Electric leakage detection method and device of distribution box and storage medium

Abstract

The invention discloses an electric leakage detection method and device for a distribution box and a storage medium, and relates to the technical field of electric leakage detection, and the method comprises the steps: building an identification influence relation between a detection environment and electric leakage detection, and the identification influence relation comprises an increment, a weakening relation and a corresponding relation coefficient; collecting a current detection environment parameter, and performing gain analysis on the detection environment parameter by using the identification influence relationship to obtain a target gain parameter and a gain adjustment amount; performing gain adjustment on the electric leakage detection sensor according to the target gain parameter and the gain adjustment amount to obtain a monitoring signal; and performing electric leakage detection according to the electric leakage monitoring signal, and identifying an electric leakage detection result and electric leakage positioning information of the distribution box. The technical problem that in the prior art, electric leakage detection is prone to being interfered by environmental factors, and consequently the detection sensitivity and accuracy are insufficient is solved, and the technical effect of improving the electric leakage detection sensitivity and accuracy is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of leakage detection, and particularly relates to a leakage detection method and device for a distribution box and a storage medium. BACKGROUND

[0002] In the leakage monitoring process of the distribution box, the detection result is often affected by various environmental factors such as humidity, temperature, power grid harmonics and load fluctuation. These factors can cause fluctuations in the amplitude of the leakage signal, making the sensitivity of the sensor inconsistent in different environments, and thus affecting the accuracy and stability of the leakage detection. Especially in the scene 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 leakage fault position in time and accurately, and increasing the safety risk of the operation of the distribution box. SUMMARY

[0003] The present application provides a leakage detection method and device for a distribution box and a storage medium, which are used to solve the technical problem that the leakage detection in the prior art is easily disturbed by environmental factors, resulting in insufficient detection sensitivity and accuracy.

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

[0005] In a first aspect of the present application, a leakage detection method for a distribution box is provided, which comprises: establishing an identification influence relationship between a detection environment and leakage detection, including an increment, a weakening relationship and a corresponding relationship coefficient; collecting current detection environment parameters, performing gain analysis on the detection environment parameters by using the identification influence relationship, obtaining target gain parameters and gain adjustment amount; gain adjusting the leakage detection sensor according to the target gain parameters and the gain adjustment amount, and obtaining a monitoring signal; performing leakage detection according to the leakage monitoring signal, and identifying the leakage detection result and the leakage positioning information of the distribution box.

[0006] In a second aspect of the present application, a leakage detection device for a distribution box is provided, which comprises: An influence relationship establishing module is configured to establish an identification influence relationship between a detection environment and leakage detection, including an increment, a weakening relationship and a corresponding relationship coefficient; a gain analysis module is configured to collect current detection environment parameters, perform gain analysis on the detection environment parameters by using the identification influence relationship, and obtain target gain parameters and gain adjustment amount; a gain adjusting module is configured to gain adjust the leakage detection sensor according to the target gain parameters and the gain adjustment amount, and obtain a leakage monitoring signal; and a leakage detection module is configured to perform leakage detection according to the leakage monitoring signal, and identify the leakage detection result and the leakage positioning information of the distribution box.

[0007] In a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the method for detecting leakage of a distribution box is implemented.

[0008] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The present application establishes the identification influence relationship between the detection environment and the leakage detection, including the increment, the weakening relationship and the corresponding relationship coefficient; the current detection environment parameters are collected, the gain analysis is performed on the detection environment parameters by using the identification influence relationship, the target gain parameter and the gain adjustment amount are obtained; the gain adjustment is performed on the leakage detection sensor according to the target gain parameter and the gain adjustment amount, and the monitoring signal is obtained; the leakage detection is performed according to the leakage monitoring signal, and the leakage detection result and the leakage positioning information of the distribution box are identified. The present application solves the technical problem that the leakage detection in the prior art is easily disturbed by environmental factors, resulting in insufficient detection sensitivity and accuracy. By establishing the identification influence relationship between the detection environment and the leakage detection and dynamically adjusting the sensor gain based on the relationship, the technical effect of improving the leakage detection sensitivity and accuracy is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0010] Figure 1 The flowchart of the method for detecting leakage of a distribution box provided by the embodiments of the present application is shown. Figure 2 The structural diagram of the leakage detection device of a distribution box provided by the embodiments of the present application is shown.

[0011] Legend of the drawings: influence relationship establishing module 11, gain analysis module 12, gain adjustment module 13, leakage detection module 14. DETAILED DESCRIPTION

[0012] The present application provides a method and device for detecting leakage of a distribution box and a storage medium. The technical problem that the leakage detection in the prior art is easily disturbed by environmental factors, resulting in insufficient detection sensitivity and accuracy, is solved. The sensor gain is dynamically adjusted based on the identification influence relationship between the detection environment and the leakage detection, and the technical effect of improving the leakage detection sensitivity and accuracy is achieved.

[0013] 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.

[0014] 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.

[0015] Example 1, as Figure 1 As shown, this application provides a leakage current detection method for a distribution box, the method comprising: Step S100: Establish the identification influence relationship between the detection environment and leakage current detection, including incremental and attenuation relationships and corresponding relationship coefficients.

[0016] Furthermore, in the method provided in the application embodiments, the step of establishing the relationship between the detection environment and the identification of leakage current detection further includes: 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.

[0017] 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.

[0018] Based on the detection of environmental parameters, the experimental data or field data of the leakage detection signal is collected, and the humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components are collected by a multi-channel synchronous collection method, and the leakage detection signal is recorded at the same time to form the experimental data or field data set. The collection process is based on the change sensitivity of the detection environment parameters and the empirical value of its influence on the leakage detection, and the appropriate sampling step is determined, and the multi-level data samples are obtained by adaptive sampling and high-density area encryption sampling, and finally the data sample set is formed by de-duplication and integration.

[0019] Next, the collected data samples are subjected to single variable and multi-variable correlation analysis. The single variable analysis calculates the linear correlation degree between each detection environment parameter and the leakage detection result by using the Pearson correlation coefficient, and judges the influence direction and strength of the detection sensitivity and signal amplitude. The multi-variable analysis calculates the overall correlation of multiple detection environment parameter combinations to identify the superposition effect and interaction of environmental factors. Through this analysis, the incremental relationship or weakening relationship between each parameter and the leakage detection result is determined. The incremental relationship means that when the environmental parameter increases, the leakage signal amplitude or sensitivity is enhanced, and the weakening relationship means that when the environmental parameter increases, the signal amplitude or sensitivity is reduced.

[0020] Subsequently, the influence relationship is fitted and quantified using the data samples to obtain the influence relationship coefficient. Specifically, the detection environment parameters are taken as independent variables, and the leakage detection result is taken as dependent variable, and the correlation result is fitted by using the multiple linear regression method. In this process, the regression coefficient corresponding to each environmental parameter obtained by regression calculation is the influence relationship coefficient. The positive sign of the influence relationship coefficient corresponds to the incremental relationship, indicating that the leakage detection sensitivity is enhanced when the environmental parameter increases; the negative sign corresponds to the weakening relationship, indicating that the detection sensitivity is weakened when the environmental parameter increases. The absolute value of the influence relationship coefficient represents the influence of the parameter on the leakage detection sensitivity and signal amplitude, and the greater the value, the more significant the influence on the detection result.

[0021] Finally, the influence relationship, the detection environment parameters and the corresponding influence relationship coefficient are mapped and associated. By one-to-one correspondence, the humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components are directly related to their influence relationship coefficients, and the action direction and degree of each environmental parameter are determined according to the mapping relationship. When the environmental parameters are collected in real time, the incremental or weakening contribution of the environmental factors to the leakage detection sensitivity can be directly calculated based on the mapping relationship, and the comprehensive action result is obtained, thereby completing the establishment of the identification influence relationship between the detection environment and the leakage detection.

[0022] Further, in the method provided by the application embodiment, based on the detection of the environmental parameters, the experimental data or field data of the leakage detection signal is collected, and the humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components are collected by a multi-channel synchronous collection method, and the leakage detection signal is recorded at the same time to form the experimental data or field data set. The collection process is based on the change sensitivity of the detection environment parameters and the empirical value of its influence on the leakage detection, and the appropriate sampling step is determined, and the multi-level data samples are obtained by adaptive sampling and high-density area encryption sampling, and finally the data sample set is formed by de-duplication and integration. A preliminary sampling step is determined based on a self-change sensitivity of a detection environment parameter and an empirical value of a leakage influence; adaptive sampling of the detection environment parameter is performed according to the preliminary sampling step to obtain a first data sample; the first data sample is used to perform identification of an influence relationship to obtain an influence sensitivity distribution; a high-density area is identified based on the influence sensitivity distribution, and a granularity fission is performed on the preliminary sampling step of the high-density area to perform secondary sampling to obtain a second data sample; and the first data sample and the second data sample are de-duplicated and integrated to obtain a data sample set.

[0023] In the embodiments of the present application, first, a preliminary sampling step is determined based on a self-change sensitivity of a detection environment parameter and an empirical value of a leakage influence. In this process, a fixed interval division method is used to divide humidity, temperature, air ions, dust, load fluctuations and power grid harmonic components and other detection environment parameters into several fixed intervals according to the historical change range. By calculating the average change rate of the leakage detection signal in each fixed interval, the target interval with the highest sensitivity is identified, and the preliminary sampling step is determined according to the average change rate of the target interval. Taking humidity as an example, when the humidity in the 40% to 60% interval corresponds to the maximum change rate of the leakage detection signal, the change rate of this interval is used as the basis for calculating the sampling frequency, thereby determining the preliminary sampling step.

[0024] Next, adaptive sampling of the detection environment parameter is performed according to the preliminary sampling step. In this process, a threshold trigger sampling method is used to set a sampling trigger threshold for humidity, temperature, air ions, dust, load fluctuations and power grid harmonic components and other detection environment parameters. When the change of any parameter exceeds the set threshold, sampling is triggered immediately, and when the change is below the threshold, periodic sampling is performed according to the preliminary sampling step. For example, when the temperature change exceeds 2°C, sampling is triggered immediately, and when the temperature is stable, sampling is performed at a sampling step of 5 minutes. Through this sampling process, the first data sample is obtained.

[0025] Subsequently, the first data sample is used to perform influence relationship identification to obtain an influence sensitivity distribution. In this process, an interval difference method is used to divide the first data sample according to the fixed interval of the detection environment parameter, and the difference value of the average value of the leakage detection signal in adjacent intervals is calculated, with the difference value as a quantitative indicator of sensitivity. The larger the difference value, the more significant the change of the leakage detection signal corresponding to the detection environment parameter interval, and the higher the sensitivity. Through interval difference calculation of humidity, temperature, air ions, dust, load fluctuations and power grid harmonic components, the influence sensitivity distribution is formed.

[0026] Afterwards, high-density areas are identified based on the impact sensitivity distribution, and the preliminary sampling step size in the high-density areas is subjected to granularity fission for secondary sampling. The high-density area refers to a parameter interval in which the sensitivity value continuously exceeds a preset threshold. After identifying the high-density area, the granularity fission method is used, that is, the preliminary sampling step size is reduced by a fixed proportion, for example, to one-half or one-third of the original, and secondary sampling is only performed in the high-density area, so as to obtain a higher sampling resolution in the key parameter interval. For example, when the preliminary sampling step size is 5 minutes, the sampling step size in the high-density area is shortened to 2.5 minutes, so as to more finely capture the corresponding change relationship between the environmental parameters and the electric leakage detection signal, and finally obtain the second data sample.

[0027] Finally, the first data sample and the second data sample are de-duplicated and integrated. In this process, the timestamp comparison method is used to compare one by one with the sampling timestamp as the primary key, to eliminate duplicate sampling points and keep unique records. Subsequently, the two types of samples are combined in chronological order to ensure data continuity and time sequence integrity, thereby obtaining the data sample set.

[0028] Further, the method provided by the application embodiment further comprises the following steps after obtaining the second data sample: For each environmental parameter interval in the impact sensitivity distribution, the sensitivity gradient or the second-order change rate is calculated; when the sensitivity gradient or the change rate is greater than a preset threshold, step fission is triggered to increase the sampling density; or the residual error is calculated based on the relationship model fitted by the current sampling data, and when the residual error exceeds a preset threshold, step fission is triggered; when the sensitivity gradient and the residual error are both lower than the threshold, the fission is stopped, and it is determined that the current sampling density has met the required accuracy for identifying the impact relationship and establishing the relationship model, thereby obtaining the data sample set.

[0029] In the application embodiment, for each detection environmental parameter interval in the impact sensitivity distribution, the sensitivity gradient or the second-order change rate is first calculated. The sensitivity gradient refers to the change rate of the impact sensitivity distribution in adjacent sampling points or intervals, which is used to measure the rising or falling trend of the sensitivity with the change of the environmental parameter; the second-order change rate refers to the change of the sensitivity change rate, which is used to identify the inflection point with a sharp change in the sensitivity curve. In actual calculation, the difference calculation method is used to obtain the change rate of the sensitivity in adjacent intervals, and the change rate is further subjected to difference calculation to obtain the second-order change rate.

[0030] When the sensitivity gradient value or the second order change rate value exceeds the preset threshold, step fission is performed to increase the sampling density. Specifically, in the corresponding detection environment parameter interval, the original sampling step is shortened by a fixed proportion, for example, shortened to half or one third of the original step, and higher frequency sampling is only performed in the sensitive area. Taking temperature as an example, when the sensitivity gradient of the temperature corresponding interval exceeds the threshold, the original 5-minute sampling step is shortened to 2.5 minutes, and supplementary sampling is performed in the temperature interval, thereby improving the accuracy of the characterization of the leakage detection signal response caused by the temperature change. Through the process, the fissioned sampling plan and the newly added sampling points are obtained.

[0031] At the same time, based on the current sampling data, a relationship model between the environmental parameters and the leakage detection signal is established, the sampling data is fitted, and the residual error is calculated. The residual error is obtained by comparing the difference between the model prediction value and the actual sampling value, and is used to reflect the influence of the sampling density on the fitting accuracy of the model. When the residual error of a certain interval exceeds the preset threshold, it indicates that the sampling density of the interval is insufficient, and step fission needs to be triggered again to shorten the sampling step and perform supplementary sampling in the interval to improve the sampling density and fitting accuracy. Through the process, the supplementary sampling plan and the newly added sampling points triggered based on the residual error are obtained.

[0032] In the above step fission and supplementary sampling process, the fission and secondary sampling can be iterated multiple times. After each iteration, the sensitivity gradient, the second order change rate and the residual error are recalculated, and it is determined whether they still exceed the preset threshold. When the sensitivity gradient, the second order change rate and the residual error of all detection environment parameter intervals are all lower than the respective thresholds, it indicates that the sampling density has reached the accuracy required for identifying the influence relationship establishment, at which time the fission process is terminated, and the sampling density is stable and no longer changes.

[0033] Finally, all the sampling data obtained by the initial sampling, the fission sampling and the supplementary sampling are unified and arranged in chronological order. By comparing the sampling time, the repeated sampling points are eliminated, the effective records are retained, and the data sample set is formed.

[0034] Further, the method provided by the application embodiment further comprises: The step fission and the secondary sampling are iterated multiple times, and the iteration end condition includes that the sensitivity gradient, the second order change rate and the fitting residual error all satisfy the preset convergence condition; wherein the sensitivity gradient or the second order change rate is calculated by the leakage signal response change caused by the change of the environmental parameter, and the residual error is calculated by the fitting error of the sampling data of the established environmental parameter-leakage detection relationship model.

[0035] In the embodiment of the present application, the execution process of step length fission and secondary sampling dynamically adjusts the sampling granularity through multiple iterations, and takes the sensitivity gradient, the second-order change rate and the fitting residual as the convergence judgment basis. In the iteration process, when the sensitivity gradient or the second-order change rate of the environmental parameter interval exceeds the preset threshold, step length fission is performed, the sampling step is shortened, and secondary sampling is performed in the corresponding interval to improve the sampling resolution and make the sampling data more accurately reflect the characteristics of the response of the leakage detection signal to the change of the detection environment parameter.

[0036] At the same time, in the iteration process, an environmental parameter-leakage detection relationship model is established based on the collected detection environment parameters and leakage detection signal data. In the establishment process, the least square linear regression method is adopted, the humidity, temperature, air ions, dust, load fluctuation, power grid harmonic components and other detection environment parameters are taken as independent variables, and the leakage detection signal is taken as dependent variable, and the regression coefficient and fitting equation are obtained through regression calculation. The fitting equation represents the mapping relationship between the change of the detection environment parameter and the response of the leakage detection signal. By inputting the actual sampling data into the environmental parameter-leakage detection relationship model, the predicted value of the model is calculated, and the predicted value and the actual sampling value are differentially calculated to obtain the residual. The residual is used to measure the fitting accuracy of the model. The larger the residual is, the less the sampling density is or the less the model description accuracy is.

[0037] In each iteration, if the residual exceeds the preset residual threshold, step length fission is triggered in the corresponding detection environment parameter interval, the sampling step is shortened again, supplementary sampling is performed to increase the sampling points and improve the data accuracy, and the environmental parameter-leakage detection relationship model is fitted and updated again. When the residual gradually decreases to below the preset residual threshold, it indicates that the model fitting accuracy has met the requirements.

[0038] Step length fission and secondary sampling are repeatedly performed in the iteration, and the sampling granularity and fitting accuracy are continuously optimized through real-time monitoring of the sensitivity gradient, the second-order change rate and the residual. When the sensitivity gradient, the second-order change rate and the residual all meet the preset convergence conditions, it indicates that the sampling accuracy has met the requirements of identifying the influence relationship, and the iteration process is terminated. In this way, high-precision sampling data is obtained, and an environmental parameter-leakage detection relationship model with high fitting accuracy and good stability is finally formed, which provides reliable data support for the identification and positioning of the leakage detection result.

[0039] Step S200: Collecting the current detection environment parameter, and performing gain analysis on the detection environment parameter by using the identified influence relationship to obtain a target gain parameter and a gain adjustment amount.

[0040] In the embodiment of the present application, first, the humidity, temperature, air ions, dust, load fluctuation, power grid harmonic components and other detection environment parameters are collected in real time by the pre-arranged sensing collection device to determine the current detection environment parameter.

[0041] Next, the detection environment parameters are analyzed for gain by using the identified influence relationship. In this process, the detection environment parameters such as humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components collected in real time are substituted into the established identified influence relationship, the incremental effect or weakening effect of each environment parameter or parameter combination on the leakage detection sensitivity is calculated, and the corresponding gain coefficient is obtained. Then, the gain coefficients are synthesized to obtain the target gain parameter in the current detection environment, and the gain adjustment amount is calculated in combination with the target gain parameter and the working state of the current leakage detection sensor.

[0042] Further, the method provided by the application embodiment further comprises the following steps: The real-time collected detection environment parameters are substituted into the identified influence relationship, the incremental effect or weakening effect of each environment parameter or parameter combination on the leakage detection sensitivity is calculated, and the gain coefficient of each environment parameter is obtained. The target gain parameter is obtained by synthesizing the gain coefficients of the environment parameters, and the gain adjustment amount is calculated according to the target gain parameter and the current sensor state.

[0043] In the application embodiment, the detection environment parameters such as humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components collected in real time are substituted into the established identified influence relationship for calculation. The regression coefficient substitution method is used to one-to-one map the measured values of each detection environment parameter and the corresponding influence relationship coefficient, and the incremental effect or weakening effect on the leakage detection sensitivity is obtained through multiplication operation. In this process, humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components are used as input variables, which are multiplied by the influence relationship coefficients obtained by regression calculation in advance. For example, the influence relationship coefficient of humidity is , the corresponding effect value is when the current humidity value is 65%; the influence relationship coefficient of temperature is , the corresponding effect value is when the current temperature value is 28℃; and the influence relationship coefficient of power grid harmonic component is , the corresponding effect value is when the current value is 5%. Through this step, the gain coefficients of humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components are obtained.

[0044] Next, the gain coefficients of each detection environment parameter are synthesized, and the linear superposition method is used to directly add the numerical values of all gain coefficients to obtain the target gain parameter of the comprehensive effect of the current environment. For example, the gain coefficients of the above-mentioned environment parameters are added to obtain the target gain parameter , indicating that the overall environmental factors have a positive gain effect on the detection sensitivity of the leakage current under the current environmental conditions.

[0045] Finally, the target gain parameter is differentially calculated with the current sensor state, and a differential calculation method is used, and 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 , and the gain adjustment amount is , indicating that the sensor gain needs to be adjusted positively; if the target gain parameter is , the gain adjustment amount is , indicating that the sensor gain needs to be adjusted reversely.

[0046] Further, the method provided by the application embodiment further comprises: For transient, intermittent or high resistance leakage signals, the target gain parameter and the gain adjustment amount are independently calculated, and differential gain processing is performed for each type of signal; wherein, for transient leakage signals, high bandwidth sampling and instantaneous high gain window are used, for intermittent leakage signals, long time accumulation sampling and high sensitivity threshold adjustment are used, and for high resistance leakage signals, high sensitivity low noise gain amplification and sampling integration extension strategy are used.

[0047] In the application embodiment, the target gain parameter and the gain adjustment amount are independently calculated for transient leakage signals, intermittent leakage signals and high resistance leakage signals. Among them, for transient leakage signals, high bandwidth sampling method is used for signal acquisition. By increasing the sampling frequency to more than ten times of the highest frequency component of the signal, the complete capture of the millisecond current mutation is ensured. For example, when the duration of the transient leakage signal is less than 2ms, the sampling frequency is set to 100kHz or more to ensure that the waveform details are not distorted. After sampling, an instantaneous high gain window is enabled in the signal mutation interval, and the gain is increased to twice or three times of the reference gain in the window, amplifying the peak part of the leakage signal and enhancing the detection sensitivity. Subsequently, the amplitude ratio of the signals before and after amplification is calculated to obtain the target gain parameter of the transient signal, and then the target gain parameter and the sensor reference gain value are subtracted to obtain the corresponding gain adjustment amount. On this basis, the transient high gain amplification processing is implemented, that is, the amplification multiple is temporarily increased in the signal mutation period, and high sensitivity response to transient leakage events is realized.

[0048] For intermittent leakage signals, long-time cumulative sampling method is adopted. By extending the sampling time window, such as 10 minutes to 30 minutes, the current signal is continuously collected and cumulatively averaged, the short-time noise interference is weakened, and the periodic characteristics of the intermittent leakage event are extracted. In this process, a high-sensitivity threshold adjustment mechanism is enabled, and the leakage event triggering threshold is reduced from 100% of the baseline sensitivity to 80% to ensure that signals with low amplitude or low occurrence frequency can also be effectively identified. According to the average amplitude change rate of the cumulative signal, the target gain parameter of the intermittent signal is calculated, and then the gain adjustment amount is calculated by combining the parameter with the difference between the current working gain of the sensor. On this basis, high-sensitivity threshold gain processing is implemented, that is, by moderately increasing the amplification factor and lowering the triggering threshold, the response capability and capture accuracy of the intermittent signal are improved.

[0049] Finally, for high-resistance leakage signals, high-sensitivity low-noise amplification method and sampling integration extension method are adopted. First, the low-noise amplification circuit is used to improve the signal amplification sensitivity and suppress the background noise, so as to ensure the effective amplification of the low-amplitude leakage signal. Then, the sampling integration time is extended, such as from 100 ms to 500 ms, to increase the 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 of the high-resistance leakage signal is obtained, and then the gain adjustment amount is obtained by differentiating the current baseline gain value of the sensor. On this basis, high-sensitivity low-noise gain processing is implemented, that is, by increasing the gain and extending the integration sampling time, the detection capability of the high-resistance small current signal is enhanced.

[0050] Step S300: Gain adjustment is performed on the leakage detection sensor according to the target gain parameter and the gain adjustment amount, and a leakage monitoring signal is obtained.

[0051] In the embodiments of the present application, gain adjustment is performed on the leakage detection sensor according to the target gain parameter and the gain adjustment amount. In this process, first, the target gain parameter is compared with the gain baseline value of the sensor to determine the gain adjustment direction and amplitude required. When the target gain parameter is greater than the gain baseline value, gain boosting operation is performed; when the target gain parameter is less than the gain baseline value, gain attenuation operation is performed, so that clear gain adjustment quantitative basis is obtained.

[0052] Subsequently, gain control adjustment method is used to adjust the preamplification circuit of the leakage detection sensor. By directly acting the gain adjustment amount on the amplification coefficient of the amplification circuit, the amplification factor of the sensor is kept consistent with the target gain parameter. For example, when the target gain parameter is 20 dB, and the gain baseline value is 0 dB, the gain adjustment amount is 20 dB, and the amplification factor of the preamplification circuit is increased to 20 dB. ​At this time, the signal amplitude is accurately raised to the target level by adjusting the amplification factor, and the amplitude is amplified or attenuated. During the entire 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.

[0053] After the gain adjustment is completed, the leakage detection sensor amplifies and converts the leakage current in the line, and outputs the adjusted leakage monitoring signal.

[0054] Step S400: According to the leakage monitoring signal, leakage detection is performed, and the leakage detection result and the leakage positioning information of the distribution box are identified.

[0055] In the embodiments of the present application, when leakage detection is performed according to the leakage monitoring signal, the leakage monitoring signal is first input to the signal processing link, and the signal feature extraction method is used to extract the amplitude feature, frequency feature and phase feature of the signal. The strength feature of the leakage current is extracted by calculating the effective value and the peak value of the signal; the power frequency component and the harmonic component are extracted by performing fast Fourier transform on the frequency component; and the phase feature of the signal is represented by calculating the phase information.

[0056] Then, the threshold judgment method is used to identify the leakage of the leakage monitoring signal. The real-time monitoring signal is compared with the preset leakage judgment threshold value, when the signal effective value exceeds the leakage action current threshold value, it is judged that there is leakage in the distribution box; when the signal amplitude suddenly changes in a short time and exceeds the transient judgment threshold value, it is identified as transient leakage; when the signal presents periodic change in a long time and exceeds the threshold value many times, it is identified as intermittent leakage; when the signal amplitude is continuously low but stably exists, it is identified as high resistance leakage. Through this process, the leakage detection result of whether there is leakage and the signal type is obtained.

[0057] Next, the current phase difference positioning method is used to analyze the leakage position. The current signals of the main circuit and each branch are synchronously collected, and the phase difference and amplitude difference between the main circuit and each branch are calculated. When the current phase of a branch significantly deviates from the main circuit and the current amplitude meets the leakage characteristic condition, the branch is marked as a suspected leakage circuit. By corresponding to the branch number information of the distribution box, the specific position of the leakage is determined, and the leakage positioning information is obtained.

[0058] Finally, the results of threshold judgment and phase difference positioning are integrated. The signal type and the positioning result are associated and output to form the leakage detection result and the leakage positioning information containing the leakage state, the signal type and the specific branch position.

[0059] Further, the method provided by the embodiments of the present application further comprises: Based on the leakage detection result, the intermittent, transient or high resistance leakage event is analyzed for historical trend and detected for short time high gain window, the leakage event state and leakage current amplitude are determined; according to the leakage positioning information, the multi-stage power distribution system topology relationship is analyzed, the fault branch belongs to the sub-circuit and the possible affected upper protector are identified, and the sub-circuit topology and fault branch list are generated; the leakage event state and sub-circuit information are exchanged through high-speed communication, and the selective protection is selected according to the leakage current amplitude, event state and sub-circuit topology, the protector closest to the fault branch is determined to act preferentially, the upper protector is delayed or kept in power supply state, and the management interface is updated in real time to visually display the fault branch and the action state of the protector.

[0060] In the embodiments of the present application, based on the leakage detection result, the intermittent, transient or high resistance leakage event is analyzed for historical trend and detected for short time high gain window, the leakage current amplitude, duration and event occurrence frequency are analyzed by time series analysis using sliding time window statistics method, the average leakage current, maximum leakage current and event occurrence number are calculated in the fixed time window; for transient leakage event, short time high gain window detection method is used in the time window to capture the peak value change of leakage current at high sampling rate. Through the process, the leakage event state and leakage current amplitude are determined.

[0061] Then, the topology relationship of the multi-stage power distribution system is analyzed according to the leakage positioning information. By using node connection mapping method, the topology mapping table between nodes is constructed according to the branch number, bus number and connection relationship of the multi-stage power distribution system, the upstream and downstream attachment relationship between nodes is calculated level by level, the fault branch belongs to the sub-circuit is identified and the upper protector electrically connected therewith is determined. Through the process, the sub-circuit topology and fault branch list are generated.

[0062] Then, the leakage event state and sub-circuit information are exchanged between the protection devices by using high-speed communication transmission method. The event data is encapsulated and cyclically checked by data frame, the leakage event state, leakage current amplitude and sub-circuit topology data are synchronously transmitted to the related protectors, and the real-time sharing of event information is realized. Through the process, the real-time interaction of leakage event state and sub-circuit information is completed.

[0063] Finally, according to the leakage current amplitude, event state and sub-circuit topology information, the selective protection judgment method is used for protection action control. By calculating the electrical distance between the fault branch and each protector and comparing with the action threshold, the protector closest to the fault branch is determined to act preferentially, and the upper protector is delayed or kept in power supply state. After the action is completed, the management interface is updated in real time, and the topology visualization display method is used to display the fault branch position and the action state of the protector. Through the process, the selective protection execution result and the visual fault display information are obtained.

[0064] In the embodiments of the present application, the above-mentioned technical effects are achieved. The present application establishes the identification influence relationship between the detection environment and the leakage detection, including the increment, the weakening relationship and the corresponding relationship coefficient; collects the current detection environment parameters, uses the identification influence relationship to analyze the gain of the detection environment parameters, obtains the target gain parameter and the gain adjustment amount; adjusts the gain of the leakage detection sensor according to the target gain parameter and the gain adjustment amount, and obtains the monitoring signal; performs leakage detection according to the leakage monitoring signal, and identifies the leakage detection result and the leakage positioning information of the distribution box. The present application solves the technical problem that the leakage detection in the prior art is easily disturbed by environmental factors, resulting in insufficient detection sensitivity and accuracy. By establishing the identification influence relationship between the detection environment and the leakage detection and dynamically adjusting the sensor gain based on the relationship, the technical effect of improving the leakage detection sensitivity and accuracy is achieved.

[0065] In the embodiments of the present application, the above-mentioned technical effects are achieved. Figure 2 As shown in the above-mentioned embodiments, the present application provides a leakage detection device for a distribution box, and the device and method embodiments in the present application are based on the same inventive concept. The device comprises: The influence relationship establishing module 11 is configured to establish the identification influence relationship between the detection environment and the leakage detection, including the increment, the weakening relationship and the corresponding relationship coefficient; the gain analysis module 12 is configured to collect the current detection environment parameters, use the identification influence relationship to analyze the gain of the detection environment parameters, and obtain the target gain parameter and the gain adjustment amount; the gain adjustment module 13 is configured to adjust the gain of the leakage detection sensor according to the target gain parameter and the gain adjustment amount, and obtain the leakage monitoring signal; and the leakage detection module 14 is configured to perform leakage detection according to the leakage monitoring signal, and identify the leakage detection result and the leakage positioning information of the distribution box.

[0066] Further, the device is further configured to implement the following functions: The detection environment parameters at least include humidity, temperature, air ions, dust, load fluctuation and power grid harmonic components. Based on the detection environment parameters, experimental data or field data of the leakage detection signal are collected; the collected data samples are subjected to single-variable and multi-variable correlation analysis, the influence characteristics of each detection environment parameter on the leakage detection result are extracted, the influence relationship between the environmental parameter variable and the leakage detection result is determined, including the increment or weakening relationship; the influence relationship is quantitatively fitted by using the data samples, the influence relationship coefficient is obtained, and the influence relationship, the detection environment parameters and the corresponding influence relationship coefficient are mapped and associated to establish the identification influence relationship between the detection environment and the leakage detection.

[0067] Further, the device is further configured to implement the following functions: determine a preliminary sampling step length based on a self-change sensitivity of the detection environment parameter and an empirical value of the leakage influence; perform adaptive sampling of the detection environment parameter according to the preliminary sampling step length to obtain a first data sample; perform identification of the influence relationship using the first data sample to obtain an influence sensitivity distribution; identify a high-density area based on the influence sensitivity distribution, perform granularity fission on the preliminary sampling step length of the high-density area, perform secondary sampling to obtain a second data sample; and perform deduplication and integration of the first data sample and the second data sample to obtain a data sample set.

[0068] Further, the apparatus is further configured to implement the following functions: For each environmental parameter interval in the influence sensitivity distribution, calculate the sensitivity gradient or the second-order change rate; when the sensitivity gradient or the change rate is greater than a preset threshold, trigger step length fission to increase the sampling density; or calculate the residual based on the relationship model fitted based on the current sampling data, and when the residual exceeds a preset threshold, trigger step length fission; when the sensitivity gradient and the residual are both lower than the threshold, stop fission, and determine that the current sampling density has met the required accuracy for identifying the influence relationship, and obtain the data sample set.

[0069] Further, the apparatus is further configured to implement the following functions: The step length fission and the secondary sampling are iterated multiple times, and the iteration end conditions include that the sensitivity gradient, the second-order change rate, and the fitting residual all meet the preset convergence condition; wherein the sensitivity gradient or the second-order change rate is calculated based on the change in the leakage signal response caused by the change in the environmental parameter, and the residual is calculated based on the fitting error of the sampling data of the established environmental parameter-leakage detection relationship model.

[0070] Further, the apparatus is further configured to implement the following functions: Substitute the real-time collected detection environment parameter into the identified influence relationship to calculate the increment or weakening effect of the environmental parameter or parameter combination on the leakage detection sensitivity, obtain the gain coefficient of each environmental parameter, integrate the gain coefficients of all environmental parameters to obtain a target gain parameter, and calculate a gain adjustment amount based on the target gain parameter and the current sensor state.

[0071] Further, the apparatus is further configured to implement the following functions: For transient, intermittent, or high-resistance leakage signals, the target gain parameter and the gain adjustment amount are calculated independently, and differential gain processing is performed for each type of signal; wherein the transient leakage signal adopts high-bandwidth sampling and instantaneous high-gain window, the intermittent leakage signal adopts long-time cumulative sampling and high-sensitivity threshold adjustment, and the high-resistance leakage signal adopts high-sensitivity low-noise gain amplification and sampling integration extension strategy.

[0072] Further, the device is also used to realize the following functions: Based on the leakage detection result, the historical trend analysis and short-time high-gain window detection are performed on the intermittent, transient or high-resistance leakage event to determine the leakage event state and leakage current amplitude; the multi-stage power distribution system topology relationship is analyzed according to the leakage positioning information to identify the fault branch belonging to the sub-circuit and the possible affected upper-level protector, and a sub-circuit topology and fault branch list is generated; the leakage event state and sub-circuit information are exchanged through high-speed communication, and the leakage current amplitude, event state and sub-circuit topology are used for optimization and selective protection to determine the protector closest to the fault branch to act first, and the upper-level protector delays or maintains power supply, while the management interface is updated in real time to visually display the fault branch and the protector action state.

[0073] In the third embodiment, based on the leakage detection method of the distribution box in the foregoing embodiments, the same inventive concept is provided, and the application also provides a computer readable storage medium, which stores a computer program. The computer program realizes the steps of the method in any one of the first embodiment when executed.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or may be advantageous.

[0075] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the present application. Any modification, equivalent change and modification of the above embodiments based on the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for detecting an electric leakage of a distribution box, characterized by, The method comprises: establishing an identification influence relationship between a detection environment and leakage detection, including an increment relationship, a weakening relationship, and corresponding relationship coefficients; collecting current detection environment parameters, performing gain analysis on the detection environment parameters using the identification influence relationship, obtaining target gain parameters and gain adjustment amounts; performing gain adjustment on a leakage detection sensor according to the target gain parameters and the gain adjustment amounts, and obtaining a leakage monitoring signal; performing leakage detection according to the leakage monitoring signal, and identifying a leakage detection result and leakage positioning information of a distribution box.

2. The method of detecting an electric leakage of a distribution box according to claim 1, wherein The establishment of the identification influence relationship between the detection environment and the leakage detection comprises: The detection environment parameters at least include humidity, temperature, air ions, dust, load fluctuation, and power grid harmonic components. Experimental data or field data of the leakage detection signal are collected based on the detection environment parameters; single-variable and multi-variable correlation analysis is performed on the collected data samples, influence characteristics of each detection environment parameter on the leakage detection result are extracted, and an influence relationship between the environment parameter variables and the leakage detection result is determined, including an increment relationship or a weakening relationship; the influence relationship is quantitatively fitted using the data samples, influence relationship coefficients are obtained, the influence relationship, the detection environment parameters, and the corresponding influence relationship coefficients are mapped and associated, and the identification influence relationship between the detection environment and the leakage detection is established.

3. The method of ground fault detection for an electrical distribution box of claim 2, wherein, The collection of the experimental data or the field data of the leakage detection signal based on the detection environment parameters comprises: a preliminary sampling step is determined based on the self-change sensitivity of the detection environment parameters and the empirical value of the leakage influence; first data samples are obtained by performing adaptive sampling of the detection environment parameters according to the preliminary sampling step; the identification influence relationship is obtained using the first data samples, and an influence sensitivity distribution is obtained; a high-density area is identified based on the influence sensitivity distribution, a granularity fission of the preliminary sampling step of the high-density area is performed, secondary sampling is performed, and second data samples are obtained; the first data samples and the second data samples are de-duplicated and integrated to obtain a data sample set.

4. The method of ground fault detection for an electrical distribution box of claim 3, wherein, After the second data samples are obtained, the following steps are further included: for each environment parameter interval in the influence sensitivity distribution, a sensitivity gradient or a second-order change rate is calculated; when the sensitivity gradient or the change rate is greater than a preset threshold, step fission is triggered to increase the sampling density; or a residual error is calculated based on a relationship model fitted based on the current sampling data; when the residual error exceeds a preset threshold, step fission is triggered; when the sensitivity gradient and the residual error are both lower than the threshold, the fission is stopped, it is determined that the current sampling density has met the required precision for establishing the identification influence relationship, and the data sample set is obtained.

5. The method of ground fault detection for an electrical distribution box of claim 4, wherein, The step fission and the secondary sampling are iterated multiple times, and the iteration end conditions include that the sensitivity gradient, the second-order change rate, and the fitting residual error all meet preset convergence conditions; The sensitivity gradient or the second-order change rate is calculated based on a leakage signal response change caused by a change in the environment parameters, and the residual error is calculated based on a fitting error of the sampling data of the established environment parameter-leakage detection relationship model.

6. The method of ground fault detection for an electrical distribution box of claim 1, wherein, The gain analysis of the detection environment parameters using the identification influence relationship to obtain target gain parameters and gain adjustment amounts comprises: The real-time collected detection environment parameters are substituted into the identified influence relationship, and the increment or weakening effect of the environment parameters or parameter combinations on the leakage detection sensitivity is calculated to obtain the gain coefficient of each environment parameter; The gain adjustment amount is calculated according to the target gain parameter and the current sensor state.

7. The method of ground fault detection for an electrical distribution box of claim 6, wherein, The target gain parameter and the gain adjustment amount are obtained, and the method further includes: For transient, intermittent or high-resistance leakage signals, the target gain parameter and the gain adjustment amount are independently calculated, and differential gain processing is performed on each type of signal; Wherein, the transient leakage signal adopts high-bandwidth sampling and instantaneous high-gain window, the intermittent leakage signal adopts long-time cumulative sampling and high-sensitivity threshold adjustment, and the high-resistance leakage signal adopts high-sensitivity low-noise gain amplification and sampling integration extension strategy.

8. The method of ground fault detection for an electrical distribution box of claim 7, wherein, After identifying the leakage detection result and the leakage positioning information of the distribution box, the method further includes: Based on the leakage detection result, the historical trend analysis and short-time high-gain window detection are performed on the intermittent, transient or high-resistance leakage event to determine the leakage event state and the leakage current amplitude; The multi-level distribution system topology relationship is analyzed based on the leakage positioning information, the fault branch belongs to the sub-circuit and the possible affected upper protector are identified, and the sub-circuit topology and the fault branch list are generated; The leakage event state and the sub-circuit information are exchanged through high-speed communication, and the selective protection is performed according to the leakage current amplitude, the event state and the sub-circuit topology, the protector closest to the fault branch is determined to act first, the upper protector is delayed or kept powered, and the management interface is updated in real time to visually display the fault branch and the protector action state.

9. An electric leakage detection device for an electric distribution box, characterized by The device is used to perform the leakage detection method of the distribution box as claimed in any one of claims 1-8, and the device includes: An influence relationship establishment module is configured to establish an identified influence relationship between a detection environment and leakage detection, including an increment, a weakening relationship and corresponding relationship coefficients; A gain analysis module is configured to collect current detection environment parameters, perform gain analysis on the detection environment parameters by using the identified influence relationship, obtain a target gain parameter and a gain adjustment amount; A gain adjustment module is configured to perform gain adjustment on a leakage detection sensor according to the target gain parameter and the gain adjustment amount, and obtain a leakage monitoring signal; A leakage detection module is configured to perform leakage detection according to the leakage monitoring signal, and identify a leakage detection result and leakage positioning information of a distribution box.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the leakage detection method of the distribution box as claimed in any one of claims 1-8.

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