Fusing optimization method of override trip prevention fast fuse, medium and equipment

Abstract

The invention discloses a fusing optimization method of an override trip prevention fast fuse, a medium and equipment, and belongs to the technical field of intelligent power distribution networks. According to the method, a nonlinear heat conduction process of a melt is dynamically modeled through a fractional order thermal state evolution equation, and temperature dependent resistance and a heat dissipation coefficient are updated in real time in combination with an online parameter identification algorithm, so that the action precision is remarkably improved; a hierarchical fusing instruction is generated based on a power distribution network equivalent impedance correlation factor, and multi-level protection cooperation is realized in cooperation with a wireless blocking signal, so that the override trip probability is effectively reduced; the fuzzy rule base enhances the adaptability to harmonic wave and load disturbance by dynamically adjusting the current change rate gradient and the threshold value of a load fluctuation factor; the fiber bragg grating and X-ray diffraction technology is adopted for melt phase change monitoring, and fusing is accelerated by triggering nanometer pulses through double criteria, so that the fusing time is greatly shortened. The method solves the problems that a traditional fuse is inaccurate in action, poor in collaboration and ineffective in complex working conditions, and is suitable for a protection scene of a high-permeability new energy power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of smart distribution networks, and in particular to a method, medium and equipment for optimizing the fusing of an anti-over-tripping fast fuse. Background Art

[0002] In traditional distribution network protection systems, fuses are key protective devices, and their operating characteristics directly determine the probability of over-tripping. Conventional fuses often use a fixed fusing model based on the I²t integral. This model assumes that the heat accumulation process follows a linear law and cannot accurately describe the nonlinear thermal conduction effect of the fuse under dynamic fault currents. Especially in scenarios with high penetration of new energy, where the grid harmonic content increases and load fluctuations intensify, traditional models ignore issues such as uneven temperature field distribution and material phase transition hysteresis, resulting in significant deviations in the setting of the operating threshold. Furthermore, existing methods lack a coordinated response mechanism to dynamic changes in the grid topology and lack spatiotemporal coordination between branch protection devices. When a fault occurs, conflicts in the operating logic of upstream and downstream devices can easily lead to over-tripping. For example, traditional impedance matching methods rely solely on static impedance parameters and cannot reflect the transient coupling characteristics of the fault current propagation path in real time, resulting in mismatched protection action timing.

[0003] Another limitation is the singleness of the fuse triggering criterion. Existing technologies mostly rely on macroscopic electrothermal parameters (such as current amplitude and temperature rise rate) as the basis for action, while ignoring the decisive influence of the melt microstructure (such as grain boundary slip and dislocation density) on the failure process. Under working conditions containing high-order harmonics, the non-uniform thermal stress distribution of the melt material will accelerate local phase changes, but traditional monitoring methods (such as thermocouples) are difficult to capture critical failure states in a timely manner due to their slow response speed and low spatial resolution. In addition, the generation of existing fuzzy control rule bases mostly relies on empirical settings and lacks the ability to adaptively learn historical fault data, resulting in an increase in the false operation rate in complex disturbance scenarios. The above defects make traditional fuses significantly insufficient in terms of action accuracy, coordinated protection and adaptability to complex working conditions. Summary of the Invention

[0004] (1) Technical issues to be resolved To solve the above problems, the present invention proposes a method, medium and equipment for optimizing the melting of anti-overtripping fast fuses, aiming to solve the problem in the prior art that the traditional impedance matching method only relies on static impedance parameters and cannot reflect the transient coupling characteristics of the fault current propagation path in real time, resulting in mismatch in protection action timing and the singleness of the melting triggering criterion.

[0005] (2) Technical solution A method for optimizing the fusing of an anti-over-trip fast fuse of the present invention comprises: Obtain the dynamic thermodynamic parameters of the fuse body, including temperature change rate, current thermal effect parameters and nonlinear heat dissipation characteristic parameters; Acquiring coordinated action characteristic data of the associated power system, the coordinated action characteristic data including a distribution network equivalent impedance correlation factor and a load disturbance propagation characteristic, wherein the coordinated action characteristic data of each branch is represented by an action logic correlation relationship of all protection devices in the associated power system; The dynamic thermodynamic parameters and collaborative action characteristic data are input into a multi-dimensional fusion decision model, and the phase change result is obtained by describing the melt phase change process through a fractional-order thermal evolution equation. The dynamic action threshold is generated by combining the phase change result with the fuzzy rule library, and the hierarchical fusing instruction is triggered according to the impedance priority sequence, and finally the spatiotemporal optimization strategy of the fusing action is output.

[0006] In the present invention, the melt phase change process adopts an adaptive adjustment mechanism of dynamic order parameters and constructs a real-time feedback loop of thermodynamic coupling terms through a discretized numerical method; Thermodynamic parameter identification is obtained through the dynamic thermodynamic parameters, a time-varying observation matrix is constructed based on the thermodynamic parameter identification, and a recursive estimation algorithm controlled by a forgetting factor is adopted to form an exponential decay update mechanism of the error covariance matrix, thereby realizing dynamic decoupling of the state space and parameter space of the associated power system.

[0007] In the present invention, the temperature change rate is modeled by a fractional differential operator as The current thermal effect parameter includes the time-varying current square integral term Temperature-dependent function of material resistance , the fractional-order thermal evolution equation is: ,in is the dynamic order parameter, is the melt temperature field distribution function, is the real-time current vector, is the temperature-dependent resistance matrix, is the nonlinear heat dissipation coefficient tensor, is the heat capacity distribution function, Indicates real-time temperature rise.

[0008] In the present invention, the order parameter of the fractional-order thermal evolution equation is Dynamic adjustment using the Grünwald-Letnikov discretization algorithm: ,in To make the discretization step consistent with the sampling period, is the generalized binomial coefficient, calculated as , is the memory length, and , is the fundamental period of the fault current, and the temperature dependence coefficient update period is .

[0009] In the present invention, the thermodynamic parameter identification adopts a recursive least squares algorithm with a forgetting factor: ,in , , forgetting factor , the initial covariance matrix .

[0010] In the present invention, the fuzzy rule base input variable is defined as: , in, Indicates the current change rate gradient, characterizes the instantaneous growth rate of the fault current, and calculates the current difference between adjacent sampling points in real time through the differential algorithm represents the load fluctuation factor, which is the ratio of the load current fluctuation amplitude to the rated current, reflecting the disturbance intensity of the associated power system. is the load current fluctuation, is the rated current of the fuse; The output variable is the action time correction coefficient , its membership function satisfies: ,in The cluster center is generated by clustering historical fault data using the fuzzy C-means algorithm to characterize the input variables and The typical eigenvalues of is the standard deviation of the Gaussian membership function, which is determined by the sample variance calculation, controls the width of the membership function, reflects the discrete degree of parameter distribution, and the size of the fuzzy rule base. .

[0011] In the present invention, the impedance priority sequence calculation satisfies: ,in is the node admittance matrix Rank Column elements, representing branches With other branches in the system The electromagnetic coupling strength; is the node admittance matrix Rank Column elements, representing branches Its own impedance characteristics, the action delay classification is: , For support Road priority factor, Indicates the delay time of the fuse action, according to The value is adjusted dynamically.

[0012] In the present invention, the melt phase change triggering condition is: , where the dislocation density Calculated by Scherrer formula , , is the dimensionless shape factor, is the average diameter of the grains in the melt, is the wavelength of the incident X-ray, is the half-height width of the X-ray diffraction peak, It is the Prague corner.

[0013] Another computer-readable storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the method for optimizing the fusing of an anti-over-tripping fast fuse as described in any one of the above technical solutions.

[0014] Another execution device of the present invention includes an execution device body and a controller, wherein the controller includes a processor and a computer program stored in a memory and runnable on the processor, and when the processor executes the program, the method for optimizing the fusing of the anti-over-tripping fast fuse described in any one of the above technical solutions is implemented.

[0015] (3) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: (1) In the present invention, the nonlinear heat conduction process of the melt is dynamically modeled by the fractional-order thermal evolution equation, and the temperature-dependent resistance and heat dissipation coefficient are updated in real time in combination with the online parameter identification algorithm. Compared with the traditional I²t model, the action error is greatly reduced. In the 10kA short-circuit current scenario, the melting time deviation is even smaller.

[0016] (2) In the present invention, hierarchical fusing instructions are generated based on the distribution network equivalent impedance correlation factor, and combined with wireless blocking signals, the spatiotemporal coordination of multi-level protection is achieved, and the probability of over-tripping is greatly reduced.

[0017] (3) The fuzzy rule base dynamically adjusts the action threshold through the fuzzy C-means clustering algorithm. The input variables include the current change rate gradient and the load fluctuation factor. It can still maintain a low false operation rate when the harmonic distortion rate (THD ≥ 10%) or the load changes suddenly. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of the logical structure of the optimization method; Figure 2 Schematic diagram of clustering structure of fuzzy rule base; Figure 3 Schematic diagram of the framework structure of the execution equipment.

[0020] 10. Processor, 11. Memory, 12. Communication interface, 13. Communication bus. DETAILED DESCRIPTION

[0021] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed only to facilitate the present disclosure. Those skilled in the art will better understand that the subject matter described herein is not intended to limit the scope of protection, applicability, or examples set forth in the claims. The functions and arrangements of the elements discussed may be changed without departing from the scope of protection of this disclosure. Various examples may omit, replace, or add various processes or components as needed. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted, or combined. In addition, features described relative to some examples may also be combined in other examples.

[0022] As used herein, the term "including" and its variations are open terms meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0023] Example 1

[0024] like Figure 1-Figure 2 The present invention discloses a method for optimizing the melting of a fast-acting fuse to prevent over-tripping. This method is based on the spatiotemporal coupling characteristics of the melting action and the cross-scale correlation mechanism of material failure. By integrating the microscopic phase change dynamics of the melt with the macroscopic transient response characteristics of the power grid, a multi-dimensional collaborative optimization system is constructed.

[0025] S100: Acquire dynamic thermodynamic parameters of the fuse body, including temperature change rate, current thermal effect parameters, and nonlinear heat dissipation characteristic parameters.

[0026] First, the thermodynamic parameters of each microelement of the fuse are collected in real time through a distributed temperature sensor array. The sensors are arranged equidistantly along the axial direction of the melt, for example, the spacing between temperature sensors is 2 mm, and the sampling frequency is not less than 1 kHz. The temperature change rate is modeled using a fractional differential equation: ,in is the dynamic order parameter, is the melt temperature field distribution function, is the real-time current vector, is the temperature-dependent resistance matrix, is the nonlinear heat dissipation coefficient tensor, is the heat capacity distribution function, Indicates real-time temperature rise.

[0027] In this embodiment, the distributed temperature sensor array adopts a MEMS thermoelectric cone structure. Each sensing unit contains 16 pairs of thermocouples, which are embedded in the alumina ceramic substrate with a spacing of 2mm along the axial direction of the melt. The signal conditioning circuit adopts an instrument amplifier with a sampling rate of at least , the temperature resolution is 0.1℃. The data is transmitted to FPGA via SPI interface, and the temperature gradient field is calculated in real time. , and mapped to the spatial discretization grid of the fractional differential equation.

[0028] S200. Acquire collaborative action characteristic data of an associated power system, wherein the collaborative action characteristic data includes a distribution network equivalent impedance correlation factor and a load disturbance propagation characteristic, wherein the collaborative action characteristic data of each branch is represented by an action logic correlation relationship of all protection devices in the associated power system.

[0029] Specifically, the dynamic order parameter Dynamic adjustment via the Grünwald-Letnikov discretization algorithm: ,in To ensure that the discretization step size is consistent with the sampling period, both are set to 0.1ms. is the generalized binomial coefficient, and the calculation is implemented using the Gamma function: , is the memory length, and , is the fundamental period of the fault current, and the temperature dependence coefficient update period is When the fundamental frequency is 50Hz, ,but ,real-time calculation is achieved through FPGA chip, one iteration is completed in each clock cycle, ensuring that the calculation delay does not exceed 2μs.

[0030] The thermodynamic parameter identification module uses a recursive least squares algorithm to construct a parameter vector and observation vector ,in is the real-time temperature rise. , the initial covariance matrix is set to , updating parameters every 0.1 ms. In hardware implementation, a dual-port RAM is used to store historical data, and a parallel multiplier array is used to accelerate matrix operations, ensuring that parameter updates are completed within 5 μs.

[0031] S300. Input the dynamic thermodynamic parameters and collaborative action characteristic data into a multi-dimensional fusion decision model, describe the melt phase change process through a fractional-order thermal evolution equation to obtain the phase change result, combine the phase change result with the fuzzy rule library to generate a dynamic action threshold, trigger the hierarchical fusing instruction according to the impedance priority sequence, and finally output the spatiotemporal optimization strategy of the fusing action.

[0032] The fuzzy rule base is generated based on the historical fault data set, and the input variables are clustered by the fuzzy C-means clustering algorithm. Divided into 7 quantization intervals, the corresponding domain is , for example when Classified as "very high" when the load fluctuation factor Divided into 5 exponential distribution intervals, covering Range. Output variables The action time correction coefficient is mapped by 49 fuzzy rules. For example, rule R12 is defined as: “If High and If it is medium, The membership function adopts Gaussian type: The cluster center Determined through offline training, Dynamically adjusted by sample variance, the overlap is no less than 40%. In the FPGA, the membership calculation unit is implemented using a lookup table, the rule activation layer is configured with 64 parallel AND gate logic circuits, and the defuzzification unit uses a weighted average method, keeping the overall processing delay within 3μs.

[0033] like Figure 2 As shown, the present disclosure includes three clusters, wherein cluster A corresponds to the combination of high current change rate gradient and strong coincidence disturbance, and its triggering condition is and That is, the current change rate gradient is extremely high and the load fluctuation factor is extremely large. At this time, the output This shortens the fusing time to 60% of the standard value. At this point, the fault current rises rapidly and fluctuates violently, triggering the fast-acting fusing mode to prevent the fault from spreading.

[0034] Cluster B corresponds to the combination of medium current change rate and moderate load disturbance, and its triggering condition is and That is, the current change rate gradient is medium-high, and the load fluctuation factor is medium-sized. At this time, the output , this is a classic overload scenario, for example, the motor starting current is , combined with moderate load fluctuations , dynamically adjust the fuse threshold to avoid false operation.

[0035] Cluster C corresponds to the combination of low current change rate and weak load disturbance, and its triggering condition is and That is, the current change rate gradient is extremely low and the load fluctuation factor is extremely small. At this time, the output , this is normal load fluctuation or slight overcurrent, and the fusing time is extended to distinguish between transient disturbance and real fault.

[0036] Specifically, Indicates the current change rate gradient, characterizes the instantaneous growth rate of the fault current, and calculates the current difference between adjacent sampling points in real time through the differential algorithm The load fluctuation factor represents the ratio of the load current fluctuation amplitude to the rated current, reflecting the disturbance intensity of the associated power system.

[0037] The impedance priority sequence is calculated by solving the distribution network node admittance matrix in real time. , and invert it to get the equivalent impedance matrix The QR decomposition algorithm is used to avoid the matrix singularity problem. The decomposition process is expressed as: in is an orthogonal matrix, is an upper triangular matrix. For each branch , calculate the priority factor: when When it is determined to be a critical fault branch, the first-level fuse instruction is triggered with a delay of 5±2 ms; When a branch is triggered, the second-level instruction is triggered with a delay of 10±3 ms; the remaining branches enter the third level with a delay of 15±5 ms. The priority calculation module is integrated into the protection controller and uses multi-core DSP parallel processing to ensure that the entire network branch is sorted within 1 ms.

[0038] Distribution network equivalent impedance matrix The solution is achieved through multi-core DSP, each core is responsible for calculating the matrix A row of elements. Node admittance matrix The construction rule is: at the same time satisfy the diagonal elements And non-diagonal elements ,in For nodes With adjacent nodes The admittance of QR decomposition uses the Householder transform parallel algorithm. The decomposition process is divided into two steps: column vector orthogonalization and upper triangularization. The computational complexity of each step is , the task scheduler is used to assign it to 8 DSP cores for synchronous execution, ensuring that the matrix inversion of the entire network of 100 nodes is completed in a relatively short time.

[0039] Melt phase change monitoring is achieved by an 8-core fiber Bragg grating array embedded in the melt. The wavelength interrogator scans the 1530-1565 nm band at 200 Hz and the Bragg wavelength shift is used to measure the phase change of the melt. Calculate local strain: in is the effective elastic-optical coefficient, is the initial wavelength. In this embodiment Grain boundary slip rate The energy proportion of the 10-50 kHz frequency band is extracted by wavelet packet decomposition, and the signal is judged when the energy entropy change rate exceeds the baseline by 15 dB / ms. . Dislocation density The grain size was calculated by online X-ray diffraction analysis using the Scherrer formula: in is the dimensionless shape factor, is the average diameter of the grains in the melt, is the wavelength of the incident X-ray, is the half-height width of the X-ray diffraction peak, is the Bragg angle. In this disclosure, , (Cu target Kα ray). When both and When the nano-pulse accelerated fusing module is triggered, a 10 ns high-voltage pulse is released with an electric field strength of 50 kV / cm, which shortens the fusing time to 30% of the conventional mode.

[0040] The harmonic adaptation module uses FFT to analyze the current spectrum in real time. When it detects a total harmonic distortion (THD) of 10% or higher, it switches to high-frequency pulse mode. The pre-charged capacitor array generates 3-5 kV pulses with a pulse width of 10-50 ns using GaN switching transistors, which, in conjunction with magnetic saturation reactors, form a directional electric field.

[0041] The dynamic thermal model provides real-time parameters for fuzzy decision making, the impedance priority sequence guides the hierarchical fusing timing, and the phase change monitoring is directly related to the fusing triggering conditions. and When the circuit breaker is tripped, the system will complete the fuse within 5 ms and lock the downstream equipment through wireless signals to avoid over-tripping.

[0042] Example 2

[0043] An embodiment of the present invention provides a computer-readable storage medium.

[0044] The computer-readable storage medium provided in the embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods for optimizing the fusing of an anti-overtrip fast fuse can be implemented.

[0045] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0046] For an introduction to the computer-readable storage medium provided in an embodiment of the present invention, please refer to the above method embodiment, and the present invention will not elaborate on it here.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0048] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0049] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0050] Example 3

[0051] An embodiment of the present invention provides an execution device.

[0052] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an execution device provided by the present invention, which may include: Memory for storing computer programs; The processor is configured to implement the steps of any one of the above-mentioned methods for optimizing the fusing of an anti-over-tripping fast fuse when executing a computer program.

[0053] like Figure 3 FIG2 is a schematic diagram of the structure of the execution device, which may include a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 communicate with each other via the communication bus 13.

[0054] In the embodiment of the present invention, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices.

[0055] The processor 10 may call a program stored in the memory 11 . Specifically, the processor 10 may execute the operations in the embodiment of the push button switch fault detection method.

[0056] The memory 11 is used to store one or more programs. The programs may include program codes, which include computer operating instructions. In the embodiment of the present invention, the memory 11 stores at least a program for implementing the following functions: Obtain the dynamic thermodynamic parameters of the fuse body, including temperature change rate, current thermal effect parameters and nonlinear heat dissipation characteristic parameters; Acquiring coordinated action characteristic data of the associated power system, the coordinated action characteristic data including a distribution network equivalent impedance correlation factor and a load disturbance propagation characteristic, wherein the coordinated action characteristic data of each branch is represented by an action logic correlation relationship of all protection devices in the associated power system; The dynamic thermodynamic parameters and collaborative action characteristic data are input into a multi-dimensional fusion decision model, and the phase change result is obtained by describing the melt phase change process through a fractional-order thermal evolution equation. The dynamic action threshold is generated by combining the phase change result with the fuzzy rule library, and the hierarchical fusing instruction is triggered according to the impedance priority sequence, and finally the spatiotemporal optimization strategy of the fusing action is output.

[0057] In one possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0058] In addition, the memory 11 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0059] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0060] Of course, it needs to be explained that Figure 3 The structure shown does not constitute a limitation on the execution device in the embodiment of the present invention. In actual applications, the execution device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0061] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Any modifications and improvements made to the technical solution of the present invention by a person of ordinary skill in the art without departing from the design concept of the present invention shall fall within the scope of protection of the present invention. The technical content for which protection is sought in the present invention is fully set forth in the claims.

Claims

1. A method for optimizing the fusing of a fast fuse for preventing over-tripping, characterized in that: include: Obtain the dynamic thermodynamic parameters of the fuse body, including temperature change rate, current thermal effect parameters and nonlinear heat dissipation characteristic parameters; Acquiring coordinated action characteristic data of the associated power system, the coordinated action characteristic data including a distribution network equivalent impedance correlation factor and a load disturbance propagation characteristic, wherein the coordinated action characteristic data of each branch is represented by an action logic correlation relationship of all protection devices in the associated power system; The dynamic thermodynamic parameters and collaborative action characteristic data are input into a multi-dimensional fusion decision model, and the phase change result is obtained by describing the melt phase change process through a fractional-order thermal evolution equation. The dynamic action threshold is generated by combining the phase change result with the fuzzy rule library, and the hierarchical fusing instruction is triggered according to the impedance priority sequence, and finally the spatiotemporal optimization strategy of the fusing action is output.

2. The method for optimizing the fusing of a fast fuse for preventing over-tripping according to claim 1, wherein: The melt phase change process adopts an adaptive adjustment mechanism of dynamic order parameters and constructs a real-time feedback loop of thermodynamic coupling terms through a discretized numerical method; Thermodynamic parameter identification is obtained through the dynamic thermodynamic parameters, a time-varying observation matrix is constructed based on the thermodynamic parameter identification, and a recursive estimation algorithm controlled by a forgetting factor is adopted to form an exponential decay update mechanism of the error covariance matrix, thereby realizing dynamic decoupling of the state space and parameter space of the associated power system.

3. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 2, wherein: The temperature change rate is modeled by the fractional differential operator as The current thermal effect parameter includes the time-varying current square integral term Temperature-dependent function of material resistance , the fractional-order thermal evolution equation is: ,in is the dynamic order parameter, is the melt temperature field distribution function, is the real-time current vector, is the temperature-dependent resistance matrix, is the nonlinear heat dissipation coefficient tensor, is the heat capacity distribution function, Indicates real-time temperature rise.

4. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 3, wherein: The order parameter of the fractional thermal evolution equation is Dynamic adjustment using the Grünwald-Letnikov discretization algorithm: ,in To make the discretization step consistent with the sampling period, is the generalized binomial coefficient, calculated as , is the memory length, and , is the fundamental period of the fault current, and the temperature dependence coefficient update period is .

5. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 4, characterized in that: The thermodynamic parameter identification adopts the recursive least squares algorithm with forgetting factor: ,in , , forgetting factor , the initial covariance matrix .

6. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 5, characterized in that: The fuzzy rule base input variables are defined as: , in, Indicates the current change rate gradient, characterizes the instantaneous growth rate of the fault current, and calculates the current difference between adjacent sampling points in real time through the differential algorithm represents the load fluctuation factor, which is the ratio of the load current fluctuation amplitude to the rated current, reflecting the disturbance intensity of the associated power system. is the load current fluctuation, is the rated current of the fuse; The output variable is the action time correction coefficient , its membership function satisfies: ,in The cluster center is generated by clustering historical fault data using the fuzzy C-means algorithm to characterize the input variables and The typical eigenvalues of is the standard deviation of the Gaussian membership function, which is determined by the sample variance calculation, controls the width of the membership function, reflects the discrete degree of parameter distribution, and the size of the fuzzy rule base. .

7. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 6, characterized in that: The impedance priority sequence calculation satisfies: ,in is the node admittance matrix Rank Column elements, representing branches With other branches in the system The electromagnetic coupling strength; is the node admittance matrix Rank Column elements, representing branches Its own impedance characteristics, the action delay classification is: , is the branch priority factor, Indicates the delay time of the fuse action, according to The value is adjusted dynamically.

8. The method for optimizing the fusing of an anti-over-trip fast fuse according to claim 7, characterized in that: The melt phase change triggering condition is: , where the dislocation density Calculated by Scherrer formula , , is the dimensionless shape factor, is the average diameter of the grains in the melt, is the wavelength of the incident X-ray, is the half-height width of the X-ray diffraction peak and is the Bragg angle.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for optimizing the fusing of the anti-over-trip fast fuse according to any one of claims 1 to 8 is implemented.

10. An execution device, comprising an execution device body and a controller, characterized in that: The controller includes a processor and a computer program stored in a memory and executable on the processor. When the processor executes the program, the method for optimizing the fusing of the anti-over-trip fast fuse according to any one of claims 1 to 8 is implemented.

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