Method for determining relation between current and voltage of MOV device of modular hybrid direct-current circuit breaker

Abstract

The invention provides a method for determining the current and voltage relation of a modular hybrid direct-current circuit breaker MOV device, and can be used in the technical field of flexible direct-current power transmission and distribution. The method comprises the following steps: acquiring voltage and current at two ends of the MOV device in a maximum short-circuit current breaking test; preprocessing the voltage and the current by using an MOV voltage starting principle to obtain a fitting voltage and a fitting current at two ends of the MOV device; according to the fitting voltage and the fitting current, fitting parameters of a mathematical model of a preset voltage and current relationship by utilizing a Warner-gull algorithm in combination with an LM algorithm to obtain optimal fitting parameters of the mathematical model; and according to the optimal fitting parameter, generating a mathematical model of a current and voltage relationship of the MOV device with the optimal fitting parameter.

Description

Technical Field

[0001] The present application relates to the technical field of flexible direct current power transmission and distribution, and in particular to a method for determining the relationship between current and voltage of a modular hybrid direct current circuit breaker MOV device. Background Art

[0002] Due to the urgent need for access to a large number of renewable energy sources, flexible DC systems have been further developed, among which multi-terminal flexible high-voltage DC systems have received great attention and research, and have also promoted the development of multi-terminal flexible DC distribution systems. However, due to the relatively low impedance and low current tolerance of these systems, when a fault occurs, a DC circuit breaker is needed to quickly remove the fault to ensure its own reliability and stability.

[0003] DC circuit breakers can be divided into solid-state DC circuit breakers, mechanical DC circuit breakers and hybrid DC circuit breakers. Hybrid DC circuit breakers combine the advantages of mechanical and solid-state types and have received widespread attention in recent years. Hybrid DC circuit breakers are widely equipped with MOV devices to absorb the energy of short-circuit current. Effective evaluation of MOV parameters can provide further protection for the voltage changes during the breaking process of modular hybrid DC circuit breakers. However, it was found in actual experiments that the MOV parameter table provided by the manufacturer cannot effectively reflect the voltage changes during the DC breaking process. Therefore, it is necessary to re-evaluate and numerically quantify the voltage-current relationship of MOV based on the actual voltage-current breaking waveform.

[0004] However, the existing modular hybrid DC circuit breaker voltage estimation is inaccurate. One is that the existing MOV voltage and current function expression is not suitable for modular hybrid DC circuit breakers for voltage estimation, because the existing MOV function expression is mainly used in the AC field, and the application scenarios of DC circuit breakers are different from those of AC; the other is that the existing parameter estimation method cannot effectively obtain the optimal parameters in the function expression between MOV voltage and current, because the existing parameter estimation method has not studied the relevant optimal parameters of MOV function expression in DC circuit breakers. Summary of the invention

[0005] In view of the problems in the prior art, an embodiment of the present application provides a method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device, which can at least partially solve the problems in the prior art.

[0006] On the one hand, the present application proposes a method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device, comprising:

[0007] Obtain the voltage and current across the MOV device of the modular hybrid DC circuit breaker in the maximum short-circuit current breaking test;

[0008] Preprocessing the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device;

[0009] According to the fitting voltage and the fitting current, the parameters of the preset mathematical model of the relationship between voltage and current are fitted using the Sooty Tern algorithm combined with the LM algorithm to obtain the best fitting parameters of the mathematical model;

[0010] According to the best fitting parameters, a mathematical model of the current and voltage relationship of the MOV device having the best fitting parameters is generated;

[0011] According to the fitting voltage and the fitting current, the parameters of the preset mathematical model of the relationship between voltage and current are fitted by using the Sooty Tern algorithm combined with the LM algorithm to obtain the best fitting parameters of the mathematical model, which include:

[0012] Generate an initial population using the Sooty Tern optimization algorithm;

[0013] Using each individual in the initialization population as an initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the preset mathematical model of the relationship between voltage and current according to the fitting voltage and the fitting current, to obtain a first fitting parameter corresponding to each individual in the initialization population;

[0014] For each of the first fitting parameters, substituting the first fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the first fitting parameter;

[0015] Calculate a first mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the first fitting parameters;

[0016] Using the Sooty Tern optimization algorithm to update the initialized population to obtain a new population;

[0017] Using each individual in the new population as the initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the mathematical model according to the fitting voltage and the fitting current to obtain a second fitting parameter corresponding to each individual in the new population;

[0018] For each of the second fitting parameters, substituting the second fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter;

[0019] Calculate a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters;

[0020] If the smallest second average square error sum is smaller than the smallest first average square error sum, the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum are used as the best fitting parameters of the mathematical model.

[0021] In some embodiments, the preprocessing of the voltage and current using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the MOV device includes:

[0022] According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein,

[0023] If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively;

[0024] If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

[0025] In some embodiments, the method further comprises:

[0026] If the smallest second average sum of square errors is greater than or equal to the smallest first average sum of square errors, the first fitting parameter corresponding to the smallest first average sum of square errors is used as the best fitting parameter of the mathematical model.

[0027] In some embodiments, the method further comprises:

[0028] The voltage of the MOV device is estimated using the mathematical model with the best fit parameters.

[0029] On the other hand, the present application proposes a device for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device, comprising:

[0030] An acquisition module, used for acquiring the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in a maximum short-circuit current breaking test;

[0031] A preprocessing module, used to preprocess the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device;

[0032] A parameter acquisition module is used to fit the parameters of the mathematical model of the relationship between voltage and current by using the Sooty Tern algorithm combined with the LM algorithm according to the fitting voltage and the fitting current, so as to obtain the best fitting parameters of the mathematical model;

[0033] A generating module, for generating a mathematical model of the current and voltage relationship of the MOV device having the best fitting parameters according to the best fitting parameters;

[0034] The parameter acquisition module is specifically used for:

[0035] Generate an initial population using the Sooty Tern optimization algorithm;

[0036] Using each individual in the initialization population as an initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the preset mathematical model of the relationship between voltage and current according to the fitting voltage and the fitting current, to obtain a first fitting parameter corresponding to each individual in the initialization population;

[0037] For each of the first fitting parameters, substituting the first fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the first fitting parameter;

[0038] Calculate a first mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the first fitting parameters;

[0039] Using the Sooty Tern optimization algorithm to update the initialized population to obtain a new population;

[0040] Using each individual in the new population as the initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the mathematical model according to the fitting voltage and the fitting current to obtain a second fitting parameter corresponding to each individual in the new population;

[0041] For each of the second fitting parameters, substituting the second fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter;

[0042] Calculate a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters;

[0043] If the smallest second average square error sum is smaller than the smallest first average square error sum, the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum are used as the best fitting parameters of the mathematical model.

[0044] In some embodiments, the preprocessing module is specifically used to:

[0045] According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein,

[0046] If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively;

[0047] If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

[0048] In some embodiments, the parameter acquisition module is further used to:

[0049] If the smallest second average sum of square errors is greater than or equal to the smallest first average sum of square errors, the first fitting parameter corresponding to the smallest first average sum of square errors is used as the best fitting parameter of the mathematical model.

[0050] In some embodiments, the apparatus further comprises:

[0051] An estimation module is used to estimate the voltage of the MOV device of the modular hybrid DC circuit breaker using the mathematical model with the best fitting parameters. The embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for determining the relationship between the current and voltage of the MOV device of the modular hybrid DC circuit breaker described in any of the above embodiments are implemented.

[0052] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device described in any of the above embodiments are implemented.

[0053] The method and device for determining the current and voltage relationship of a modular hybrid DC circuit breaker MOV device provided in the embodiment of the present application obtains the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in the maximum short-circuit current breaking test; the voltage and current are pre-processed using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the MOV device; according to the fitting voltage and fitting current, the parameters of the mathematical model of the preset voltage and current relationship are fitted using the Sooty Tern algorithm combined with the LM algorithm to obtain the best fitting parameters of the mathematical model; according to the best fitting parameters, a mathematical model of the current and voltage relationship of the MOV device with the best fitting parameters is generated. In this way, the parameters of the mathematical model of the current and voltage relationship obtained are more accurate, and the voltage estimation of the modular hybrid DC circuit breaker MOV device using this model is more accurate and more applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0055] Figure 1 It is a topological diagram of a modular hybrid DC circuit breaker.

[0056] Figure 2 yes Figure 1 Schematic diagram of the breaking current and voltage waveforms of the modular hybrid DC circuit breaker.

[0057] Figure 3 It is a flow chart of a method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in one embodiment of the present application.

[0058] Figure 4 It is a partial flow chart of a method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in one embodiment of the present application.

[0059] Figure 5 yes Figure 1 Schematic diagram of the breaking voltage and current waveforms of the modular hybrid DC circuit breaker MOV device in the maximum short-circuit current breaking test.

[0060] Figure 6 The method for obtaining the fitting current and fitting voltage provided in this application is used to obtain the fitting current and fitting voltage of Figure 5 Schematic diagram of the waveform of the fitted current and the fitted voltage obtained after fitting the breaking voltage and current in .

[0061] Figure 7 Figure (a) is the use of Figure 6 The waveform comparison of the estimated voltage and actual voltage obtained by the LM algorithm is shown in the figure.

[0062] Figure 7 Figure (b) is the use of Figure 6 Schematic diagram of the waveform of the data and the absolute value of the error between the estimated voltage and the actual voltage obtained by the LM algorithm.

[0063] Figure 8 Figure (a) is the use of Figure 6 The waveform comparison of the estimated voltage and actual voltage obtained by the data and the Sooty Tern algorithm.

[0064] Figure 8 Figure (b) is the use of Figure 6Schematic diagram of the waveform of the data and the absolute value of the error between the estimated voltage and the actual voltage obtained by the Sooty Tern algorithm.

[0065] Fig. 9 Figure (a) is the use of Figure 6 The waveform comparison diagram of the estimated voltage and actual voltage obtained by the Sooty Tern algorithm combined with the LM algorithm proposed in this application.

[0066] Fig. 9 Figure (b) is the use of Figure 6 The data and the waveform diagram of the absolute value of the error between the estimated voltage and the actual voltage obtained by combining the Black Tern algorithm proposed in this application with the LM algorithm.

[0067] Fig.10 It is a structural schematic diagram of a device for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in one embodiment of the present application.

[0068] Fig.11 It is a schematic diagram of the physical structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily arranged with each other.

[0070] The terms “first”, “second”, etc. used in this document do not specifically refer to an order or sequence, nor are they used to limit this application. They are only used to distinguish elements or operations described with the same technical terms.

[0071] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0072] As used herein, "and / or" includes any and all permutations of the items described.

[0073] In order to better understand the present application, the research background of the present application is first introduced in detail below.

[0074] (I) The breaking principle of modular hybrid DC circuit breaker is as follows:

[0075] The voltage zero-crossing hybrid DC circuit breaker has attracted much attention due to its advantages such as no conduction loss, no need for additional water cooling devices, suitability for different voltage levels, simple structure and reliable commutation. The following example illustrates the breaking principle of the modular hybrid DC circuit breaker. Figure 1 This is a topological diagram of a modular hybrid DC circuit breaker. Figure 1 It can be seen that the modular hybrid DC circuit breaker includes an isolating switch, a fast mechanical switch, a main module and a coupled negative pressure circuit, wherein the main module is mainly composed of power electronic switch devices and MOVs in parallel, and the MOVs between each main module are discrete, and m is the number of main modules. Figure 1 In the figure, R and C are the resistance and capacitance of the main module buffer branch, D1, D2, D3, D4 and D5 are 5 diodes, L1 and L2 are inductors, L s11 and L s21 is the stray inductance, M is the mutual inductance between L1 and L2, C1 is the pre-charge capacitor, T is the thyristor, U bus is the bus voltage, U br is the circuit breaker voltage, I FMS is the current flowing through the fast mechanical switch, I br is the current flowing through the entire circuit breaker, I PES is the current flowing through the main module, L p is the stray inductance, L Line is the line inductance, U Line is the line voltage. Figure 2 The breaking current and voltage waveforms of the modular hybrid DC circuit breaker are shown in Figure 2. Figure 2 In, t f is the time when the fault occurs, t a is the fault arrival time, t tp is the moment when the mechanical switch is opened, t cc is the first current transfer moment, t po is the second current transfer moment, t ds The moment when the isolating switch is opened.

[0076] Depend on Figure 1 and Figure 2 It can be seen that the operation sequence of the circuit breaker is as follows:

[0077] Time t a ~ time t tp : Fault detection time, and at t tp When a fault is detected, a fast mechanical switch opening signal and a power electronic switch conduction signal are sent;

[0078] Time t tp ~ time t cc :t tpThe mechanical switch is operated quickly at all times. When the contacts are separated to a certain distance, the coupled negative pressure circuit is turned on. The coupled negative pressure circuit generates positive and reverse voltages in turn. cc At this moment, the current is completely transferred to the main module, which is also the starting moment of the first commutation;

[0079] Time t cc ~ time t po :The power electronic switch conducts current and the mechanical switch contact distance continues to increase. po Before the moment, the contact gap establishes an insulation distance that can withstand the breaking overvoltage, t po The power electronic switch is turned off at the moment, which is also the start time of the second commutation. In this process, the power electronic switch needs to withstand and turn off the maximum short-circuit current that the system may reach;

[0080] Time t po ~ time t ds : After the power electronic switch is turned off, the current is commutated to the energy absorption branch MOV until the current decays, and the isolation switch is opened when the isolation switch is allowed to open to complete the fault current clearing. During this process, the mechanical switch and the power electronic branch need to withstand the system overvoltage limited by the MOV.

[0081] (II) MOV mathematical model

[0082] MOV is a metal oxide with nonlinear electrical characteristics, which is widely used in overvoltage protection and lightning protection of electronic power systems. Nonlinear volt-ampere characteristic is the most important electrical characteristic of MOV. The typical electric field (E)-current density (J) curve can be expressed by formula (1):

[0083] logE=A+BlogJ+Ce (-logJ) +De (logJ) (1)

[0084] Where A, B, C, and D are standardized parameters of the MOV, usually constants related to the material and manufacturing process. The logarithmic representation is standard because the voltage and current vary over many orders of magnitude.

[0085] In the low current region, the varistor is an ohmic resistor, the EJ curve is close to linear, and there is a special "critical point" corresponding to the critical voltage V of the MOV. N When the voltage applied to the MOV exceeds V N When the MOV conductivity increases sharply, the curve approaches a straight line parallel to the horizontal axis after exceeding the critical point, which is the core feature of MOV as a nonlinear element. In this region, the relationship between voltage and current is usually expressed as formula (2).

[0086] V∝I α (2)

[0087] Where α is the nonlinear coefficient of MOV.

[0088] In the high current region, the curve becomes linear again. In this case, the MOV acts like a small resistor, so the voltage increases faster as the current increases.

[0089] However, the complex behavior of MOV is not only caused by nonlinear resistance, but also includes some distributed parameters, such as parasitic inductance, parasitic capacitance, etc. Therefore, in order to more effectively express the relationship between the voltage U and current I of MOV, equation (3) can be derived from equation (1), where α1, α2, α3 and α4 are the parameters of the MOV mathematical model, and w is the base of the logarithmic function.

[0090]

[0091] However, the above MOV mathematical models are all applied in the AC field. Existing studies have found that they are not suitable for the application scenarios of DC circuit breakers. In order to further improve the accuracy of the fitting model and its applicability to the application scenarios of DC circuit breakers, different factors in practice are taken into account as much as possible. By referring to the idea of ​​adding bias in machine learning, this application proposes a new MOV mathematical model formula based on the above model, as shown in formula (4). In the formula, α is the nonlinear coefficient of MOV, n represents the number of logarithms of the MOV parameters, and G is 1 to n; in this application, n can be 5, then there are 5 pairs of parameters in the MOV model, specifically α1, α2, α3, α4, α5, α6, α7, α8, α9 and α1. 10 (Research has found that when n is 5, it meets the application scenario of DC circuit breakers. Choosing n too large will increase the calculation burden).

[0092]

[0093] The execution subject of the method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in the embodiment of the present application includes but is not limited to a computer.

[0094] Figure 3 FIG. 1 is a flow chart of a method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in an embodiment of the present application. Figure 3 As shown, the method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in an embodiment of the present application includes:

[0095] S101, obtaining the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in the maximum short-circuit current breaking test;

[0096] In step S101, a maximum short-circuit current breaking test is performed on the modular hybrid DC circuit breaker. During the test, voltage and current data at both ends of the MOV device of the circuit breaker are obtained, wherein the MOV device can be a MOV combination of the entire energy-consuming branch of the modular hybrid DC circuit breaker, or a single MOV on the energy-consuming branch or several MOVs connected in series.

[0097] S102, preprocessing the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device;

[0098] In step S102, the MOV voltage starting principle is used to determine the fitting voltage and fitting current at both ends of the MOV device of the circuit breaker in the maximum short-circuit current breaking test according to the voltage and current at both ends of the modular hybrid DC circuit breaker.

[0099] S103, fitting the parameters of a preset mathematical model of the relationship between voltage and current using a Sooty Tern algorithm combined with an LM algorithm according to the fitting voltage and the fitting current, to obtain the best fitting parameters of the mathematical model;

[0100] In step S103, the LM algorithm and the Sooty Tern algorithm are combined, and the fitting voltage and the fitting current are used to solve the optimal parameters of the mathematical model of the relationship between voltage and current.

[0101] S104: Generate a mathematical model of the relationship between current and voltage of the MOV device having the best fitting parameters according to the best fitting parameters.

[0102] In step S104, the best fitting parameters are substituted into a preset mathematical model of the relationship between current and voltage, so as to obtain a mathematical model of the relationship between current and voltage of the MOV device having the best fitting parameters.

[0103] The method for determining the current and voltage relationship of a modular hybrid DC circuit breaker MOV device provided in the present application is to obtain the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in the maximum short-circuit current breaking test; pre-process the voltage and current using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the MOV device; according to the fitting voltage and fitting current, use the Sooty Tern algorithm combined with the LM algorithm to fit the parameters of the preset mathematical model of the voltage and current relationship to obtain the best fitting parameters of the mathematical model; according to the best fitting parameters, generate the mathematical model of the current and voltage relationship of the MOV device with the best fitting parameters. In this way, the parameters of the mathematical model of the current and voltage relationship obtained are more accurate, and the voltage estimation of the modular hybrid DC circuit breaker MOV device using this model is more accurate and more applicable.

[0104] In some embodiments, the method further includes: using the mathematical model with the best fitting parameters to estimate the voltage of the MOV device of the modular hybrid DC circuit breaker. Specifically, using the fitted current at both ends of the MOV device obtained, the fitted current is brought into the MOV mathematical model for which the optimal parameters have been obtained, and the estimated fitted voltage can be obtained.

[0105] In some embodiments, the preprocessing of the voltage and current by using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the modular hybrid DC circuit breaker includes:

[0106] According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein,

[0107] If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively;

[0108] If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

[0109] Specifically, step 1.1, obtain the voltage U across the MOV device of the modular hybrid DC circuit breaker in the maximum short-circuit current breaking test sr and current I sr The recorded data is used to obtain U at t = 1. sr (t) and I sr (t), t is the sampling point number;

[0110] Step 1.2: Determine U sr (t) Whether the starting voltage U of MOV is reached cr , if U sr (t)≥U cr , then go to step 1.3; otherwise, go to step 1.4;

[0111] Step 1.3: Calculate the fitting current I fr (t) and fitting voltage U fr (t), then go to step 1.5;

[0112]

[0113] Step 1.4: Calculate the fitting current I fr (t) and fitting voltage U fr (t), then let t = t + 1, and obtain Usr (t) and I sr (t), and proceed to Step 1.2;

[0114]

[0115] Step 1.5, Determine whether t reaches the set time length tset. If t < tset, then let t = t + 1, and obtain U sr (t) and I sr (t), and proceed to Step 1.3; If t ≥ tset, then record the value of t as y, and output the fitted current I fr and the fitted voltage U fr ;

[0116]

[0117] As Figure 4 shown, in some embodiments, the method for fitting the parameters of the mathematical model of the preset voltage and current relationship by using the Sooty Tern algorithm in combination with the LM algorithm according to the fitted voltage and the fitted current to obtain the optimal fitting parameters of the mathematical model includes:

[0118] S1031. Generate an initial population by using the Sooty Tern optimization algorithm;

[0119] S1032. Take each individual in the initial population as the initial value of the LM algorithm, and use the LM algorithm to fit the parameters of the mathematical model of the preset voltage and current relationship according to the fitted voltage and the fitted current, so as to obtain the first fitting parameter corresponding to each individual in the initial population;

[0120] S1033. For each of the first fitting parameters, substitute the first fitting parameter and the fitted current into the mathematical model to obtain the estimated voltage corresponding to each of the fitted currents under the first fitting parameter;

[0121] S1034. Calculate the sum of the first mean square errors of the fitted voltage and the estimated voltage corresponding to the fitted current under each of the first fitting parameters;

[0122] S1035. Use the Sooty Tern optimization algorithm to update the initial population to obtain a new population;

[0123] S1036. Take each individual in the new population as the initial value of the LM algorithm, and use the LM algorithm to fit the parameters of the mathematical model according to the fitted voltage and the fitted current, so as to obtain the second fitting parameter corresponding to each individual in the new population;

[0124] S1037. For each of the second fitting parameters, substitute the second fitting parameter and the fitting current into the mathematical model of the relationship between the voltage and the current to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter;

[0125] S1038, calculating a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters;

[0126] S1039: If the smallest second average square error sum is smaller than the smallest first average square error sum, taking the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum as the best fitting parameter of the mathematical model.

[0127] In the above embodiment, the method may further include: if the smallest second average square error sum is greater than or equal to the smallest first average square error sum, taking the first fitting parameter corresponding to the smallest first average square error sum as the best fitting parameter of the mathematical model.

[0128] Specifically, step 2.1, setting the MOV mathematical model, wherein α is a parameter of the MOV mathematical model, n represents the logarithm of α (that is, how many pairs of α there are), n can be determined according to an empirical value, for example, n is 5, or can be determined according to the effect of the mathematical model, etc., U represents the voltage across the MOV device, and I represents the current flowing through the MOV device;

[0129]

[0130] Step 2.2: Set the total population of the black tern algorithm to P op , the maximum number of iterations is I iter The optimization range of each parameter of the MOV model is [-α max , α max ];

[0131] Step 2.3: Get the initial population α, the kth individual in the population is denoted as α k , α k for k=1,2,…P op , and then use each individual as the initial value of the Levenberg-Marquardt (LM) algorithm, and let the number of iterations I it is 1;

[0132] Step 2.4: Use the fitted current I fr , fitting voltage U frThe MOV mathematical model parameters are fitted with the LM algorithm to obtain the fitting parameters αgk corresponding to each individual. αgk is and I fr Substitute into the MOV mathematical model to obtain the estimated voltage U αk ;

[0133] Step 2.5: Calculate U αk and U fr The mean square error between the two and E, record the smallest E in the population αm and its corresponding best fitting parameter α gm ; Where R is the maximum value of the voltage sampling point;

[0134]

[0135] Step 2.6: Update the population according to the Sooty Tern Optimization Algorithm to obtain a new population β. The k individuals in the population are denoted as β k , β k for k=1,2,…P op , and then use each individual as the initial value of the LM algorithm;

[0136] Step 2.7: Using the fitted current fr , fitting voltage U fr The MOV mathematical model parameters are fitted with the LM algorithm to obtain the fitting parameters β corresponding to each individual. gk , β gk for and I fr Substitute into the MOV mathematical model to obtain the estimated voltage U βk ;

[0137] Step 2.8: Calculate U βk and U fr The mean square error between the two and E, record the smallest E in the population βm and its corresponding fitting parameter β gm ;

[0138] Step 2.9, determine E βm Is it less than E? αm If so, then E βm Assign the value of E αm , and β gm Assign value to α gm If not, keep E αm and α gm ;

[0139] Step 2.10, Judgment I it Is it greater than I iterIf so, then output E at this time αm and α gm is the final optimization result; if not, proceed to step 2.6.

[0140] In some embodiments, the sooty tern algorithm may also be replaced by a particle swarm algorithm, a cuckoo search algorithm, a grey wolf algorithm, or a whale algorithm.

[0141] Specifically, the population update method of the Sooty Tern Optimization Algorithm (STOA) is replaced by the Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Grey Wolf Algorithm (GWO) and Whale Algorithm (WOA), and the other parameters are the same as the Sooty Tern. Their test results are shown in Table 1, where E in Table 1 represents the mean square error between the estimated voltage and the actual voltage obtained by each algorithm. It can be seen from Table 1 that through the idea of ​​combining the LM algorithm and the intelligent swarm algorithm proposed in this application, different swarm optimization algorithms can obtain effective optimization results.

[0142] Table 1 Test results of improved swarm optimization algorithm

[0143] algorithm E algorithm E LM-PSO 2.04 LM-GWO 2.04 LM-CSA 2.00 LM-WOA 2.00

[0144] In order to better understand the present application, the method for determining the current and voltage relationship of the modular hybrid DC circuit breaker MOV device provided by the present application is described in detail below through a specific embodiment.

[0145] Select Figure 1 The specific parameters of the modular hybrid DC circuit breaker shown are shown in Table 2.

[0146] Table 2 Parameters of modular hybrid DC circuit breaker

[0147]

[0148]

[0149] according to Figure 1 And Table 2, you can set U cr 220kV. Then, by conducting the maximum current breaking test on the circuit breaker, we can obtain Figure 5 The breaking voltage and current waveforms shown in the figure, where the circuit breaker is in a normal current-carrying state before 1.5s, and the circuit breaker starts to break after 1.5s. Figure 5 And the fitting current and fitting voltage acquisition method described above can be obtained Figure 6 The fitted current and voltage waveforms shown in the figure are then used to Figure 6 The data and LM algorithm can be obtained Figure 7 , Figure 7 Figure (a) shows the estimated voltage and actual voltage waveforms. Figure 7 Figure (b) in the figure is a waveform diagram of the absolute value of the error between the estimated voltage and the actual voltage, where the initial value of the LM algorithm is 10 and the coefficient is. Figure 6 The data and the Sooty Tern algorithm can be obtained Figure 8 , Figure 8 Figure (a) shows the estimated voltage and actual voltage waveforms. Figure 8 Figure (b) is the waveform of the absolute value of the error between the estimated voltage and the actual voltage. The parameters of the black tern algorithm are as follows: the population size is 5000, the maximum number of iterations is 5000, and C f is 2. Figure 7 and Figure 8 It can be seen that the LM algorithm and the Sooty Tern algorithm alone cannot effectively evaluate the actual voltage. Figure 6 The data and the black tern algorithm proposed in this application combined with the LM algorithm can be obtained Fig. 9 , Fig. 9 Figure (a) shows the estimated voltage and actual voltage waveforms. Fig. 9 Figure (b) is the waveform of the absolute value of the error between the estimated voltage and the actual voltage. The parameters of the black tern algorithm are as follows: the population size is 50, the maximum number of iterations is 50, and C f is 2, the parameter search range is from -10 to 10. Fig. 9 It can be seen that the estimated voltage waveform can effectively follow the actual voltage waveform, and the mean square error between them is only 2.1073, that is, the method for determining the current and voltage relationship of the modular hybrid DC circuit breaker MOV device proposed in the present application can effectively estimate the transient voltage of the modular hybrid DC circuit breaker.

[0150] Fig.10 is a structural schematic diagram of a device for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in an embodiment of the present application, such as Fig.10 As shown, the device for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device provided in an embodiment of the present application includes:

[0151] An acquisition module 21 is used to acquire the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in a maximum short-circuit current breaking test;

[0152] A preprocessing module 22, used to preprocess the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device;

[0153] A parameter acquisition module 23 is used to fit the parameters of the mathematical model of the relationship between voltage and current by using the Sooty Tern algorithm combined with the LM algorithm according to the fitting voltage and the fitting current, so as to obtain the best fitting parameters of the mathematical model;

[0154] The generating module 24 is used to generate a mathematical model of the current and voltage relationship of the MOV device having the best fitting parameters according to the best fitting parameters.

[0155] The device for determining the current and voltage relationship of the modular hybrid DC circuit breaker MOV device provided in the embodiment of the present application obtains the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in the maximum short-circuit current breaking test; pre-processes the voltage and current using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the MOV device; according to the fitting voltage and fitting current, uses the Sooty Tern algorithm combined with the LM algorithm to fit the parameters of the preset mathematical model of the voltage and current relationship to obtain the best fitting parameters of the mathematical model; according to the best fitting parameters, generates the mathematical model of the current and voltage relationship of the MOV device with the best fitting parameters. In this way, the parameters of the mathematical model of the current and voltage relationship obtained are more accurate, and the voltage estimation of the modular hybrid DC circuit breaker MOV device using this model is more accurate and more applicable.

[0156] In some embodiments, the preprocessing module is specifically used to:

[0157] According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein,

[0158] If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively;

[0159] If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

[0160] In some embodiments, the sooty tern algorithm includes a sooty tern optimization algorithm, a particle swarm algorithm, a cuckoo search algorithm, a grey wolf algorithm, or a whale algorithm.

[0161] In some embodiments, the Sooty Tern algorithm is a Sooty Tern optimization algorithm; and the parameter acquisition module is specifically used for:

[0162] Generate an initial population using the Sooty Tern optimization algorithm;

[0163] Using each individual in the initialization population as an initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the preset mathematical model of the relationship between voltage and current according to the fitting voltage and the fitting current, to obtain a first fitting parameter corresponding to each individual in the initialization population;

[0164] For each of the first fitting parameters, substituting the first fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the first fitting parameter;

[0165] Calculate a first mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the first fitting parameters;

[0166] Using the Sooty Tern optimization algorithm to update the initialized population to obtain a new population;

[0167] Using each individual in the new population as the initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the mathematical model according to the fitting voltage and the fitting current to obtain a second fitting parameter corresponding to each individual in the new population;

[0168] For each of the second fitting parameters, substituting the second fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter;

[0169] Calculate a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters;

[0170] If the smallest second average square error sum is smaller than the smallest first average square error sum, the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum are used as the best fitting parameters of the mathematical model.

[0171] In some embodiments, the parameter acquisition module is further used to:

[0172] If the smallest second average sum of square errors is greater than or equal to the smallest first average sum of square errors, the first fitting parameter corresponding to the smallest first average sum of square errors is used as the best fitting parameter of the mathematical model.

[0173] In some embodiments, the apparatus further comprises:

[0174] The estimation module is used to estimate the voltage of the MOV device of the modular hybrid DC circuit breaker using the mathematical model with the best fitting parameters. The embodiment of the device provided in the embodiment of the present application can be specifically used to execute the processing flow of the above-mentioned method embodiments, and its functions are not repeated here, and the detailed description of the above-mentioned method embodiments can be referred to.

[0175] Fig.11 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig.11 As shown, the electronic device may include: a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304. The processor 301 may call the logic instructions in the memory 303 to execute the method described in any of the above embodiments.

[0176] In addition, the logic instructions in the above-mentioned memory 303 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0177] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0178] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program enables the computer to execute the methods provided by the above-mentioned method embodiments.

[0179] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0180] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0183] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0184] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device, characterized in that: include: Obtain the voltage and current across the MOV device of the modular hybrid DC circuit breaker in the maximum short-circuit current breaking test; Preprocessing the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device; According to the fitting voltage and the fitting current, the parameters of the preset mathematical model of the relationship between voltage and current are fitted using the Sooty Tern algorithm combined with the LM algorithm to obtain the best fitting parameters of the mathematical model; According to the best fitting parameters, a mathematical model of the current and voltage relationship of the MOV device having the best fitting parameters is generated; According to the fitting voltage and the fitting current, the parameters of the preset mathematical model of the relationship between voltage and current are fitted by using the Sooty Tern algorithm combined with the LM algorithm to obtain the best fitting parameters of the mathematical model, which include: Generate an initial population using the Sooty Tern optimization algorithm; Using each individual in the initialization population as an initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the preset mathematical model of the relationship between voltage and current according to the fitting voltage and the fitting current, to obtain a first fitting parameter corresponding to each individual in the initialization population; For each of the first fitting parameters, substituting the first fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the first fitting parameter; Calculate a first mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the first fitting parameters; Using the Sooty Tern optimization algorithm to update the initialized population to obtain a new population; Using each individual in the new population as the initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the mathematical model according to the fitting voltage and the fitting current to obtain a second fitting parameter corresponding to each individual in the new population; For each of the second fitting parameters, substituting the second fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter; Calculate a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters; If the smallest second average square error sum is smaller than the smallest first average square error sum, the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum are used as the best fitting parameters of the mathematical model.

2. The method according to claim 1, characterized in that The method of preprocessing the voltage and current by using the MOV voltage starting principle to obtain the fitting voltage and fitting current at both ends of the MOV device includes: According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein, If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively; If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

3. The method according to claim 1, characterized in that The method further comprises: If the smallest second average sum of square errors is greater than or equal to the smallest first average sum of square errors, the first fitting parameter corresponding to the smallest first average sum of square errors is used as the best fitting parameter of the mathematical model.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The voltage of the MOV device is estimated using the mathematical model with the best fit parameters.

5. A device for determining the relationship between current and voltage of a modular hybrid DC circuit breaker MOV device, characterized in that: include: An acquisition module, used for acquiring the voltage and current at both ends of the modular hybrid DC circuit breaker MOV device in a maximum short-circuit current breaking test; A preprocessing module, used to preprocess the voltage and current using the MOV voltage starting principle to obtain a fitting voltage and a fitting current at both ends of the MOV device; A parameter acquisition module is used to fit the parameters of the mathematical model of the relationship between voltage and current by using the Sooty Tern algorithm combined with the LM algorithm according to the fitting voltage and the fitting current, so as to obtain the best fitting parameters of the mathematical model; A generating module, for generating a mathematical model of the current and voltage relationship of the MOV device having the best fitting parameters according to the best fitting parameters; The parameter acquisition module is specifically used for: Generate an initial population using the Sooty Tern optimization algorithm; Using each individual in the initialization population as an initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the preset mathematical model of the relationship between voltage and current according to the fitting voltage and the fitting current, to obtain a first fitting parameter corresponding to each individual in the initialization population; For each of the first fitting parameters, substituting the first fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the first fitting parameter; Calculate a first mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the first fitting parameters; Using the Sooty Tern optimization algorithm to update the initialized population to obtain a new population; Using each individual in the new population as the initial value of the LM algorithm, and using the LM algorithm to fit the parameters of the mathematical model according to the fitting voltage and the fitting current to obtain a second fitting parameter corresponding to each individual in the new population; For each of the second fitting parameters, substituting the second fitting parameter and the fitting current into the mathematical model to obtain an estimated voltage corresponding to each of the fitting currents under the second fitting parameter; Calculate a second mean square error sum of a fitting voltage and an estimated voltage corresponding to the fitting current under each of the second fitting parameters; If the smallest second average square error sum is smaller than the smallest first average square error sum, the smallest second average square error sum and the second fitting parameter corresponding to the second average square error sum are used as the best fitting parameters of the mathematical model.

6. The device according to claim 5, characterized in that The preprocessing module is specifically used for: According to the sampling order of the voltage and current, it is judged in turn whether the voltage at each sampling point reaches the starting voltage of the MOV, until the sampling sequence number of the sampling point is greater than or equal to the preset threshold for the first time, then the judgment is stopped; wherein, If the voltage at a sampling point does not reach the start voltage of the MOV, the fitting voltage and fitting current corresponding to the sampling point are set to 0 respectively; If the voltage at a sampling point reaches the starting voltage of the MOV, the voltage and current at the sampling point are respectively used as the fitting voltage and fitting current corresponding to the sampling point.

7. The device according to claim 5, characterized in that The parameter acquisition module is also used for: If the smallest second average sum of square errors is greater than or equal to the smallest first average sum of square errors, the first fitting parameter corresponding to the smallest first average sum of square errors is used as the best fitting parameter of the mathematical model.

8. The device according to any one of claims 5 to 7, characterized in that The device also includes: An estimation module is used to estimate the voltage of the MOV device of the modular hybrid DC circuit breaker by using the mathematical model with the best fitting parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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