Fast version analog leakage current sensor and its use method

Abstract

The present application discloses a fast version of an analog leakage current sensor and its use method. The sensor includes: an open-loop magnetic core module, including a single set of oscillation coils; an AC / DC composite detection module, integrated at the sensing end of the open-loop magnetic core module, including a DC leakage current detection unit, an AC leakage current detection unit, and a dual-channel switch detection interface; a control module, which performs real-time baseline tracking and compensation for DC leakage current and AC leakage current based on a sliding time window; dynamically adjusts the digital filter parameters corresponding to DC leakage current and AC leakage current according to the ambient electromagnetic noise spectrum; performs timestamp alignment on the DC leakage current, AC leakage current, and the switch signals corresponding to the dual-channel switch detection interface; outputs leakage detection results based on the DC leakage current, AC leakage current, and switch signals; including normal operating conditions, transient leakage, and continuous leakage. The sensor function can be realized through a single set of oscillation coils, eliminating the secondary winding and shortening the production process.

Description

Technical Field

[0001] The present application relates to the field of power electronics technology, and in particular to a fast analog leakage current sensor and a method for using the same. Background Art

[0002] Leakage current detection technology is a crucial safeguard for the safe operation of power systems and electrical equipment. Especially in industrial automation, smart homes, and new energy sectors, the accuracy and real-time performance of leakage current detection are directly related to equipment reliability and personal safety. Traditional leakage current sensors typically use a closed-loop magnetic core structure, detecting changes in the core's magnetic flux to indicate leakage current. However, this approach has the following limitations in practical applications:

[0003] Single detection mode: Traditional sensors can usually only detect one of the two leakage currents: AC leakage current or DC leakage current, which is difficult to meet the complex AC / DC mixed scenario requirements of modern power systems.

[0004] Limited detection accuracy: In complex electromagnetic environments, traditional sensors are easily interfered by environmental noise, resulting in reduced detection accuracy, especially poor performance in the detection of low leakage current or transient leakage current.

[0005] Insufficient dynamic response: The signal processing method of traditional sensors is relatively fixed, and it is difficult to dynamically adjust the filtering parameters according to environmental changes. As a result, the real-time and accuracy of the detection results cannot be guaranteed under rapidly changing working conditions.

[0006] Single function: Traditional sensors usually only focus on leakage current detection and lack the ability to collect and comprehensively analyze switching signals, making it difficult to meet the integration requirements of multi-dimensional signals in modern intelligent systems.

[0007] Therefore, there is an urgent need for a sensor to solve at least one of the above technical problems. Summary of the Invention

[0008] The present application provides a fast version of an analog leakage current sensor and a method for using the same, aiming to solve the problems of traditional leakage current sensors generally adopting a closed-loop magnetic core structure, having a single detection mode, limited detection accuracy, insufficient dynamic response and single function.

[0009] In a first aspect, the present application provides a fast version analog leakage current sensor, comprising:

[0010] Open-loop magnetic core module, the open-loop magnetic core module includes a single set of oscillation coils;

[0011] An AC / DC composite detection module is integrated into the sensing end of the open-loop magnetic core module and includes a DC leakage current detection unit, an AC leakage current detection unit, and a dual-channel switch detection interface. The DC leakage current detection unit converts the collected DC current analog signal into DC leakage current based on the Hall element. The AC leakage current detection unit converts the analog value of the AC leakage current based on the fluxgate circuit. The dual-channel switch detection interface includes two independent status signal acquisition channels.

[0012] A control module, wherein the control module performs real-time baseline tracking and compensation for the DC leakage current and the AC leakage current based on a sliding time window; the control module dynamically adjusts the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum; the control module is also used to timestamp align the DC leakage current, the AC leakage current, and the switch signal corresponding to the dual-channel switch detection interface; the control module outputs a leakage detection result based on the DC leakage current, the AC leakage current, and the switch signal; the leakage detection result includes at least any one of normal operating conditions, transient leakage, and continuous leakage.

[0013] In some embodiments, the number of turns of the oscillation coil ranges from 300 to 500.

[0014] In some embodiments, it also includes: a fast response compensation circuit, including a transient magnetic flux compensation coil and a response time calibration circuit; the transient magnetic flux compensation coil forms an electromagnetic coupling with the open-loop magnetic core module; the response time calibration circuit controls the response time of the fast version analog leakage current sensor to be less than 4.8ms through negative feedback regulation.

[0015] In some embodiments, the control module outputs a leakage detection result based on the DC leakage current, the AC leakage current and the switch signal, including: the control module obtains a normalized waveform corresponding to the waveform of the DC leakage current and the AC leakage current; the control module obtains a switch state code corresponding to the switch signal; the control module generates a multi-channel input matrix based on the switch state code and the normalized waveform corresponding to the waveform of the DC leakage current and the AC leakage current; the multi-channel input convolution layer is input to at least three levels of void convolution layers to obtain transient features, medium-range features and long-range features respectively; a feature pyramid is constructed based on the transient features, medium-range features, long-range features, switch state codes, and normalized waveforms corresponding to the waveforms of the DC leakage current and the AC leakage current; and the output of the feature pyramid is mapped to a preset fault category space to obtain the leakage detection result.

[0016] Exemplarily, the dynamic perception range corresponding to the first-level hole convolution layer is 0.7 to 20ms, which is used to output the transient feature, and the physical phenomenon corresponding to the transient feature is the transient leakage of the switching action; the dynamic perception range corresponding to the second-level hole convolution layer is 20 to 50ms, which is used to output the medium-range feature, and the physical phenomenon corresponding to the medium-range feature is the equipment startup surge current; the dynamic perception range corresponding to the third-level hole convolution layer is 50 to 100ms, which is used to output the long-range feature, and the physical phenomenon corresponding to the long-range feature is the gradual leakage caused by the equipment startup surge current and the aging of the equipment insulation.

[0017] It should be noted that, in some embodiments, the weight corresponding to the first-level atrous convolution layer is greater than the weight corresponding to the second-level atrous convolution layer and the weight corresponding to the third-level atrous convolution layer.

[0018] Exemplarily, the control module calculates a cross entropy loss function and an environmental noise adversarial function based on the leakage detection result output by the feature pyramid, calculates a total loss function based on the cross entropy loss function and the environmental noise adversarial function, and completes the training of the feature pyramid based on the value of the total loss function.

[0019] It should be noted that, in some embodiments, the expression of the total loss function includes:

[0020] ;

[0021] in, is the value of the total loss function; is the environmental noise countermeasure function; is the value of the cross entropy loss function; is the expectation operator, which means taking the average over all possible noise samples; To calculate the expected gradient norm under the noise distribution; To obey the normal distribution A noise vector for simulating the environmental noise; The cross entropy loss function is the input The L2 norm square of the gradient is used to reflect the sensitivity of the output to the input change.

[0022] In some embodiments, the control module dynamically adjusts the digital filtering parameters corresponding to the DC leakage current and the AC leakage current according to the environmental electromagnetic noise spectrum, including: the control module obtains the electromagnetic noise signal of the environment in which the fast version analog leakage current sensor is set, and converts the electromagnetic noise signal into a frequency domain signal; the control module analyzes the noise spectrum corresponding to the frequency domain signal to obtain the noise frequency and intensity; the control module determines the digital filtering parameters according to the noise frequency and intensity.

[0023] In a second aspect, the present application provides a method for using a fast analog leakage current sensor, which is applied to the control module of the fast analog leakage current sensor provided in any embodiment of the present application, and the method includes:

[0024] Based on a sliding time window, real-time baseline tracking and compensation are performed on the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit;

[0025] Dynamically adjust the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum;

[0026] Performing time stamp alignment on the DC leakage current, the AC leakage current, and the switching value signals corresponding to the dual-channel switching value detection interface;

[0027] Output a leakage detection result based on the DC leakage current, the AC leakage current and the switching signal; the leakage detection result includes at least any one of normal operating conditions, transient leakage and continuous leakage.

[0028] In a third aspect, the present application provides a device for using a fast version analog leakage current sensor, comprising:

[0029] A dynamic compensation unit, configured to perform real-time baseline tracking and compensation for the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit based on a sliding time window;

[0030] A digital filtering unit, configured to dynamically adjust digital filtering parameters corresponding to the DC leakage current and the AC leakage current according to an ambient electromagnetic noise spectrum;

[0031] A time alignment unit, configured to perform time stamp alignment on the DC leakage current, the AC leakage current, and the switching value signals corresponding to the dual-channel switching value detection interface;

[0032] A leakage detection unit is used to output a leakage detection result based on the DC leakage current, the AC leakage current and the switching signal; the leakage detection result includes at least one of normal operating conditions, transient leakage and continuous leakage.

[0033] In a fourth aspect, the present application provides a control module comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0034] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors execute the method provided in any embodiment of the present application.

[0035] This application discloses a fast analog leakage current sensor and its use method. The sensor mainly includes:

[0036] 1. Open-loop magnetic core module: Traditional leakage current sensors typically use a closed-loop magnetic core structure, which suffers from problems such as a single detection mode, limited detection accuracy, and insufficient dynamic response. This application uses an open-loop magnetic core module, including a single set of oscillating coils, which can more flexibly adapt to different detection needs while also improving dynamic response speed.

[0037] 2. AC / DC Combined Detection Module: Integrated into the sensing end of the open-loop magnetic core module, it includes the following functional units: DC leakage current detection unit: Based on a Hall effect element, it converts the collected DC current analog signal into DC leakage current. AC leakage current detection unit: Based on a fluxgate circuit, it converts the AC leakage current analog signal. Dual-channel switching detection interface: Includes two independent status signal acquisition channels to support the acquisition of switching signals. This module can simultaneously detect DC and AC leakage current and support the acquisition of switching signals, achieving multifunctional integration.

[0038] 3. Control Module: Real-time Baseline Tracking and Compensation: Performs real-time baseline tracking and compensation for DC and AC leakage currents based on a sliding time window to eliminate environmental interference and drift. Dynamic Filter Parameter Adjustment: Dynamically adjusts the digital filter parameters corresponding to DC and AC leakage currents based on the ambient electromagnetic noise spectrum to improve detection accuracy. Timestamp Alignment: Performs timestamp alignment on the DC leakage current, AC leakage current, and the switching signals corresponding to the dual-channel switching detection interface to ensure signal synchronization. Leakage Detection Result Output: Outputs leakage detection results based on the DC leakage current, AC leakage current, and switching signals. Results include normal operating conditions, transient leakage, and continuous leakage.

[0039] The provided sensor and the method of using the same have at least the following beneficial effects:

[0040] 1. Diverse detection modes: Through the AC / DC composite detection module and dual-channel switch detection interface, it can simultaneously detect DC leakage current, AC leakage current and switch signals to meet various detection needs.

[0041] 2. High detection accuracy: The control module effectively eliminates environmental interference and drift, improving detection accuracy through technologies such as real-time baseline tracking and compensation, and dynamic filter parameter adjustment.

[0042] 3. Fast dynamic response: The combination of the open-loop magnetic core module and the control module enables the sensor to have a fast response capability and can capture transient leakage and continuous leakage in real time.

[0043] 4. Comprehensive functions: The sensor can not only detect leakage current, but also support the acquisition and analysis of switching signals, suitable for a variety of application scenarios.

[0044] 5. Strong adaptability: Dynamic filter parameter adjustment and timestamp alignment functions enable the sensor to adapt to complex electromagnetic environments, improving detection stability and reliability.

[0045] In summary, this fast-acting analog leakage current sensor, through its combination of an open-loop magnetic core module, an AC / DC composite detection module, and an intelligent control module, overcomes the limitations of traditional leakage current sensors, such as their single detection mode, limited accuracy, and insufficient dynamic response. Its technical content and beneficial effects promise broad application prospects in industrial and civilian applications, effectively improving both electrical safety and intelligent functionality.

[0046] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 This is a front view of a fast version of an analog leakage current sensor provided in one embodiment of the present application;

[0049] Figure 2 1 is a side view of a fast version analog leakage current sensor provided in one embodiment of the present application;

[0050] Figure 3 This is a bottom view of a fast version analog leakage current sensor provided by an embodiment of the present application;

[0051] Figure 4 This is a schematic block diagram of the structure of a fast version analog leakage current sensor provided by an embodiment of the present application;

[0052] Figure 5 This is a flowchart illustrating the steps of a method for using a fast version of an analog leakage current sensor provided in one embodiment of the present application;

[0053] Figure 6 This is a schematic block diagram of the structure of a control module provided in one embodiment of the present application.

[0054] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0057] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0058] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0059] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0060] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0061] Leakage current detection technology is a crucial safeguard for the safe operation of power systems and electrical equipment. Especially in industrial automation, smart homes, and new energy sectors, the accuracy and real-time performance of leakage current detection are directly related to equipment reliability and personal safety. Traditional leakage current sensors typically use a closed-loop magnetic core structure, detecting changes in the core's magnetic flux to indicate leakage current. However, this approach has the following limitations in practical applications:

[0062] Single detection mode: Traditional sensors can usually only detect one of the two leakage currents: AC leakage current or DC leakage current, which is difficult to meet the complex AC / DC mixed scenario requirements of modern power systems.

[0063] Limited detection accuracy: In complex electromagnetic environments, traditional sensors are easily interfered by environmental noise, resulting in reduced detection accuracy, especially poor performance in the detection of low leakage current or transient leakage current.

[0064] Insufficient dynamic response: The signal processing method of traditional sensors is relatively fixed, and it is difficult to dynamically adjust the filtering parameters according to environmental changes. As a result, the real-time and accuracy of the detection results cannot be guaranteed under rapidly changing working conditions.

[0065] Single function: Traditional sensors usually only focus on leakage current detection and lack the ability to collect and comprehensively analyze switching signals, making it difficult to meet the integration requirements of multi-dimensional signals in modern intelligent systems.

[0066] Therefore, there is an urgent need for a sensor to solve at least one of the above technical problems.

[0067] To solve the above problems, please refer to Figures 1 to 4 ( Figure 1The +, -, M, and G in the figure are respectively the positive current interface, the negative current interface, the signal input interface, and the signal output interface. The application provides a fast version of an analog leakage current sensor, including: an open-loop magnetic core module, the open-loop magnetic core module includes a single set of oscillation coils; an AC / DC composite detection module, integrated at the sensing end of the open-loop magnetic core module, including a DC leakage current detection unit, an AC leakage current detection unit, and a dual-channel switch detection interface; the DC leakage current detection unit converts the collected DC current analog signal into a DC leakage current according to the Hall element; the AC leakage current detection unit converts the analog value of the AC leakage current according to the fluxgate circuit; the dual-channel switch detection interface includes two independent status signal acquisition channels; A control module, wherein the control module performs real-time baseline tracking and compensation for the DC leakage current and the AC leakage current based on a sliding time window; the control module dynamically adjusts the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum; the control module is also used to timestamp align the DC leakage current, the AC leakage current, and the switch signal corresponding to the dual-channel switch detection interface; the control module outputs a leakage detection result based on the DC leakage current, the AC leakage current, and the switch signal; the leakage detection result includes at least any one of normal operating conditions, transient leakage, and continuous leakage.

[0068] Specifically, the fast-acting analog leakage current sensor is an integrated, intelligent detection device primarily used for leakage current detection in power systems and electrical equipment. Its core technologies include the following:

[0069] 1. Open-Loop Magnetic Core Module: This open-loop magnetic core design offers lower hysteresis loss and higher sensitivity than traditional closed-loop magnetic core structures. The open-loop magnetic core module contains a single oscillating coil, effectively capturing the subtle magnetic field changes generated by leakage current.

[0070] 2. AC / DC composite detection module: integrated into the sensing end of the open-loop magnetic core module, it contains the following three functional units:

[0071] DC leakage current detection unit: Based on the Hall element, it converts the collected DC current analog signal into a DC leakage current value.

[0072] AC leakage current detection unit: converts the analog signal of AC leakage current through the fluxgate circuit.

[0073] Dual-channel switch detection interface: provides two independent switch signal acquisition channels for collecting status signals of external devices (such as relays, switches, etc.).

[0074] 3. Control module

[0075] As the core control unit of the sensor, the control module's functions include:

[0076] Real-time baseline tracking and compensation: Based on the sliding time window algorithm, real-time baseline tracking and compensation are performed on DC leakage current and AC leakage current to eliminate the impact of environmental noise on detection accuracy.

[0077] Dynamic filter parameter adjustment: Dynamically adjust the digital filter parameters corresponding to DC leakage current and AC leakage current based on the spectrum characteristics of the ambient electromagnetic noise, improving the real-time performance and accuracy of signal processing.

[0078] Signal timestamp alignment: Perform timestamp alignment on DC leakage current, AC leakage current, and dual-channel switching signals to ensure synchronous acquisition and analysis of multi-dimensional signals.

[0079] Leakage detection result output: Based on the comprehensive analysis of DC leakage current, AC leakage current and switching signals, the leakage detection results are output, including normal working conditions, transient leakage and continuous leakage.

[0080] The open-loop magnetic core module utilizes high-permeability, low-hysteresis materials to create an open-loop core. A single oscillating coil is wound around the core to sense the magnetic field changes generated by leakage current. The AC / DC composite detection module utilizes Hall effect sensors and fluxgate circuits for DC and AC leakage current detection, respectively. The dual-channel switch detection interface utilizes optoelectronic isolation technology for signal acquisition to prevent signal interference. The control module utilizes a high-performance microcontroller (MCU) or digital signal processor (DSP), integrating a sliding time window algorithm, dynamic filter parameter adjustment, and timestamp alignment algorithms.

[0081] The baseline tracking and compensation algorithm uses a sliding time window algorithm to track baseline changes in the leakage current signal in real time and compensate for them, eliminating the impact of environmental noise. The dynamic filter parameter adjustment algorithm dynamically adjusts the filter's cutoff frequency and order based on the spectral characteristics of the ambient electromagnetic noise, optimizing signal processing. The timestamp alignment algorithm uses hardware timestamps and software synchronization mechanisms to ensure time consistency between DC leakage current, AC leakage current, and switching signals. The leakage detection algorithm uses a comprehensive analysis of multi-dimensional signals to determine the leakage status and output the results.

[0082] The fast-acting analog leakage current sensor integrates an open-loop magnetic core module, an AC / DC combined detection module, and a control module in a compact sensor housing, ensuring stable signal transmission and interference immunity. Standard interfaces (such as RS-485 and CAN bus) facilitate communication between the sensor and a host computer or other intelligent devices.

[0083] This fast-version analog leakage current sensor, through the combination of an open-loop magnetic core design, Hall elements, and fluxgate circuits, can achieve high-precision detection of AC and DC leakage currents, especially in the detection of low leakage currents and transient leakage currents. Based on a sliding time window algorithm and a dynamic filter parameter adjustment algorithm, the control module can track changes in the leakage current signal in real time, improving the real-time performance and accuracy of detection. The integrated dual-channel switch detection interface can collect status signals from external devices and align them with the leakage current signal for comprehensive analysis of multi-dimensional signals. Through dynamic filter parameter adjustment and baseline tracking and compensation algorithms, it can effectively eliminate noise interference in complex electromagnetic environments and improve detection accuracy. This fast-version analog leakage current sensor is suitable for industrial automation, smart homes, new energy, and other fields, and can meet the complex AC and DC mixed scenarios in modern power systems.

[0084] In summary, this fast-acting analog leakage current sensor has broad application prospects in power systems, industrial automation, smart homes, and new energy. Its high precision, real-time performance, and multi-dimensional signal integration can effectively improve equipment reliability and personal safety, providing important guarantees for the safe operation of intelligent power systems and electrical equipment.

[0085] In some embodiments, the number of turns of the oscillation coil ranges from 300 to 500.

[0086] Design and wind an oscillating coil with 300 to 500 turns in the open-loop magnetic core module. The number of turns in the coil directly affects the sensor's sensitivity and detection capabilities. More turns result in more pronounced changes in the induced magnetic field, but too high a number of turns may lead to signal saturation or increased noise. Therefore, choosing a turn count between 300 and 500 is a balance between ensuring sufficient sensitivity and minimizing interference. Ensure uniform distribution of the coils to improve magnetic field uniformity and detection accuracy. Use high-precision winding equipment to ensure consistent coil turns and uniform coil spacing.

[0087] By increasing the number of coil turns, the sensor can detect lower leakage current values, improving detection sensitivity. An appropriate range of turns reduces noise interference, improves the signal-to-noise ratio of the detection signal, and thus enhances detection accuracy. An evenly distributed coil design effectively reduces the impact of external electromagnetic interference on the detection signal, improving the sensor's stability and reliability.

[0088] In some embodiments, it also includes: a fast response compensation circuit, including a transient magnetic flux compensation coil and a response time calibration circuit; the transient magnetic flux compensation coil forms an electromagnetic coupling with the open-loop magnetic core module; the response time calibration circuit controls the response time of the fast version analog leakage current sensor to be less than 4.8ms through negative feedback regulation.

[0089] A transient flux compensation coil is installed near the open-loop magnetic core module to create electromagnetic coupling. This coil compensates for rapidly changing magnetic fields, ensuring the sensor accurately captures transient leakage current signals. The response time calibration circuit utilizes a negative feedback mechanism to adjust the sensor's response parameters in real time, ensuring a response time of less than 4.8ms. This dynamic adjustment of circuit parameters optimizes the sensor's dynamic performance. The control module utilizes the fast-response compensation circuit to adjust the signal processing algorithm in real time, ensuring the sensor's ability to quickly respond to transient leakage current signals and improving real-time detection accuracy.

[0090] By utilizing a transient magnetic flux compensation coil and negative feedback regulation mechanism, the sensor can quickly respond to transient leakage current signals, enhancing its ability to detect transient leakage current. Dynamic adjustment of circuit parameters and signal processing algorithms ensures the sensor's dynamic performance under complex operating conditions, reducing response time and improving real-time detection. The fast-response compensation circuit effectively reduces signal processing delays, improving sensor reliability in rapidly changing environments and ensuring the accuracy of detection results.

[0091] In some embodiments, the control module outputs a leakage detection result based on the DC leakage current, the AC leakage current and the switch signal, including: the control module obtains a normalized waveform corresponding to the waveform of the DC leakage current and the AC leakage current; the control module obtains a switch state code corresponding to the switch signal; the control module generates a multi-channel input matrix based on the switch state code and the normalized waveform corresponding to the waveform of the DC leakage current and the AC leakage current; the multi-channel input convolution layer is input to at least three levels of void convolution layers to obtain transient features, medium-range features and long-range features respectively; a feature pyramid is constructed based on the transient features, medium-range features, long-range features, switch state codes, and normalized waveforms corresponding to the waveforms of the DC leakage current and the AC leakage current; and the output of the feature pyramid is mapped to a preset fault category space to obtain the leakage detection result.

[0092] The control module collects DC leakage current, AC leakage current and switch signals, and normalizes these signals to ensure data standardization and consistency. The normalized DC leakage current, AC leakage current and switch signals are constructed into a multi-channel input matrix as the input of the deep learning model. At least three levels of dilated convolution layers are used to extract transient features, medium-range features and long-range features respectively. The extraction of signal features at different time scales is achieved through the design of multi-level dilated convolution layers. The extracted transient features, medium-range features and long-range features are fused with the switch state code and the normalized waveforms of DC leakage current and AC leakage current to construct a feature pyramid. The output of the feature pyramid is mapped to the preset fault category space to generate leakage detection results, including normal operating conditions, transient leakage and continuous leakage.

[0093] Through deep learning algorithms and multi-feature extraction, intelligent leakage detection is achieved, improving detection accuracy and reliability. By extracting and fusing signal features at different time scales, the sensor can adapt to complex and changing operating conditions and improve its ability to distinguish different physical phenomena. The design of a feature pyramid and deep learning model enables rapid leakage detection, improving detection efficiency and real-time performance.

[0094] Exemplarily, the dynamic perception range corresponding to the first-level hole convolution layer is 0.7 to 20ms, which is used to output the transient feature, and the physical phenomenon corresponding to the transient feature is the transient leakage of the switching action; the dynamic perception range corresponding to the second-level hole convolution layer is 20 to 50ms, which is used to output the medium-range feature, and the physical phenomenon corresponding to the medium-range feature is the equipment startup surge current; the dynamic perception range corresponding to the third-level hole convolution layer is 50 to 100ms, which is used to output the long-range feature, and the physical phenomenon corresponding to the long-range feature is the gradual leakage caused by the equipment startup surge current and the aging of the equipment insulation.

[0095] The first-level dilated convolutional layer has a dynamic range of 0.7 to 20 milliseconds, making it suitable for capturing rapidly changing transient signals. It is specifically designed to detect transient leakage caused by switching operations. This leakage typically occurs when switching devices operate rapidly, lasting only a short time but experiencing dramatic fluctuations. High sampling rate signal acquisition and fast-response algorithms ensure timely capture of transient signal changes.

[0096] The second-level dilated convolutional layer has a dynamic range of 20 to 50 milliseconds, making it suitable for detecting signals with moderate rates of change. This layer is primarily used to detect inrush current during device startup. During device startup, current changes are significant, but the duration is slightly longer than that of transient signals. By extracting features at this medium timescale and combining them with typical signal patterns during device startup, detection accuracy is improved.

[0097] The third-level dilated convolutional layer has a dynamic range of 50 to 100 milliseconds, making it suitable for detecting slowly changing signals. It is primarily used to detect gradual leakage caused by aging insulation. This leakage typically changes slowly but gradually intensifies over time. Through long-term signal accumulation and feature extraction, it can effectively identify insulation aging trends and prevent potential failures.

[0098] Different levels of dilated convolutional layers can extract features at different time scales, comprehensively covering transient, medium-range, and long-range leakage phenomena, improving the comprehensiveness and accuracy of detection. Each level of dilated convolutional layer is designed for specific physical phenomena, enabling more accurate identification and classification of different leakage types, enhancing detection sensitivity and specificity. Through hierarchical signal processing, the effects of signal interference and noise are reduced, the signal-to-noise ratio of the detection signal is improved, and detection reliability is enhanced.

[0099] It should be noted that, in some embodiments, the weight corresponding to the first-level atrous convolution layer is greater than the weight corresponding to the second-level atrous convolution layer and the weight corresponding to the third-level atrous convolution layer.

[0100] In the deep learning model, the weight of the first-level atrous convolutional layer is set to be greater than the weights of the second and third-level atrous convolutional layers. This allows the model to focus more on extracting transient features, improving its ability to detect transient leakage. Furthermore, the weights of each atrous convolutional layer can be dynamically adjusted based on the self-attention matrix.

[0101] By increasing the weight of the first-level atrous convolutional layer, the model can focus more on extracting transient features, improving the accuracy and sensitivity of transient leakage detection. Proper weight distribution optimizes model performance, enhances its ability to distinguish different physical phenomena, and strengthens the intelligence of detection.

[0102] Exemplarily, the control module calculates a cross entropy loss function and an environmental noise adversarial function based on the leakage detection result output by the feature pyramid, calculates a total loss function based on the cross entropy loss function and the environmental noise adversarial function, and completes the training of the feature pyramid based on the value of the total loss function.

[0103] Based on the leakage detection results output by the feature pyramid, the control module calculates the cross-entropy loss function and the environmental noise countermeasure function to produce a total loss function. By simulating environmental noise, the squared gradient norm of the cross-entropy loss function with respect to the input signal is calculated to reflect the output's sensitivity to input changes and optimize the model's noise tolerance. By minimizing the total loss function, the feature pyramid is trained, improving the model's noise tolerance and detection performance.

[0104] By designing an environmental noise adversarial function, the model can effectively suppress interference from ambient noise, improving detection robustness and anti-interference capabilities. Combining the cross-entropy loss function with the environmental noise adversarial function optimizes the model training process, improving detection accuracy and stability. By optimizing the total loss function, the model can better adapt to complex electromagnetic environments, enhancing the performance and reliability of leakage detection.

[0105] It should be noted that, in some embodiments, the expression of the total loss function includes:

[0106] ;

[0107] in, is the value of the total loss function; is the environmental noise countermeasure function; is the value of the cross entropy loss function; is the expectation operator, which means taking the average over all possible noise samples; To calculate the expected gradient norm under the noise distribution; To obey the normal distribution A noise vector for simulating the environmental noise; The cross entropy loss function is the input The L2 norm square of the gradient is used to reflect the sensitivity of the output to the input change.

[0108] Cross entropy loss function ( Cross-entropy loss (CEL) measures the difference between the model's predictions and the true labels, commonly used in classification tasks. By comparing the probability distribution of the model output with the distribution of the true labels, the cross-entropy loss is calculated. This optimizes the model's classification performance and improves its ability to distinguish between different leakage states.

[0109] Environmental noise countermeasure function ( ): By simulating environmental noise, the gradient norm square of the cross entropy loss function to the input signal is calculated to reflect the sensitivity of the output to the input change. Introducing a noise vector that obeys the normal distribution , calculates the expected gradient norm under the noise distribution. Through adversarial training, the model's robustness to environmental noise is improved, reducing the impact of noise on detection results.

[0110] The total loss function ( ): Combine the cross-entropy loss function and the environmental noise adversarial function to comprehensively consider the model's classification performance and noise immunity. The cross-entropy loss function and the environmental noise adversarial function are weighted and added together to obtain the total loss function. By minimizing the total loss function, the model parameters are optimized, improving the model's detection performance and anti-interference capabilities.

[0111] By introducing an environmental noise adversarial function, the model can effectively suppress interference from ambient noise, improving detection robustness and anti-interference capabilities. Combining the cross-entropy loss function with the environmental noise adversarial function to design a total loss function can more comprehensively optimize model performance and enhance detection accuracy and stability. By dynamically adjusting model parameters, the model's sensitivity to noise is optimized, improving the reliability and stability of leakage detection.

[0112] In some embodiments, the control module dynamically adjusts the digital filtering parameters corresponding to the DC leakage current and the AC leakage current according to the environmental electromagnetic noise spectrum, including: the control module obtains the electromagnetic noise signal of the environment in which the fast version analog leakage current sensor is set, and converts the electromagnetic noise signal into a frequency domain signal; the control module analyzes the noise spectrum corresponding to the frequency domain signal to obtain the noise frequency and intensity; the control module determines the digital filtering parameters according to the noise frequency and intensity.

[0113] The control module collects electromagnetic noise signals from the sensor's environment, converts them into frequency-domain signals, and analyzes the noise spectrum. By analyzing the frequency-domain signals, the module determines the noise frequency and intensity, identifying the primary source and characteristics of the noise. Based on the noise frequency and intensity, the module dynamically adjusts digital filter parameters, such as the cutoff frequency and order, to optimize signal processing. By dynamically adjusting digital filter parameters, the control module optimizes signal processing in real time, improving the signal-to-noise ratio and detection accuracy.

[0114] By dynamically adjusting digital filter parameters, the sensor can effectively suppress interference from environmental noise and improve the signal-to-noise ratio of the detection signal. Dynamic adjustment of filter parameters based on the noise spectrum ensures real-time and accurate signal processing, enhancing detection performance. The sensor can adapt to the electromagnetic noise characteristics of different environments and dynamically adjust filter parameters to improve detection adaptability and reliability.

[0115] For example, if the noise is mainly concentrated in the high-frequency range, a low-pass filter can be selected; if the noise is concentrated in the low-frequency range, a high-pass filter can be selected. DC leakage current is usually in the low-frequency range, so a low-pass filter needs to be selected and a lower cutoff frequency needs to be set. AC leakage current may be in the medium-frequency or high-frequency range, so a band-pass or high-pass filter needs to be selected and the corresponding cutoff frequency needs to be set. The control module monitors the changes in environmental noise in real time, regularly collects the noise spectrum and updates the filter parameters. According to the changes in the noise spectrum, the type and cutoff frequency of the filter are dynamically adjusted to adapt to changes in the noise environment. It is also possible that this may involve a feedback mechanism, that is, evaluating the effectiveness of the current filter parameters based on the quality of the filtered signal, and further adjusting the parameters based on the evaluation results. By ensuring that the update of the filter parameters can reflect the changes in environmental noise in a timely manner, the measurement accuracy and stability of the sensor can be maintained.

[0116] See also Figure 5 , Figure 5 This is a schematic flow chart of a method for using a fast version of an analog leakage current sensor provided in one embodiment of the present application. The execution device of the method is the control module of the sensor provided in any embodiment of the present application.

[0117] like Figure 5As shown, the provided method includes steps S101 to S104. The control module can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc., for implementing steps S101 to S104 and their corresponding embodiments.

[0118] Step S101 : performing real-time baseline tracking and compensation on the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit based on a sliding time window.

[0119] Specifically, this step performs real-time baseline tracking and compensation on DC and AC leakage current signals through sliding time window technology to eliminate baseline drift and noise in the signals and improve signal stability and accuracy.

[0120] Set a fixed-length time window (for example, 1 second) to collect the leakage current signal. The time window continuously slides forward to update the signal samples in real time.

[0121] In each time window, calculate the average or median of the leakage current signal as the current baseline. Use robust statistical methods (such as the median) to reduce the impact of outliers.

[0122] Subtracting the calculated baseline value from the original leakage current signal yields a compensated signal. This compensated signal better reflects the true leakage current variation and reduces drift effects.

[0123] By tracking and compensating for baselines in real time, long-term drift in the signal is effectively eliminated, improving detection accuracy. Average or median calculations based on a sliding time window can reduce the impact of random noise and improve signal quality. Dynamic updates of the sliding time window ensure real-time baseline tracking and adaptability to signal changes.

[0124] Step S102: Dynamically adjust the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum.

[0125] Specifically, according to the spectrum characteristics of the environmental electromagnetic noise, the digital filtering parameters of the DC and AC leakage current signals are dynamically adjusted to optimize the signal filtering effect and suppress the noise.

[0126] Use sensors to collect ambient electromagnetic noise signals in real time. Obtain the noise's spectral distribution through Fourier transform or other spectrum analysis methods. Identify the noise's primary frequency components and intensity. Adjust digital filter parameters, such as the low-pass filter's cutoff frequency and bandwidth, based on the noise characteristics to better suppress the noise.

[0127] Use an FIR or IIR filter and reconfigure the filter coefficients based on the adjusted parameters. Apply the filter to process the leakage current signal to remove noise components.

[0128] Filter parameters are adjusted based on the real-time noise spectrum to ensure optimal filtering performance and adaptability to varying environmental conditions. This effectively suppresses environmental noise, improves the signal-to-noise ratio of the leakage current signal, and enhances detection accuracy. The ability to dynamically adjust filter parameters enables the system to adapt to changing electromagnetic environments and enhances system robustness.

[0129] Step S103: performing time stamp alignment on the DC leakage current, the AC leakage current and the switching value signals corresponding to the dual-channel switching value detection interface.

[0130] Specifically, timestamp alignment is performed on DC leakage current, AC leakage current, and switching signals to ensure temporal synchronization of data from different signal sources, facilitating subsequent analysis and processing. Each signal (DC leakage current, AC leakage current, and switching signal) is assigned a precise timestamp to record the time of signal acquisition. Timestamps can be generated based on the system clock or a hardware synchronization signal. Hardware synchronization methods (such as a synchronous clock signal) or software timestamp adjustment methods are used to ensure consistent timestamps for all signals. For hardware synchronization, signal acquisition devices share the same clock source to ensure timestamp consistency. For software adjustment, timestamps are adjusted to achieve synchronization by calculating signal transmission delays.

[0131] Based on timestamps, data from different signals is aligned to the same time base. This ensures that data points for each signal are consistent in time, facilitating subsequent correlation analysis and processing. Timestamp alignment accurately correlates changes in different signals, improving the accuracy of leakage detection. It is suitable for processing multiple signal sources simultaneously, enhancing system versatility.

[0132] Step S104. Output a leakage detection result based on the DC leakage current, the AC leakage current, and the switching signal; the leakage detection result includes at least one of normal operating conditions, transient leakage, and continuous leakage.

[0133] Specifically, the system outputs leakage detection results based on the processed DC leakage current, AC leakage current, and switching signals, determining whether the current operating condition is normal, transient leakage, or continuous leakage. Features such as signal amplitude, frequency content, and variation trends are extracted from the processed leakage current signal. The leakage condition is comprehensively analyzed based on the status of the switching signal (such as switch operation). Threshold comparison, pattern recognition, or other machine learning methods are used to determine the leakage status. For example, leakage is determined when the leakage current amplitude exceeds a preset threshold. Transient leakage and continuous leakage are distinguished based on the variation in the switching signal. Based on the detection results, the corresponding status information (normal, transient leakage, continuous leakage) is output. Detection results can be provided to the user via a display, alarm, or other means. Comprehensive analysis of multiple signal features improves the accuracy and reliability of leakage detection. Timely output of leakage detection results facilitates the implementation of appropriate measures to prevent accidents. This system is suitable for leakage detection needs in diverse scenarios, enhancing its applicability and practicality.

[0134] In summary, the detailed description of the four steps above clearly demonstrates the advantages of the fast-track analog leakage current sensor in terms of technical content, implementation, and beneficial effects. Each step addresses specific signal processing requirements and employs advanced techniques to ensure the accuracy and reliability of leakage detection. Furthermore, the system's dynamic adjustment and multi-source signal processing capabilities enable it to adapt to complex electromagnetic environments and diverse application scenarios, providing strong technical support for the safe operation of power systems.

[0135] It should be noted that, those skilled in the art can clearly understand that, for the convenience and conciseness of description, the usage method of the fast version of the analog leakage current sensor described above and the specific working process of each step can refer to the corresponding process in the fast version of the analog leakage current sensor embodiments described in the above embodiments, and will not be repeated here.

[0136] The present application also provides a schematic diagram of a device for using a fast analog leakage current sensor. This device is used to perform the steps of the method for using a fast analog leakage current sensor described in each of the above embodiments. This device can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0137] The fast version of the analog leakage current sensor is used in the following devices:

[0138] A dynamic compensation unit, configured to perform real-time baseline tracking and compensation for the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit based on a sliding time window;

[0139] A digital filtering unit, configured to dynamically adjust digital filtering parameters corresponding to the DC leakage current and the AC leakage current according to an ambient electromagnetic noise spectrum;

[0140] A time alignment unit, configured to perform time stamp alignment on the DC leakage current, the AC leakage current, and the switching value signals corresponding to the dual-channel switching value detection interface;

[0141] A leakage detection unit is used to output a leakage detection result based on the DC leakage current, the AC leakage current and the switching signal; the leakage detection result includes at least one of normal operating conditions, transient leakage and continuous leakage.

[0142] It should be noted that those skilled in the art can clearly understand that, for the convenience and conciseness of description, the above-described usage device of the fast version analog leakage current sensor and the specific working process of each unit can refer to the corresponding process in the usage method embodiments of the fast version analog leakage current sensor described in the above-mentioned embodiments, and will not be repeated here.

[0143] The above-mentioned monitoring based on the data asset management platform can be implemented in the form of a computer program, which can be run on the above-mentioned device.

[0144] See also Figure 6 , Figure 6 1 is a schematic block diagram of the structure of a control module provided in an embodiment of the present application. The control module includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0145] The storage medium can store an operating device and a computer program. The computer program includes program instructions, which, when executed, can cause the processor to perform any type of monitoring based on the data asset management platform.

[0146] The processor is used to provide computing and control capabilities and support the operation of the entire control module.

[0147] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can perform any type of monitoring based on the data asset management platform.

[0148] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific control module may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0149] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0150] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0151] Based on a sliding time window, real-time baseline tracking and compensation are performed on the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit;

[0152] Dynamically adjust the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum;

[0153] Performing time stamp alignment on the DC leakage current, the AC leakage current, and the switching value signals corresponding to the dual-channel switching value detection interface;

[0154] Output a leakage detection result based on the DC leakage current, the AC leakage current and the switching signal; the leakage detection result includes at least any one of normal operating conditions, transient leakage and continuous leakage.

[0155] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the processor described above can refer to the corresponding process in the method embodiments described in the above embodiments, and will not be repeated here.

[0156] A computer-readable storage medium is also provided in an embodiment of the present application. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. The processor executes the program instructions to implement the steps of the method for using the fast version of the analog leakage current sensor provided in the above embodiments of the present application.

[0157] The computer-readable storage medium may be an internal storage unit of the control module described in the aforementioned embodiment, such as a hard disk or memory of the control module. The computer-readable storage medium may also be an external storage device of the control module, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the control module.

[0158] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A fast version analog leakage current sensor, characterized in that: include: Open-loop magnetic core module, the open-loop magnetic core module includes a single set of oscillation coils; An AC / DC composite detection module is integrated into the sensing end of the open-loop magnetic core module and includes a DC leakage current detection unit, an AC leakage current detection unit, and a dual-channel switch detection interface. The DC leakage current detection unit converts the collected DC current analog signal into DC leakage current based on the Hall element. The AC leakage current detection unit converts the analog value of the AC leakage current based on the fluxgate circuit. The dual-channel switch detection interface includes two independent status signal acquisition channels. A control module, wherein the control module performs real-time baseline tracking and compensation for the DC leakage current and the AC leakage current based on a sliding time window; the control module dynamically adjusts the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum; the control module is further used to perform timestamp alignment on the DC leakage current, the AC leakage current, and the switch signal corresponding to the dual-channel switch detection interface; the control module outputs a leakage detection result based on the DC leakage current, the AC leakage current, and the switch signal, including: the control module obtains a normalized waveform corresponding to the waveform of the DC leakage current and the AC leakage current; the control module obtains a switch state code corresponding to the switch signal; The control module generates a multi-channel input matrix based on the normalized waveforms corresponding to the waveforms of the switch state code, DC leakage current, and AC leakage current; inputs the multi-channel input convolution layer into at least three levels of dilated convolution layers to obtain transient features, medium-range features, and long-range features respectively; constructs a feature pyramid based on the normalized waveforms corresponding to the transient features, medium-range features, long-range features, switch state code, DC leakage current, and AC leakage current waveforms; maps the output of the feature pyramid to a preset fault category space to obtain the leakage detection result; the dynamic perception range corresponding to the first-level dilated convolution layer is 0.7 The dynamic perception range of the second-level hole convolution layer is 20 to 20ms, which is used to output the transient feature, and the physical phenomenon corresponding to the transient feature is the transient leakage of the switching action; the dynamic perception range corresponding to the second-level hole convolution layer is 20 to 50ms, which is used to output the medium-range feature, and the physical phenomenon corresponding to the medium-range feature is the equipment startup surge current; the dynamic perception range corresponding to the third-level hole convolution layer is 50 to 100ms, which is used to output the long-range feature, and the physical phenomenon corresponding to the long-range feature is the gradual leakage caused by the equipment startup surge current and the aging of the equipment insulation; the leakage detection result includes at least any one of normal operating conditions, transient leakage and continuous leakage.

2. The sensor according to claim 1, characterized in that The number of turns of the oscillation coil corresponds to a range of 300 to 500.

3. The sensor according to claim 1, wherein Also includes: A fast response compensation circuit, including a transient magnetic flux compensation coil and a response time calibration circuit; The transient magnetic flux compensation coil forms an electromagnetic coupling with the open-loop magnetic core module; The response time calibration circuit controls the response time of the fast version analog leakage current sensor to be less than 4.8 ms through negative feedback regulation.

4. The sensor according to claim 1, characterized in that The weight corresponding to the first-level dilated convolution layer is greater than the weight corresponding to the second-level dilated convolution layer and the weight corresponding to the third-level dilated convolution layer.

5. The sensor according to claim 1, wherein The control module calculates a cross entropy loss function and an environmental noise adversarial function based on the leakage detection result output by the feature pyramid, calculates a total loss function based on the cross entropy loss function and the environmental noise adversarial function, and completes the training of the feature pyramid based on the value of the total loss function.

6. The sensor according to claim 5, characterized in that The expression of the total loss function includes: ； in, is the value of the total loss function; is the environmental noise countermeasure function; is the value of the cross entropy loss function; is the expectation operator, which means taking the average over all possible noise samples; To calculate the expected gradient norm under the noise distribution; To obey the normal distribution A noise vector for simulating the environmental noise; The cross entropy loss function is the input The L2 norm square of the gradient is used to reflect the sensitivity of the output to the input change.

7. The sensor according to claim 1, characterized in that The control module dynamically adjusts the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum, including: The control module obtains an electromagnetic noise signal of an environment in which the fast version analog leakage current sensor is set, and converts the electromagnetic noise signal into a frequency domain signal; The control module analyzes the noise spectrum corresponding to the frequency domain signal to obtain the noise frequency and intensity; The control module determines the digital filtering parameters according to the noise frequency and intensity.

8. A method for using a fast version analog leakage current sensor, characterized in that: The control module of the fast version analog leakage current sensor applied to any one of claims 1 to 7, the method comprising: Based on a sliding time window, real-time baseline tracking and compensation are performed on the DC leakage current and AC leakage current detected by the DC leakage current detection unit and the AC leakage current detection unit; Dynamically adjust the digital filter parameters corresponding to the DC leakage current and the AC leakage current according to the ambient electromagnetic noise spectrum; Performing time stamp alignment on the DC leakage current, the AC leakage current, and the switching value signals corresponding to the dual-channel switching value detection interface; Output a leakage detection result based on the DC leakage current, the AC leakage current and the switching signal; the leakage detection result includes at least any one of normal operating conditions, transient leakage and continuous leakage.

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