Surge protection device modeling method and system
By constructing an initial particle swarm and verifying it using a surge protection system simulation platform, the problem of insufficient accuracy and efficiency of existing surge protection device models under nanosecond pulses was solved, achieving efficient parameter optimization and accurate simulation circuit generation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-15
- Publication Date
- 2026-03-10
AI Technical Summary
Existing surge protection device models are mostly built based on microsecond-level pulses, which simplifies parameters such as parasitic capacitance and lead inductance, which are significantly affected by nanosecond pulses. This makes it difficult to meet the dual requirements of model accuracy and adaptation efficiency in practical applications.
By acquiring the test waveform data of surge protection devices, electromagnetic simulation tools are used to construct response characteristic simulation circuits. An initial particle swarm is constructed based on preset parasitic parameter constraints, and the parasitic parameters are optimized through iterative optimization and particle swarm algorithm. The simulation circuits are then verified using a surge protection system simulation platform to obtain an accurate surge protection device simulation circuit.
It significantly improves the model accuracy and adaptation efficiency of surge protection device simulation circuits, achieves efficient parameter optimization, and meets the high precision and high efficiency requirements of practical applications.
Smart Images
Figure CN121638148A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of surge protection device modeling technology, and in particular to a surge protection device modeling method and system. Background Technology
[0002] As electronic devices evolve towards higher frequencies and greater precision, the threat posed by nanosecond-level pulses is becoming increasingly prominent. Scenarios such as the 1ns rise time of high-altitude nuclear electromagnetic pulses (HEMP) and the 2ns rise time of lightning fast pulses place stringent demands on the surge protection capabilities of electronic devices. Surge protection devices, such as gas discharge tubes (GDTs), are core components for limiting transient overvoltages and discharging surge currents. The accurate modeling of their response characteristics directly determines the design reliability of surge protection systems and is a crucial prerequisite for ensuring the stable operation of electronic devices in complex electromagnetic environments such as lightning strikes and electrostatic discharge.
[0003] Currently, most existing surge protection device models are built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance, which have a significant impact under nanosecond pulses. This results in an inability to accurately simulate the device response under nanosecond-level pulses. Furthermore, traditional models rely on manual trial and error to adjust parasitic parameters, requiring hours to adapt to different GDT models or pulse scenarios, leading to extremely low engineering efficiency and failing to meet the dual requirements of model accuracy and adaptation efficiency in practical applications. Summary of the Invention
[0004] This invention provides a surge protection device modeling method and system, which solves the technical problem that existing surge protection device models are mostly built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance that are significantly affected by nanosecond pulses, making it difficult to meet the dual requirements of model accuracy and adaptation efficiency in practical applications.
[0005] The first aspect of this invention provides a surge protection device modeling method, comprising:
[0006] Obtain the test waveform data of the surge protection device, and call the preset electromagnetic simulation tool to construct the simulation circuit of the response characteristics of the surge protection device;
[0007] The initial particle swarm is constructed based on the preset parasitic parameter constraints.
[0008] Based on the response characteristic simulation circuit and the experimental waveform data, the initial particle swarm is iteratively optimized to obtain the corresponding characteristic simulation circuit to be verified.
[0009] The simulation circuit with the characteristics to be verified is verified based on the preset surge protection system simulation platform to obtain the corresponding surge protection device simulation circuit.
[0010] Optionally, the step of iteratively optimizing the initial particle swarm based on the response characteristic simulation circuit and the experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified includes:
[0011] The parasitic parameters corresponding to each particle in each initial particle swarm are input into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0012] Accelerated aging tests were conducted using simulation circuits for each of the target response characteristics, resulting in multiple simulation waveform data.
[0013] Each of the simulated waveform data and the experimental waveform data is input into a preset root mean square error function to obtain multiple root mean square error values;
[0014] Determine whether the number of iterations of the initial particle swarm is less than a preset first iteration threshold;
[0015] When the number of iterations is less than the first iteration threshold, the fitness function value corresponding to each particle is determined based on the number of iterations, according to each root mean square error value, each simulation waveform data and the experimental waveform data.
[0016] Based on the particle swarm optimization algorithm, the initial particle swarm is updated according to the fitness function values to obtain a new initial particle swarm, and then the process jumps to execute the step of inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0017] When the number of iterations is greater than or equal to the first iteration threshold, the target response characteristic simulation circuit corresponding to the minimum value among the various fitness function values is selected as the characteristic simulation circuit to be verified.
[0018] Optionally, the step of determining the fitness function value corresponding to each particle based on the number of iterations, according to each root mean square error value, each simulation waveform data, and the experimental waveform data, includes:
[0019] Determine whether the number of iterations is less than a preset second iteration threshold;
[0020] When the number of iterations is less than the second iteration threshold, the root mean square error value corresponding to each particle is determined as the fitness function value;
[0021] When the number of iterations is greater than or equal to the second iteration threshold, the fitness function value corresponding to each particle is determined based on each root mean square error value, each simulation waveform data, and the experimental waveform data.
[0022] Optionally, the step of determining the fitness function value corresponding to each particle based on each root mean square error value, each simulation waveform data, and the experimental waveform data includes:
[0023] Calculate the cosine similarity between each of the simulated waveform data and the experimental waveform data;
[0024] The preset cosine reference value and each cosine similarity are respectively subjected to difference processing to obtain multiple first differences;
[0025] Based on the preset adaptation weights, the root mean square error values and the corresponding first difference values are weighted and calculated to obtain the adaptation function values corresponding to each particle.
[0026] Optionally, the step of verifying the simulation circuit with the characteristics to be verified based on a preset surge protection system simulation platform to obtain the corresponding surge protection device simulation circuit includes:
[0027] The simulation circuit with the characteristics to be verified is imported into a preset surge protection system simulation platform to obtain the corresponding target surge protection system simulation platform.
[0028] Surge protection was performed using the target surge protection system simulation platform, and the corresponding simulation response waveform data was obtained.
[0029] The simulated response waveform data and the pre-acquired response waveform data are input into a preset target function to obtain the corresponding target function value;
[0030] When the objective function value is greater than or equal to the preset error threshold, the process jumps to the step of constructing the corresponding initial particle swarm based on the preset parasitic parameter constraints.
[0031] When the objective function value is less than the error threshold, a surge protection device simulation circuit is generated.
[0032] Optionally, the parasitic parameter constraints include parasitic capacitance constraints and lead inductance constraints.
[0033] A surge protection device modeling system is provided in a second aspect of the present invention, comprising:
[0034] The first construction module is used to acquire the test waveform data of the surge protection device and call the preset electromagnetic simulation tool to construct the response characteristic simulation circuit corresponding to the surge protection device.
[0035] The second construction module is used to construct the corresponding initial particle swarm based on preset parasitic parameter constraints.
[0036] The optimization module is used to iteratively optimize the initial particle swarm based on the response characteristic simulation circuit and the experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified.
[0037] The verification module is used to verify the simulation circuit of the characteristics to be verified based on a preset surge protection system simulation platform, so as to obtain the corresponding surge protection device simulation circuit.
[0038] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the surge protection device modeling method as described in any of the preceding claims.
[0039] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the surge protection device modeling method as described in any of the preceding claims.
[0040] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the surge protection device modeling method as described in any of the preceding claims.
[0041] As can be seen from the above technical solutions, the present invention has the following advantages:
[0042] This invention constructs an initial particle swarm based on preset parasitic parameter constraints. Iterative optimization of the initial particle swarm using response characteristic simulation circuits and experimental waveform data yields the corresponding simulation circuit for the characteristics to be verified. Verification of this simulation circuit is then performed using a preset surge protection system simulation platform, resulting in the corresponding surge protection device simulation circuit. This overcomes the technical problem that existing surge protection device models are mostly built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance, which are significantly affected by nanosecond pulses, making it difficult to meet the dual requirements of model accuracy and adaptation efficiency in practical applications. Compared to traditional surge protection device model building methods, this invention achieves efficient parameter optimization of the response characteristic simulation circuit by iteratively optimizing the initial particle swarm using response characteristic simulation circuits and experimental waveform data. Simultaneously, verification of the simulation circuit based on a preset surge protection system simulation platform significantly improves the model accuracy of the surge protection device simulation circuit, enhancing both the modeling accuracy and adaptation efficiency of surge protection devices. Attached Figure Description
[0043] 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.
[0044] Figure 1 This is a flowchart illustrating the steps of a surge protection device modeling method provided in Embodiment 1 of the present invention.
[0045] Figure 2 This is a flowchart illustrating the steps of a surge protection device modeling method provided in Embodiment 2 of the present invention;
[0046] Figure 3 This is a schematic diagram of the response characteristic simulation circuit provided in Embodiment 2 of the present invention;
[0047] Figure 4 This is a structural block diagram of a surge protection device modeling system provided in Embodiment 3 of the present invention;
[0048] Figure 5 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation
[0049] This invention provides a surge protection device modeling method and system to address the technical problem that surge protection device models are mostly built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance, which are significantly affected by nanosecond pulses, and thus cannot meet the dual requirements of model accuracy and adaptation efficiency in practical applications.
[0050] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0051] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a surge protection device modeling method provided in Embodiment 1 of the present invention.
[0052] This invention provides a surge protection device modeling method, comprising:
[0053] Step 101: Obtain the test waveform data of the surge protection device, and call the preset electromagnetic simulation tool to construct the simulation circuit of the response characteristics of the surge protection device.
[0054] Test waveform data refers to the waveform data acquired using professional measuring equipment (such as a high-speed oscilloscope) during the performance testing of surge protection devices (such as gas discharge tubes, GDTs). This waveform data reflects the change of the device's electrical signal over time under specific excitation conditions. For example, in a conventional testing scenario, a nanosecond-level double exponential pulse source with adjustable parameters (voltage amplitude 1kV~50kV, rise time 1ns~10ns, pulse width 10ns~100ns) is connected to the surge protection device under test. The waveform data of the response current generated by the device under this nanosecond pulse excitation is captured in real time using a high-speed oscilloscope. In an accelerated aging testing scenario, the surge protection device is placed in a harsh environment of T=85℃ and RH=85% and 1.2 times the rated voltage is applied. The above nanosecond pulse excitation test is repeated at aging times of 0h, 200h, 400h, 600h, 800h, and 1000h, and the response current waveform data of the device at different aging stages are collected simultaneously.
[0055] In this embodiment of the invention, test waveform data of surge protection devices are obtained, and preset electromagnetic simulation tools (i.e., electromagnetic simulation software such as PSpice and LTspice) are called to construct the simulation circuit of the response characteristics of surge protection devices.
[0056] Step 102: Construct the corresponding initial particle swarm based on the preset parasitic parameter constraints.
[0057] In this embodiment of the invention, an initial particle swarm is constructed based on a preset parasitic parameter constraint, wherein each particle in the initial particle swarm corresponds to a parasitic parameter.
[0058] Step 103: Iteratively optimize the initial particle swarm based on the response characteristic simulation circuit and experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified.
[0059] The simulated circuit with characteristics to be verified refers to the simulated circuit with response characteristics whose parasitic parameters have been initially adjusted, but has not yet undergone system-level verification. It needs to be compared with the test waveform through the surge protection system simulation platform to confirm whether it meets the requirements of actual application.
[0060] In this embodiment of the invention, the parasitic parameters corresponding to each particle in each initial particle swarm are input into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits. Accelerated aging tests are performed using each target response characteristic simulation circuit to obtain multiple simulation waveform data. It is determined whether the number of iterations of the initial particle swarm is less than a preset first iteration threshold. When the number of iterations is less than the first iteration threshold, the fitness function value corresponding to each particle is determined based on the number of iterations, the simulation waveform data, and the test waveform data. The initial particle swarm is updated according to the fitness function values to obtain a new initial particle swarm, and the process jumps to the step of inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits. When the number of iterations is greater than or equal to the first iteration threshold, the target response characteristic simulation circuit corresponding to the minimum value among the fitness function values is selected as the characteristic simulation circuit to be verified.
[0061] Step 104: Verify the simulation circuit of the characteristics to be verified based on the preset surge protection system simulation platform to obtain the corresponding surge protection device simulation circuit.
[0062] A surge protection system simulation platform refers to a professional simulation tool (such as EMTP-RV) used to simulate the overall working state of a surge protection system. It can build a system-level model that includes surge protection devices, power supply circuits, loads, and related components. It can input surge pulse signals from actual scenarios and output simulated waveforms of the electrical characteristics of the system and its components to verify the adaptability and accuracy of the protection device model in the system.
[0063] In this embodiment of the invention, the simulation circuit of the characteristic to be verified is imported into a preset surge protection system simulation platform for surge protection (i.e., simulating an environment where multiple surge protection devices work together) to obtain the corresponding simulation response waveform data. The simulation response waveform data and the pre-acquired response waveform data are input into a preset objective function to obtain the corresponding objective function value. When the objective function value is greater than or equal to a preset error threshold, steps 103-104 are executed. When the objective function value is less than the error threshold, the simulation circuit of the characteristic to be verified is determined to be a surge protection device simulation circuit.
[0064] In this embodiment of the invention, an initial particle swarm is constructed based on preset parasitic parameter constraints. The initial particle swarm is iteratively optimized using response characteristic simulation circuits and experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified. This circuit is then verified using a preset surge protection system simulation platform to obtain the corresponding surge protection device simulation circuit. This overcomes the technical problem that existing surge protection device models are mostly built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance, which have significant effects under nanosecond pulses, making it difficult to meet the dual requirements of model accuracy and adaptation efficiency in practical applications. Compared with traditional surge protection device model building methods, this invention achieves efficient parameter optimization of the response characteristic simulation circuit by iteratively optimizing the initial particle swarm using response characteristic simulation circuits and experimental waveform data. Simultaneously, the verification of the characteristic simulation circuit to be verified using a preset surge protection system simulation platform significantly improves the model accuracy of the surge protection device simulation circuit, thereby enhancing the accuracy and adaptation efficiency of surge protection device modeling.
[0065] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a surge protection device modeling method provided in Embodiment 2 of the present invention.
[0066] This invention provides a surge protection device modeling method, comprising:
[0067] Step 201: Obtain the test waveform data of the surge protection device, and call the preset electromagnetic simulation tool to construct the simulation circuit of the response characteristics of the surge protection device.
[0068] In this embodiment of the invention, test waveform data of the surge protection device is obtained, and a preset electromagnetic simulation tool is used to construct a simulation circuit for the response characteristics of the surge protection device. See also... Figure 3 As shown in the simulation circuit, C6 is mainly used to simulate the parasitic capacitance exhibited by the GDT under nanosecond pulses; L7 and L8 are used to simulate the lead inductance generated in the circuit connection and device package.
[0069] It should be noted that, for reference Figure 3As shown, the inter-electrode capacitance of a surge protection device (GDT) is typically 1-5pF. Therefore, C7 is set to 5pF to simulate the inter-electrode capacitance of the GDT. The extremely large resistance R6 is used to simulate the near-open, high-resistance state when the device is not activated. The extremely small resistance R7 is used to simulate the near-conductive, low-resistance state after the device activates. The three sets of inductor-based time-delay switches on the far right are used to simulate the inductance change during device activation. The specific simulation process is as follows: When the surge protection device is not activated, L4, L5, and L6 are not connected to the circuit, and the large resistor R6 is connected in series in the circuit to simulate the high-resistance state of the device. After a 2ns reaction time, U2 begins to close, and L4 begins to be connected in series in the circuit, while simultaneously short-circuiting the large resistor R6. The activation time of switch U2 is set to 14ns to simulate the change in resistance from high to low and the change in inductance. At 38ns, L5 begins to be connected in series in the circuit, with the activation times of U4 and U3 being 4ns, similarly simulating the inductance change. At 64ns, L6 begins to be connected in series in the circuit, with an activation time of 4ns. In the circuit section simulating the response characteristics of the device, all adjustable parameters are obtained by continuously adjusting the voltage waveform of the experimental device.
[0070] Step 202: Construct the corresponding initial particle swarm based on the preset parasitic parameter constraints.
[0071] In this embodiment of the invention, a particle swarm optimization algorithm is used to construct an initial particle swarm based on preset parasitic parameter constraints, wherein each particle in the initial particle swarm corresponds to a parasitic parameter.
[0072] It should be noted that the parasitic parameter constraints include parasitic capacitance constraints and lead inductance constraints.
[0073] The parasitic capacitance constraint is specifically as follows:
[0074]
[0075] in, Parasitic capacitance, This is the inter-electrode capacitance.
[0076] The lead inductance constraint is specifically as follows:
[0077]
[0078] in, For the fourth lead inductor, For the fifth lead inductor, For the sixth lead inductor, For the seventh lead inductor, This is the inductance of the eighth lead.
[0079] Step 203: Iteratively optimize the initial particle swarm based on the response characteristic simulation circuit and experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified.
[0080] Furthermore, step 203 includes the following sub-steps:
[0081] S11. Input the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0082] In this embodiment of the invention, the parasitic parameters corresponding to each particle in each initial particle swarm are imported into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0083] S12. Accelerated aging tests were conducted using simulation circuits for each target response characteristic to obtain multiple simulation waveform data.
[0084] In this embodiment of the invention, accelerated aging tests were conducted using various target response characteristic simulation circuits at T=85℃, RH=85%, and 1.2 times the rated voltage, to obtain multiple simulation waveform data. The simulation waveform data are the response current waveforms of the GDT (surge protection device) under accelerated aging, measured by the target response characteristic simulation circuit at aging times t=0h, 200h, 400h, 600h, 800h, and 1000h every 200h.
[0085] S13. Input each simulated waveform data and experimental waveform data into the preset root mean square error function to obtain multiple root mean square error values.
[0086] In this embodiment of the invention, each simulated waveform data and experimental waveform data is input into a preset root mean square error function to obtain multiple root mean square error values.
[0087] It should be noted that the root mean square error function is as follows:
[0088]
[0089] in, This is the root mean square error value. Let be the total number of time nodes, and i be the index of the time node. Let be the simulated current value at the i-th time node. This represents the actual current value at the i-th time node.
[0090] S14. Determine whether the number of iterations of the initial particle swarm is less than the preset first iteration threshold.
[0091] The first iteration threshold refers to the upper limit of the number of iterations (such as 300 or 500 times) set based on the accuracy requirements and engineering efficiency of surge protection device modeling.
[0092] In this embodiment of the invention, it is determined that the number of iterations of the initial particle swarm has reached a preset first iteration threshold.
[0093] S15. When the number of iterations is less than the first iteration threshold, the fitness function value corresponding to each particle is determined based on the number of iterations, the root mean square error values, the simulation waveform data, and the experimental waveform data.
[0094] Furthermore, S15 includes the following sub-steps:
[0095] S151. Determine whether the number of iterations is less than the preset second iteration threshold.
[0096] The second iteration threshold is a critical value used to determine whether to perform fine optimization on the initial particle swarm, and its value is 50.
[0097] S152. When the number of iterations is less than the second iteration threshold, the root mean square error value corresponding to each particle is determined as the fitness function value.
[0098] In this embodiment of the invention, when the number of iterations is less than 50, the root mean square error value corresponding to each particle is determined as the fitness function value.
[0099] S153. When the number of iterations is greater than or equal to the second iteration threshold, the fitness function value corresponding to each particle is determined based on the root mean square error value, the simulation waveform data, and the experimental waveform data.
[0100] Furthermore, S153 includes the following sub-steps:
[0101] S1531. Calculate the cosine similarity between each simulated waveform data and the experimental waveform data.
[0102] In this embodiment of the invention, each simulated waveform data and the experimental waveform data are input into a preset cosine similarity function to obtain multiple cosine similarities.
[0103] It should be noted that the cosine similarity function is as follows:
[0104]
[0105] in, Let be the cosine similarity.
[0106] S1532. Perform difference processing on the preset cosine benchmark value and each cosine similarity to obtain multiple first differences.
[0107] The cosine reference value refers to the pre-set cosine similarity target value, which is set to 1, based on the accuracy requirements of waveform consistency in the actual application scenario of surge protection devices.
[0108] In this embodiment of the invention, the difference between the preset cosine benchmark value and each cosine similarity is calculated to obtain multiple first differences.
[0109] S1533. Based on the preset adaptation weights, perform weighted calculations on each root mean square error value and the corresponding first difference value to obtain the adaptation function value corresponding to each particle.
[0110] In this embodiment of the invention, based on preset adaptation weights, each root mean square error value and its corresponding first difference are weighted and calculated to obtain the adaptation function value for each particle. For example, the adaptation function value = first adaptation weight coefficient * root mean square error value + second adaptation weight coefficient * first difference value.
[0111] S16. Based on the particle swarm optimization algorithm, update the initial particle swarm according to the values of each fitness function to obtain a new initial particle swarm, and then jump to execute the step of inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0112] In this embodiment of the invention, based on the particle swarm optimization algorithm, the initial particle swarm is updated according to the values of each fitness function to obtain a new initial particle swarm, and then the process jumps to execute S11-S14.
[0113] It should be noted that the process of updating the initial particle swarm based on each fitness function value is as follows: A1. Record the minimum fitness function value (i.e., the individual optimal fitness value) and the corresponding parameter combination (individual optimal position) for each particle, and simultaneously select the minimum fitness function value (i.e., the global optimal fitness value) and the corresponding parameter combination (global optimal position) from the entire initial particle swarm. A2. According to the velocity update formula of the particle swarm optimization algorithm, combined with the preset inertia weight, individual learning factor, swarm learning factor, and the deviation between the current particle position and the individual optimal position and the global optimal position, calculate the new velocity of each particle, thereby determining the direction and magnitude of particle parameter adjustment. A3. Update the current position of each particle according to the new velocity to obtain the updated parasitic parameter combination for each group of particles. A4. Integrate the updated parameter combinations of all particles to form a new initial particle swarm.
[0114] S17. When the number of iterations is greater than or equal to the first iteration threshold, the target response characteristic simulation circuit corresponding to the minimum value among the various fitness function values is selected as the characteristic simulation circuit to be verified.
[0115] In this embodiment of the invention, when the number of iterations is greater than or equal to the first iteration threshold, the target response characteristic simulation circuit corresponding to the minimum value among the various fitness function values is taken as the characteristic simulation circuit to be verified.
[0116] Step 204: Import the simulation circuit of the characteristics to be verified into the preset surge protection system simulation platform to obtain the corresponding target surge protection system simulation platform.
[0117] In this embodiment of the invention, the simulation circuit of the characteristic to be verified is input into a preset surge protection system simulation platform to obtain the corresponding target surge protection system simulation platform.
[0118] Step 205: Perform surge protection using the target surge protection system simulation platform to obtain the corresponding simulation response waveform data.
[0119] In this embodiment of the invention, the target surge protection system simulation platform is used for surge protection (i.e., to simulate the collaborative protection effect of multiple GDTs) to obtain the corresponding simulation response waveform data.
[0120] Step 206: Input the simulation response waveform data and the pre-acquired response waveform data into the preset objective function to obtain the corresponding objective function value.
[0121] In this embodiment of the invention, based on a preset objective function, the corresponding objective function value is determined according to the simulation response waveform data (i.e., the GDT response waveform data obtained by the simulation platform of the target surge protection system) and the pre-acquired response waveform data (i.e., the GDT response waveform data under the actual nanosecond pulse).
[0122] It should be noted that the objective function is as follows:
[0123]
[0124] in, The objective function value, The first adaptive weighting coefficient, This is the second adaptive weighting coefficient.
[0125] Step 207: When the objective function value is greater than or equal to the preset error threshold, proceed to the step of constructing the corresponding initial particle swarm based on the preset parasitic parameter constraints.
[0126] Error threshold refers to the upper limit of the target function value set in advance according to the accuracy requirements of the surge protection system design (such as the protection reliability requirements of substations and communication base stations), and the value is 8%.
[0127] In this embodiment of the invention, when the objective function value is greater than or equal to 8%, the process jumps to steps 202-206.
[0128] Step 208: When the objective function value is less than the error threshold, a surge protection device simulation circuit is generated.
[0129] In this embodiment of the invention, when the objective function value is less than 8%, the simulation circuit of the characteristic to be verified is determined to be the surge protection device simulation circuit.
[0130] In this embodiment of the invention, an initial particle swarm is constructed based on preset parasitic parameter constraints. The initial particle swarm is iteratively optimized using response characteristic simulation circuits and experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified. This circuit is then verified using a preset surge protection system simulation platform to obtain the corresponding surge protection device simulation circuit. This overcomes the technical problem that existing surge protection device models are mostly built based on microsecond-level pulses, simplifying parameters such as parasitic capacitance and lead inductance, which have significant effects under nanosecond pulses, making it difficult to meet the dual requirements of model accuracy and adaptation efficiency in practical applications. Compared with traditional surge protection device model building methods, this invention achieves efficient parameter optimization of the response characteristic simulation circuit by iteratively optimizing the initial particle swarm using response characteristic simulation circuits and experimental waveform data. Simultaneously, the verification of the characteristic simulation circuit to be verified using a preset surge protection system simulation platform significantly improves the model accuracy of the surge protection device simulation circuit, thereby enhancing the accuracy and adaptation efficiency of surge protection device modeling.
[0131] Please see Figure 3 , Figure 3 This is a structural block diagram of a surge protection device modeling system provided in Embodiment 3 of the present invention.
[0132] This invention provides a surge protection device modeling system, comprising:
[0133] The first construction module 301 is used to acquire the test waveform data of the surge protection device and call the preset electromagnetic simulation tool to construct the response characteristic simulation circuit corresponding to the surge protection device.
[0134] The second construction module 302 is used to construct the corresponding initial particle swarm based on preset parasitic parameter constraints.
[0135] The optimization module 303 is used to iteratively optimize the initial particle swarm based on the response characteristic simulation circuit and experimental waveform data to obtain the corresponding characteristic simulation circuit to be verified.
[0136] The verification module 304 is used to verify the simulation circuit of the characteristics to be verified based on the preset surge protection system simulation platform, so as to obtain the corresponding surge protection device simulation circuit.
[0137] Furthermore, module 303 is optimized, including:
[0138] The loading submodule is used to input the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0139] The test submodule is used to conduct accelerated aging tests using simulation circuits for each target response characteristic to obtain multiple simulation waveform data.
[0140] The first analysis submodule is used to input each simulated waveform data and experimental waveform data into a preset root mean square error function to obtain multiple root mean square error values.
[0141] The second analysis submodule is used to determine whether the number of iterations of the initial particle swarm is less than the preset first iteration threshold.
[0142] The first adaptation submodule is used to determine the adaptation function value corresponding to each particle based on the number of iterations, the root mean square error value, the simulation waveform data, and the experimental waveform data when the number of iterations is less than the first iteration threshold.
[0143] Based on the particle swarm optimization algorithm, the initial particle swarm is updated according to the values of each fitness function to obtain a new initial particle swarm. Then, the process jumps to execute the step of inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain multiple target response characteristic simulation circuits.
[0144] A submodule is selected to select the target response characteristic simulation circuit corresponding to the minimum value among the various fitness function values as the characteristic simulation circuit to be verified when the number of iterations is greater than or equal to the first iteration threshold.
[0145] Furthermore, the first adaptation submodule includes:
[0146] The first analysis unit is used to determine whether the number of iterations is less than a preset second iteration threshold.
[0147] The second analysis unit is used to determine the root mean square error value corresponding to each particle as the fitness function value when the number of iterations is less than the second iteration threshold.
[0148] The third analysis unit is used to determine the fitness function value corresponding to each particle based on the root mean square error value, the simulation waveform data, and the experimental waveform data when the number of iterations is greater than or equal to the second iteration threshold.
[0149] Furthermore, the third analysis unit includes:
[0150] The cosine similarity subunit is used to calculate the cosine similarity between each simulated waveform data and the experimental waveform data.
[0151] The difference subunit is used to perform difference processing on the preset cosine benchmark value and each cosine similarity to obtain multiple first differences;
[0152] The weighted sub-unit is used to perform weighted operations on each root mean square error value and the corresponding cosine similarity based on preset adaptation weights, so as to obtain the adaptation function value corresponding to each particle.
[0153] Furthermore, the verification module 304 includes:
[0154] The import submodule is used to import the simulation circuit with characteristics to be verified into the preset surge protection system simulation platform to obtain the corresponding target surge protection system simulation platform.
[0155] The surge protection submodule is used to perform surge protection using the target surge protection system simulation platform and obtain the corresponding simulation response waveform data.
[0156] The target submodule is used to input the simulation response waveform data and the pre-acquired response waveform data into a preset target function to obtain the corresponding target function value;
[0157] The verification submodule is used to jump to the step of constructing the corresponding initial particle swarm based on the preset parasitic parameter constraints when the objective function value is greater than or equal to the preset error threshold.
[0158] When the objective function value is less than the error threshold, a surge protection device simulation circuit is generated.
[0159] Furthermore, the parasitic parameter constraints include parasitic capacitance constraints and lead inductance constraints.
[0160] Please see Figure 4 , Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.
[0161] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the surge protection device modeling method as described in any of the above embodiments.
[0162] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing processing device, it causes the device to perform the various steps in the surge protection device modeling method described above.
[0163] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the surge protection device modeling method as described in any of the above embodiments.
[0164] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the surge protection device modeling method as described in any of the above embodiments.
[0165] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0166] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.
[0167] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0168] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0169] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0170] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method of modeling a surge protection device, the method comprising: The method comprises the following steps: acquiring test waveform data of a surge protection device, and calling a preset electromagnetic simulation tool to construct a response characteristic simulation circuit corresponding to the surge protection device; constructing an initial particle swarm based on a preset parasitic parameter constraint condition; iteratively optimizing the initial particle swarm according to the response characteristic simulation circuit and the test waveform data to obtain a corresponding to-be-verified characteristic simulation circuit; verifying the to-be-verified characteristic simulation circuit based on a preset surge protection system simulation platform to obtain a corresponding surge protection device simulation circuit.
2. The surge protection device modeling method of claim 1, wherein, The step of iteratively optimizing the initial particle swarm according to the response characteristic simulation circuit and the test waveform data to obtain a corresponding to-be-verified characteristic simulation circuit comprises the following steps: inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain a plurality of target response characteristic simulation circuits; respectively performing accelerated aging tests by using each target response characteristic simulation circuit to obtain a plurality of simulation waveform data; inputting each simulation waveform data and the test waveform data into a preset root mean square error function to obtain a plurality of root mean square error values; judging whether the iteration number of the initial particle swarm is less than a preset first iteration threshold value; when the iteration number is less than the first iteration threshold value, determining the fitness function value corresponding to each particle based on the iteration number, each root mean square error value, each simulation waveform data and the test waveform data; updating the initial particle swarm according to each fitness function value based on a particle swarm optimization algorithm to obtain a new initial particle swarm, and jumping to execute the step of inputting the parasitic parameters corresponding to each particle in each initial particle swarm into the response characteristic simulation circuit to obtain a plurality of target response characteristic simulation circuits; when the iteration number is greater than or equal to the first iteration threshold value, selecting the target response characteristic simulation circuit corresponding to the minimum value in each fitness function value as the to-be-verified characteristic simulation circuit.
3. The surge protection device modeling method of claim 2, wherein, The step of determining the fitness function value corresponding to each particle based on the iteration number, each root mean square error value, each simulation waveform data and the test waveform data comprises the following steps: judging whether the iteration number is less than a preset second iteration threshold value; when the iteration number is less than the second iteration threshold value, determining the root mean square error value corresponding to each particle as the fitness function value; when the iteration number is greater than or equal to the second iteration threshold value, determining the fitness function value corresponding to each particle according to each root mean square error value, each simulation waveform data and the test waveform data.
4. The surge protection device modeling method of claim 3, wherein, The step of determining the fitness function value corresponding to each particle according to each root mean square error value, each simulation waveform data and the test waveform data comprises the following steps: calculating the cosine similarity between each simulation waveform data and the test waveform data; respectively performing difference processing on a preset cosine reference value and each cosine similarity to obtain a plurality of first differences; Based on the preset adaptive weight, the respective first difference value and the respective root mean square error value are subjected to weighted operation to obtain an adaptive function value corresponding to each particle.
5. The surge protection device modeling method of claim 1, wherein, The step of verifying the to-be-verified characteristic simulation circuit based on the preset surge protection system simulation platform to obtain a corresponding surge protection device simulation circuit comprises: The to-be-verified characteristic simulation circuit is imported into a preset surge protection system simulation platform to obtain a corresponding target surge protection system simulation platform; The target surge protection system simulation platform is used to perform surge protection to obtain corresponding simulation response waveform data; The simulation response waveform data and the pre-acquired response waveform data are input into a preset target function to obtain a corresponding target function value; When the target function value is greater than or equal to a preset error threshold value, the step of constructing a corresponding initial particle swarm based on the preset parasitic parameter constraint condition is executed; When the target function value is less than the error threshold value, a surge protection device simulation circuit is generated.
6. The surge protection device modeling method of claim 1, wherein, The parasitic parameter constraint condition comprises a parasitic capacitance constraint and a lead inductance constraint.
7. A surge protection device modeling system, comprising: Comprise: The first construction module is configured to acquire test waveform data of a surge protection device, and to call a preset electromagnetic simulation tool to construct a response characteristic simulation circuit corresponding to the surge protection device; The second construction module is configured to construct a corresponding initial particle swarm based on a preset parasitic parameter constraint condition; The optimization module is configured to perform iterative optimization on the initial particle swarm according to the response characteristic simulation circuit and the test waveform data to obtain a to-be-verified characteristic simulation circuit; The verification module is configured to verify the to-be-verified characteristic simulation circuit based on a preset surge protection system simulation platform to obtain a corresponding surge protection device simulation circuit.
8. An electronic device, comprising: The computer program is executed by the processor to cause the processor to perform the steps of the surge protection device modeling method according to any one of claims 1-6.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the surge protection device modeling method according to any one of claims 1-6.
10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer performs the surge protection device modeling method according to any one of claims 1-6.