC chip performance, makes it difficult to capture microsecond-level wavefront transients. This solution overcomes hardware limitations by using equivalent time sampling technology, enabling high-frequency signal reconstruction at a limited sampling rate.

[0046] This application effectively eliminates the time difference calculation error caused by the asynchronous clock of the sensor network, ensuring that the detection accuracy of the time difference of electromagnetic wave propagation reaches the sub-nanosecond level. At the same time, through the dynamic sampling configuration, the transient characteristics of the current wavefront are fully captured, providing a reliable data basis for the accurate calculation of the spatial coordinates of the short-circuit fault point.

[0047] The present application further proposes a precision time synchronization unit including an atomic clock module and an optical fiber bus. The extraction of wavefront features is achieved by processing current differential response data using a time-frequency joint analysis algorithm. The time-frequency joint analysis algorithm decouples the signal energy distribution in the time-frequency plane and locates the energy focusing point characterizing the fault electromagnetic disturbance; the signal spectrum characteristics are obtained by decoupling the current differential response data using the time-frequency joint analysis algorithm.

[0048] The atomic clock module is a device that provides a high-precision time reference. It can be implemented using a rubidium atomic clock, whose phase noise is kept to an extremely low level, establishing an absolute time reference for the sensor network. The fiber optic bus is a medium used to transmit time synchronization signals. It can be implemented using a single-mode optical fiber combined with an optical transceiver module. Its transmission delay is calibrated, ensuring the comparability of timestamps across sensor nodes. The joint time-frequency analysis algorithm is a method for jointly analyzing signal characteristics in the time-frequency domain. It can be implemented using a windowed S-transform, which adaptively adjusts the time-frequency resolution surface to separate the transient wavefront and noise components in the current differential response.

[0049] Specifically, the reference clock signal output by the atomic clock module is distributed to each magnetoresistive sensor node via a fiber optic bus, ensuring that the sampling times of all nodes remain synchronized. After the current differential response data is collected, a joint time-frequency analysis algorithm decomposes the signal in the time-frequency domain and identifies the energy focus region on the time-frequency plane, which corresponds to the arrival time of the wavefront caused by the electromagnetic disturbance. The algorithm dynamically adjusts the analysis window width based on the signal frequency component, using a narrow window to improve the time resolution in the high-frequency band of the wavefront and a wide window to suppress arc noise in the low-frequency band. The authenticity of the energy focus region is verified by the phase consistency criterion, eliminating the influence of interference events such as contact bounce, and finally extracting the time-frequency feature points that represent the arrival time of the wavefront.

[0050] Traditional methods rely on GPS or cable timing systems, which have time synchronization errors exceeding 100 nanoseconds and cannot meet the requirements of millimeter-level positioning in compact structures. Conventional time-domain threshold detection or fixed-window frequency-domain analysis are susceptible to interference from electromagnetic noise within the molded case circuit breaker, leading to misjudgment of wavefront timing. This solution achieves sub-nanosecond synchronization accuracy through optical fiber transmission of atomic clock signals. Combined with an adaptive time-frequency resolution algorithm, it accurately extracts microsecond-level wavefront features in a strong noise environment, addressing the existing technology's inability to resolve tiny propagation time differences and transient signal capture distortion.

[0051] This application achieves the improvement of the time synchronization accuracy of the sensor network to the sub-nanosecond level, ensuring the accurate measurement of the time difference of electromagnetic wave propagation; the time-frequency joint analysis algorithm effectively distinguishes the real fault wavefront from the interference signal, accurately extracts the transient characteristics of the current differential response, and provides reliable input data for subsequent wave velocity calibration and fault point calculation.

[0052] Furthermore, the present application further proposes an electromagnetic wave propagation compensation method based on multi-dimensional data input and dynamic wave velocity modeling, which specifically includes: the first data set contains current parameters, sensor position parameters, and signal spectrum parameters; the wave velocity calibration model receives a set of conductor physical property parameters and signal spectrum characteristics as input, and outputs an electromagnetic propagation velocity function that changes with frequency, which quantifies the influence mechanism of conductor structure and material properties on propagation velocity.

[0053] The expression of the wave velocity calibration model is: , represents the speed of light, Indicates the equivalent dielectric constant of the cable insulation layer, represents the frequency-dependent skin depth, represents the wire radius. In the expression of skin depth, represents the conductor resistivity, represents the vacuum permeability, Values are called from the material database, Is a physical constant. In high temperature environment, the dielectric constant temperature drift model is used Compensation, including is the temperature coefficient of the insulation material. The output of the wave velocity calibration model is represents the frequency-dependent wave velocity function. This model, by quantifying the physical properties of the conductor, the signal spectrum, and environmental factors, limits the wave velocity calculation error to less than 2%, providing an accurate electromagnetic propagation benchmark for millimeter-level positioning within the compact space of molded case circuit breakers. It should be noted that the units in the above formulas are all internationally standardized units, ensuring dimensional consistency.

[0054] The set of physical property parameters of a conductor refers to a group of parameters that describe the material and structural characteristics of the conductor. Specifically, it can be implemented using conductivity, dielectric constant of the insulation layer, conductor cross-sectional area, and twist spacing data. It is used to characterize the skin effect and proximity effect of the conductor under the action of a high-frequency electromagnetic field. The signal spectrum characteristics refer to the frequency domain energy distribution characteristics of the current differential response signal. Specifically, it can be implemented by extracting the dominant frequency component through a time-frequency joint analysis algorithm, and is used to dynamically match the main frequency of electromagnetic wave propagation for different short-circuit types. The electromagnetic propagation velocity function refers to a frequency-dependent wave velocity calculation model. Specifically, it can be implemented using the bound wave analytical formula derived from Maxwell's equations. Its functional form includes dielectric loss tangent and grain boundary scattering correction terms, which are used to eliminate the wave velocity offset caused by polarization relaxation of insulating materials in high-temperature environments. Reference Figure 2 As shown in the figure, the solid black line represents the basic velocity curve; the dark gray long dashed line clearly shows the temperature effect; the medium gray dotted line is clearly distinguished from the velocity curve; and the light gray double dashed line is used as the reference baseline. The fixed velocity assumption will lead to significant positioning errors. At a frequency of 10 MHz, the fixed velocity model (1.5×10 8 m / s) and the actual wave speed (1.53×10 8 m / s) will produce an error of approximately 13 cm over a distance of 10 meters. This patented frequency-variable wave velocity calibration model controls positioning errors to the millimeter level, meeting the requirements for precise positioning of molded case circuit breaker faults.

[0055] Specifically, the wave velocity calibration model constructs a dynamic propagation velocity function by integrating the inherent properties of the conductor with the real-time spectrum characteristics. The current parameters provide the time-domain amplitude information of the fault current, the sensor position parameters establish the spatial topological relationship, and the signal spectrum parameters identify the dominant frequency components of electromagnetic wave propagation. The set of conductor physical property parameters is input into the basic physical model of the wave velocity function to quantify the nonlinear effects of the conductor material skin depth and twisted structure on the wave velocity. The signal spectrum characteristics trigger the frequency selection mechanism. For metallic short circuits, the excitation pulse fundamental frequency is used to calculate the wave velocity, while for high-resistance short circuits, the response signal main frequency is used for dynamic adaptation. The electromagnetic propagation velocity function thus generated continuously corrects the wave velocity value in the frequency domain dimension, so that the propagation delay differences corresponding to different frequency components are accurately compensated, thereby eliminating the positioning deviation of the traditional fixed wave velocity model in complex wiring scenarios.

[0056] Traditional methods use only a single fixed frequency to calculate electromagnetic wave velocity, ignoring the impact of conductor material dispersion and fault type on the main propagation frequency. This approach, by incorporating a set of conductor physical property parameters and a dynamic spectrum selection mechanism, establishes a frequency-adaptive wave velocity calculation model that accurately reflects the frequency-dependent physical laws governing electromagnetic wave propagation velocity in the control circuit of a molded case circuit breaker. Furthermore, by incorporating polarization relaxation and proximity effects into the wave velocity function, it effectively addresses the issue of inaccurate wave velocity predictions caused by dielectric constant drift under high-temperature conditions.

[0057] Through the above technical solution, this application achieves dynamic and accurate modeling of electromagnetic wave propagation velocity, significantly reducing fault location errors caused by dispersion effects. By matching the characteristic frequencies of different short-circuit types, the deviation in wave velocity calculation caused by arc nonlinearity in high-resistance faults is eliminated. By quantifying the influence of conductor material and structural parameters, the reliability of wave velocity prediction in complex wiring environments is improved, providing basic support for millimeter-level positioning of molded case circuit breaker short-circuit faults.

[0058] This application further proposes that the short-circuit fault location model is constructed as an optimization problem based on spatial distance constraints. The optimization problem takes the wavefront arrival time difference and the sensor spatial topology as input, and solves the spatial coordinates of the fault point by minimizing the weighted residual between the observed time difference and the theoretical time difference; in the process of solving the optimization problem, a propagation path compensation term related to the equipment structure is introduced. This compensation term corrects the deviation of the original time difference observation value caused by the reflection path of the electromagnetic wave in the closed metal structure.

[0059] Assume the fault point location is ,sensor Location is . Establish the time difference equations: ,in is the earliest arriving sensor index, is the measurement error, Indicates the fault point to the sensor The Euclidean distance is , Indicates the distance from the fault point to the sensor where the first pulse is detected The distance is , Indicates sensor and The wavefront arrival time difference, represents the propagation velocity of the current pulse in the conductor, which is determined by the velocity calibration model. The location of the fault point is converted into an optimization problem by weighted least squares estimation: , is the sensor weight and , is the standard deviation of the time difference measurement, is the candidate value of the fault point coordinate to be solved. Weight Determined by the signal quality, , where the signal-to-noise ratio , ensuring that high signal-to-noise ratio sensors dominate positioning.

[0060] Optimization based on spatial distance constraints involves transforming the relationship between the wavefront arrival time difference collected by the sensors and the spatial distribution of the wires within the device into a mathematical optimization model. Specifically, a nonlinear least squares method is used to construct an objective function, and the coordinates of the fault point are obtained by minimizing this function. This method incorporates physical layout information into the calculation process, eliminating positioning errors caused by uneven sensor distribution.

[0061] The propagation path compensation term is a parameter that quantifies the additional propagation delay caused by electromagnetic waves reflecting inside the metal casing. Specifically, the geometric diffraction theory is used to model the reflection path length and the equivalent wave velocity is calculated based on the electromagnetic properties of the material at the reflection point. This compensation term eliminates the multipath effect that interferes with the original time difference data, making the electromagnetic wave propagation model more realistic.

[0062] After the wavefront arrival time difference and sensor spatial topology are input into the optimization model, the fault point coordinates are calculated using a weighted residual minimization method. During this process, the observed time difference of each sensor is assigned a weight coefficient related to the signal-to-noise ratio, with data with high signal-to-noise ratios dominating the optimization process. Simultaneously, a geometric parameter database of the metal casing reflection path is pre-established based on the 3D structural model of the molded case circuit breaker. The additional time delay compensation caused by the reflection path is superimposed when calculating the theoretical time difference. The fault point coordinates are iteratively adjusted to minimize the weighted residual between the compensated theoretical time difference and the observed time difference, ultimately outputting the corrected spatial location of the fault point.

[0063] Traditional location methods use a fixed wave velocity model and fail to account for reflections from metal structures, resulting in calculations that deviate from the true fault location. This solution, through a dynamic wave velocity compensation mechanism and reflection path modeling, performs real-time corrections for electromagnetic wave propagation speed and path loss, effectively resolving the problem of location deviation caused by multipath interference in enclosed spaces.

[0064] Through the above technical solution, this application can accurately identify the distortion of electromagnetic wave propagation paths caused by reflections from the metal casing inside molded case circuit breakers, dynamically correct the systematic errors in time-of-day observations, and thus achieve millimeter-level accuracy in short-circuit fault location in complex electromagnetic environments. This method significantly improves the reliability of fault point coordinate calculation, maintaining stable output, especially under abnormal operating conditions such as contact welding or coil deformation.

[0065] This application further proposes a confidence assessment method to construct a spatial error covariance model through the statistical distribution characteristics of the positioning residual, and decide the coordinate output conditions based on the credibility index derived from the model. When the credibility does not meet the predetermined threshold, the fault re-detection mechanism is triggered.

[0066] The spatial error covariance matrix is defined as ,in is the Jacobian matrix of the residual coordinates, , , weight Sensor signal-to-noise ratio Calculation. Credibility Prefabrication ,in Corresponding to 99% confidence level.

[0067] The statistical distribution characteristics of the positioning residual refer to the probability distribution law of the deviation between the calculated value and the actual value of the fault point coordinate in different spatial dimensions. Specifically, this can be achieved by fitting the historical positioning data using a Gaussian mixture model to characterize the error characteristics caused by differences in the electromagnetic wave propagation path.

[0068] The spatial error covariance model refers to a mathematical matrix that describes the distribution characteristics of positioning error in three-dimensional space. It can be constructed through the inverse operation of the Jacobian matrix and the weight matrix, and is used to quantify the impact of sensor geometric layout and time measurement accuracy on positioning results.

[0069] The credibility index refers to a quantitative parameter that measures the reliability of the fault point coordinates. It can be calculated by comparing the trace of the covariance matrix with the dynamic threshold, and is used to determine whether the current positioning result meets the requirements of the engineering application.

[0070] The fault re-detection mechanism refers to a secondary detection process that is automatically initiated when the credibility is insufficient. This can be achieved by adjusting pulse voltage parameters, switching time synchronization mode, or activating the sensor self-test function. It is used to optimize signal acquisition conditions to improve subsequent positioning accuracy.

[0071] Specifically, the confidence assessment process first calculates the covariance matrix based on the residual of the wavefront arrival time of each sensor. This matrix reflects the coupling effect of the difference in the electromagnetic wave propagation path and the time measurement error. By decomposing the eigenvalues of the covariance matrix, the distribution range of the positioning error in the direction of each main axis in space is determined. The credibility index dynamically adjusts the threshold according to the current working conditions, such as automatically relaxing the error tolerance in a strong electromagnetic interference environment. When the indicator is lower than the threshold, the system automatically triggers the pulse amplitude enhancement and sensor self-test program to optimize the positioning conditions by enhancing the signal-to-noise ratio or eliminating abnormal nodes, and then re-executes the data acquisition and calculation process to form a closed-loop verification mechanism. When the credibility is insufficient, the following are executed in sequence: 1. Increase the pulse amplitude by 50V and re-inject; 2. Switch to the backup time synchronization channel; 3. Based on Eliminate abnormal sensor thickness and solve again.

[0072] Compared with existing technologies, traditional methods use fixed thresholds to determine the validity of positioning results, which cannot adapt to the complex electromagnetic environment changes within molded case circuit breakers. This leads to a significant increase in false positives when high-resistance short circuits or partial sensor failures occur. Existing solutions lack a closed-loop re-detection mechanism, requiring manual intervention to adjust parameters after positioning failures, making it difficult to meet the requirements of rapid fault recovery. This solution effectively overcomes the limitations of fixed thresholds through dynamic error modeling and an adaptive decision-making mechanism, while also enabling automated optimization of detection conditions and significantly improving system reliability under harsh operating conditions.

[0073] This application addresses the problem of misjudgment caused by the unmodeled statistical characteristics of positioning residuals. It accurately quantifies positioning uncertainty through a spatial error covariance model, preventing the transmission of erroneous coordinates to the maintenance terminal. A dynamic threshold decision mechanism adjusts the judgment criteria based on real-time operating conditions to prevent frequent false triggering due to environmental interference. The closed-loop re-detection process automatically optimizes acquisition parameters to ensure that signal quality meets accuracy requirements during secondary positioning, thereby maintaining system robustness in complex electromagnetic environments.

[0074] Example 2

[0075] An embodiment of the present invention provides a computer-readable storage medium.

[0076] The computer-readable storage medium provided in the embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for locating a short-circuit fault of a molded case circuit breaker can be implemented.

[0077] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0078] For an introduction to the computer-readable storage medium provided in an embodiment of the present invention, please refer to the above method embodiment, and the present invention will not elaborate on it here.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0080] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0081] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0082] Example 3

[0083] An embodiment of the present invention provides an execution device.

[0084] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an execution device provided by the present invention, which may include: memory for storing computer programs; The processor is configured to implement the steps of any one of the above-mentioned methods for locating a short-circuit fault of a molded case circuit breaker when executing a computer program.

[0085] like Figure 3 FIG2 is a schematic diagram of the structure of the execution device, which may include a processor 1, a memory 2, a communication interface 3, and a communication bus 4. The processor 1, the memory 2, and the communication interface 3 communicate with each other via the communication bus 4.

[0086] In the embodiment of the present invention, the processor 1 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices.

[0087] The processor 1 may call a program stored in the memory 2. Specifically, the processor 1 may execute the operations in the embodiment of the push button switch fault detection method.

[0088] The memory 2 is used to store one or more programs. The programs may include program codes, and the program codes include computer operating instructions. In the embodiment of the present invention, the memory 2 stores at least a program for implementing the following functions: obtaining current differential response data of all branches of the molded case circuit breaker control loop during a short circuit fault using a magnetoresistive sensor to obtain a first data set, and analyzing the first data set to extract wavefront features of each branch; Based on the wavefront characteristics, a wavefront arrival time is obtained, the first data set is input into a wave velocity calibration model to obtain a frequency-varying wave velocity of each branch, and the wavefront arrival time and the frequency-varying wave velocity are input into a short-circuit fault location model to obtain the spatial coordinates of the fault point; A confidence evaluation is performed on the spatial coordinates of the fault point to obtain an evaluation result, and based on the evaluation result, it is determined whether to send the spatial coordinates of the fault point to a terminal user.

[0089] In one possible implementation, the memory 2 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function, etc.; the data storage area may store data created during use.

[0090] In addition, the memory 2 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0091] The communication interface 3 may be an interface of a communication module, used for connecting to other devices or systems.

[0092] Of course, it needs to be explained that Figure 3 The structure shown does not constitute a limitation on the execution device in the embodiment of the present invention. In actual applications, the execution device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0093] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Any modifications and improvements made to the technical solution of the present invention by a person of ordinary skill in the art without departing from the design concept of the present invention shall fall within the scope of protection of the present invention. The technical content for which protection is sought in the present invention is fully set forth in the claims.

Claims

1. A method for locating a short-circuit fault of a molded case circuit breaker, characterized in that: include: obtaining current differential response data of all branches of the molded case circuit breaker control loop during a short circuit fault using a magnetoresistive sensor to obtain a first data set, and analyzing the first data set to extract wavefront features of each branch; Based on the wavefront characteristics, a wavefront arrival time is obtained, the first data set is input into a wave velocity calibration model to obtain a frequency-varying wave velocity of each branch, and the wavefront arrival time and the frequency-varying wave velocity are input into a short-circuit fault location model to obtain the spatial coordinates of the fault point; A confidence evaluation is performed on the spatial coordinates of the fault point to obtain an evaluation result, and based on the evaluation result, it is determined whether to send the spatial coordinates of the fault point to a terminal user.

2. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 1, characterized in that: When the molded case circuit breaker determines that a short circuit fault has occurred, a rectangular pulse voltage is injected into the control loop to trigger the magnetoresistive sensor to perform data sampling.

3. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 2, characterized in that: The pulse width of the rectangular pulse voltage is greater than or equal to 50ms and less than or equal to 100ms, the pulse amplitude is greater than or equal to 180V and less than or equal to 250V, and the duration of the rectangular pulse voltage is configured to allow the fault characteristics to be fully established and avoid equipment damage.

4. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 2 or 3, characterized in that: The magnetoresistive sensor network achieves full network clock alignment through a precise time synchronization unit. The time synchronization accuracy meets the requirements of electromagnetic wave propagation time difference detection, and the sensor sampling frequency is configured to a rate that can resolve the transient characteristics of the current wavefront.

5. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 4, characterized in that: The precision time synchronization unit includes an atomic clock module and an optical fiber bus. The wavefront feature is extracted by processing the current differential response data using a time-frequency joint analysis algorithm. The time-frequency joint analysis algorithm decouples the signal energy distribution in the time-frequency plane and locates the energy focus point that characterizes the fault electromagnetic disturbance. The signal spectrum characteristics are obtained by decoupling the current differential response data through a time-frequency joint analysis algorithm.

6. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 5, characterized in that: The first data set includes current parameters, sensor position parameters, and signal spectrum parameters. The wave velocity calibration model receives a set of conductor physical property parameters and signal spectrum characteristics as input, and outputs an electromagnetic propagation velocity function that changes with frequency. This function quantifies the influence mechanism of conductor structure and material properties on propagation velocity.

7. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 6, wherein: The short-circuit fault location model is constructed as an optimization problem based on spatial distance constraints. The optimization problem takes the wavefront arrival time difference and the sensor spatial topology as input, and solves the spatial coordinates of the fault point by minimizing the weighted residual between the observed time difference and the theoretical time difference. In the process of solving the optimization problem, a propagation path compensation term related to the device structure is introduced, which corrects the deviation of the original time difference observation value caused by the reflection path of the electromagnetic wave in the closed metal structure.

8. The method for locating a short-circuit fault of a molded case circuit breaker according to claim 7, characterized in that: The confidence assessment constructs a spatial error covariance model through the statistical distribution characteristics of the positioning residual, and determines the coordinate output conditions based on the confidence index derived from the model. When the confidence does not meet the predetermined threshold, the fault re-detection mechanism is triggered.

9. A computing device, characterized in that include: at least one processor; and a memory storing instructions, which, when executed by the at least one processor, enable the at least one processor to execute the molded case circuit breaker short circuit fault locating method according to any one of claims 1 to 8.

10. A non-transitory machine-readable storage medium, characterized in that It stores executable instructions, which, when executed, enable the machine to execute the molded case circuit breaker short circuit fault locating method according to any one of claims 1 to 8.

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