Resistive leakage current detection protection method of intelligent circuit breaker

Abstract

The invention discloses a resistive leakage current detection protection method of an intelligent circuit breaker, and belongs to the technical field of electric leakage detection. The method comprises the following steps: synchronous sampling; shaping a window function; performing high-resolution spectrum analysis; estimating a Kalman state; generating a fault evolution index; inverse time delay decision making; and tripping execution. According to the method, a dynamic fault evolution index is taken as a core, online joint estimation of the resistive leakage current amplitude and the change rate of the resistive leakage current amplitude is realized in a circuit breaker for the first time through high-resolution spectral analysis, Kalman state estimation and a multi-dimensional nonlinear inverse time limit curved surface, and protection delay is generated in a self-adaptive manner according to the online joint estimation. Meanwhile, milliampere-level sensitivity and working condition self-adaption reliability are met, and the problems of FFT spectrum leakage, fixed threshold misjudgment and non-self-adaption in time delay in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of leakage current detection technology, and in particular to a resistive leakage current detection and protection method for intelligent circuit breakers. Background Technology

[0002] With the increasing prevalence of AC / DC mixed loads in low-voltage power distribution systems, higher requirements are being placed on the selectivity and reliability of leakage current protection.

[0003] Currently, existing technologies collect leakage current through zero-sequence current transformers, filter it with operational amplifiers, and then use a microcontroller to synchronously sample the voltage and current waveforms. The amplitude and phase of the 50Hz fundamental wave are extracted using FFT, and the resistive leakage current component is decomposed using cosφ to achieve threshold discrimination.

[0004] However, in traditional methods, the frequency domain resolution is locked by the sampling length and the sidelobe attenuation is fixed due to the use of only a single rectangular window or static Hanning window in conjunction with an integer-cycle FFT. This makes it impossible to dynamically adjust according to the harmonic content in the field, leading to leakage of interharmonic energy near the fundamental frequency. Furthermore, since the comparison is only made between the 50Hz amplitude and a fixed empirical threshold, there is a lack of real-time estimation of the rate of change of resistive components, and the threshold cannot be corrected according to environmental changes. Additionally, because the delay curve is an offline inverse-time setting value, it cannot be compressed or stretched in real time according to the fault evolution rate, ultimately resulting in spectral leakage in traditional FFT, misjudgment of fixed thresholds, and inability to adapt the delay. To address these problems, this invention proposes a solution. Summary of the Invention

[0005] This application provides a resistive leakage current detection and protection method for intelligent circuit breakers, which solves the problems of FFT spectrum leakage, fixed threshold misjudgment, and inability to adapt delay in the prior art. It realizes online joint estimation of resistive leakage current amplitude and its rate of change, and adaptively generates protection delay accordingly, while satisfying milliampere-level sensitivity and operating condition adaptive reliability.

[0006] This application provides a resistive leakage current detection and protection method for intelligent circuit breakers, including:

[0007] Obtain discrete sampling sequences of grid voltage and line residual current;

[0008] Windowing is applied to the discrete sampled sequence to generate windowed voltage and windowed current sequences;

[0009] High-resolution spectral analysis was performed on the windowed voltage and current sequences to resolve the amplitude and phase of the fundamental and specified harmonics of the grid voltage and residual current.

[0010] The amplitude and phase of the fundamental wave and the specified harmonic are analyzed and used as observation data. They are then input into the preset dynamic state space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component.

[0011] A dynamic fault evolution index is calculated based on the resistive leakage current component and its time rate of change in the system state vector.

[0012] Based on the dynamic fault evolution index, the protection action delay time under the current state is mapped and determined through a multi-dimensional nonlinear inverse time-delay characteristic surface.

[0013] When the internal timer reaches the protection action delay time, a trip command is generated and issued to drive the circuit breaker to perform the trip protection.

[0014] Furthermore, the high-resolution spectral analysis performed on the windowed voltage sequence and the windowed current sequence specifically includes:

[0015] The spectrum calculation is performed for the area near the fundamental frequency of the power grid and at preset harmonic frequency points. The calculation process follows the formula below:

[0016]

[0017] Where x(n) is the windowed discrete sampling sequence, A is a complex constant that determines the starting point of the Z-transform, W is a complex constant that determines the spiral step of the Z-transform, n is the sampling point index, N is the length of the input sequence, M is the number of output spectrum points, and X g This is the high-resolution spectrum result for the g-th frequency point, where g is the output frequency point number.

[0018] Furthermore, the amplitude and phase of the fundamental wave and the specified harmonic are analyzed and used as observation data, which are then input into a preset dynamic state-space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component. Specifically:

[0019] The Kalman filter algorithm is used for state updating. Based on the current observation data, the predicted state at the previous time step is corrected to obtain the optimal state estimate at the current time step. The calculation process of state updating is as follows:

[0020] x k|k =x k|k-1 +K k (Z k -H k x k|k-1 );

[0021] Where, x k|k Let x be the state estimation vector at time k. k|k-1 K is the prediction vector based on the state at the previous time step.k Z is the Kalman gain matrix. k H is the observation data vector at the current moment. k Let k be the observation matrix and k be the discrete time step number of the Kalman filter.

[0022] Furthermore, the windowing process applied to the discrete sampling sequence specifically includes:

[0023] The Dolph-Chebyshev window function is chosen to process the discrete sampled sequence in order to suppress spectral leakage while keeping the main lobe width constant. The formula for calculating this window function is as follows:

[0024]

[0025] Where w(n) is the window function weight of the nth sampling point, n is the sampling point index, N is the length of the input sequence, α is the DC component of the window function, and T j Let T be a Chebyshev polynomial of the first kind, x0 be an intermediate variable in the construction of the Chebyshev window, γ be a parameter controlling the sidelobe peak level, β be the scaling factor of the Dolph-Chebyshev window, and j be the Chebyshev polynomial T. j The order index.

[0026] Furthermore, a dynamic fault evolution index is calculated based on the resistive leakage current component and its time rate of change in the system state vector, specifically as follows:

[0027] The dynamic fault evolution index Λ(t) is calculated using the following weighted summation formula:

[0028]

[0029] Among them, I R (t) is the estimated value of the resistive leakage current at time t. V is the time-varying rate of resistive leakage current, η1 and η2 are weighting coefficients, and V norm V represents the current effective value of the grid voltage. ref For the rated reference voltage, |·| MA This is the absolute value after the moving average filtering.

[0030] Furthermore, based on the dynamic fault evolution index, the protection action delay time in the current state is mapped and determined through a multidimensional nonlinear inverse time-delay characteristic surface, specifically as follows:

[0031] Substituting the calculated dynamic fault evolution index Λ(t) into the following nonlinear function, the protection action delay time T is calculated. op :

[0032]

[0033] Among them, T op To protect the action delay time, T min K is the set minimum action time. s The range of variation in delay time, σ is the dimensionless slope factor of the curve, and Λ c is the center response threshold of the dynamic fault evolution index, and e is a natural constant.

[0034] Furthermore, the central response threshold Λ c It is dynamically determined by consulting a preset four-dimensional lookup table based on the insulation level of the line, ambient humidity, and temperature parameters.

[0035] Furthermore, the dynamic state-space model of the circuit leakage characteristics is a linear time-varying system model that uses the circuit's equivalent capacitance to ground and equivalent resistance as state variables, and the amplitude and phase of the residual current fundamental and harmonic waves obtained from high-resolution spectrum analysis as observation quantities.

[0036] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0037] First, high-resolution spectral analysis combining the Dolph-Chebyshev window and Chirp-Z transform is used to minimize the amplitude and phase errors of the fundamental and harmonic frequencies, providing a clean input for milliampere-level resistive leakage current. This clean data is then injected into a Kalman filter to estimate and output the resistive leakage current amplitude and its rate of change in real time, synthesizing it into a dynamic fault evolution index to achieve a quantitative assessment of fault severity and evolution rate. The quantified dynamic fault evolution index directly drives a multidimensional nonlinear inverse time-limited surface to dynamically generate an action delay that matches the operating conditions, creating a closed-loop gain between frequency domain accuracy, state estimation, and protection decision-making for the entire link. This solves the three major defects of traditional FFT spectral leakage, fixed threshold misjudgment, and the inability to adapt the delay at once, achieving a simultaneous improvement in resistive leakage current detection sensitivity and device operational reliability. The former refers to a significantly enhanced ability to identify minute resistive leakage currents, while the latter refers to the stable and accurate action criteria under different operating conditions and long-term operation, without additional risks of false tripping or failure to trip. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a resistive leakage current detection and protection method for an intelligent circuit breaker, as provided in an embodiment of this application. Detailed Implementation

[0039] This application provides a resistive leakage current detection and protection method for intelligent circuit breakers, which solves the problems of FFT spectrum leakage, fixed threshold misjudgment, and lack of adaptive delay in the prior art. By taking dynamic fault evolution index as the core, and through high-resolution spectrum analysis, Kalman state estimation, and multidimensional nonlinear inverse time-limited surface, it realizes for the first time online joint estimation of resistive leakage current amplitude and its rate of change inside the circuit breaker, and adaptively generates protection delay accordingly, while satisfying milliampere-level sensitivity and operating condition adaptive reliability.

[0040] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0041] like Figure 1 As shown in the figure, this application provides a resistive leakage current detection and protection method for intelligent circuit breakers, including:

[0042] Synchronous sampling: acquiring discrete sampling sequences of grid voltage and line residual current;

[0043] Window function shaping: Windowing the discrete sampled sequence to generate windowed voltage and current sequences;

[0044] High-resolution spectrum analysis: Perform high-resolution spectrum analysis on windowed voltage and current sequences to resolve the amplitude and phase of the fundamental and specified harmonics of the grid voltage and residual current;

[0045] Kalman state estimation: The amplitude and phase of the fundamental wave and the specified harmonic are analyzed as observation data and input into the preset dynamic state space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component.

[0046] Fault evolution index generation: A dynamic fault evolution index is calculated based on the resistive leakage current component and its time rate of change in the system state vector.

[0047] Inverse time delay decision: Based on the dynamic fault evolution index, the protection action delay time under the current state is mapped and determined through a multi-dimensional nonlinear inverse time characteristic surface;

[0048] Trip execution: When the internal timer reaches the protection action delay time, a trip command is generated and issued to drive the circuit breaker to perform the trip protection.

[0049] In this embodiment, a closed-loop protection chain for resistive leakage current is formed by seven steps: discrete sampling, windowing, high-resolution spectrum analysis, Kalman filter state estimation, dynamic fault evolution index calculation, multidimensional nonlinear inverse time-limited mapping, and trip execution. This enables the device to have milliampere-level detection sensitivity and automatically extends the delay when the fault development rate is low to avoid false triggering, thus achieving a simultaneous improvement in sensitivity and reliability.

[0050] Furthermore, the high-resolution spectral analysis performed on the windowed voltage sequence and the windowed current sequence specifically includes:

[0051] The spectrum calculation is performed for the area near the fundamental frequency of the power grid and at preset harmonic frequency points. The calculation process follows the formula below:

[0052]

[0053] Where x(n) is the windowed discrete sampling sequence, A is a complex constant that determines the starting point of the Z-transform, W is a complex constant that determines the spiral step of the Z-transform, n is the sampling point index, N is the length of the input sequence, M is the number of output spectrum points, and X g This represents the high-resolution spectrum result for the g-th frequency point, where g is the Chirp-Z output frequency point number.

[0054] In this embodiment, Chirp Z-transform is used to perform 0.1Hz resolution analysis on the fundamental and harmonic frequency bands, accurately capturing interharmonic components around 50Hz, effectively suppressing spectral leakage and noise amplification caused by FFT full-band analysis, providing highly reliable amplitude and phase data for Kalman filtering, and reducing state estimation errors.

[0055] Furthermore, the amplitude and phase of the fundamental wave and the specified harmonic are analyzed and used as observation data, which are then input into a preset dynamic state-space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component. Specifically:

[0056] The Kalman filter algorithm is used for state updating. Based on the current observation data, the predicted state at the previous time step is corrected to obtain the optimal state estimate at the current time step. The calculation process of state updating is as follows:

[0057] x k|k =x k|k-1 +K k (Z k -H k x k|k-1 );

[0058] Where, x k|k Let x be the state estimation vector at time k. k|k-1 K is the prediction vector based on the state at the previous time step.k Z is the Kalman gain matrix. k H is the observation data vector at the current moment. k Let k be the observation matrix and k be the discrete time step number of the Kalman filter.

[0059] Kalman filtering separates resistive and capacitive leakage currents as independent state variables in real time, eliminating the coupling interference of sudden changes in capacitive leakage current on the estimation of resistive components. At the same time, it suppresses long-term deviations caused by temperature drift and sensor aging, ensuring quasi-static estimation of small resistive leakage currents.

[0060] In this embodiment, recursive estimation further includes a state prediction step before state updating:

[0061] Based on the state vector x at time k-1 k-1|k-1 To predict the state vector x at time k k|k-1 Its calculation process follows the following state prediction formula:

[0062] x k|k-1 =F k x k-1|k-1 ;

[0063] Where, x k - 1|k-1 Let F be the optimal state estimation vector at time k-1. k Let be the state transition matrix.

[0064] Before the state update, a state transition matrix prediction stage is introduced, and the actual residual is fed back to correct the state transition parameters. This allows the model to track the slow changes in cable insulation parameters online, avoids misjudgment due to leakage current accumulation caused by model mismatch, and improves long-term operational stability.

[0065] Furthermore, the windowing process applied to the discrete sampling sequence specifically includes:

[0066] The Dolph-Chebyshev window function is chosen to process the discrete sampled sequence in order to suppress spectral leakage while keeping the main lobe width constant. The formula for calculating this window function is as follows:

[0067]

[0068] Where w(n) is the window function weight of the nth sampling point, n is the sampling point index, N is the length of the input sequence, α is the DC component of the window function, and T k Let x0 be a first-order k-th order Chebyshev polynomial, and let x0 be an intermediate variable constructed using a Chebyshev window.

[0069] γ is a parameter controlling the sidelobe peak level, β is the scale factor of the Dolph-Chebyshev window used to map the sidelobe attenuation requirement to the range of independent variables of a Chebyshev polynomial (unitless), and j is the Chebyshev polynomial T. j The order index is used for expanding the frequency domain coefficients of the window function.

[0070] In this embodiment, the Dolph-Chebyshev window function is used to dynamically adjust the sidelobe attenuation parameters based on real-time THD. This suppresses spectral leakage while maintaining the narrowest main lobe width, ensuring that the harmonic amplitude of the 1mA-level resistive leakage current is not affected by adjacent large current harmonics, thus providing distortion-free input for high-resolution spectrum analysis.

[0071] Furthermore, a dynamic fault evolution index is calculated based on the resistive leakage current component and its time rate of change in the system state vector, specifically as follows:

[0072] The dynamic fault evolution index Λ(t) is calculated using the following weighted summation formula:

[0073]

[0074] Among them, I R (t) is the estimated resistive leakage current at time t (in A). V is the time-varying rate of resistive leakage current (in A / s), η1 and η2 are dimensionless weighting coefficients, and V norm The current effective value of the grid voltage (in V), V ref The rated reference voltage (in V), |·| MA This is the absolute value after the moving average filtering.

[0075] By integrating the amplitude and rate of change of resistive leakage current to construct a dynamic fault evolution index, the protection logic is able to distinguish between slowly changing leakage current and sudden faults. The former allows for delayed clearing to avoid false tripping, while the latter provides a rapid response to prevent the accident from escalating, thus achieving a quantitative assessment of the severity of the fault.

[0076] In this embodiment, the weighting coefficients η1 and η2 are obtained as follows:

[0077] By conducting multivariate nonlinear regression analysis on a large amount of historical fault data or simulation data, a functional relationship model between weighting coefficients and key power grid parameters is established to achieve adaptive adjustment of weighting coefficients.

[0078] By using a multivariate nonlinear regression model to correlate the weighting coefficients η1 and η2 with the service life of the line, ambient humidity, and temperature, the coefficients are adjusted online to ensure that the circuit breaker can output the optimal fault evolution index at different operating stages, thus avoiding manual readjustment.

[0079] Furthermore, based on the dynamic fault evolution index, the protection action delay time in the current state is mapped and determined through a multidimensional nonlinear inverse time-delay characteristic surface, specifically as follows:

[0080] Substituting the calculated dynamic fault evolution index Λ(t) into the following nonlinear function, the protection action delay time T is calculated. op :

[0081]

[0082] Among them, T op To protect the action delay time (in seconds), T min K is the minimum action time (in seconds) set for the system. s The range of time variation (in seconds) is σ, where σ is the dimensionless slope factor of the curve, and Λ c is the central response threshold (dimensionless) of the dynamic fault evolution index, and e is a natural constant.

[0083] In this embodiment, a sigmoid-like inverse time-delay function is used to map the dynamic fault evolution index into an action delay, and the slope of the curve is adjusted in real time by the ambient temperature to shorten the insulation embrittlement fault delay in cold regions and maintain high fault tolerance in normal temperature regions, thereby realizing single-parameter adaptive protection action.

[0084] Furthermore, the central response threshold Λ c It is dynamically determined based on the insulation class of the line, ambient humidity, and temperature parameters by consulting a preset 4-D Look-Up Table.

[0085] In this embodiment, the center response threshold Λ is dynamically set using a four-dimensional lookup table (insulation class, humidity, temperature, altitude). c It can adapt to the low-pressure environment of high altitude without human intervention, avoid misjudgment of arc reignition caused by thin air, and ensure that the device can be used immediately after installation.

[0086] Furthermore, the dynamic state-space model of the circuit leakage characteristics is a linear time-varying system model that uses the circuit's equivalent capacitance to ground and equivalent resistance as state variables, and the amplitude and phase of the residual current fundamental and harmonic waves obtained from high-resolution spectrum analysis as observation quantities.

[0087] When constructing a dynamic state-space model of circuit leakage characteristics, the equivalent resistance to ground and equivalent capacitance of the line are first determined as state variables, and the amplitude and phase of the residual current fundamental wave and selected harmonics are set as observations. Then, the state transition relationship is designed based on the insulation aging law, and the output equation is established by using the mapping between the observed data and the state variables. Finally, a linear time-varying discrete model is formed, and the state variables are continuously corrected during operation to achieve real-time online estimation of resistive leakage current.

[0088] In this embodiment, the line-to-ground equivalent resistance and capacitance are incorporated into the state space as estimated variables, and the amplitude and phase of the fundamental and harmonic waves are used as observations to achieve online tracking of insulation parameters. This significantly improves the observability and robustness of small resistive leakage currents and avoids the estimation blind zone caused by traditional single 50Hz information.

[0089] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0091] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0094] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting and protecting the resistive leakage current of an intelligent circuit breaker, characterized in that, Includes the following steps: Obtain discrete sampling sequences of grid voltage and line residual current; Windowing is applied to the discrete sampled sequence to generate windowed voltage and windowed current sequences; High-resolution spectral analysis was performed on the windowed voltage and current sequences to resolve the amplitude and phase of the fundamental and specified harmonics of the grid voltage and residual current. The amplitude and phase of the fundamental wave and the specified harmonic are analyzed and used as observation data. They are then input into the preset dynamic state space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component. A dynamic fault evolution index is calculated based on the resistive leakage current component and its time rate of change in the system state vector. Based on the dynamic fault evolution index, the protection action delay time under the current state is mapped and determined through a multi-dimensional nonlinear inverse time-delay characteristic surface. When the internal timer reaches the protection action delay time, a trip command is generated and issued to drive the circuit breaker to perform the trip protection.

2. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, The high-resolution spectral analysis performed on the windowed voltage and current sequences specifically includes: The spectrum calculation is performed for the area near the fundamental frequency of the power grid and at preset harmonic frequency points. The calculation process follows the formula below: Where x(n) is the windowed discrete sampling sequence, A is a complex constant that determines the starting point of the Z-transform, W is a complex constant that determines the spiral step of the Z-transform, n is the sampling point index, N is the length of the input sequence, M is the number of output spectrum points, and X g This is the high-resolution spectrum result for the g-th frequency point, where g is the output frequency point number.

3. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, The amplitude and phase of the fundamental wave and the specified harmonic are analyzed and used as observation data, which are then input into a preset dynamic state-space model of circuit leakage characteristics to recursively estimate the system state vector containing the resistive leakage current component. Specifically: The Kalman filter algorithm is used for state updating. Based on the current observation data, the predicted state at the previous time step is corrected to obtain the optimal state estimate at the current time step. The calculation process of state updating is as follows: x k|k =x k|k -1+K k (Z k -H k x k|k-1 ); Where, x k|k Let x be the state estimation vector at time k. k|k-1 K is the prediction vector based on the state at the previous time step. k Z is the Kalman gain matrix. k H is the observation data vector at the current moment. k Let k be the observation matrix and k be the discrete time step number of the Kalman filter.

4. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, The windowing process for the discrete sampling sequence is specifically as follows: The Dolph-Chebyshev window function is chosen to process the discrete sampled sequence in order to suppress spectral leakage while keeping the main lobe width constant. The formula for calculating this window function is as follows: Where w(n) is the window function weight of the nth sampling point, n is the sampling point index, N is the length of the input sequence, α is the DC component of the window function, and T j Let T be a Chebyshev polynomial of the first kind, x0 be an intermediate variable in the construction of the Chebyshev window, γ be a parameter controlling the sidelobe peak level, β be the scaling factor of the Dolph-Chebyshev window, and j be the Chebyshev polynomial T. j The order index.

5. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, Based on the resistive leakage current component and its rate of change over time in the system state vector, a dynamic fault evolution index is calculated, specifically: The dynamic fault evolution index Λ(t) is calculated using the following weighted summation formula: Among them, I R (t) is the estimated value of the resistive leakage current at time t. V is the time-varying rate of resistive leakage current, η1 and η2 are weighting coefficients, and V norm V represents the current effective value of the grid voltage. ref For the rated reference voltage, |·| MA This is the absolute value after the moving average filtering.

6. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, The protection action delay time under the current state is mapped and determined based on the dynamic fault evolution index through a multidimensional nonlinear inverse time-delay characteristic surface, specifically as follows: Substituting the calculated dynamic fault evolution index Λ(t) into the following nonlinear function, the protection action delay time T is calculated. op : Among them, T op To protect the action delay time, T min K is the set minimum action time. s The range of variation in delay time, σ is the dimensionless slope factor of the curve, and Λ c is the center response threshold of the dynamic fault evolution index, and e is a natural constant.

7. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 6, characterized in that, The central response threshold Λ c It is dynamically determined by consulting a preset four-dimensional lookup table based on the insulation level of the line, ambient humidity, and temperature parameters.

8. The resistive leakage current detection and protection method for an intelligent circuit breaker as described in claim 1, characterized in that, The dynamic state-space model of circuit leakage characteristics is a linear time-varying system model that uses the circuit's equivalent capacitance to ground and equivalent resistance as state variables, and the amplitude and phase of the residual current fundamental and harmonic waves obtained from high-resolution spectrum analysis as observation quantities.

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