Method and device for detecting electric leakage of distribution box and storage medium

Abstract

The application discloses a leakage detection method and device of a distribution box and a storage medium, relates to the technical field of leakage detection, and comprises the following steps: establishing a recognition influence relationship of a detection environment and leakage detection, including an increment, a weakening relationship and corresponding relationship coefficients; collecting current detection environment parameters, performing gain analysis on the detection environment parameters by using the recognition influence relationship, obtaining target gain parameters and gain adjustment amounts; gain adjusting the leakage detection sensor according to the target gain parameters and the gain adjustment amounts, and acquiring a monitoring signal; and performing leakage detection according to the leakage monitoring signal, recognizing a leakage detection result and leakage positioning information of the distribution box. The application solves the technical problem that the leakage detection in the prior art is susceptible to environmental factors, and the detection sensitivity and accuracy are insufficient, and achieves the technical effect of improving the leakage detection sensitivity and accuracy.

Description

Technical Field

[0001] This invention relates to the field of leakage current detection technology, specifically to leakage current detection methods, devices, and storage media for distribution boxes. Background Technology

[0002] During leakage current monitoring in distribution boxes, detection results are often affected by various environmental factors such as humidity, temperature, power grid harmonics, and load fluctuations. These factors cause fluctuations in the amplitude of the leakage current signal, resulting in inconsistent sensor sensitivity under different environments, thus affecting the accuracy and stability of leakage current detection. Especially in scenarios with high humidity, high dust, or electromagnetic interference, the detection signal is prone to noise amplification or response attenuation, making it difficult to identify the location of leakage current faults in a timely and accurate manner, increasing the safety risks of operating the distribution box. Summary of the Invention

[0003] This application provides a leakage current detection method, device, and storage medium for distribution boxes, which addresses the technical problem that leakage current detection in the prior art is easily affected by environmental factors, resulting in insufficient detection sensitivity and accuracy.

[0004] In view of the above problems, this application provides a leakage current detection method, device and storage medium for distribution boxes.

[0005] The first aspect of this application provides a method for detecting leakage current in a distribution box, the method comprising:

[0006] Establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; collect the current detection environment parameters, and use the identification influence relationship to perform gain analysis on the detection environment parameters to obtain the target gain parameter and gain adjustment amount; adjust the gain of the leakage current detection sensor according to the target gain parameter and gain adjustment amount to obtain the monitoring signal; perform leakage current detection according to the leakage current monitoring signal, and identify the leakage current detection result and leakage current location information of the distribution box.

[0007] A second aspect of this application provides a leakage current detection device for a distribution box, the device comprising:

[0008] The system includes several modules: an influence relationship establishment module for establishing the influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; a gain analysis module for collecting current detection environment parameters and using the identified influence relationship to perform gain analysis on the detection environment parameters to obtain target gain parameters and gain adjustment amounts; a gain adjustment module for adjusting the gain of the leakage current detection sensor according to the target gain parameters and gain adjustment amounts to obtain leakage current monitoring signals; and a leakage current detection module for performing leakage current detection based on the leakage current monitoring signals, identifying the leakage current detection results and leakage current location information of the distribution box.

[0009] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the leakage current detection method for a distribution box provided in this application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application establishes an identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; it collects current detection environment parameters, uses the identification influence relationship to perform gain analysis on the detection environment parameters, obtains target gain parameters and gain adjustment amounts; it adjusts the gain of the leakage current detection sensor according to the target gain parameters and gain adjustment amounts, and acquires monitoring signals; it performs leakage current detection based on the leakage current monitoring signals, and identifies the leakage current detection results and leakage current location information of the distribution box. This invention solves the technical problem in the prior art where leakage current detection is easily interfered with by environmental factors, leading to insufficient detection sensitivity and accuracy. By establishing an identification influence relationship between the detection environment and leakage current detection and dynamically adjusting the sensor gain based on this relationship, it achieves the technical effect of improving the sensitivity and accuracy of leakage current detection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic flowchart of the leakage current detection method for a distribution box provided in an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of the leakage current detection device for a distribution box provided in an embodiment of this application.

[0015] Figure labeling: Module 11 for establishing influence relationship, Module 12 for gain analysis, Module 13 for gain adjustment, and Module 14 for leakage current detection. Detailed Implementation

[0016] This application provides a leakage current detection method, device, and storage medium for distribution boxes. It addresses the technical problem in the prior art where leakage current detection is easily affected by environmental factors, resulting in insufficient detection sensitivity and accuracy. By establishing the relationship between the detection environment and the leakage current detection and dynamically adjusting the sensor gain based on this relationship, the technical effect of improving the sensitivity and accuracy of leakage current detection is achieved.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0019] Example 1, as Figure 1 As shown, this application provides a leakage current detection method for a distribution box, the method comprising:

[0020] Step S100: Establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients.

[0021] Furthermore, in the method provided in the application embodiments, the step of establishing the relationship between the detection environment and the identification influence of leakage current detection further includes:

[0022] The detection environment parameters include at least: humidity, temperature, air ions, dust, load fluctuation, and power grid harmonic components. Based on these environmental parameters, experimental or field data related to the leakage current detection signal are collected. Univariate and multivariate correlation analyses are performed on the collected data samples to extract the influence characteristics of each environmental parameter on the leakage current detection results, determining the influence relationship between the environmental parameter variables and the leakage current detection results, including incremental or weakening relationships. The influence relationship is fitted and quantified using the data samples to obtain the influence relationship coefficients. The influence relationship, the detection environment parameters, and the corresponding influence relationship coefficients are mapped and correlated to establish the identification influence relationship between the detection environment and leakage current detection.

[0023] In this embodiment, the environmental parameters detected include at least humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components. Humidity refers to the water vapor content in the air. Temperature refers to the thermal state of the environment. Air ions refer to the concentration level of charged particles in the air. Dust refers to suspended particulate matter in the air. Load fluctuations refer to the dynamic changes in current and power in the power system. Power grid harmonic components refer to harmonic components other than the fundamental frequency in the power grid.

[0024] Based on the detection environment parameters, when collecting experimental or field data for leakage current detection signals, a multi-channel synchronous acquisition method is used to collect parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components, while simultaneously recording the leakage current detection signal to form experimental or field datasets. The acquisition process is based on the sensitivity of changes in detection environment parameters and their empirical impact on leakage current detection to determine an appropriate sampling step size. Multi-level data samples are obtained through adaptive sampling and high-density area encryption sampling, and finally, a data sample set is formed through deduplication and integration.

[0025] Next, univariate and multivariate correlation analyses were performed on the collected data samples. Univariate analysis used Pearson correlation coefficients to calculate the linear correlation between each detection environmental parameter and the leakage current detection results, determining the direction and intensity of their influence on detection sensitivity and signal amplitude. Multivariate analysis calculated the overall correlation of multiple combinations of detection environmental parameters, identifying the cumulative and interactive effects of environmental factors. This analysis clarified the incremental or attenuating relationships between each parameter and the leakage current detection results. An incremental relationship indicates that the leakage current signal amplitude or sensitivity increases when environmental parameters increase, while an attenuating relationship indicates that the signal amplitude or sensitivity decreases when environmental parameters increase.

[0026] Subsequently, the influence relationship was fitted and quantified using data samples to obtain the influence relationship coefficient. Specifically, the detection environment parameter was used as the independent variable, and the leakage current detection result was used as the dependent variable. A multiple linear regression method was employed to fit the correlation results. In this process, the regression coefficient corresponding to each environmental parameter obtained through regression calculation is the influence relationship coefficient. A positive sign of the influence relationship coefficient corresponds to an incremental relationship, indicating that the leakage current detection sensitivity increases when the environmental parameter increases; a negative sign corresponds to a weakening relationship, indicating that the detection sensitivity decreases when the environmental parameter increases. The absolute value of the influence relationship coefficient represents the strength of the parameter's influence on the leakage current detection sensitivity and signal amplitude; the larger the value, the more significant the influence on the detection result.

[0027] Finally, the influencing relationships, detection environmental parameters, and corresponding influence coefficients are mapped and correlated. Through a one-to-one correspondence, humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components are directly linked to their influence coefficients, and the direction and degree of influence of each environmental parameter are determined based on this mapping relationship. When environmental parameters are collected in real time, the incremental or diminishing contribution of environmental factors to leakage current detection sensitivity can be directly calculated based on this mapping relationship, and the comprehensive effect result can be obtained, thus completing the establishment of the identification and influence relationship between the detection environment and leakage current detection.

[0028] Furthermore, in the method provided in the application embodiments, based on the detection environment parameters, the acquisition of experimental data or field data related to the leakage current detection signal further includes:

[0029] Based on the sensitivity of the detection environment parameters to their own changes and empirical values ​​for their impact on leakage current, a preliminary sampling step size is determined. Adaptive sampling of the detection environment parameters is performed according to the preliminary sampling step size to obtain a first data sample. Using the first data sample, influence relationships are identified to obtain an influence sensitivity distribution. Based on the influence sensitivity distribution, high-density regions are identified, and the preliminary sampling step size for these high-density regions is granularized and subjected to secondary sampling to obtain a second data sample. The first and second data samples are deduplicated and integrated to obtain a data sample set.

[0030] In this embodiment, the initial sampling step size is first determined based on the sensitivity of the environmental parameters to changes and empirical values ​​of their impact on leakage current. In this process, a fixed interval division method is used, dividing the environmental parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components into several fixed intervals according to their historical variation range. By calculating the average rate of change of the leakage current detection signal within each fixed interval, the target interval with the highest sensitivity is identified, and the initial sampling step size is determined based on the average rate of change of this target interval. Taking humidity as an example, when the rate of change of the leakage current detection signal is largest in the humidity range of 40% to 60%, the rate of change in this interval is used as the basis for calculating the sampling frequency, thereby determining the initial sampling step size.

[0031] Next, adaptive sampling of environmental parameters is performed based on the initial sampling step size. In this process, a threshold-triggered sampling method is used, setting sampling trigger thresholds for environmental parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components. Sampling is triggered immediately when the change in any parameter exceeds the set threshold; when the change is below the threshold, periodic sampling is performed according to the initial sampling step size. For example, sampling is triggered immediately when the temperature change exceeds 2°C, while sampling is performed at 5-minute sampling steps when the temperature is stable. This sampling process yields the first data sample.

[0032] Subsequently, the first data sample was used to identify influence relationships and obtain the influence sensitivity distribution. In this process, the interval difference method was employed, dividing the first data sample into fixed intervals based on the detection environmental parameters. The difference between the average values ​​of leakage current detection signals in adjacent intervals was calculated, and the magnitude of the difference was used as a quantitative indicator of sensitivity. The larger the difference value, the more significant the change in the leakage current detection signal corresponding to that detection environmental parameter interval, and the higher the sensitivity. The influence sensitivity distribution was formed by calculating the interval differences for humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components.

[0033] Then, based on the influence sensitivity distribution, high-density regions are identified. The initial sampling step size in these high-density regions is then granularized and secondary sampling is performed. High-density regions refer to parameter ranges where sensitivity values ​​continuously exceed a preset threshold. After identifying high-density regions, a granularity reduction method is used, where the initial sampling step size is reduced by a fixed proportion, such as to half or one-third of its original size. Secondary sampling is performed only within these high-density regions, thereby achieving higher sampling resolution in key parameter ranges. For example, when the initial sampling step size is 5 minutes, the sampling step size in the high-density region is shortened to 2.5 minutes to more precisely capture the corresponding changes in environmental parameters and leakage current detection signals, ultimately obtaining the second data sample.

[0034] Finally, the first and second data samples are deduplicated and integrated. This process uses a timestamp comparison method, comparing each sample with its primary key to eliminate duplicates and retain unique records. The two types of samples are then merged in chronological order to ensure data continuity and temporal integrity, thus obtaining the data sample set.

[0035] Furthermore, in the method provided in the application embodiments, after obtaining the second data sample, it further includes:

[0036] For each environmental parameter interval in the sensitivity distribution, calculate the sensitivity gradient or second-order rate of change; when the sensitivity gradient or rate of change is greater than a preset threshold, trigger step-size fission to increase the sampling density; or calculate the residual based on the relationship model fitted by the current sampled data, and trigger step-size fission when the residual exceeds a preset threshold; when both the sensitivity gradient and the residual are lower than the threshold, stop fission, determine that the current sampling density has met the accuracy required to establish the influence relationship, and obtain the data sample set.

[0037] In this embodiment, for each range of environmental parameters affecting the sensitivity distribution, the sensitivity gradient or second-order rate of change is first calculated. The sensitivity gradient refers to the rate of change affecting the sensitivity distribution among adjacent sampling points or ranges, used to measure the upward or downward trend of sensitivity as environmental parameters change; the second-order rate of change refers to the change in the sensitivity rate of change, used to identify inflection points with sharp changes in the sensitivity curve. In actual calculations, the rate of change of sensitivity between adjacent ranges is obtained using a differential calculation method, and the second-order rate of change is obtained by further differential calculation of the rate of change.

[0038] When the sensitivity gradient or second-order rate of change exceeds a preset threshold, step size fission is performed to increase the sampling density. Specifically, within the corresponding detection environment parameter range, the original sampling step size is shortened by a fixed proportion, such as to half or one-third of the original step size, and higher-frequency sampling is performed only in sensitive areas. Taking temperature as an example, when the sensitivity gradient in the corresponding temperature range exceeds the threshold, the original 5-minute sampling step size is shortened to 2.5 minutes, and supplementary sampling is performed within this temperature range, thereby improving the characterization accuracy of the leakage current detection signal response caused by temperature changes. Through this process, the fission-based sampling plan and new sampling points are obtained.

[0039] Simultaneously, based on the current sampling data, a relationship model between environmental parameters and leakage current detection signals is established. The sampled data is then fitted, and residuals are calculated. The residuals are obtained by comparing the model's predicted values ​​with the actual sampled values, reflecting the impact of sampling density on the model's fitting accuracy. When the residual in a certain interval exceeds a preset threshold, it indicates that the sampling density in that interval is insufficient, requiring a further step-size split to shorten the sampling step and perform supplementary sampling within that interval, thereby improving the sampling density and fitting accuracy. Through this process, a supplementary sampling plan and new sampling points based on residual triggering are obtained.

[0040] In the aforementioned step-size fission and supplementary sampling process, fission and secondary sampling can be iterated multiple times. After each iteration, the sensitivity gradient, second-order rate of change, and residual are recalculated, and it is determined whether they still exceed the preset threshold. When the sensitivity gradient, second-order rate of change, and residual for all detection environmental parameter intervals are lower than their respective thresholds, it indicates that the sampling density has reached the accuracy required to establish the influence relationship. At this point, the fission process is terminated, and the sampling density stabilizes and no longer changes.

[0041] Finally, all sampling data obtained from the initial sampling, fission sampling, and supplementary sampling were organized in chronological order. By comparing the sampling times, duplicate sampling points were eliminated, and valid records were retained to form a data sample set.

[0042] Furthermore, the method provided in the application embodiments also includes:

[0043] The step-size fission and secondary sampling are iterated multiple times. The iteration termination condition includes that the sensitivity gradient, the second-order rate of change and the fitting residual all meet the preset convergence condition. The sensitivity gradient or the second-order rate of change is calculated by the change in leakage signal response caused by the change in environmental parameters, and the residual is calculated by the fitting error of the sampling data by the established environmental parameter-leakage detection relationship model.

[0044] In this embodiment, the step-size fission and secondary sampling process dynamically adjusts the sampling granularity through multiple iterations, and uses the sensitivity gradient, second-order rate of change, and fitting residual as the convergence criteria. During the iteration process, when the sensitivity gradient or second-order rate of change of the detection environment parameter range exceeds a preset threshold, step-size fission is executed. By shortening the sampling step size and performing secondary sampling in the corresponding range, the sampling resolution is improved, making the sampling data more accurately reflect the characteristics of the response of the detection environment parameter changes to the leakage current detection signal.

[0045] Simultaneously, during the iterative process, an environmental parameter-leakage current detection relationship model is established based on the collected detection environment parameters and leakage current detection signal data. The model is built using the least squares linear regression method, with detection environment parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components as independent variables, and the leakage current detection signal as the dependent variable. Regression coefficients and a fitting equation are obtained through regression calculation. This fitting equation characterizes the mapping relationship between changes in detection environment parameters and the leakage current detection signal response. By inputting actual sampled data into the environmental parameter-leakage current detection relationship model, the model's predicted values ​​are calculated, and the difference between the predicted values ​​and the actual sampled values ​​is calculated to obtain the residuals. The residuals are used to measure the model's fitting accuracy; a larger residual indicates insufficient sampling density or inadequate model description accuracy.

[0046] In each iteration, if the residual exceeds a preset residual threshold, a step size split is triggered within the corresponding detection environment parameter range. This shortens the sampling step size again, performs supplementary sampling to increase sampling points, improve data accuracy, and refit and update the environmental parameter-leakage detection relationship model. When the residual gradually decreases below the preset residual threshold, it indicates that the model fitting accuracy has met the requirements.

[0047] Step-size fission and secondary sampling are repeatedly performed in the iteration. By monitoring the sensitivity gradient, second-order rate of change, and residuals in real time, the sampling granularity and fitting accuracy are continuously optimized. When the sensitivity gradient, second-order rate of change, and residuals all meet the preset convergence conditions, it indicates that the sampling accuracy has met the requirements for establishing the influence relationship, and the iteration process is terminated. In this way, high-precision sampling data is obtained, and a high-accuracy and stable environmental parameter-leakage detection relationship model is finally formed, providing reliable data support for the identification and localization of leakage detection results.

[0048] Step S200: Collect the current detection environment parameters, and use the identified influence relationship to perform gain analysis on the detection environment parameters to obtain the target gain parameter and gain adjustment amount.

[0049] In this embodiment of the application, the detection environment parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components are first collected in real time by a pre-arranged sensing and acquisition device to determine the current detection environment parameters.

[0050] Next, the gain analysis of the detection environment parameters is performed using the identified influence relationships. In this process, real-time collected environmental parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components are substituted into the established influence relationships. By calculating the incremental or diminishing effect of each environmental parameter or combination on the leakage current detection sensitivity, the corresponding gain coefficient is obtained. Subsequently, the gain coefficients are synthesized to obtain the target gain parameter under the current detection environment. Then, combining the target gain parameter with the current operating state of the leakage current detection sensor, the gain adjustment amount is calculated.

[0051] Furthermore, in the method provided in the application embodiments, the method further includes: using the identified influence relationship to perform gain analysis on the detection environment parameters to obtain the target gain parameter and the gain adjustment amount;

[0052] Substitute the real-time collected detection environment parameters into the identification influence relationship, calculate the incremental or decremental effect of the environment parameter or parameter combination on the leakage current detection sensitivity, and obtain the gain coefficient of each environment parameter; combine the gain coefficients of each environment parameter to obtain the target gain parameter, and calculate the gain adjustment amount based on the target gain parameter and the current sensor state.

[0053] In this embodiment, real-time collected environmental parameters such as humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components are sequentially substituted into the established identification influence relationships for calculation. A regression coefficient substitution method is used to map the measured values ​​of each environmental parameter to their corresponding influence relationship coefficients. The incremental or decremental effect on leakage current detection sensitivity is obtained through multiplication. In this process, humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components are used as input variables and multiplied by the influence relationship coefficients obtained through pre-calculated regression. For example, the influence relationship coefficient for humidity is... The current humidity level is 65%, and the corresponding effective value is... The coefficient of influence of temperature is The current temperature is 28℃, and the corresponding effect value is The influence coefficient of power grid harmonic components is: The current value is 5%, and the corresponding effect value is This step yields the gain coefficients for humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components.

[0054] Next, the gain coefficients of each environmental parameter are synthesized using a linear superposition method, directly adding all the gain coefficients to obtain the target gain parameter representing the overall effect of the current environment. For example, adding the gain coefficients of the above environmental parameters yields the target gain parameter as follows: This indicates that under the current environmental conditions, environmental factors as a whole have a positive gain effect on the sensitivity of leakage current detection.

[0055] Finally, the target gain parameter is differentially calculated with the current sensor state. The difference between the target gain parameter and the sensor gain reference value is used as the gain adjustment amount. For example, when the sensor gain reference value is 0.00, the target gain parameter is... The gain adjustment amount is This indicates that the sensor gain needs to be adjusted positively; if the target gain parameter is... The gain adjustment amount is This indicates that the sensor gain needs to be adjusted in reverse.

[0056] Furthermore, the method provided in the application embodiments, in order to obtain the target gain parameter and the gain adjustment amount, further includes:

[0057] For transient, intermittent, or high-impedance leakage signals, the target gain parameters and gain adjustment amounts are calculated independently, and differentiated gain processing is performed for each type of signal. Specifically, high-bandwidth sampling and instantaneous high-gain windows are used for transient leakage signals, long-term cumulative sampling and high-sensitivity threshold adjustment are used for intermittent leakage signals, and high-sensitivity low-noise gain amplification and sampling integral extension strategies are used for high-impedance leakage signals.

[0058] In this embodiment, the target gain parameter and gain adjustment amount are calculated independently for transient leakage signals, intermittent leakage signals, and high-resistance leakage signals. For transient leakage signals, a high-bandwidth sampling method is used for signal acquisition. By increasing the sampling frequency to more than ten times the highest frequency component of the signal, complete capture of millisecond-level current surges is ensured. For example, when the duration of the transient leakage signal is less than 2ms, the sampling frequency is set to above 100kHz to ensure that waveform details are not distorted. After sampling, a transient high-gain window is enabled during the signal surge interval. Within this window, the gain is increased to two or three times the reference gain to amplify the peak portion of the leakage signal and enhance detection sensitivity. Subsequently, the amplitude ratio of the signal before and after amplification is calculated to obtain the target gain parameter of the transient signal. The difference between the target gain parameter and the sensor's reference gain value is then used to obtain the corresponding gain adjustment amount. Based on this, transient high-gain amplification processing is implemented, that is, temporarily increasing the amplification factor during the signal surge period to achieve a highly sensitive response to transient leakage events.

[0059] For intermittent leakage current signals, a long-term cumulative sampling method is employed. By extending the sampling time window, such as from 10 to 30 minutes, current signals are continuously acquired and accumulated, reducing short-term noise interference and extracting the periodic characteristics of intermittent leakage current events. During this process, a high-sensitivity threshold adjustment mechanism is activated, reducing the leakage current event trigger threshold from 100% of the baseline sensitivity to 80% to ensure effective identification of signals with low amplitude or low frequency of occurrence. Based on the average amplitude change rate of the accumulated signal, the target gain parameter for the intermittent signal is calculated. Then, combining this parameter with the difference between the sensor's current operating gain, the gain adjustment amount is calculated. Based on this, high-sensitivity threshold gain processing is implemented, i.e., by appropriately increasing the amplification factor and lowering the trigger threshold, improving the response capability and acquisition accuracy for intermittent signals.

[0060] Finally, for high-impedance leakage signals, a high-sensitivity, low-noise amplification method and a sampling integration extension method are employed. First, a low-noise amplification circuit is used to improve signal amplification sensitivity and suppress background noise, ensuring effective amplification of low-amplitude leakage signals. Then, the sampling integration time is extended, for example, from 100ms to 500ms, to increase effective signal energy and reduce the influence of random noise. By calculating the average amplitude increase ratio of the signal before and after amplification, the target gain parameter for the high-impedance leakage signal is obtained. This parameter is then differentially compared with the sensor's current reference gain value to obtain the gain adjustment amount. Based on this, high-sensitivity, low-noise gain processing is implemented, that is, by increasing the gain and extending the integration sampling time, the detection capability of high-impedance, low-current signals is enhanced.

[0061] Step S300: Adjust the gain of the leakage current detection sensor according to the target gain parameter and the gain adjustment amount to obtain the leakage current monitoring signal.

[0062] In this embodiment, the gain of the leakage current detection sensor is adjusted based on the calculated target gain parameter and the gain adjustment amount. In this process, the target gain parameter is first compared with the sensor's gain reference value to determine the required gain adjustment direction and magnitude. When the target gain parameter is greater than the gain reference value, a gain boost operation is performed; when the target gain parameter is less than the gain reference value, a gain attenuation operation is performed, thereby obtaining a clear quantitative basis for gain adjustment.

[0063] Subsequently, the preamplifier circuit of the leakage current detection sensor was adjusted using a gain control method. By directly applying the gain adjustment to the amplification factor of the amplifier circuit, the sensor amplification factor was made consistent with the target gain parameter. For example, when the target gain parameter is... When the gain reference value is 0dB, the gain adjustment is... At this point, by adjusting the amplification factor, the signal amplitude is precisely increased to the target level, achieving amplitude amplification or attenuation. Throughout the adjustment process, the amplitude change of the output signal is monitored in real time and compared with the target value to ensure that the adjustment result is stable and reliable.

[0064] After gain adjustment, the leakage current detection sensor amplifies and converts the leakage current in the circuit, and outputs the adjusted leakage current monitoring signal.

[0065] Step S400: Perform leakage detection based on the leakage monitoring signal, and identify the leakage detection result and leakage location information of the distribution box.

[0066] In this embodiment, when performing leakage current detection based on the leakage current monitoring signal, the leakage current monitoring signal is first input to the signal processing stage, and the signal feature extraction method is used to extract the amplitude feature, frequency feature, and phase feature of the signal. By calculating the effective value and peak amplitude of the signal, the intensity feature of the leakage current is extracted; by performing a fast Fourier transform on the frequency components, the power frequency component and harmonic components are extracted; and by calculating the phase information, the phase feature of the signal is characterized.

[0067] Subsequently, a threshold method was used to identify leakage current in the leakage monitoring signal. The real-time monitoring signal was compared with a preset leakage current threshold. If the effective signal value exceeded the leakage current threshold, leakage was identified in the distribution box; if the signal amplitude suddenly changed within a short period and exceeded the transient threshold, it was identified as transient leakage; if the signal exhibited periodic changes over a longer period and repeatedly exceeded the threshold, it was identified as intermittent leakage; and if the signal amplitude remained consistently low but stable, it was identified as high-resistance leakage. Through this process, the leakage detection results, including whether leakage existed and the type of signal, were obtained.

[0068] Next, the location of the leakage is analyzed using the current phase difference method. Current signals from the main circuit and each branch are simultaneously acquired, and the phase difference and amplitude difference between the main circuit and each branch are calculated. When the current phase of a branch deviates significantly from that of the main circuit, and the current amplitude meets the leakage characteristic conditions, that branch is marked as a suspected leakage circuit. By comparing this with the branch number information in the distribution box, the specific location of the leakage is determined, obtaining the leakage location information.

[0069] Finally, the results of threshold determination and phase difference location are integrated. The signal type is associated with the location result and output to form leakage detection results and leakage location information that include leakage status, signal type, and specific branch location.

[0070] Furthermore, in the method provided in the application embodiments, after identifying the leakage detection result and leakage location information of the distribution box, it further includes:

[0071] Based on leakage current detection results, historical trend analysis and short-time high-gain window detection are performed on intermittent, transient, or high-resistance leakage current events to determine the leakage current event status and leakage current amplitude. The multi-level power distribution system topology is analyzed based on leakage current location information to identify the sub-circuit to which the faulty branch belongs and the potentially affected upstream protectors, generating a sub-circuit topology and a list of faulty branches. Leakage current event status and sub-circuit information are exchanged via high-speed communication, and optimal selective protection is performed based on leakage current amplitude, event status, and sub-circuit topology. The protector closest to the faulty branch is prioritized for operation, while the upstream protector delays or maintains power supply. Simultaneously, the management interface is updated in real-time to visually display the faulty branch and protector operation status.

[0072] In this embodiment, based on leakage current detection results, when performing historical trend analysis and short-time high-gain window detection for intermittent, transient, or high-resistance leakage events, a sliding time window statistical method is used to perform time series analysis on the leakage current amplitude, duration, and event frequency. Within a fixed time window, the average leakage current, maximum leakage current, and the number of event occurrences are calculated. For transient leakage events, a short-time high-gain window detection method is activated within the time window to capture the peak value change of the leakage current at a high sampling rate. Through this process, the leakage event state and leakage current amplitude are determined.

[0073] Subsequently, the topology of the multi-level power distribution system is analyzed based on the leakage current location information. Using a node connection mapping method, a topology mapping table between nodes is constructed based on the branch numbers, bus numbers, and connection relationships of the multi-level power distribution system. The upstream and downstream dependencies between nodes are calculated level by level to identify the sub-circuit to which the faulty branch belongs and determine the upstream protector electrically connected to it. Through this process, the sub-circuit topology and a list of faulty branches are generated.

[0074] Next, a high-speed communication transmission method is used to exchange leakage event status and sub-circuit information among the various protection devices. By encapsulating the event data into data frames and performing cyclic verification, the leakage event status, leakage current amplitude, and sub-circuit topology data are synchronously transmitted to the relevant protectors, achieving real-time sharing of event information. Through this process, real-time interaction between leakage event status and sub-circuit information is completed.

[0075] Finally, based on the leakage current amplitude, event status, and circuit topology information, a selective protection determination method is used for protection action control. By calculating the electrical distance between the faulty branch and each protector and comparing it with the action threshold, the protector closest to the faulty branch is determined to act first, while the upstream protector implements a delayed action or maintains power supply. After the action is completed, the management interface is updated in real time, and the location of the faulty branch and the action status of the protectors are displayed using a topology visualization method. Through this process, the selective protection execution results and visualized fault display information are obtained.

[0076] In summary, the embodiments of this application have at least the following technical effects:

[0077] This application establishes an identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; it collects current detection environment parameters, uses the identification influence relationship to perform gain analysis on the detection environment parameters, obtains target gain parameters and gain adjustment amounts; it adjusts the gain of the leakage current detection sensor according to the target gain parameters and gain adjustment amounts, and acquires monitoring signals; it performs leakage current detection based on the leakage current monitoring signals, and identifies the leakage current detection results and leakage current location information of the distribution box. This invention solves the technical problem in the prior art where leakage current detection is easily interfered with by environmental factors, leading to insufficient detection sensitivity and accuracy. By establishing an identification influence relationship between the detection environment and leakage current detection and dynamically adjusting the sensor gain based on this relationship, it achieves the technical effect of improving the sensitivity and accuracy of leakage current detection.

[0078] Example 2, based on the same inventive concept as the leakage current detection method for the distribution box in the previous examples, such as... Figure 2 As shown, this application provides a leakage current detection device for a distribution box. The device and method embodiments in this application are based on the same inventive concept. The device includes:

[0079] The influence relationship establishment module 11 is used to establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; the gain analysis module 12 is used to collect the current detection environment parameters, use the identification influence relationship to perform gain analysis on the detection environment parameters, and obtain the target gain parameter and gain adjustment amount; the gain adjustment module 13 is used to adjust the gain of the leakage current detection sensor according to the target gain parameter and gain adjustment amount, and obtain the leakage current monitoring signal; the leakage current detection module 14 is used to perform leakage current detection according to the leakage current monitoring signal, and identify the leakage current detection result and leakage current location information of the distribution box.

[0080] Furthermore, the device is also used to perform the following functions:

[0081] The detection environment parameters include at least: humidity, temperature, air ions, dust, load fluctuation, and power grid harmonic components. Based on these environmental parameters, experimental or field data related to the leakage current detection signal are collected. Univariate and multivariate correlation analyses are performed on the collected data samples to extract the influence characteristics of each environmental parameter on the leakage current detection results, determining the influence relationship between the environmental parameter variables and the leakage current detection results, including incremental or weakening relationships. The influence relationship is fitted and quantified using the data samples to obtain the influence relationship coefficients. The influence relationship, the detection environment parameters, and the corresponding influence relationship coefficients are mapped and correlated to establish the identification influence relationship between the detection environment and leakage current detection.

[0082] Furthermore, the device is also used to perform the following functions:

[0083] Based on the sensitivity of the detection environment parameters to their own changes and empirical values ​​for their impact on leakage current, a preliminary sampling step size is determined. Adaptive sampling of the detection environment parameters is performed according to the preliminary sampling step size to obtain a first data sample. Using the first data sample, influence relationships are identified to obtain an influence sensitivity distribution. Based on the influence sensitivity distribution, high-density regions are identified, and the preliminary sampling step size for these high-density regions is granularized and subjected to secondary sampling to obtain a second data sample. The first and second data samples are deduplicated and integrated to obtain a data sample set.

[0084] Furthermore, the device is also used to perform the following functions:

[0085] For each environmental parameter interval in the sensitivity distribution, calculate the sensitivity gradient or second-order rate of change; when the sensitivity gradient or rate of change is greater than a preset threshold, trigger step-size fission to increase the sampling density; or calculate the residual based on the relationship model fitted by the current sampled data, and trigger step-size fission when the residual exceeds a preset threshold; when both the sensitivity gradient and the residual are lower than the threshold, stop fission, determine that the current sampling density has met the accuracy required to establish the influence relationship, and obtain the data sample set.

[0086] Furthermore, the device is also used to perform the following functions:

[0087] The step-size fission and secondary sampling are iterated multiple times. The iteration termination condition includes that the sensitivity gradient, the second-order rate of change and the fitting residual all meet the preset convergence condition. The sensitivity gradient or the second-order rate of change is calculated by the change in leakage signal response caused by the change in environmental parameters, and the residual is calculated by the fitting error of the sampling data by the established environmental parameter-leakage detection relationship model.

[0088] Furthermore, the device is also used to perform the following functions:

[0089] Substitute the real-time collected detection environment parameters into the identification influence relationship, calculate the incremental or decremental effect of the environment parameter or parameter combination on the leakage current detection sensitivity, and obtain the gain coefficient of each environment parameter; combine the gain coefficients of each environment parameter to obtain the target gain parameter, and calculate the gain adjustment amount based on the target gain parameter and the current sensor state.

[0090] Furthermore, the device is also used to perform the following functions:

[0091] For transient, intermittent, or high-impedance leakage signals, the target gain parameters and gain adjustment amounts are calculated independently, and differentiated gain processing is performed for each type of signal. Specifically, high-bandwidth sampling and instantaneous high-gain windows are used for transient leakage signals, long-term cumulative sampling and high-sensitivity threshold adjustment are used for intermittent leakage signals, and high-sensitivity low-noise gain amplification and sampling integral extension strategies are used for high-impedance leakage signals.

[0092] Furthermore, the device is also used to perform the following functions:

[0093] Based on leakage current detection results, historical trend analysis and short-time high-gain window detection are performed on intermittent, transient, or high-resistance leakage current events to determine the leakage current event status and leakage current amplitude. The multi-level power distribution system topology is analyzed based on leakage current location information to identify the sub-circuit to which the faulty branch belongs and the potentially affected upstream protectors, generating a sub-circuit topology and a list of faulty branches. Leakage current event status and sub-circuit information are exchanged via high-speed communication, and optimal selective protection is performed based on leakage current amplitude, event status, and sub-circuit topology. The protector closest to the faulty branch is prioritized for operation, while the upstream protector delays or maintains power supply. Simultaneously, the management interface is updated in real-time to visually display the faulty branch and protector operation status.

[0094] In Embodiment 3, based on the leakage current detection method of the distribution box in the foregoing embodiments and with the same inventive concept, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of any one of the methods in Embodiment 1 above.

[0095] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for detecting leakage current in a distribution box, characterized in that, include: Establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; Collect current detection environment parameters, and use the identified influence relationship to perform gain analysis on the detection environment parameters to obtain the target gain parameter and gain adjustment amount; The gain of the leakage current detection sensor is adjusted according to the target gain parameter and the gain adjustment amount to obtain the leakage current monitoring signal. Based on the leakage current monitoring signal, leakage current detection is performed to identify the leakage current detection result and leakage current location information of the distribution box; The establishment of the relationship between the detection environment and the identification of leakage current detection includes: The environmental parameters to be detected include at least: humidity, temperature, air ions, dust, load fluctuations, and power grid harmonic components. Based on the environmental parameters to be detected, experimental data or field data of leakage current detection signals are collected. Univariate and multivariate correlation analysis was performed on the collected data samples to extract the influence characteristics of each detection environment parameter on the leakage current detection results, and to determine the influence relationship between the environmental parameter variables and the leakage current detection results, including the incremental or weakening relationship. The influence relationship is fitted and quantified using data samples to obtain the influence relationship coefficient. The influence relationship, detection environment parameters and corresponding influence relationship coefficients are then mapped and associated to establish the identification influence relationship between the detection environment and leakage current detection. Among them, based on the detection environment parameters, the experimental data or field data collected for the leakage current detection signal include: Based on the sensitivity of the detection environment parameters to changes and empirical values ​​of their impact on leakage current, the initial sampling step size is determined. Based on the initial sampling step size, adaptive sampling of environmental parameters is performed to obtain the first data sample; Using the first data sample, the influence relationships are identified, and the influence sensitivity distribution is obtained; Based on the influence sensitivity distribution, high-density regions are identified. The initial sampling step size of the high-density regions is then subjected to granular fission, and secondary sampling is performed to obtain the second data sample. The first data sample and the second data sample are deduplicated and integrated to obtain a data sample set; After obtaining the second data sample, the process also includes: For each environmental parameter interval in the sensitivity distribution, calculate the sensitivity gradient or second-order rate of change; when the sensitivity gradient or rate of change is greater than a preset threshold, trigger step size fission to increase the sampling density; or calculate the residual based on the relational model fitted by the current sampling data, and trigger step size fission when the residual exceeds a preset threshold. When both the sensitivity gradient and the residual are below the threshold, the fission is stopped, and it is determined that the current sampling density has met the accuracy required to establish the influence relationship, and the data sample set is obtained. The step-size fission and secondary sampling are iterated multiple times, and the iteration termination conditions include the sensitivity gradient, the second-order rate of change and the fitting residual all satisfying the preset convergence conditions. The sensitivity gradient or second-order rate of change is calculated by the change in leakage signal response caused by changes in environmental parameters, and the residual is calculated by the fitting error of the sampled data to the established environmental parameter-leakage detection relationship model.

2. The leakage current detection method for a distribution box according to claim 1, characterized in that, Using the identified influence relationship, gain analysis is performed on the detection environment parameters to obtain the target gain parameter and gain adjustment amount, including: Substitute the real-time collected detection environment parameters into the identification influence relationship, calculate the incremental or weakening effect of the environmental parameters or parameter combinations on the leakage current detection sensitivity, and obtain the gain coefficient of each environmental parameter. By combining the gain coefficients of various environmental parameters, the target gain parameter is obtained, and the gain adjustment amount is calculated based on the target gain parameter and the current sensor state.

3. The leakage current detection method for a distribution box according to claim 2, characterized in that, Obtaining the target gain parameter and the gain adjustment amount also includes: For transient, intermittent, or high-impedance leakage signals, the target gain parameters and gain adjustment amounts are calculated independently, and differentiated gain processing is performed for each type of signal. Among them, transient leakage signals adopt high-bandwidth sampling and instantaneous high-gain window, intermittent leakage signals adopt long-term cumulative sampling and high-sensitivity threshold adjustment, and high-impedance leakage signals adopt high-sensitivity low-noise gain amplification and sampling integration extension strategy.

4. The leakage current detection method for a distribution box according to claim 3, characterized in that, After identifying the leakage current detection results and leakage current location information of the distribution box, the following is also included: Based on the leakage current detection results, historical trend analysis and short-time high-gain window detection are performed on intermittent, transient or high-resistance leakage current events to determine the leakage current event status and leakage current amplitude. Based on the leakage current location information, the topology of the multi-level power distribution system is analyzed to identify the sub-circuit to which the faulty branch belongs and the upstream protectors that may be affected, and a sub-circuit topology and a list of faulty branches are generated. The system exchanges leakage event status and branch circuit information through high-speed communication, and performs optimal selective protection based on leakage current amplitude, event status and branch circuit topology. It determines the protector closest to the faulty branch to take priority action, while the upper-level protector delays or maintains power supply. At the same time, the management interface is updated in real time to visualize the faulty branch and the protector action status.

5. A leakage current detection device for a distribution box, characterized in that, The device is used to perform the leakage current detection method for the distribution box as described in any one of claims 1-4, and the device comprises: The influence relationship establishment module is used to establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients; The gain analysis module is used to collect the current detection environment parameters, and use the identified influence relationship to perform gain analysis on the detection environment parameters to obtain the target gain parameter and the gain adjustment amount. The gain adjustment module is used to adjust the gain of the leakage current detection sensor according to the target gain parameter and the gain adjustment amount, so as to obtain the leakage current monitoring signal. The leakage current detection module is used to detect leakage current based on the leakage current monitoring signal, and to identify the leakage current detection results and leakage current location information of the distribution box.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the leakage current detection method for the distribution box as described in any one of claims 1-4.

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