Fault self-diagnosis and open circuit cooperative control method for intelligent power distribution switch equipment

Abstract

An intelligent power distribution switchgear fault self-diagnosis and circuit break cooperative control method comprises the following steps: acquiring mechanism parameters corresponding to a mechanical pendulum and an electrical loop, and calling a Swing-Load hybrid model to initialize the mechanism parameters so as to output initialization characteristics; and building a simulation environment based on the initialization features to generate an inertial fallback template and a low-resistance short-circuit template of the mechanical pendulum. When an equipment fault occurs, a voltage fall-back waveform of a microgrid node is intercepted, and a feature vector is extracted based on the voltage fall-back waveform. And performing similarity comparison on the feature vector with an inertial fallback template and a low-resistance short-circuit template, so as to determine a fault type corresponding to the voltage fallback waveform according to the similarity. And based on the fault type corresponding to the voltage drop waveform, combining the current current distribution to determine the branch node position where the fault occurs. And sending an open circuit control instruction according to a preset priority sequence, and performing fault isolation on the branch node position where the fault occurs in response to the open circuit control instruction.

Description

Technical Field

[0001] This invention belongs to the field of circuit fault diagnosis technology, and more specifically, relates to a method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear. Background Technology

[0002] Currently, existing fault detection technologies for distribution switchgear coupled with multiple microgrids primarily rely on internal sensors within the switchgear to collect real-time data on three-phase currents and neutral point displacement voltages in microgrid branches, establishing zero-sequence voltage and current waveform samples within a sliding time window (e.g., 100 milliseconds). Subsequently, envelope detection is performed on these zero-sequence signals, analyzing statistical indicators such as maximum value, peak occurrence frequency, and peak duration. These statistical values ​​are then compared with empirical characteristic templates for high-resistance grounding. A Fast Fourier Transform (FFT) is performed on the suspicious waveform window to extract the signal's energy distribution in the high-frequency band (e.g., 200Hz–2kHz). Combined with spectral peak shift changes, the presence of intermittent resonant interference is determined. Simultaneously, the distribution network topology information and switchgear location parameters are used, along with the sampling synchronization timing of the affected area, to comprehensively determine if multiple branches exhibit similar grounding characteristics simultaneously. If only a single branch consistently exhibits high-resistance characteristics, it is identified as a grounding point. Finally, when a branch is identified as having a high-resistance ground fault, the system generates a circuit breaker coordination strategy: first, the end load switch of the target branch is tripped; if the voltage recovery is not significant, the action is extended to the sectional circuit breaker or tie circuit breaker. At the same time, the microgrid controller issues an islanding switching command to ensure uninterrupted power supply after the faulty section is isolated.

[0003] However, in isolated microgrids with flywheel energy storage or motor-type inertial loads, when a high-resistance ground fault occurs, the traction characteristics of the inertial load cause the branch node voltage to drop rapidly due to the lack of main grid support. This drop trend is extremely similar to that of a low-resistance short circuit. In existing technologies, high-resistance ground faults are primarily identified based on zero-sequence current fluctuations and spectral energy ratios. However, in these microgrids, the inertial load startup or impulse response can "suppress" the zero-sequence voltage characteristics and is accompanied by non-periodic oscillations, causing the system to misinterpret the high-resistance ground fault as a transient load impulse, thus bypassing fault isolation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to resolve the aforementioned deficiencies and propose a method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention discloses a method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear, the method comprising: Obtain the mechanistic parameters corresponding to the mechanical pendulum and electrical circuit, and call the Swing-Load hybrid model to initialize the mechanistic parameters, so as to output the oscillation frequency range, circuit damping ratio and inductor resistance characteristic values ​​of the mechanical pendulum; Based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values, a simulation environment is built to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum. When a device failure occurs, the voltage drop waveform of the microgrid node is captured, and the Swing-Load hybrid model is called to extract feature vectors based on the voltage drop waveform. The feature vector is compared with the inertial fallback template and the low-resistance short-circuit template respectively to determine the fault type corresponding to the voltage fallback waveform based on the similarity. Based on the fault type corresponding to the voltage drop waveform and the current current distribution, the location of the branch node where the fault occurred is determined. The circuit breaker control command is sent in a preset priority order, and the fault is isolated at the branch node where the fault occurred in response to the circuit breaker control command.

[0007] Furthermore, the step of obtaining the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and calling the Swing-Load hybrid model to initialize the mechanistic parameters to output the oscillation frequency range, circuit damping ratio, and inductor-resistance characteristic values ​​of the mechanical pendulum, includes: Voltage and current sensors are installed at the voltage sampling terminal of the power distribution switchgear and at the current transformers of each branch. The gain correction coefficient is calculated by a calibrator based on the measured values ​​of voltage and current of each branch and the standard values ​​to obtain the calibrated voltage and current. Under the no-load condition of the microgrid, a step voltage test pulse is applied to record the current response curve, and the circuit time constant is calculated based on the current response curve. The circuit time constant is the ratio between the equivalent inductance and the equivalent resistance.

[0008] Furthermore, the step of obtaining the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and calling the Swing-Load hybrid model to initialize the mechanistic parameters to output the oscillation frequency range, circuit damping ratio, and inductor-resistance characteristic values ​​of the mechanical pendulum, also includes: When the microgrid is unloaded and excitation is on, a short-time excitation current pulse or speed disturbance is applied to record the speed oscillation curve, and the spectrum analysis of the speed oscillation curve is performed to identify the main oscillation frequency and damping ratio. The equivalent inductance, equivalent resistance, main oscillation frequency, and damping ratio are normalized according to a preset range, and the normalization result is combined with the gain correction coefficient and synchronization timing to generate a parameter vector. The parameter vector includes the oscillation frequency range of the mechanical pendulum, the circuit damping ratio, and the characteristic values ​​of the inductor resistance.

[0009] Furthermore, based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values, a simulation environment is built to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum, including: Deploy real-time simulation software inside an industrial control computer or DSP, and write the parameter vector into the model initialization interface of the real-time simulation software to generate a simulation model; In the simulation model, a high-resistance grounding condition is set up, and the traction effect of the mechanical pendulum is triggered to simulate the main oscillation frequency and damping ratio, while recording the voltage drop waveform of the simulation feedback. Based on the voltage drop waveform, the simplified response formula is called to calculate the waveform characteristics, and the initialization template is smoothed to extract multiple feature points and generate the inertial drop template.

[0010] Furthermore, the step of building a simulation environment based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values ​​to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum also includes: In the simulation model, a preset short-circuit impedance is set at the target node to run the simulation, and the voltage drop waveform is recorded to characterize the voltage drop waveform and obtain the low-resistance short-circuit template. Index tags are assigned to the inertial fallback template and the low-resistance short-circuit template to store the inertial fallback template, the low-resistance short-circuit template and the corresponding features in the embedded database.

[0011] Furthermore, the step of capturing the voltage drop waveform of the microgrid node when a device failure occurs, and then calling the Swing-Load hybrid model to extract feature vectors based on the voltage drop waveform, includes: When the change in node voltage exceeds a set threshold, the voltage data within the corresponding time period before and after the voltage change is written into a circular buffer through a waveform buffer, and the voltage data in the circular buffer is clock-synchronized and corrected to obtain the fault voltage fall-off waveform. The fault voltage drop waveform is linearly scaled by a gain correction coefficient, and the sampling time axis of the voltage data is finely adjusted by a timing deviation value. At the same time, the fault voltage drop waveform is normalized based on the average voltage value within the first time period before the voltage change. The initial fall slope and initial drop amount are determined based on the normalized fault voltage fall waveform, and the peak value of the fault voltage fall waveform is detected to output the oscillation period and amplitude attenuation ratio.

[0012] Furthermore, the step of comparing the feature vector with the inertial fallback template and the low-resistance short-circuit template respectively, to determine the fault type corresponding to the voltage fallback waveform based on the similarity, includes: Calculate the cosine similarity between the feature vector and the inertial fallback template and the low-resistance short-circuit template to construct similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template respectively; Calculate the maximum similarity in the similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template respectively to identify inertial artifacts and low-resistance short circuits.

[0013] Furthermore, determining the location of the branch node where the fault occurred based on the fault type corresponding to the voltage drop waveform and the current current distribution includes: Based on the maximum similarity between the inertial artifact and the low-resistance short circuit, the difference between the maximum similarity between the inertial artifact and the low-resistance short circuit is calculated, and the fault type is output according to the preset judgment threshold. When the fault type is determined to be a low-resistance short circuit, the current distribution vectors corresponding to all branches of the power distribution switchgear are collected, and the branch number corresponding to the low-resistance short circuit is located by combining the branch topology information table, and a fault diagnosis report is output at the same time.

[0014] The second aspect of this invention discloses a smart power distribution switchgear fault self-diagnosis and circuit breaking coordinated control device, the device comprising: The parameter initialization module is used to obtain the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and call the Swing-Load hybrid model to initialize the mechanistic parameters, so as to output the oscillation frequency range, circuit damping ratio and inductor resistance characteristic value of the mechanical pendulum. The fault template generation module is used to build a simulation environment based on the oscillation frequency range, circuit damping ratio and inductor resistance characteristic value to generate the inertial fall template and low resistance short circuit template of the mechanical pendulum. The fault feature extraction module is used to capture the voltage drop waveform of the microgrid node when a device fault occurs, and call the Swing-Load hybrid model to extract feature vectors based on the voltage drop waveform. The fault type determination module is used to compare the feature vector with the inertial fallback template and the low-resistance short-circuit template respectively, so as to determine the fault type corresponding to the voltage fallback waveform based on the similarity. The fault location determination module is used to determine the location of the branch node where the fault occurred based on the fault type corresponding to the voltage drop waveform and the current current distribution. The fault isolation module is used to send circuit breaker control commands in a preset priority order, and to isolate the branch node where the fault occurs in response to the circuit breaker control commands.

[0015] A third aspect of the present invention discloses a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0017] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages: (1) This invention collects the mechanistic parameters of the mechanical pendulum (flywheel or motor-type inertia) and the electrical loop (RLE) based on the Swing-Load hybrid model during the initial operation phase of the microgrid. Based on the mechanistic parameters of the mechanical pendulum and the electrical loop, two fallback characteristic templates are simulated and generated respectively: the inertial fallback template and the low-resistance short-circuit template, to reflect the slow fluctuations caused by the mechanical pendulum and the rapid fallback of pure electrical circuits. In this way, when a high-resistance grounding or suspected fault occurs, the intelligent device can immediately capture the voltage fallback waveform of the relevant nodes of the microgrid and extract representative features such as fallback slope, oscillation period and attenuation trend based on the Swing-Load hybrid model mechanism. Through comprehensive analysis of each feature point, it provides a guarantee for subsequent fault type determination and fault isolation.

[0018] (2) Based on the determination of fault type and the comprehensive analysis of various feature points during the fault, this invention compares the real-time extracted feature vectors with the inertial fallback template and the low-resistance short-circuit template for similarity. Based on the similarity score, those higher than the threshold are determined to be the corresponding type: if the similarity of "inertial fallback" is higher, it is identified as an artifact; if the similarity of "low-resistance short circuit" is higher, it is identified as a real fault. At the same time, the specific branch node position is determined by combining the current distribution, which strengthens the distinction between artifacts and faults. In this way, when a high-resistance grounding occurs, it can effectively avoid the influence of the inertial load start-up or impact response "suppressing" the zero-sequence voltage characteristics and accompanied by non-periodic oscillations, thereby preventing the system from misjudging the high-resistance grounding as a transient load impact and skipping the fault isolation, improving the accuracy of fault type and fault location determination, and further providing a guarantee for fault isolation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention. Figure 2 This is a schematic diagram of the structure of the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control device provided by the present invention. Detailed Implementation

[0020] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0021] like Figure 1 As shown, in one embodiment, a method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear includes the following steps: Step S110: Obtain the mechanism parameters corresponding to the mechanical pendulum and the electrical circuit, and call the Swing-Load hybrid model to initialize the mechanism parameters, so as to output the oscillation frequency range, circuit damping ratio and inductor resistance characteristic values ​​of the mechanical pendulum.

[0022] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes the following steps in step S110: Step S111: Voltage sensors and current sensors are installed at the voltage sampling terminal of the power distribution switchgear and at the current transformers of each branch. The gain correction coefficient is calculated by the calibrator based on the measured values ​​of each voltage and current and the standard values ​​to obtain the calibrated voltage and current.

[0023] Step S112: Under the no-load condition of the microgrid, a step voltage test pulse is applied to record the current response curve, and the circuit time constant is calculated based on the current response curve. The circuit time constant is the ratio between the equivalent inductance and the equivalent resistance.

[0024] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention further includes the following steps in step S110: Step S113: When the microgrid is unloaded and the excitation is on, apply a short-time excitation current pulse or speed disturbance to record the speed oscillation curve, and perform spectrum analysis on the speed oscillation curve to identify the main oscillation frequency and damping ratio.

[0025] Step S114: Normalize the equivalent inductance, equivalent resistance, main oscillation frequency and damping ratio according to the preset range, and merge the normalization result with the gain correction coefficient and synchronization timing to generate a parameter vector.

[0026] The parameter vector includes the oscillation frequency range of the mechanical pendulum, the circuit damping ratio, and the characteristic values ​​of the inductor resistance.

[0027] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention addresses the high-resistance grounding fault + intermittent resonance in the coupled main grid and multi-microgrid systems of the prior art, as well as the node voltage drop artifact caused by inertial loads in asynchronous microgrids. Specifically, the main distribution network is connected to two microgrids, both of which can operate in islanded mode; insulation failure in a device in a microgrid branch leads to high-resistance grounding; during system operation, irregular resonance amplification occurs, affecting signal sampling.

[0028] In this embodiment, steps 1 through 6 are included: Step 1: Obtain and initialize model parameters.

[0029] During the initial operation phase of the microgrid, the mechanical pendulum (flywheel or motor-type inertia) and electrical loop (RLE) mechanism parameters are collected based on the Swing-Load hybrid model to provide dynamic features for subsequent fault identification.

[0030] Includes the following sub-steps: Sub-step 1.1, sensor device deployment and benchmark calibration.

[0031] Specifically, high-precision voltage and current sensors are installed at the voltage sampling terminals of the power distribution switchgear and at the current transformers of each branch. A known voltage / current signal is applied to each sensor using a standard calibrator, and the ratio of the measured output to the standard value is calculated to obtain the gain correction coefficient. Simultaneously, GPS synchronization or a network clock is used to timestamp and calibrate the sampling modules of each sensor, recording the time deviation of each channel. This sub-step ensures the accuracy of subsequent data acquisition and the consistency of timing across channels, providing an accurate foundation for identifying mechanistic parameters.

[0032] Sub-step 1.2, Measurement of RLE parameters of electrical circuit.

[0033] Specifically, under the no-load condition of the microgrid, a step voltage test pulse is applied, the current response curve is recorded, and then the circuit time constant is calculated based on the current response curve. The circuit time constant is the ratio between the equivalent inductance and the equivalent resistance, expressed as: ; In the formula, The time constant for the current response to decay to steady state, in seconds; The equivalent inductance, expressed in Henry, can be inferred from the current rise slope. This is the equivalent resistance value in ohms, calculated using the ratio of steady-state voltage to current.

[0034] Finally, the step test was repeated multiple times, the average value was taken and the standard deviation was calculated to obtain stable R and L values, providing a quantitative basis for the electrical oscillation and attenuation behavior in the hybrid model.

[0035] Sub-step 1.3, Swing-Load characteristic identification.

[0036] Specifically, under microgrid no-load and excitation-on conditions, short-duration excitation current pulses or speed disturbances are applied, and the speed sensor sampling curves are recorded. Spectral analysis is performed on the speed oscillation curves to identify the dominant oscillation frequency and damping ratio. The dominant oscillation frequency ranges from ±10% of the dominant oscillation frequency, and the damping ratio ranges from 0.05 to 0.2, to cover different mechanical pendulum load conditions. Finally, repeated tests are conducted in two typical scenarios: flywheel energy storage and motor-type loads, forming two sets of mechanical pendulum characteristic parameters. These parameters are used to clarify the oscillation characteristics generated by the mechanical inertial load under high-resistance grounding, and to distinguish artifacts from electrical waveforms.

[0037] Sub-step 1.4: Data normalization and model initialization.

[0038] Specifically, the mechanistic parameters (equivalent inductance, equivalent resistance, main oscillation frequency, and damping ratio) obtained in the preceding steps are normalized according to a preset interval to generate parameter values ​​within the range [0,1]. The normalization result is then combined with the gain correction coefficient and timing correction data to form the corresponding parameter vector. Afterward, the parameter vector is written to the local storage of the power distribution switchgear, and the model computation engine is initialized. This step unifies the format of the sensing and identification results, constructing an initial parameter set that can be directly used for Swing-Load hybrid model simulation and fault comparison.

[0039] Step S120: Based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values, a simulation environment is built to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum.

[0040] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes the following steps in step S120: Step S121: Deploy real-time simulation software inside the industrial control computer or DSP, and write the parameter vector into the model initialization interface of the real-time simulation software to generate a simulation model.

[0041] Step S122: Set up a high-resistance grounding condition in the simulation model and trigger the traction effect of the mechanical pendulum to simulate the main oscillation frequency and damping ratio, while recording the voltage drop waveform of the simulation feedback.

[0042] Step S123: Based on the voltage drop waveform, the simplified response formula is called to calculate the waveform characteristics, and the initialization template is smoothed to extract multiple feature points and generate an inertial drop template.

[0043] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention further includes the following steps in step S120: Step S124: Set a preset short-circuit impedance at the target node in the simulation model and run the simulation. Record the voltage drop waveform to characterize the voltage drop waveform and obtain a low-resistance short-circuit template.

[0044] Step S125: Assign index labels to the inertial fallback template and the low-resistance short-circuit template to store the inertial fallback template, the low-resistance short-circuit template and the corresponding features to the embedded database.

[0045] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes step 2, feature template construction. Based on the mechanical pendulum and electrical circuit mechanism parameters, two types of fall-off feature templates are simulated and generated respectively—the "inertial fall-off template" and the "low-resistance short-circuit template". The inertial fall-off template reflects the gradual fluctuations caused by the mechanical pendulum, while the short-circuit template reflects the rapid fall of pure electrical circuits.

[0046] Includes the following sub-steps: Sub-step 2.1: Simulation environment setup and model integration.

[0047] Specifically, real-time simulation software (e.g., embeddable C code generated by MATLAB / Simulink) is deployed within an industrial control computer / DSP. The aforementioned parameter vectors are written into the model initialization interface of the simulation software, forming an overall simulation framework that includes a mechanical oscillation submodule and an RLE electrical submodule. Subsequently, the real-time running capability of the simulation framework is verified, ensuring that a complete voltage drop waveform simulation is completed within 10ms. This step provides a simulation basis that is highly consistent with the field environment for generating feature templates, enabling subsequent template data to realistically reflect the mechanistic behavior of microgrid faults.

[0048] Sub-step 2.2, Inertial fallback feature generation.

[0049] Specifically, a high-resistance grounding condition is set in the simulation model, but no short-circuit current is injected; only the traction effect of the mechanical pendulum module is triggered. Simulations are performed on the main oscillation frequency and damping ratio parameters for each group, and the voltage drop waveform returned from the simulation is recorded. The sampling frequency is consistent with the field sampling. The expression for the voltage drop waveform is: ; In the formula, The voltage drop at time t. The initial drop voltage amplitude (in volts) is equal to the pre-drop voltage difference at the moment of triggering; The mechanical damping ratio is dimensionless and ranges from 0.05 to 0.2. For mechanical eigenfrequency, , These are simulation parameters, ranging from 1 to 5 Hz. The damped oscillation frequency is... ; The time (unit: seconds) is calculated from the moment the fault is triggered, and the value ranges from 0 to 5 seconds.

[0050] Finally, each template is smoothed and feature points (amplitude decay curve, peak time point, period interval) are extracted to construct an inertial fallback template set. This step, by constructing a voltage fallback curve dominated by mechanical oscillation, reflects the characteristics of slow oscillation and energy release, providing a dedicated template for distinguishing artifacts.

[0051] Sub-step 2.3: Generation of low-resistance short-circuit characteristics.

[0052] Specifically, a typical low-resistance short-circuit condition is set in the simulation model. This involves injecting a known short-circuit impedance (<1Ω) into the target node, then running the simulation and recording the rapid voltage drop waveform, with the sampling frequency matching the field conditions. Subsequently, the waveform is characterized by: rapid drop duration (<50ms), steady-state drop value (determined by the source internal resistance ratio), and no or extremely weak oscillations. Finally, the response curves under multiple short-circuit impedance conditions are summarized, and typical curves and extreme value distributions are extracted to construct a low-resistance short-circuit template set. This step, by constructing a voltage drop curve under a purely electrical short-circuit condition, forms a "rapid, oscillatory" characteristic, which contrasts sharply with the mechanical vibration artifact template.

[0053] Sub-step 2.4: Template library organization and indexing.

[0054] Specifically, based on the inertial fallback template set and low-resistance short-circuit template set established in the preceding steps, a corresponding index is assigned to each template in both sets, and the corresponding mechanism parameters are recorded. Then, the template data and response feature point information are stored in a high-speed local cache or embedded database, and a lookup table indexed by key features such as "amplitude decay rate," "peak period," and "fall rate" is established to support subsequent real-time matching.

[0055] Step S130: When a device failure occurs, capture the voltage drop waveform of the microgrid node and call the Swing-Load hybrid model to extract feature vectors based on the voltage drop waveform.

[0056] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes the following steps in step S130: Step S131: When the change in node voltage exceeds a set threshold, the voltage data within the corresponding time period before and after the voltage change is written into the circular buffer through the waveform buffer, and the voltage data in the circular buffer is clock-synchronized and corrected to obtain the fault voltage drop waveform.

[0057] Step S132: The fault voltage drop waveform is linearly scaled by the gain correction coefficient, and the sampling time axis of the voltage data is finely adjusted by the timing deviation value. At the same time, the fault voltage drop waveform is normalized by taking the average voltage value in the first time period before the voltage change as the reference.

[0058] Step S133: Determine the initial fall slope and initial drop amount based on the normalized fault voltage fall waveform, and perform peak detection on the fault voltage fall waveform to output the oscillation period and amplitude attenuation ratio.

[0059] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes step 3, real-time waveform feature extraction. When a high-resistance grounding or suspected fault occurs, the device immediately captures the voltage drop waveform of the relevant nodes of the microgrid and extracts representative features such as the drop slope, oscillation period, and attenuation trend based on the Swing-Load hybrid model mechanism.

[0060] Includes the following sub-steps: Sub-step 3.1, fault trigger detection and waveform capture.

[0061] Specifically, the sensor sampling frequency is set to 5kHz to ensure that voltage is sampled once every 0.2ms during the fallback process. When the node voltage drop exceeds a set threshold, a waveform buffer is activated, storing the data segment from 10ms before triggering to 190ms after triggering into a circular buffer. Then, the data in the buffer is clock-synchronized to obtain a continuous, uninterrupted fallback waveform. This step ensures that the raw data segments relied upon for subsequent feature extraction are sufficiently detailed by quickly locking and capturing the complete voltage fallback process.

[0062] Sub-step 3.2, waveform preprocessing and normalization.

[0063] Specifically, a gain correction coefficient is applied to linearly scale the fallback waveform to eliminate channel errors, and the sampling time axis is fine-tuned using timing deviation values ​​to ensure time axis consistency. Then, the fallback waveform is normalized using the average voltage 10ms before the trigger moment as a reference; the normalization result is the ratio between the fallback waveform and the average voltage. Finally, the time axis is mapped to the [0,1] interval for easier subsequent template comparison. This step, after removing dimensional differences and channel errors, normalizes the waveform into a unified format, facilitating consistent comparison across different operating conditions and devices.

[0064] Sub-step 3.3: Extraction of fall slope and initial drop amount.

[0065] Specifically, within the first 10% interval of the normalized time axis, a straight line is fitted using the least squares method. The slope of this fitted line is then calculated, and subsequently, the initial drop is calculated. This initial drop is the difference between the least squares fitted line results when time is 0 and when time is equal to the normalized time of the 10% interval (i.e., 20 ms). Finally, the slope and initial drop are recorded as important indicators of the difference between electrical rapid drop and mechanical traction initial motion.

[0066] Among them, the least squares method fits the straight line. The expression is: ; In the formula, The slope is denoted as .

[0067] In this embodiment, the drop slope characterizes the rate of voltage drop after the fault is triggered, and the initial drop reflects the drop magnitude. Both are crucial for distinguishing between low-resistance short circuits (large slope, large drop) and inertial artifacts (small slope, small drop).

[0068] Sub-step 3.4, analysis of oscillation period and decay trend.

[0069] Specifically, within the range of 20ms to 100ms... Peak point detection is performed to identify the two previous peak times, tp1 and tp2, and the oscillation period, i.e., the difference between tp1 and tp2, is calculated. Then, the amplitude attenuation ratio is calculated. This is used to reflect the energy loss of the waveform within one cycle, and its expression is: ; Subsequently, the amplitude attenuation ratio was compared and verified with the main oscillation frequency and damping ratio in the aforementioned mechanistic parameters. If obvious periodic oscillations were observed and D was between 0.7 and 0.95, it tended to be a mechanical pendulum artifact; if there were no obvious oscillations or D was close to 1, it was more likely to be an electrical short circuit. The oscillation period and attenuation trend can directly reflect the characteristics of mechanical oscillations, complementing the attenuation characteristics of the electrical circuit, and are used to improve the accuracy of differentiation.

[0070] Sub-step 3.5: Feature vector assembly and output.

[0071] Specifically, the four scalars—initial fall slope, fall amount, oscillation period, and decay ratio—are assembled into a feature vector in a fixed order. Each feature in the vector is then normalized within a preset interval. Finally, the normalized feature vector is cached locally. This step integrates multi-dimensional features into the same vector space, simplifying subsequent similarity calculations and improving real-time performance and scalability.

[0072] Step S140: The feature vector is compared with the inertial fallback template and the low-resistance short-circuit template respectively to determine the fault type corresponding to the voltage fallback waveform based on the similarity.

[0073] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes the following steps in step S140: Step S141: Calculate the cosine similarity between the feature vector and the inertial fallback template and the low-resistance short-circuit template to construct similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template respectively.

[0074] Step S142: Calculate the maximum similarity in the similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template, respectively, to determine the inertial artifact and the low-resistance short circuit.

[0075] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes step 4, template matching and similarity evaluation. The real-time extracted feature vectors are compared with the "inertial fallback template" and the "low-resistance short-circuit template" respectively to determine which mechanism's returned features the waveform is closer to, thereby distinguishing between artifacts and faults.

[0076] It should be noted that in sub-steps 2.2 (inertial fallback template generation) and 2.3 (low-resistance short-circuit template generation), multiple sets of voltage fallback waveforms are obtained through simulation. For each waveform, the same feature extraction process as in step 3 can be used—that is, extracting the slope, fall amount, oscillation period, and attenuation ratio. After assembling these four scalars in a fixed order and normalizing them to the same numerical range, a template vector set is obtained. M in sub-step 4.2 below is any element in this template vector set.

[0077] Includes the following sub-steps: Sub-step 4.1: Template library loading and feature vector preparation.

[0078] Specifically, the indexed template vector set is read from an embedded database or local memory. Each template vector solution is normalized to the same dimension as the real-time waveform feature vector (i.e., a feature vector obtained by assembling four scalars—initial fall slope, fall amount, oscillation period, and decay ratio—in a fixed order). Then, the templates to be matched and the real-time features are uniformly loaded into the matching engine's memory buffer. This step ensures that all templates and real-time features use the same dimension and format, constructing a unified vector space for subsequent batch similarity calculations.

[0079] Sub-step 4.2: Setting the similarity measurement function.

[0080] Specifically, cosine similarity is chosen as the metric, and the defined function expression is: ; In the formula, All are feature vectors of length 4, which can be represented as [fall slope, drop amount, oscillation period, decay ratio]; "·" represents the vector dot product operation; For vector norms, any vector norm The expression is: .

[0081] Then, the cosine similarity result is limited to the interval [0,1], so that the metric function can simultaneously consider the consistency of each feature in direction, which is suitable for fast matching of multi-dimensional feature templates.

[0082] Sub-step 4.3: Batch similarity calculation and classification score.

[0083] Specifically, the similarity of each inertial fallback template and each low-resistance short-circuit template in the aforementioned template set is calculated, and all similarity calculations are performed simultaneously using a multi-core DSP or FPGA acceleration module. The calculation results are then stored in the corresponding lists according to the template type.

[0084] Sub-step 4.4: Aggregate score and determine fault type.

[0085] Specifically, the maximum similarity of all inertial fallback templates and low-resistance short-circuit templates is calculated, and the difference between the maximum similarity of the two is calculated. If the difference exceeds the set threshold (0.1 is recommended), the type is determined to be an inertial fallback artifact; otherwise, it is determined to be a low-resistance short-circuit fault.

[0086] Step S150: Based on the fault type corresponding to the voltage drop waveform and the current current distribution, determine the location of the branch node where the fault occurred.

[0087] In some embodiments, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes the following steps in step S150: Step S151: Based on the maximum similarity corresponding to the inertial artifact and the low-resistance short circuit, calculate the difference between the maximum similarity corresponding to the inertial artifact and the low-resistance short circuit, and output the fault type according to the preset judgment threshold.

[0088] Step S152: When the determined fault type is low-resistance short circuit, collect the current distribution vector corresponding to all branches of the power distribution switch equipment, locate the branch number corresponding to the low-resistance short circuit in combination with the branch topology information table, and output a fault diagnosis report.

[0089] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method provided by the present invention includes step 5, fault determination and branch location. Based on the similarity score, those exceeding the threshold are determined to be of the corresponding type: if the similarity of "inertial fallback" is higher, it is identified as an artifact; if the similarity of "low resistance short circuit" is higher, it is identified as a real fault. At the same time, the specific branch node location is determined in combination with the current distribution.

[0090] Includes the following sub-steps: Sub-step 5.1: Current distribution acquisition under short-circuit scenario.

[0091] Specifically, when the determination result in sub-step 4.4 is a low-resistance short-circuit fault, high-frequency full-channel capture of all branch current sampling channels is immediately triggered to ensure that the latest current value is obtained within at least 10ms. Afterwards, a gain correction factor and timing calibration are performed on each current value to obtain an accurate current value. Finally, the current values ​​of each branch are sorted according to the branch number to construct a current distribution vector.

[0092] Sub-step 5.2: Execute the branch localization algorithm.

[0093] Specifically, the branch number containing the maximum current value is identified in the current distribution vector. Using the branch topology information table (i.e., the topology of the entire circuit), the current gradient from the main bus to each circuit breaker branch on that branch is calculated. If the current in the parent branch of that branch also increases significantly, the process is traced upwards until the branch point with the largest current abrupt change is found as the location result. Then, the location result is set as the branch number corresponding to the branch point with the largest current abrupt change, and the location confidence is quantified based on the ratio between the maximum current and the second largest current.

[0094] Sub-step 5.3: Encapsulate and report the judgment result.

[0095] Specifically, the aforementioned four characteristics (fall slope, drop amount, oscillation period, and attenuation ratio) are encapsulated into a diagnostic report, which is then written into the device log and pushed to the upper-level monitoring system via the communication bus, while simultaneously triggering fault isolation.

[0096] Step S160: Send circuit breaker control commands in a preset priority order, and in response to the circuit breaker control commands, isolate the fault location of the branch node where the fault occurred.

[0097] In a specific embodiment, the intelligent power distribution switchgear fault self-diagnosis and circuit breaker coordinated control method provided by the present invention includes step 6, circuit breaker coordinated control and closed-loop feedback. For real short-circuit faults, circuit breaker control commands are issued according to a preset priority order to complete fault isolation; for inertial artifacts, malfunctions are suppressed and switch protection parameters are adjusted to achieve local closed-loop self-diagnosis and protection configuration optimization.

[0098] Includes the following sub-steps: Sub-step 6.1, control strategy branch.

[0099] Specifically, read the diagnostic report. If the fault type is determined to be a low-resistance short-circuit fault, set the corresponding control execution path flag. If it is an inertial fall-back artifact, set another control execution path flag.

[0100] Sub-step 6.2, short-circuit isolation control.

[0101] Specifically, based on the control execution path flag set in sub-step 6.1, the circuit breaker corresponding to the fault node and its upstream bus circuit breaker are located. Opening commands are generated in order of proximity (isolation first, then disconnection), and then sent sequentially through the control bus of the power distribution switchgear. The opening status of each circuit breaker is monitored in real time. If any circuit breaker fails to report an opening status failure, the backup upstream circuit breaker protection isolation is immediately triggered.

[0102] Sub-step 6.3, optimization of inertial artifact protection.

[0103] Specifically, if the template matching degree is lower than the set threshold (e.g., 0.2), it indicates that the inertial fall-off artifact and the low-resistance short-circuit score are quite close. In this case, the voltage drop threshold is increased by up to 10%, and the short-circuit action delay is extended by up to 50ms. If the template matching degree is not lower than the set threshold, only parameter fine-tuning is performed to ensure the sensitivity of the short-circuit protection. Finally, the adjusted parameters are written to the protection configuration storage area of ​​the circuit breaker control unit, taking effect in real time, and a comparison log before and after the adjustment is recorded.

[0104] The following describes the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control device provided by the present invention. The intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control device described below can be referred to in correspondence with the intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control method described above.

[0105] like Figure 2 As shown in one embodiment, an intelligent power distribution switchgear fault self-diagnosis and circuit breaking coordinated control device includes a parameter initialization module, a fault template generation module, a fault feature extraction module, a fault type determination module, a fault location determination module, and a fault isolation module.

[0106] The parameter initialization module is used to obtain the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and calls the Swing-Load hybrid model to initialize the mechanistic parameters, so as to output the oscillation frequency range, circuit damping ratio and inductor resistance characteristic values ​​of the mechanical pendulum.

[0107] The fault template generation module is used to build a simulation environment based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristics to generate inertial fall templates and low-resistance short-circuit templates for mechanical pendulums.

[0108] The fault feature extraction module is used to capture the voltage drop waveform of the microgrid node when a device failure occurs, and call the Swing-Load hybrid model to extract feature vectors based on the voltage drop waveform.

[0109] The fault type determination module is used to compare the feature vector with the inertial fallback template and the low-resistance short-circuit template respectively, so as to determine the fault type corresponding to the voltage fallback waveform based on the similarity.

[0110] The fault location determination module is used to determine the location of the branch node where the fault occurred based on the fault type corresponding to the voltage drop waveform and the current current distribution.

[0111] The fault isolation module is used to send circuit breaker control commands in a preset priority order and, in response to the circuit breaker control commands, isolate the fault location of the branch node where the fault occurred.

[0112] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

Claims

1. A method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear, characterized in that, The method includes: Obtain the mechanistic parameters corresponding to the mechanical pendulum and electrical circuit, and call the Swing-Load hybrid model to initialize the mechanistic parameters, so as to output the oscillation frequency range, circuit damping ratio and inductor resistance characteristic values ​​of the mechanical pendulum; Based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values, a simulation environment is built to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum. When a device failure occurs, the voltage drop waveform of the microgrid node is captured, and the Swing-Load hybrid model is called to extract feature vectors based on the voltage drop waveform. The feature vector is compared with the inertial fallback template and the low-resistance short-circuit template respectively to determine the fault type corresponding to the voltage fallback waveform based on the similarity. Based on the fault type corresponding to the voltage drop waveform and the current current distribution, the location of the branch node where the fault occurred is determined. The circuit breaker control command is sent in a preset priority order, and the fault is isolated at the branch node where the fault occurred in response to the circuit breaker control command.

2. The method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear according to claim 1, characterized in that, The process of obtaining the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and initializing the mechanistic parameters by calling the Swing-Load hybrid model to output the oscillation frequency range, circuit damping ratio, and inductor-resistance characteristic values ​​of the mechanical pendulum, includes: Voltage and current sensors are installed at the voltage sampling terminal of the power distribution switchgear and at the current transformers of each branch. The gain correction coefficient is calculated by a calibrator based on the measured values ​​of voltage and current of each branch and the standard values ​​to obtain the calibrated voltage and current. Under the no-load condition of the microgrid, a step voltage test pulse is applied to record the current response curve, and the circuit time constant is calculated based on the current response curve. The circuit time constant is the ratio between the equivalent inductance and the equivalent resistance.

3. The method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear according to claim 2, characterized in that, The process of obtaining the mechanistic parameters corresponding to the mechanical pendulum and the electrical circuit, and initializing the mechanistic parameters by calling the Swing-Load hybrid model to output the oscillation frequency range, circuit damping ratio, and inductor-resistance characteristic values ​​of the mechanical pendulum, further includes: When the microgrid is unloaded and excitation is on, a short-time excitation current pulse or speed disturbance is applied to record the speed oscillation curve, and the spectrum analysis of the speed oscillation curve is performed to identify the main oscillation frequency and damping ratio. The equivalent inductance, equivalent resistance, main oscillation frequency, and damping ratio are normalized according to a preset range, and the normalization result is combined with the gain correction coefficient and synchronization timing to generate a parameter vector. The parameter vector includes the oscillation frequency range of the mechanical pendulum, the circuit damping ratio, and the characteristic values ​​of the inductor resistance.

4. The method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear according to claim 3, characterized in that, The simulation environment is built based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values ​​to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum, including: Deploy real-time simulation software inside an industrial control computer or DSP, and write the parameter vector into the model initialization interface of the real-time simulation software to generate a simulation model; In the simulation model, a high-resistance grounding condition is set up, and the traction effect of the mechanical pendulum is triggered to simulate the main oscillation frequency and damping ratio, while recording the voltage drop waveform of the simulation feedback. Based on the voltage drop waveform, the simplified response formula is called to calculate the waveform characteristics, and the initialization template is smoothed to extract multiple feature points and generate the inertial drop template.

5. The method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear according to claim 4, characterized in that, The process of building a simulation environment based on the oscillation frequency range, circuit damping ratio, and inductor resistance characteristic values ​​to generate the inertial fall template and low-resistance short-circuit template of the mechanical pendulum also includes: In the simulation model, a preset short-circuit impedance is set at the target node to run the simulation, and the voltage drop waveform is recorded to characterize the voltage drop waveform and obtain the low-resistance short-circuit template. Index tags are assigned to the inertial fallback template and the low-resistance short-circuit template to store the inertial fallback template, the low-resistance short-circuit template and the corresponding features in the embedded database.

6. The method for self-diagnosis of faults and coordinated control of circuit breaking in intelligent power distribution switchgear according to claim 1, characterized in that, When a device failure occurs, the voltage drop waveform of the microgrid node is captured, and the Swing-Load hybrid model is invoked to extract feature vectors based on the voltage drop waveform, including: When the change in node voltage exceeds a set threshold, the voltage data within the corresponding time period before and after the voltage change is written into the circular buffer through the waveform buffer, and the voltage data in the circular buffer is clock-synchronized and corrected to obtain the fault voltage fall-off waveform. The fault voltage drop waveform is linearly scaled by a gain correction coefficient, and the sampling time axis of the voltage data is finely adjusted by a timing deviation value. At the same time, the fault voltage drop waveform is normalized based on the average voltage value within the first time period before the voltage change. The initial fall slope and initial drop amount are determined based on the normalized fault voltage fall waveform, and the peak value of the fault voltage fall waveform is detected to output the oscillation period and amplitude attenuation ratio.

7. The method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear according to claim 1, characterized in that, The step of comparing the feature vector with the inertial fallback template and the low-resistance short-circuit template respectively, and determining the fault type corresponding to the voltage fallback waveform based on the similarity, includes: Calculate the cosine similarity between the feature vector and the inertial fallback template and the low-resistance short-circuit template to construct similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template respectively; Calculate the maximum similarity in the similarity lists corresponding to the inertial fallback template and the low-resistance short-circuit template respectively to identify inertial artifacts and low-resistance short circuits.

8. The method for fault self-diagnosis and circuit breaking coordinated control of intelligent power distribution switchgear according to claim 7, characterized in that, The determination of the branch node location where the fault occurred based on the fault type corresponding to the voltage drop waveform and the current current distribution includes: Based on the maximum similarity between the inertial artifact and the low-resistance short circuit, the difference between the maximum similarity between the inertial artifact and the low-resistance short circuit is calculated, and the fault type is output according to the preset judgment threshold. When the fault type is determined to be a low-resistance short circuit, the current distribution vectors corresponding to all branches of the power distribution switchgear are collected, and the branch number corresponding to the low-resistance short circuit is located by combining the branch topology information table, and a fault diagnosis report is output at the same time.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.

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