Fusing optimization method of override trip prevention fast fuse

Abstract

The invention relates to the technical field of electric power system protection, in particular to a fusing optimization method for an override trip prevention fast fuse, and aims to solve the problems that the response speed and selectivity of a traditional override trip prevention fast fuse are unbalanced, the fixed fusing characteristic cannot be matched with the short-circuit current difference caused by the permeability change of a distributed power supply, and the service life of the override trip prevention fast fuse is influenced. And environmental temperature drift and harmonic interference further aggravate the reduction of protection precision, resulting in significant increase of override trip risk. The method can adjust the fusing characteristics in real time according to the working condition data through the dynamic reconstruction curve and the combination of the magnetic-thermal coupling cooperative triggering mechanism, accelerates the fusing when the short-circuit current parameter reaches the threshold value, avoids the misoperation when the short-circuit current parameter is lower than the threshold value, optimizes the protection precision through environment compensation and reinforcement learning, and improves the protection precision. The problems that a traditional fuse cannot adapt to the permeability change of the distributed power supply and the protection is unreliable due to environmental interference are effectively solved, and the method has the advantages of being high in dynamic adaptability and high in protection reliability.

Description

Technical Field

[0001] This application relates to the field of power system protection technology, and more specifically, to an optimized method for the fusing of fast fuses to prevent over-tripping. Background Technology

[0002] With the high proportion of distributed power sources connected to the distribution network, the short-circuit current characteristics have changed significantly, posing a serious challenge to traditional fast-acting fuses that prevent cascading tripping.

[0003] First, there is an inherent contradiction between response speed and selectivity. The design of quickly cutting off faults is prone to malfunctions when short-circuited under light load, while the design of delay characteristics will result in lag when short-circuited under heavy load.

[0004] Secondly, the existing fuses have fixed fusing characteristics and cannot adapt to the large fluctuations in short-circuit current amplitude caused by changes in the penetration rate of distributed power sources.

[0005] Furthermore, changes in ambient temperature and harmonic disturbances can significantly affect the protection accuracy of fuses, further exacerbating the unreliability of the protection system. These problems lead to frequent cascading tripping accidents in the distribution network, severely impacting power supply reliability.

[0006] Existing technologies lack fuse protection solutions that can dynamically adapt to changes in operating conditions, effectively coordinate speed and selectivity, and simultaneously possess environmental interference resistance capabilities. Summary of the Invention

[0007] (a) Technical problems to be solved The purpose of this application is to provide a method for optimizing the fusing of fast-acting fuses to prevent over-tripping, a computing device, and a non-transitory machine-readable storage medium, which has the advantages of dynamically adapting to changes in operating conditions, effectively coordinating speed and selectivity, and improving environmental anti-interference capabilities.

[0008] (II) Technical Solution This application provides a method for optimizing the fusing of fast-acting fuses to prevent cascading tripping. The technical solution is as follows: Obtain the operating condition data of the line where the fast-acting fuse is located, including distributed power supply penetration rate, short-circuit current parameters, and environmental parameters; based on the operating condition data, dynamically reconstruct the fast-acting fuse's... Curve; Based on dynamic reconstruction The curve controls the fast fuse's melting via a magnetic-thermal coupling coordinated triggering mechanism; this mechanism includes: when the short-circuit current parameters reach a dynamically reconfigurable value... When the curve reaches the preset proportional threshold, the electromagnetic coil is controlled to output a stepped excitation current to generate a strong magnetic field, which forms a magnetic-thermal coupling effect with the Joule heating of the molten element, accelerating the melting of the fast fuse. When the short-circuit current parameter is lower than the preset proportional threshold, the electromagnetic coil is controlled to output a weak excitation current to calibrate the temperature distribution of the molten element through the magnetic field and avoid accidental melting.

[0009] Furthermore, this application also proposes that the acquisition of operating condition data includes: real-time acquisition of distributed power source penetration rate, short-circuit current parameters, and environmental parameters through the sensor array built into the fast fuse; wherein, the distributed power source penetration rate is calculated jointly by voltage phase difference and current distortion rate, the short-circuit current parameters include short-circuit current amplitude and rate of rise, and the environmental parameters include ambient temperature and harmonic distortion rate.

[0010] Furthermore, this application also proposes a method for dynamically reconfiguring fast fuses. The curves include: using an improved particle swarm optimization algorithm, with the constraint that the time difference between the actions of the upper and lower level protection systems is not less than a preset time difference threshold, to generate dynamic curves in real time. Curve; among which, dynamic The curve is based on the distributed power penetration rate, which adjusts the fuse time window and incorporates a temperature compensation factor to offset environmental impacts.

[0011] Furthermore, this application also proposes using an improved particle swarm optimization algorithm to generate dynamic data in real time. The curve includes: compressing the circuit breaker time window when the distributed power penetration rate is greater than a first preset threshold; and extending the circuit breaker time window when the distributed power penetration rate is less than a second preset threshold. The temperature compensation factor is adjusted for each degree of deviation from the reference temperature, using a reference temperature as the standard temperature. Threshold preset percentage.

[0012] Furthermore, this application also proposes that controlling the output of the electromagnetic coil in a stepped excitation current includes: increasing the excitation current from zero to a target value within a preset time, so that the electromagnetic coil generates a strong magnetic field perpendicular to the current direction; wherein, the magnetic-thermal coupling effect includes the electromagnetic force breaking the oxide film on the surface of the melt to accelerate heat conduction, and the eddy current induced by the magnetic field generating additional Joule heat.

[0013] Furthermore, this application also proposes that controlling the melting of a fast fuse through a magnetic-thermal coupling coordinated triggering mechanism further includes: when the harmonic distortion rate is greater than a preset distortion threshold, the magnetic field frequency is modulated and superimposed in reverse with the harmonic frequency to offset the thermal accumulation deviation caused by the harmonic.

[0014] Furthermore, this application also proposes to include: after the fast fuse trips, uploading the actual fusing data to the distribution network edge node, the actual fusing data including the actual fusing time, short-circuit current parameters, and operating condition data; and using a reinforcement learning algorithm to iteratively optimize the dynamic reconstruction based on the actual fusing data. curve.

[0015] Furthermore, this application also proposes to include: when the time difference between the action of the upper and lower level fuses is detected to be less than a preset time difference threshold, automatically triggering a wireless interlocking signal to control the weakening of the magnetic field of the upper level fuse and avoid cascading tripping.

[0016] Furthermore, this application also proposes a computing device, comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the aforementioned optimized method for preventing over-tripping of fast fuses.

[0017] Furthermore, this application also proposes a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the aforementioned optimized method for preventing cascading tripping of fast fuses.

[0018] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: In this invention, dynamic reconstruction is used. The curve, combined with the magnetic-thermal coupling collaborative triggering mechanism, can adjust the fuse characteristics in real time according to the operating condition data. It accelerates the fuse when the short-circuit current parameter reaches the threshold and avoids false tripping when it is below the threshold. At the same time, it optimizes the protection accuracy through environmental compensation and reinforcement learning, effectively solving the problems that traditional fuses cannot adapt to changes in the penetration rate of distributed power sources and the unreliability of protection caused by environmental interference. It has the advantages of strong dynamic adaptability and high protection reliability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic diagram of the overall logical structure of the optimization method; Figure 2 for A comparison diagram showing the curve before and after reconstruction; Figure 3 For different temperatures A diagram illustrating the comparison of the curves. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In existing technologies, with the high proportion of distributed generation connected to the distribution network, short-circuit currents exhibit characteristics such as large amplitude fluctuations, severe waveform distortion, and bidirectional direction. Traditional fast-acting fuses for preventing cascading trips suffer from an imbalance between response speed and selectivity. Fixed fuse characteristics cannot match the differences in short-circuit currents caused by variations in the penetration rate of distributed generation. Ambient temperature drift and harmonic interference further exacerbate the decrease in protection accuracy, leading to a significant increase in the risk of cascading trips.

[0023] To solve the above problems, the inventors discovered that traditional fuses, due to their fixed shape... The curve cannot adapt to dynamic operating conditions, leading to frequent mismatches in protection settings. Analysis revealed the need to establish a multi-dimensional operating condition sensing system to capture real-time changes in grid status and dynamically adjust fuse characteristics. Further considering the need for the fuse triggering mechanism to coordinate with algorithm reconstruction, a three-level linkage control system was proposed, using the magnetic-thermal coupling effect as the hardware execution layer to form operating condition sensing, curve reconstruction, and trigger execution. Example

[0024] Therefore, as Figure 1 As shown, this application proposes an optimized method for preventing cascading tripping of fast-acting fuses, comprising the following steps: S100. Obtain the operating condition data of the circuit where the fast fuse is located. The operating condition data includes the penetration rate of distributed power sources, short-circuit current parameters, and environmental parameters. The built-in sensor array of the fast fuse consists of a surface-mount Hall current sensor, a PT1000 platinum resistance temperature sensor, and a voltage sampling divider resistor group. The sensor array is installed at both ends of the fuse and outside the electromagnetic coil, respectively. The data acquisition frequency is set to 1kHz, which can capture transient parameter changes during line operation in real time.

[0025] The penetration rate of distributed power sources is calculated jointly by voltage phase difference and current distortion rate. The calculation adopts a weighted coefficient allocation method, with the voltage phase difference weighting coefficient set to 0.02 and the current distortion rate weighting coefficient set to 0.15. The final penetration rate value ranges from 0 to 100%, which can accurately reflect the degree of influence of distributed power source access on the short-circuit characteristics of the line. The short-circuit current parameters include the short-circuit current amplitude and the rate of rise. The short-circuit current amplitude is directly collected by a Hall current sensor with a collection resolution set to 0.01kA. The rate of rise of the short-circuit current is obtained by performing a 5-point sliding differential operation on the collected current signal.

[0026] Environmental parameters include ambient temperature and harmonic distortion rate. Ambient temperature is collected by a PT1000 platinum resistance temperature sensor, with a collection range covering -50℃ to 150℃. Harmonic distortion rate is obtained by performing a 128-point fast Fourier transform analysis on the current signal, extracting the proportions of the 3rd, 5th, and 7th harmonic components, and then summing them.

[0027] S200, a fast fuse that can be dynamically reconfigured based on operating condition data. curve; Specifically, an improved particle swarm optimization algorithm is adopted. The core improvement of this algorithm lies in the introduction of a dynamic adjustment mechanism for inertia weight. The formula for calculating the inertia weight is as follows: ,in This represents the current iteration number. The total number of iterations is set to 50. This formula allows the algorithm to maintain a strong global search capability in the early stages of iteration, while focusing on local optimization in the later stages, thus significantly improving the convergence speed of curve reconstruction.

[0028] Meanwhile, the constraint condition is that the time difference between the actions of the upper and lower level protections is not less than the preset time difference threshold. The preset time difference threshold is set to 0.3s to ensure that the reconstructed curve can meet the selective requirements of the distribution network protection.

[0029] dynamic The curve adjusts the fuse time window based on the distributed power penetration rate. When the distributed power penetration rate is greater than a first preset threshold, the fuse time window is compressed. The first preset threshold is set to 30%. For example, under a 1kA short-circuit current, the fuse time is compressed from 5ms of the traditional fixed curve to 2ms. When the distributed power penetration rate is less than a second preset threshold, the fuse time window is extended. The second preset threshold is set to 10%. For example, under a 0.5kA short-circuit current, the fuse time is extended from 3ms of the traditional fixed curve to 8ms.

[0030] The curve also incorporates a temperature compensation factor, with 25℃ as the reference temperature, and adjustments made for every 1℃ deviation from the reference temperature. The threshold is 0.8%, and this compensation ratio is determined by fitting experimental data on the resistivity-temperature characteristics of silver alloy melt, which can effectively offset the interference of ambient temperature changes on the melting threshold.

[0031] S300, based on dynamic reconfiguration The curve controls the rapid fuse breaking through a magnetic-thermal coupling synergistic triggering mechanism; The electromagnetic coil of the fast-acting fuse is made of enameled copper wire with 500 turns and a conductor cross-sectional area of ​​0.2 mm². It is arranged coaxially with the fuse body, and the inner diameter of the coil is 2 mm larger than the diameter of the fuse body to ensure that the direction of the generated magnetic field is perpendicular to the direction of the current in the fuse body.

[0032] The control of fuse tripping via magnetic-thermal coupling coordinated triggering mechanism includes: when the short-circuit current parameter reaches the preset proportional threshold of the dynamically reconstructed I²t curve (the preset proportional threshold is set to 80%), controlling the electromagnetic coil to output a stepped excitation current. The excitation current increases from zero to the target value of 5A within 0.05s to 0.1s. The specific rise time is adjusted according to the short-circuit current rise rate. When the short-circuit current rise rate is greater than 0.5kA / ms, it is set to 0.05s; when it is less than 0.2kA / ms, it is set to 0.1s.

[0033] The strong magnetic field and the Joule heating of the melt form a magnetic-thermal coupling effect. The electromagnetic force can break the oxide film with a thickness of about 5μm on the surface of the melt, which improves the heat conduction efficiency by 35%. At the same time, the eddy current induced by the magnetic field generates additional Joule heating with an eddy current density of about 200A / mm², which can raise the temperature of the melt by an additional 150°C on the basis of the original short-circuit current heating, thus accelerating the melting of the fast fuse.

[0034] When the short-circuit current parameter is lower than the preset proportional threshold, the electromagnetic coil is controlled to output a weak excitation current of 0.5A. The temperature distribution of the melt is calibrated by the magnetic field, so that the radial temperature difference of the melt is controlled within 5℃ to avoid accidental melting.

[0035] Specifically, controlling the fuse's tripping via a magnetic-thermal coupling coordinated triggering mechanism includes: when the short-circuit current parameters reach a dynamically reconfigurable state... When the curve reaches the preset proportional threshold, the electromagnetic coil is controlled to output a stepped excitation current to generate a strong magnetic field, which forms a magnetic-thermal coupling effect with the Joule heating of the melt to accelerate melting. When the short-circuit current parameter is lower than the preset proportional threshold, the electromagnetic coil is controlled to output a weak excitation current, and the temperature distribution of the melt is calibrated by the magnetic field to avoid accidental melting.

[0036] Among them, operating condition data refers to a set of real-time parameters that reflect the operating status of the power grid. Specifically, it can be collected by a built-in sensor array, including voltage phase difference, current distortion rate, short-circuit current amplitude and rise rate, ambient temperature and harmonic distortion rate, to quantify the impact of distributed power source access on short-circuit characteristics.

[0037] Dynamic Reconfiguration The curve refers to the curve that adjusts the fuse-off time characteristics according to real-time operating conditions. Specifically, it uses an improved particle swarm optimization algorithm combined with the time difference constraint of upper and lower level protection actions to generate a dynamic curve with embedded temperature compensation factors, so that the fuse-off time window automatically expands and contracts with the change of distributed power penetration rate.

[0038] The magnetic-thermal coupling synergistic triggering mechanism refers to controlling the melting process through the interaction between the electromagnetic field and the thermal effect of the melt. Specifically, it uses an electromagnetic coil to output a stepped or weak excitation current, which respectively achieves strong magnetic field to accelerate the heat conduction of the melt and weak magnetic field to balance the temperature distribution.

[0039] Specifically, sensors are used to collect real-time grid operating parameters, and the distributed generation penetration rate is calculated to determine the degree of change in the grid structure. The penetration rate and short-circuit current parameters are then input into an optimization algorithm to generate a dynamic [database / power supply] while ensuring the time difference between upstream and downstream protection systems. The curve is calculated, and a temperature compensation factor is superimposed to eliminate environmental interference.

[0040] When the short-circuit current reaches the threshold of the dynamic curve, the electromagnetic coil increases the excitation current in a stepped manner, generating a strong magnetic field perpendicular to the current direction. This magnetic force breaks the oxide layer on the surface of the melt, accelerating heat conduction, and simultaneously induces eddy currents to generate additional Joule heating, achieving rapid melting. When the short-circuit current does not reach the threshold, a weak excitation current is output to form a calibration magnetic field, suppressing local overheating caused by uneven temperature distribution and avoiding malfunctions.

[0041] Compared with existing technologies, traditional fuses use fixed... Traditional curve-based and single-element heating mechanisms cannot cope with short-circuit current amplitude fluctuations and direction changes caused by distributed power supply connections. This solution dynamically reconstructs the fuse curve to match real-time operating conditions, and combines graded magnetic field control to achieve a dynamic balance between fusing speed and selectivity, thus resolving the contradiction between rapid response and protection selectivity in traditional technologies.

[0042] Through the above technical solution, this application can dynamically adjust the fuse characteristics according to the real-time state of the power grid, accelerate fuse breaking to prevent cascading tripping during heavy load short circuits, and suppress malfunctions to ensure power supply continuity during light load short circuits. The synergistic effect of magnetic field and thermal effect counteracts ambient temperature drift and harmonic interference, improving protection accuracy and adaptability to operating conditions.

[0043] Appendix Figure 2 for The diagram shows a comparison of the curve before and after reconstruction. The horizontal axis represents the current I in kA, and the vertical axis represents the fusing time in ms. The "fixed I²t curve before reconstruction" in the diagram is the fixed fusing characteristic curve of a traditional fast-acting fuse. Its fusing time only varies with the current and does not consider the influence of distributed power supply penetration. For example, the fusing time is fixed at 4ms under 3kA current and fixed at 2ms under 7kA current.

[0044] The "Reconstructed Curve (High Penetration)" represents the dynamic curve when the penetration rate of distributed power sources exceeds 30%. The curve significantly compresses the fuse time window compared to a fixed curve. For example, the fuse time is shortened to 2ms at 3kA current and to 1ms at 7kA current, so as to quickly respond to high short-circuit current scenarios.

[0045] The "Reconstructed Curve (Low Penetration)" represents the dynamic curve when the penetration rate of distributed power generation is less than 10%. The dynamic reconfiguration curve extends the fusing time window compared to a fixed curve. For example, the fusing time is extended to 6ms under 3kA current and to 3ms under 7kA current, ensuring protection selectivity under light load short circuits. A comparison of the three curves clearly shows the difference after dynamic reconfiguration. The curve can flexibly adjust the fuse characteristics according to the penetration rate of distributed power sources to adapt to different operating conditions.

[0046] Appendix Figure 3 For different temperatures The diagram illustrates the curve comparison. The horizontal axis represents current I (kA), and the vertical axis represents fusing time (ms). The "Reference Temperature (25℃)" curve in the diagram represents the dynamics at an ambient temperature of 25℃. The curve serves as a reference for temperature compensation; for example, the fusing time is 5ms at a current of 2kA and 2ms at a current of 6kA.

[0047] The "High Temperature Environment (40℃)" curve represents the ambient temperature when it reaches 40℃. The curve, due to the embedded temperature compensation factor, appropriately lowers the fusing threshold compared to the reference temperature curve. For example, the fusing time is shortened to 4ms at 2kA current and to 1.8ms at 6kA current, thus offsetting the effect of the decrease in melt resistivity caused by high temperature.

[0048] The "Low Temperature Environment (-20℃)" curve represents the ambient temperature as low as -20℃. The curves show that the fusing threshold is appropriately increased compared to the reference temperature curve. For example, the fusing time is extended to 6ms at 2kA current and to 2.2ms at 6kA current to offset the effect of increased melt resistivity caused by low temperature. The differences between the three curves indicate that the temperature compensation factor can effectively correct the interference of ambient temperature fluctuations on fusing characteristics and ensure the stability of protection settings under different temperature environments.

[0049] This application further proposes to acquire operating condition data, including real-time acquisition of distributed power source penetration rate, short-circuit current parameters, and environmental parameters through the sensor array built into the fast fuse; wherein, the distributed power source penetration rate is calculated by jointly calculating the voltage phase difference and current distortion rate, the short-circuit current parameters include the short-circuit current amplitude and rate of rise, and the environmental parameters include ambient temperature and harmonic distortion rate.

[0050] The sensor array refers to multiple miniature sensors integrated inside the fuse body. Specifically, it can be implemented by combining surface-mount temperature sensors, Hall current sensors, and voltage transformers to synchronously collect multi-dimensional physical quantities during line operation.

[0051] Distributed generation penetration rate refers to the proportion of distributed generation output power to total load in a distribution network. This can be achieved by calculating the weighted average of voltage phase difference and current distortion rate, used to quantify the impact of renewable energy access on short-circuit current. Short-circuit current amplitude refers to the peak value of the current when a fault occurs, which can be captured in real-time using high-speed sampling circuits, used to characterize the intensity of short-circuit faults.

[0052] The short-circuit current rise rate refers to the rate of change of current over time, which can be calculated using differentiating circuits or digital signal processing algorithms to reflect the dynamic characteristics of fault development. Ambient temperature refers to the temperature of the space where the fuse is located, which can be measured using a thermistor or infrared sensor to compensate for changes in the thermal properties of the molten material. Harmonic distortion rate refers to the ratio of harmonic components to the fundamental component in the current waveform, which can be achieved through Fast Fourier Transform analysis to identify the risk of localized overheating of the molten material caused by harmonic interference.

[0053] Specifically, the sensor array is directly embedded inside the fuse, enabling real-time acquisition of voltage and current signals in the line during short-circuit faults. The voltage phase difference is obtained by comparing the phase offset of the voltage waveforms at the distributed power source access point and the main grid connection point, while the current distortion rate is calculated by analyzing the proportion of harmonic components in the current waveform. The two are then weighted and fused to generate the distributed power source penetration rate.

[0054] The short-circuit current amplitude is continuously sampled by a high-precision current sensor, and the rate of rise is extracted by time differentiation of the current signal. Ambient temperature is monitored in real time by a temperature sensor located inside the fuse housing, and harmonic distortion rate is dynamically calculated by a current signal spectrum analysis module. These multi-dimensional data are processed in parallel using a field-programmable gate array (FPGA) to generate a complete operating condition dataset containing fault characteristics and environmental interference, providing real-time input for subsequent dynamic adjustment of fuse characteristics.

[0055] Compared to existing technologies, traditional fuses rely on external monitoring devices to obtain operating data, resulting in data transmission delays and signal interference, and can only collect a single current parameter. This solution, however, achieves physical integration of data acquisition and fuse protection through a built-in sensor array, eliminating external signal transmission links; it overcomes the bias in estimating renewable energy penetration rates using a single parameter by jointly calculating voltage phase difference and current distortion rate; and it constructs a multi-dimensional correlation model between fault characteristics and environmental interference by simultaneously collecting amplitude, rate of rise, temperature, and harmonic data, providing comprehensive data support for dynamic adjustment of protection settings.

[0056] Through the above technical solution, this application achieves high real-time acquisition and high-precision calculation of operating condition data, effectively solves the problem of protection setting adaptation deviation caused by data lag or missing data in traditional fuses, and improves the matching ability of fuse characteristics with dynamic changes of distributed power supply and environmental interference.

[0057] This application further proposes a dynamically reconfigurable fast fuse. The curve-based approach includes using an improved particle swarm optimization algorithm, with the constraint that the time difference between the actions of the upper and lower level protection systems is not less than a preset time difference threshold, to generate dynamic curves in real time. Curves, in which dynamic The curve is based on the distributed power penetration rate, which adjusts the fuse time window and incorporates a temperature compensation factor to offset environmental impacts.

[0058] Among them, the improved particle swarm optimization algorithm refers to a swarm intelligence optimization algorithm that introduces a dynamic adjustment mechanism for inertia weights. Specifically, it can be implemented using a particle swarm algorithm with nonlinear decreasing inertia weights. By dynamically adjusting the particle search step size and direction, it can quickly converge to the global optimum in multi-parameter coupled scenarios.

[0059] The time difference constraint of upper and lower level protection action refers to using the time difference of action of adjacent protection devices as the optimization boundary condition. Specifically, it can be achieved by setting the time parameters of time relays or digital protection devices to ensure that the lower level fuses act first to avoid over-level tripping.

[0060] Adjusting the fuse time window refers to dynamically modifying the time interval for fuse activation based on changes in the penetration rate of distributed power sources. Specifically, this can be achieved using a mapping model between penetration rate and short-circuit current amplitude, by matching the changes in short-circuit current caused by fluctuations in penetration rate in real time.

[0061] The temperature compensation factor is a dynamic coefficient used to correct the influence of ambient temperature on the fusing threshold. Specifically, it can be implemented by combining temperature sensor data with a polynomial fitting algorithm, through dynamic adjustment. The threshold offsets the effects of temperature drift.

[0062] Specifically, when the penetration rate of distributed power sources fluctuates, the improved particle swarm optimization algorithm constructs a multi-objective optimization model using real-time collected operating data. It uses the fuse time window adjustment and temperature compensation factor as optimization variables, and the action time difference constraint as the boundary condition for iterative calculation. When the increased penetration rate leads to a larger short-circuit current amplitude, the algorithm automatically compresses the fuse time window to accelerate the fuse response speed, while dynamically correcting the fuse threshold at the current ambient temperature using the temperature compensation factor.

[0063] Throughout this process, the algorithm consistently uses the time difference between the actions of upstream and downstream protection devices as a hard constraint to ensure that the optimized fuse-breaking time window does not compromise protection selectivity. For example, in scenarios with rapidly changing photovoltaic output, the algorithm can complete the process within milliseconds. The dynamic reconstruction of the curve not only adapts to the changes in short-circuit current caused by permeability fluctuations, but also eliminates the deviation of high melting threshold under low temperature conditions through temperature compensation.

[0064] Compared with existing technologies, traditional fuse curve adjustment methods usually use fixed formulas or single parameter threshold control, which cannot simultaneously handle the multi-parameter coupling effects of distributed power penetration rate and ambient temperature, and lack active constraints on protection selectivity.

[0065] Existing optimization schemes based on the standard particle swarm optimization algorithm are prone to getting trapped in local optima when optimizing multiple parameters, causing the fuse curve adjustment to lag behind changes in operating conditions. This proposed scheme, through the collaborative design of an improved particle swarm optimization algorithm and action time difference constraints, achieves dynamic optimization of multiple parameters while ensuring protection selectivity. It can simultaneously respond to permeability fluctuations and changes in ambient temperature, solving the problem of coexisting protection setting mismatch and cascading tripping risks in traditional methods.

[0066] Through the above technical solution, this application achieves dynamic matching of fuse characteristics with distributed power supply access scenarios. It automatically adjusts the fuse time window to adapt to changes in short-circuit current amplitude when penetration fluctuates rapidly, and eliminates the influence of ambient temperature on the fuse threshold through a temperature compensation factor. This method effectively solves the problems of traditional fixed... The curve addresses the protection setting mismatch issue in high-penetration scenarios of distributed power sources, improving the operational adaptability and environmental interference resistance of fuse action while maintaining protection selectivity.

[0067] This application further proposes using an improved particle swarm optimization algorithm to generate dynamic data in real time. The curve shows that when the distributed power penetration rate exceeds a first preset threshold, the circuit breaker time window is compressed; when the distributed power penetration rate falls below a second preset threshold, the circuit breaker time window is extended. The temperature compensation factor is adjusted based on a reference temperature, changing for every degree Celsius deviation from the reference temperature. Threshold preset percentage.

[0068] Among them, the improved particle swarm optimization algorithm refers to the rapid optimization under multi-objective constraints by introducing dynamic adjustment of inertia weights and a global optimal solution perturbation mechanism. Specifically, it can use a fitness function with constraints to iteratively calculate the particle swarm, which is used to generate dynamic values ​​that meet the time difference requirements of upper and lower level protection in real time. The curve. The first and second preset thresholds refer to the penetration rate boundary values ​​divided according to the typical output scenarios of distributed power sources. Specifically, they can be determined by statistical analysis of historical operating data and correlation analysis of short-circuit current amplitude, and are used to establish a graded control mechanism for the fuse time window in high, medium and low penetration rate scenarios.

[0069] The temperature compensation factor is a linear correction coefficient established based on the temperature characteristics of the melt material. Specifically, it can be obtained by fitting experimental data on the relationship between melt resistivity and temperature, and is used to offset the influence of ambient temperature changes on the melting threshold.

[0070] Specifically, in high-penetration scenarios where the penetration rate of distributed power sources exceeds the first preset threshold, the short-circuit current amplitude increases significantly. By compressing the fuse time window, the protection action speed is accelerated, preventing the fault current from continuously impacting the equipment. In low-penetration scenarios where the penetration rate is below the second preset threshold, the short-circuit current amplitude is smaller. Extending the fuse time window provides sufficient action time for downstream protection devices, avoiding cascading tripping caused by maloperation of this level.

[0071] The temperature compensation factor is adjusted by a preset percentage based on the difference between the real-time ambient temperature and the reference temperature. The threshold temperature allows the melting characteristics to adaptively correct for deviations caused by temperature drift. The reference temperature is usually set to the standard operating temperature of the melt material, while the preset percentage is determined based on the temperature coefficient of resistance of the melt material to ensure that the temperature compensation is precisely matched with the physical properties of the material.

[0072] Compared with existing technologies, traditional fuses use fixed... The existing curve-based temperature compensation, which only performs tiered temperature compensation, cannot adapt to the short-circuit current amplitude fluctuations caused by continuous changes in distributed power source penetration, and its temperature compensation accuracy is insufficient. This solution achieves dynamic expansion and contraction of the fusing time window through graded control of the penetration threshold. Combined with a temperature compensation factor based on material properties, it enables the fusing characteristics to adapt to both changes in grid operating conditions and ambient temperature fluctuations, solving the problem of cascading tripping caused by the superposition of protection setting mismatch and temperature drift in traditional technologies.

[0073] Through the above technical solution, this application achieves dynamic matching between the fuse time window and the changes in the penetration rate of distributed power sources, avoiding the imbalance of protection action speed caused by the difference in short-circuit current amplitude; at the same time, it eliminates the influence of ambient temperature on the fuse threshold through precise temperature compensation, ensuring the stability of protection settings under different temperature conditions and effectively reducing the risk of over-tripping.

[0074] This application further proposes controlling the output of the electromagnetic coil with a stepped excitation current, including increasing the excitation current from zero to a target value within a preset time, so that the electromagnetic coil generates a strong magnetic field perpendicular to the current direction. The magneto-thermal coupling effect includes the electromagnetic force breaking the oxide film on the melt surface to accelerate heat conduction, and the eddy currents induced by the magnetic field generating additional Joule heating.

[0075] Among them, stepped excitation current refers to the current output mode that increases in stages according to a preset time interval. Specifically, it can be achieved by using a slope controller that dynamically adjusts the rise rate of short-circuit current, and achieves precise adjustment of magnetic field strength by matching the fault development speed.

[0076] A strong magnetic field perpendicular to the current direction refers to the magnetic field distribution formed by arranging the electromagnetic coil windings orthogonally to the current path of the melt. This can be achieved using a toroidal coil winding process, ensuring the magnetic force forms a 90-degree angle with the melt axis to maximize its effect. Electromagnetic force breaking the oxide film on the melt surface refers to the mechanical peeling of the oxide layer on the melt surface using the Lorentz force generated by the magnetic field. This can be achieved by setting a magnetic field strength threshold to trigger high-frequency vibration, thereby eliminating the oxide layer's obstruction to heat conduction.

[0077] The additional Joule heating generated by magnetic field-induced eddy currents refers to the heat generation effect of the closed current induced by the alternating magnetic field inside the melt. Specifically, it can be achieved by adjusting the matching relationship between the magnetic field frequency and the resistivity of the melt, forming a superposition effect with the heating of the original short-circuit current.

[0078] Specifically, when the short-circuit current parameter reaches the preset threshold of the dynamic reconfiguration curve, the control system initiates a stepped excitation program. This program dynamically adjusts the ramp rate of the excitation current based on the real-time monitored current rise rate. For example, a steep ramp rate is used during the rapid current rise phase to quickly establish a strong magnetic field, while a gentle ramp rate is used during the gradual change phase to maintain the necessary field strength.

[0079] The generated strong vertical magnetic field works synergistically through two physical mechanisms: firstly, the electromagnetic force generated by the magnetic field peels off the oxide layer on the surface of the melt through high-frequency vibration, restoring the thermal conductivity of the metal; secondly, the alternating magnetic field induces annular eddies inside the melt, and the additional Joule heating generated by these eddies has a superposition effect with the Joule heating of the original short-circuit current. These two mechanisms are synchronized through time-synchronized control, with the eddy current effect reaching its peak when the oxide layer peeling is complete, thus achieving a rapid increase in melt temperature while ensuring thermal conductivity.

[0080] Compared to existing technologies, traditional fuse devices mostly employ a fixed-rate linear excitation method, which cannot dynamically adjust the magnetic field establishment speed according to the fault development, easily leading to response lag or energy waste. Existing magnetic field triggering technologies typically utilize only a single thermal effect, neglecting the hindering effect of the oxide layer on heat conduction. This solution, through dynamic stepped excitation and the synergy of dual physical effects, not only solves the problem of matching magnetic field strength with fault rhythm, but also overcomes the efficiency bottleneck of traditional single heating modes through oxide layer stripping and thermal effect superposition.

[0081] Through the above technical solution, this application can precisely control the magnetic field establishment process when a short-circuit fault occurs, accelerating the melting of the fusible element through physical effects, while maintaining a low-intensity magnetic field to avoid false triggering in non-fault conditions. This solution effectively solves the problem of balancing response speed and reliability in traditional fuse technology, achieving rapid action in fault conditions and stable operation in non-fault conditions.

[0082] This application further proposes a magnetic-thermal coupling coordinated triggering mechanism to control the melting of fast fuses. When the harmonic distortion rate is greater than the preset distortion threshold, the magnetic field frequency is modulated and superimposed on the harmonic frequency in reverse to counteract the thermal accumulation deviation caused by the harmonics.

[0083] The harmonic distortion rate refers to the degree to which the current or voltage waveform deviates from a sinusoidal waveform. This can be achieved by analyzing the current spectrum components using a Fast Fourier Transform (FFT) to quantify the intensity of harmonic interference. The preset distortion threshold refers to the critical condition that triggers magnetic field frequency modulation. Specifically, the threshold range can be set according to the thermal stability of the melt material; for example, setting the threshold to 5% can prevent harmonic heat accumulation from exceeding the melt's tolerance limit.

[0084] Among them, the modulation magnetic field frequency refers to adjusting the frequency of the alternating magnetic field generated by the electromagnetic coil. Specifically, this can be achieved by adjusting the frequency of the coil drive signal through the inverter, so that the magnetic field frequency and the harmonic components form an inverse correspondence.

[0085] Among them, the reverse superposition of harmonic frequencies refers to keeping the magnetic field frequency and the harmonic frequency at the same value but opposite in phase. Specifically, this can be achieved by tracking the harmonic frequency and generating a reverse phase signal through a phase-locked loop circuit, thereby forming an electromagnetic cancellation effect inside the melt.

[0086] Specifically, when the harmonic distortion rate in the circuit exceeds a set threshold, the harmonic current generates additional Joule heat in the melt, leading to abnormal local temperature distribution. At this time, the driving signal frequency of the electromagnetic coil is adjusted to be the same as the frequency of the main harmonic component but opposite in phase. For example, when the third harmonic at 150Hz is detected, the magnetic field frequency is synchronously adjusted to 150Hz with a phase difference of 180 degrees.

[0087] A reverse magnetic field induces eddy currents on the surface of the melt, which are opposite in direction to the harmonic current. According to Lenz's law, these eddy currents weaken the effective flow of the harmonic current and reduce the generation of harmonic components. Heat. At the same time, the Lorentz force generated by the interaction of the reverse magnetic field and the harmonic current can refine the internal grain structure of the melt and equalize the temperature gradient distribution, thereby blocking the path of deviation expansion in the early stage of heat accumulation.

[0088] Compared with existing technologies, traditional methods rely on external filtering devices to suppress harmonics or use temperature sensors to correct the fusing threshold, resulting in high equipment costs and slow response times. This solution directly utilizes the electromagnetic coil in the magnetic-thermal coupling mechanism to suppress the source of harmonic thermal effects through frequency modulation without adding hardware. This avoids harmonic energy injection into the fusible link while maintaining the real-time performance of the fusing trigger mechanism.

[0089] Through the above technical solution, this application can actively eliminate the abnormal heat accumulation caused by harmonic current on the fuse when the line harmonic distortion exceeds the standard, maintain the uniformity of the fuse temperature distribution, and ensure the fuse's operation and dynamic stability. Precise curve threshold matching effectively prevents protection malfunctions or failures to operate due to harmonic interference.

[0090] This application further proposes uploading the actual fusing data to the distribution network edge node after the fast fuse blows. The actual fusing data includes the actual fusing time, short-circuit current parameters, and operating condition data. A reinforcement learning algorithm is then used to iteratively optimize and dynamically reconstruct the data based on the actual fusing data. curve.

[0091] Among them, actual fusing data refers to the key operating parameters generated during the fuse's operation. Specifically, the actual fusing time, short-circuit current amplitude and rise rate, ambient temperature, and harmonic distortion rate can be collected through the built-in sensor array and uploaded to the distribution network edge node through the wireless communication module to record the characteristic parameters of the fusing event.

[0092] Distribution network edge nodes refer to computing nodes deployed near the distribution network. Specifically, they can be embedded devices with data caching and preprocessing capabilities, used to receive and process circuit breaker data, avoiding data lag caused by remote transmission delays. Reinforcement learning algorithms are adaptive optimization algorithms based on reward mechanisms. Specifically, they can use a deep Q-network model, setting the reward function to reduce the probability of cascading tripping and improve the matching degree of circuit breaker termination time, dynamically adjusting... The reconstructed parameters of the curve enable continuous optimization of the circuit breaker strategy.

[0093] Specifically, after the fuse completes its blowing action, the built-in sensor transmits the collected actual blowing time, short-circuit current parameters, and current operating condition data to the nearest distribution network edge node via a wireless transmission module. The edge node preprocesses the received data, including removing abnormal data points, associating it with historical operating records, and inputting the valid data into the reinforcement learning model.

[0094] Reinforcement learning algorithms aim to reduce the probability of cascading trips, analyzing actual circuit breaker times and dynamics. The deviation of the curve's preset threshold, combined with the current distributed power penetration rate and environmental parameters, generates... The adjustment parameters of the curve. For example, when multiple circuit breaker data indicate that fluctuations in the output of distributed power sources in a certain area lead to a mismatch in the circuit breaker time window, the algorithm iteratively learns and gradually corrects the scaling factor of the circuit breaker time window, so that... The curve can adaptively adjust to adapt to dynamic operating conditions. For example, if the deviation between the fusing time and the curve threshold is less than 3% for five consecutive times, the current iteration stops. The optimized curve parameters are sent to the fuse controller through edge nodes to guide the execution of the fusing strategy in the next cycle, forming a closed-loop optimization mechanism.

[0095] Compared to existing technologies, traditional fuses lack data feedback and optimization mechanisms after fusing, relying solely on preset fixed values. The existing circuit curves cannot cope with the mismatch of protection settings caused by changes in the penetration rate of distributed power sources and environmental disturbances. Current technologies employ manual periodic adjustments or simple statistical corrections, which suffer from response lag and insufficient optimization accuracy. This solution, however, achieves real-time processing of circuit breaker data and online iteration of reinforcement learning algorithms through edge nodes. This allows the circuit breaker strategy to continuously evolve based on actual operational results, solving the problem of poor adaptability to operating conditions caused by the fixed circuit breaker characteristics in traditional technologies.

[0096] Through the above technical solution, this application achieves dynamic optimization and self-adaptation capabilities of the circuit breaker strategy, effectively reducing the risk of protection setting mismatch. The uploading and processing of actual circuit breaker data ensures the real-time nature of the optimization process, and the iterative mechanism of the reinforcement learning algorithm enables… The curve can adapt to changes in different operating conditions, avoiding malfunctions or delayed tripping caused by fluctuations in ambient temperature or changes in the output of distributed power sources, which is common with traditional fuses. The closed-loop feedback mechanism further improves the accuracy of fuse action, reduces the probability of cascading tripping, and enables continuous optimization of fuse characteristics without manual intervention.

[0097] This application further proposes that when the time difference between the action of the upper and lower level fuses is less than a preset time difference threshold, a wireless interlocking signal is automatically triggered to control the magnetic field of the upper level fuse to weaken and avoid cascading tripping.

[0098] Among them, the time difference between the operation of upper and lower level fuses refers to the time difference between the operation times of two adjacent level fuses through which the same fault current flows. Specifically, it can be realized through the timestamp recording and comparison module, which is used to quantify the selectivity requirements of protection operation.

[0099] The preset time difference threshold refers to the minimum allowable time difference of action set in advance. It can be determined by statistical analysis of historical fault data or calculation by simulation model. It is used to identify the risk of over-level tripping. In this embodiment, the preset time difference threshold is consistent with the time difference constraint of the upper and lower level protection actions of S200, that is, 0.3s.

[0100] Wireless interlocking signals refer to control commands independent of the distribution network master station communication link. They can be implemented using low-power wide-area networks or dedicated radio frequency channels to ensure that interlocking actions are triggered even when communication is interrupted. Magnetic field weakening refers to changing the magnetic field force by adjusting the excitation current intensity of the electromagnetic coil. This can be achieved using a programmable current source to dynamically adjust the melting delay time of the upstream fuse.

[0101] Specifically, when a fault occurs, the time difference between the actions of upstream and downstream fuses is monitored in real time. When this difference falls below a preset threshold, it indicates a risk of cascading tripping. At this point, the independent communication link immediately sends a blocking signal to the control unit of the upstream fuse, triggering a magnetic field adjustment mechanism. Based on the difference between the actual time difference and the preset threshold, the excitation current of the upstream fuse's electromagnetic coil is dynamically adjusted: when the difference is small, the current intensity is slightly reduced to moderately delay the fuse's activation; when the difference is large, the current intensity is significantly reduced to significantly extend the activation time. This tiered control method ensures that downstream fuses activate first while preventing upstream fuses from losing their protective capability due to excessive delay.

[0102] Compared to existing technologies, traditional solutions rely on fixed differential settings or master station communication-based interlocking mechanisms, which cannot effectively intervene in the event of communication interruption or short-circuit current fluctuations. This solution achieves reliable transmission of the interlocking signal through an independent wireless link and achieves flexible control of the fuse breaking time through dynamic adjustment of the magnetic field strength, thus solving the problems of interlocking failure or excessive intervention in traditional technologies.

[0103] Through the above technical solution, this application can promptly trigger the magnetic field adjustment mechanism when insufficient time difference between the action of the upper and lower level fuses is detected, effectively blocking the triggering conditions of over-level tripping, ensuring the reliability of protection level coordination, and maintaining the backup protection function of the upper level fuse when the fault continues. Example

[0104] This application further proposes a computing device, including at least one processor and a memory, wherein the memory stores instructions that, when executed by at least one processor, cause at least one processor to perform a fast-acting fuse tripping optimization method to prevent overstepping.

[0105] The processor is a computing unit capable of executing computer program instructions. It can be implemented using a multi-core central processing unit or an embedded microcontroller, and is used to process operating data in real time and execute dynamic... Curve reconstruction algorithm. Here, memory refers to a non-volatile storage medium used to store executable instructions, specifically flash memory or solid-state drives, used to solidify the program logic of the circuit breaker optimization method, ensuring that the instructions can be repeatedly invoked under different operating conditions. Instructions refer to computer program code containing the steps of the circuit breaker optimization method, specifically stored in compiled machine language or intermediate code, used to dynamically... Curve generation and magnetic-thermal coupling trigger control are transformed into an executable computational process.

[0106] Specifically, the computing device receives real-time operating data from sensors via a processor, including distributed power source penetration rate, short-circuit current parameters, and environmental parameters. Based on an improved particle swarm optimization algorithm, the processor generates dynamic parameters with protection action time difference as a constraint. The system generates a curve and uses a temperature compensation factor to offset the effects of ambient temperature changes. When the short-circuit current parameter reaches a preset threshold, the processor controls the electromagnetic coil to output a stepped excitation current, utilizing the coupling effect between the magnetic field and the Joule heating of the melt to accelerate melting. When the short-circuit current is below the threshold, a weak magnetic field is used to calibrate the melt temperature distribution. The instruction set stored in memory ensures that the above control logic continues to execute under complex scenarios such as fluctuations in distributed power supply penetration and harmonic interference.

[0107] In some specific implementations, the processor uses reinforcement learning algorithms to iteratively analyze historical circuit breaker data and optimize dynamic circuit breakers. The parameters for generating the curve; an independent storage partition is set in the memory to cache the short-circuit current waveform data acquired in real time, so that the processor can calculate the harmonic distortion rate.

[0108] Compared with existing technologies, traditional fuses rely on fixed hardware circuits to achieve fuse breaking control and cannot dynamically adjust protection characteristics. In contrast, this solution uses programmed control of computing devices to enable fuse breaking characteristics to match changes in distributed power penetration rate in real time. Existing technologies use mechanical fuse structures, which are susceptible to temperature drift. In contrast, this solution uses a processor to execute a temperature compensation algorithm to eliminate fuse breaking threshold deviations caused by environmental interference.

[0109] Through the above technical solutions, this application achieves dynamic adaptation of the fusing characteristics to the power grid operating conditions, automatically adjusts the fusing time window when the short-circuit current amplitude fluctuates, avoids false tripping under light load short circuits and delayed tripping under heavy load short circuits; and improves protection selectivity and anti-harmonic interference capability by programmatically controlling the excitation current of the electromagnetic coil and accurately switching between strong magnetic field acceleration fusing and weak magnetic field calibration temperature. Example

[0110] This application further proposes a non-transitory machine-readable storage medium storing executable instructions, which, when executed, cause the machine to perform an optimized method for fast-acting fuses to prevent cascading tripping.

[0111] Non-transitory machine-readable storage media refers to physical storage media capable of long-term data retention. This can be implemented using solid-state drives (SSDs) or flash memory chips, ensuring that instructions are fully preserved after power failure or restart, preventing the loss of fuse control logic due to storage media failure. Executable instructions refer to a set of program instructions composed of machine code or bytecode. This can be implemented using compiled binary files or virtual machine bytecode, which are parsed and executed line by line by the processor, directly driving the fuse's built-in controller to perform dynamic... Curve reconstruction, magnetic-thermal coupling triggering, and data upload operations.

[0112] Specifically, when the instructions stored in the storage medium are executed, they first trigger the sensor array to collect data on distributed power source penetration, short-circuit current parameters, and ambient temperature. Then, an improved particle swarm optimization algorithm is invoked to generate dynamic data. The curve automatically adjusts the fusing time window based on permeability changes and incorporates a temperature compensation factor. When the short-circuit current parameter reaches a dynamic... When the curve reaches a preset proportional threshold, the command controls the electromagnetic coil to output a stepped excitation current, accelerating melting through the coupling effect of the magnetic field and the Joule heating of the molten element. If the short-circuit current is below the threshold, a weak excitation current is output to calibrate the temperature distribution. During command execution, the actual melting data is uploaded to the distribution network edge node in real time, and dynamic optimization is iteratively performed using a reinforcement learning algorithm. Curve parameters.

[0113] Compared to existing technologies, traditional fuses rely on fixed storage for their circuit-breaking logic, making it impossible to update control strategies via external commands, resulting in poor adaptability to operating conditions. This solution, by embedding the circuit-breaking optimization method into a non-volatile storage medium, retains the stability of traditional hardware storage while enabling dynamic iteration of the circuit-breaking logic. It can directly respond to fluctuations in distributed power penetration and changes in environmental parameters, solving the problem of control strategy loss caused by the temporary nature of storage media in traditional fuses.

[0114] Through the above technical solution, this application realizes the persistent storage and standardized execution of the fuse control command, ensuring that the fuse threshold is dynamically adjusted under different operating conditions, effectively suppressing false fuses caused by temperature drift and harmonic interference, and optimizing the fuse logic through real-time data feedback to reduce the risk of cascading trips.

[0115] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing the fusing of fast-acting fuses to prevent cascading tripping, comprising: Obtain the operating condition data of the circuit where the fast-acting fuse is located, including distributed power supply penetration rate, short-circuit current parameters, and environmental parameters; Based on the aforementioned operating condition data, the fast-acting fuse can be dynamically reconstructed. curve; Based on dynamic reconstruction The curve controls the melting of the fast fuse through a magnetic-thermal coupling coordinated triggering mechanism; Controlling the melting of the fast fuse through a magnetic-thermal coupling coordinated triggering mechanism includes: When the short-circuit current parameter reaches the dynamic reconfiguration... When the curve reaches the preset proportional threshold, the electromagnetic coil is controlled to output a stepped excitation current to generate a strong magnetic field, which forms a magnetic-thermal coupling effect with the Joule heating of the molten element, thereby accelerating the melting of the fast fuse. When the short-circuit current parameter is lower than the preset proportional threshold, the electromagnetic coil is controlled to output a weak excitation current in order to calibrate the temperature distribution of the melt through the magnetic field and avoid accidental melting.

2. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 1, characterized in that, Obtaining the operating condition data includes: The distributed power penetration rate, short-circuit current parameters, and environmental parameters are collected in real time by the sensor array built into the fast fuse. The distributed power penetration rate is calculated by combining voltage phase difference and current distortion rate. The short-circuit current parameters include short-circuit current amplitude and rate of rise. The environmental parameters include ambient temperature and harmonic distortion rate.

3. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 1 or 2, characterized in that, Dynamically reconfigure the fast fuse The curves include: Using an improved particle swarm optimization algorithm, with the constraint that the time difference between upper and lower level protection actions is not less than a preset time difference threshold, dynamic data is generated in real time. curve; Among them, the dynamic The curve adjusts the fuse time window based on the penetration rate of the distributed power source and incorporates a temperature compensation factor to offset environmental impacts.

4. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 3, characterized in that, Dynamics are generated in real time using an improved particle swarm optimization algorithm. The curves include: When the distributed power penetration rate exceeds a first preset threshold, the circuit breaker time window is compressed. When the penetration rate of the distributed power supply is less than the second preset threshold, the circuit breaker time window is extended. The temperature compensation factor is referenced to a reference temperature and is adjusted for every degree of deviation from the reference temperature. Threshold preset percentage.

5. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 1, characterized in that, Controlling the stepped excitation current output of the electromagnetic coil includes: Within a preset time, the excitation current is increased from zero to a target value, causing the electromagnetic coil to generate a strong magnetic field perpendicular to the current direction. The magnetic-thermal coupling effect includes electromagnetic force breaking the oxide film on the surface of the melt to accelerate heat conduction, and magnetic field-induced eddy currents generating additional Joule heat.

6. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 1, characterized in that, Controlling the melting of the fast fuse through the magnetic-thermal coupling coordinated triggering mechanism also includes: When the harmonic distortion rate is greater than the preset distortion threshold, the magnetic field frequency is modulated and superimposed in reverse with the harmonic frequency to counteract the thermal accumulation deviation caused by the harmonics.

7. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 1, characterized in that, It also includes reconstruction: After the fast fuse blows, the actual blowing data is uploaded to the distribution network edge node. The actual blowing data includes the actual blowing time, short-circuit current parameters, and operating condition data. The dynamic reconstruction is iteratively optimized based on the actual circuit breaker data using a reinforcement learning algorithm. curve.

8. The optimized fusing method for anti-over-trip fast-acting fuses according to claim 7, characterized in that, Also includes: When the time difference between the action of the upper and lower level fuses is less than the preset time difference threshold, the wireless interlock signal is automatically triggered to control the magnetic field of the upper level fuse to weaken and prevent cascading tripping.

9. A computing device, characterized in that, include: At least one processor; as well as The memory stores instructions that, when executed by the at least one processor, cause the at least one processor to perform the fast-acting fuse blowing optimization method for preventing over-tripping as described in any one of claims 1-8.

10. A non-transitory machine-readable storage medium, characterized in that, It stores executable instructions that, when executed, cause the machine to perform the fusing optimization method for anti-overlapping fast fuses as described in any one of claims 1-8.

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