Fast estimation method and device of MMC loss in electromagnetic transient model and medium

Abstract

The application relates to a rapid estimation method, device and medium for MMC loss in an electromagnetic transient model, wherein the method comprises the following steps: S1, constructing a Thevenin equivalent model of the MMC; S2, based on the Thevenin equivalent model of the MMC, establishing a on-state loss model of a sub-module by discretizing the sub-module capacitor based on a numerical integration method; S3, correcting the turn-on loss and turn-off loss of IGBT and the reverse recovery loss of a diode; S4, in a simulation step, calculating the on-state loss based on the on-state loss model, calculating the total switching frequency according to the port data of the Thevenin equivalent model, and combining the turn-on loss and turn-off loss of IGBT, the reverse recovery loss of the diode and the switching frequency to obtain the switching loss; and S5, taking the sum of the on-state loss and the switching loss as the total loss. Compared with the prior art, the application can be updated in real time in a simulation step, the calculation complexity is controllable, the engineering precision is ensured, and the cost is significantly reduced.

Description

Technical Field

[0001] This invention relates to the field of power system simulation technology, and in particular to a method, apparatus and medium for rapid estimation of MMC losses in electromagnetic transient models. Background Technology

[0002] Modular multilevel converters (MMCs) are a mainstream power electronic converter structure widely used in flexible direct current (HVDC) transmission systems. They are widely applied in HVDC engineering projects both domestically and internationally due to their advantages such as modular structure, rapid decoupling and independent control of active and reactive power, low switching frequency, and ease of constructing two- or multi-terminal flexible HVDC transmission systems. As a core component of flexible HVDC transmission systems, the operating status of MMC converter valves is crucial. Rapid and accurate assessment of converter valve losses is a key aspect of heat dissipation system design and power electronic device selection, and is also an important indicator for evaluating converter valve performance, directly impacting the project's economy and reliability.

[0003] Existing methods for electromagnetic transient simulation of MMC mainly include:

[0004] (1) The electromagnetic transient simulation model based on detailed switching devices can reflect the microscopic processes such as device turn-on / turn-off and diode reverse recovery. It has high accuracy, but the computational load increases rapidly with the number of sub-modules, making it difficult to meet the needs of large systems, long-term or real-time simulation.

[0005] (2) The equivalent model based on the average value reduces the dimension of the solution matrix by averaging the switching action or equivalently equipping the bridge arm, which greatly speeds up the simulation. However, the characterization of loss depends on additional empirical parameters or preset switching frequency assumptions, making it difficult to balance efficiency and accuracy under multiple operating conditions and multiple modulation methods.

[0006] (3) Based on the Thevenin equivalent MMC model, the coupling between MMC and external system is simplified to equivalent power supply and impedance, which is convenient for embedding in large systems. However, since the IGBT and anti-parallel diode are equivalent to a variable resistor, loss assessment is often missing or requires offline compensation.

[0007] Existing MMC loss assessment techniques often rely on device datasheet curves to establish on-state voltage drop and single-switching energy models, and combine these with modulation schemes to calculate the equivalent switching frequency to obtain on-state and switching losses. This often faces a trade-off between computational efficiency and system scale. While detailed switch-level models can accurately simulate device on / off states and accumulate MMC losses point by point, resulting in high loss calculation accuracy, their computational overhead is significant when the number of submodules is large. Furthermore, when using average value models or Thevenin equivalent models, the average switching frequency often depends on fixed assumptions or offline experience, which is inconsistent with the actual sequence bit changes of modulation strategies such as carrier phase-shifted PWM (CPS-PWM) and nearest-level approximation (NLM) under different operating conditions, leading to biases in loss assessment. Summary of the Invention

[0008] The purpose of this invention is to provide a fast estimation method, device, and medium for MMC loss in electromagnetic transient models, which can synchronously adapt to the dynamics of submodule terminal voltage and modulation sequencing behavior, can be updated in real time within the simulation step, and has controllable computational complexity, while ensuring engineering accuracy and significantly reducing computational overhead.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] A fast estimation method for MMC losses in an electromagnetic transient model includes:

[0011] Step S1: Construct the Thevenin equivalent model of MMC;

[0012] Step S2: Based on the MMC Thevenin equivalent model, a through-state loss model of the sub-module is established by discretizing the sub-module capacitor using the numerical integration method.

[0013] Step S3: Obtain the IGBT's turn-on loss, turn-off loss, and diode's reverse recovery loss by correcting the curves according to the device datasheet.

[0014] Step S4: Within one simulation step, the on-state loss is calculated based on the on-state loss model, and the total number of switching operations is calculated based on the port data of the Thevenin equivalent model. The switching loss is obtained by combining the IGBT's turn-on loss, turn-off loss, and the diode's reverse recovery loss with the number of switching operations.

[0015] Step S5: The sum of the conduction loss and the switching loss is taken as the total loss.

[0016] Step S1 includes:

[0017] The overall structure consisting of an IGBT and a corresponding anti-parallel freewheeling diode is equivalent to a switch group. The switching state of the submodule determines whether the switch group is on or off. When on, the equivalent resistance of the switch group is 0.01 ohms, and when off, the equivalent resistance of the switch group is 100 kiloohms.

[0018] The on-state loss model does not include losses caused by the equivalent resistance of capacitance.

[0019] The mathematical expression for the flux loss model is:

[0020]

[0021] in: Let be the flux loss at time t. For the number of bridge arm submodules, The on-state resistance of the switch group in the Thevenin equivalent is given. Let be the current flowing through the bridge arm at time t. This is the on-state bias voltage.

[0022] The turn-on loss, turn-off loss, and reverse recovery loss of the IGBT are as follows:

[0023]

[0024]

[0025]

[0026] in: For IGBT turn-on losses, The fitting coefficients for the first and second quadratic terms are... The fitting coefficients for the first-order term are... The current flowing through the IGBT, The fitting coefficients for the first 0th term are... U This is the actual voltage across the device. The voltage specified in the datasheet. For IGBT turn-off losses, The fitting coefficients are the second and quadratic terms. The fitting coefficients for the second-order term are... The fitting coefficients for the 20th term are... This refers to the reverse recovery loss of the diode. The fitting coefficients for the third and second quadratic terms are... The fitting coefficients for the third-order term are... c 3 is the fitting coefficient for the third term of degree 0. This represents the current flowing through the diode.

[0027] The switching loss is:

[0028]

[0029] in: The total number of switches is the simulation step corresponding to time t.

[0030] The process of calculating the total number of switches based on the port data from the Thevenin equivalent model includes:

[0031] Step S4-1: Obtain the port data of the Thevenin equivalent model at the current time t and the previous time, respectively;

[0032] Step S4-2: Based on port data parsing, obtain the switching status of each submodule at time t and the previous time.

[0033] Step S4-3: Calculate the action count value of each submodule based on the switching state of each submodule at time t:

[0034]

[0035] in: This is the action count value for submodule k. Let represent the switching state of submodule k at time t. Let Δt be the switching state of submodule k at the previous time step, and Δt be the simulation step size.

[0036] Step S4-4: Sum the count values ​​of all sub-modules to obtain the total number of switches.

[0037] Step S4-2 includes:

[0038] Step S4-2-1: Based on port data parsing, obtain the upper IGBT trigger signals of each submodule at time t and the previous time.

[0039] Step S4-2-2: Based on the IGBT trigger signal of each submodule, obtain the switching state of each submodule at time t and the previous time.

[0040] A device for rapid estimation of MMC loss in an electromagnetic transient model includes a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the method described above.

[0041] A storage medium having a program stored thereon, which, when executed, implements the method described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. Based on the Thevenin equivalent model of MMC, a conduction loss model and a switching loss model based on switching frequency are proposed, which greatly improves the efficiency of loss calculation. At the same time, an average switching frequency acquisition method is proposed, which determines whether a switching action has occurred by comparing the switching states of sub-modules before and after sorting. It is not necessary to obtain PWM pulse or IGBT trigger signal waveforms, which has high simulation efficiency and is suitable for large-scale high-level MMC simulation scenarios. It can evaluate MMC loss relatively quickly and accurately.

[0044] 2. The designed through-state loss model can be used to calculate the through-state loss quickly based on the MMC Thevenin equivalent model, and the accuracy is high. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the modular structure of the MMC topology in the modeling method of this invention;

[0046] Figure 2 This is a structural diagram of the Thevenin equivalent process of the submodule in the modeling method of this invention;

[0047] Figure 3 This is a schematic diagram of the main steps of the method of the present invention;

[0048] Figure 4 This is a schematic diagram of a typical dual-ended MMC-HVDC simulation system in an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the fitting results for the on-state loss parameters in an embodiment of the present invention. Figure 1 ;

[0050] Figure 6 This is a schematic diagram of the fitting results for the on-state loss parameters in an embodiment of the present invention. Figure 2 ;

[0051] Figure 7 This is a schematic diagram comparing the calculation results of the through-state loss in an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of the IGBT turn-on loss fitting results in an embodiment of the present invention;

[0053] Figure 9 This is a schematic diagram of the IGBT turn-off loss fitting results in an embodiment of the present invention;

[0054] Figure 10 This is a schematic diagram of the reverse recovery loss fitting results of the diode in an embodiment of the present invention;

[0055] Figure 11 This is a schematic diagram comparing the switching loss calculation results in an embodiment of the present invention. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0057] Example 1

[0058] A fast estimation method for MMC losses in an electromagnetic transient model, such as... Figure 3 As shown, it includes:

[0059] Step S1: Construct the Thevenin equivalent model of MMC, including:

[0060] The overall structure consisting of an IGBT and a corresponding anti-parallel freewheeling diode is regarded as a switch group. The switching state of the sub-module determines whether the switch group is conducting. When conducting, the equivalent resistance of the switch group is regarded as a forward resistance close to 0, and when disconnected, the equivalent resistance of the switch group is regarded as a very large off-state resistance.

[0061] In this step, Thevenin single-port equivalent is performed on each sub-module of MMC. Based on the switching state of the sub-module, the IGBT and anti-parallel diode are equivalent to the on-resistance or off-resistance. The capacitor is discretized by numerical integration method and equivalent to the series connection of voltage source and equivalent resistance. Thevenin equivalent is performed on the sub-module. Based on this, Thevenin equivalent is further performed on the bridge arm.

[0062] In this embodiment, the modular structure of the MMC topology is as follows: Figure 1 As shown. Thevenin single-port equivalent is performed on each submodule of the MMC. The overall structure consisting of an IGBT and its corresponding anti-parallel freewheeling diode is considered as a switching group. Whether the switching group is on is determined based on the switching state of the submodule. When on, the equivalent resistance of the switching group is considered to be a near-zero on-state resistance. R on When disconnected, the equivalent resistance of the switch group is considered as a very large off-state resistance. R off Numerical integration methods, such as the trapezoidal integration method and the back Euler method, can be used to discretize the capacitor, which can then be represented as a series connection of a voltage source and an equivalent resistor. Figure 1 middle, u a The AC phase voltage of phase A on the MMC valve side. u b This refers to the AC phase voltage of phase B on the MMC valve side. u c This refers to the AC phase voltage of phase C on the MMC valve side. u a1 This refers to the voltage of phase A of the upper arm of the MMC bridge. u a2This refers to the voltage of phase A of the lower arm of the MMC bridge. i a For the A-phase AC current of the MMC valve, i b For the B-phase AC current of the MMC valve, i c For the C-phase AC current of the MMC valve, i a1 This refers to the A-phase current of the upper arm of the MMC bridge. SM2 is the second submodule. n For the nth submodule, R To simulate the equivalent resistance of the MMC bridge arm and converter transformer losses, L For the inductance of the bridge arm reactor, i a2 This refers to the A-phase current of the lower bridge arm of the MMC, where SM1 is the first submodule. i b1 This refers to the B-phase current of the upper arm of the MMC. u b1 This refers to the voltage of phase B of the upper arm of the MMC bridge. u b2 This refers to the voltage of phase B of the lower arm of the MMC bridge. i b2 This refers to the B-phase current of the lower arm of the MMC. i c1 This refers to the C-phase current of the upper arm of the MMC. u c1 The voltage of phase C of the upper arm of the MMC bridge. u c2 This refers to the C-phase voltage of the lower arm of the MMC bridge. i c2 This refers to the C-phase current of the lower arm of the MMC bridge. I d This refers to the DC component of the MMC DC-side current. U d This refers to the DC component of the MMC DC-side output voltage. O This is the neutral point between the positive and negative terminals of the MMC DC voltage.

[0063] The capacitor voltage can be expressed using the trapezoidal integral method as:

[0064]

[0065] in: for t The capacitor voltage at time t. This is the capacitance value of the capacitor. The current flowing through the capacitor, The capacitor voltage from the previous simulation step. This represents the current flowing through the capacitor in the previous simulation step. This is the simulated step size.

[0066] The Thevenin equivalent capacitor voltage can be expressed as:

[0067]

[0068] in: This represents the equivalent voltage source value of the capacitor after the Thevenin equivalent. R C This is the equivalent resistance value of the capacitor after the Thevenin equivalent.

[0069] By comparing the coefficients in the formal analysis, we can see that:

[0070]

[0071] Furthermore, it can be seen that:

[0072]

[0073] This yields the discrete circuit of the sub-module, and the parameters can be obtained through Thevenin equivalent circuitry. R SMEQ , V SMEQ The equivalent process is as follows Figure 2 As shown.

[0074]

[0075] in: R 1 represents the equivalent resistance value of the upper switch group. R 2 represents the equivalent resistance value of the lower switch group.

[0076] Figure 2 In the diagram, T1 is the upper IGBT of the MMC half-bridge sub-module, T2 is the lower IGBT of the MMC half-bridge sub-module, D1 is the upper anti-parallel diode of the MMC half-bridge sub-module, and D2 is the lower anti-parallel diode of the MMC half-bridge sub-module. u SM This refers to the port voltage of the MMC half-bridge submodule. U C This refers to the capacitor voltage of the MMC half-bridge submodule. C 0 represents the capacitor of the MMC half-bridge submodule. i SM The current flowing through the MMC half-bridge submodule, V SM This represents the port voltage of the MMC submodule after Thevenin's equivalent.

[0077] Step S2: Based on the MMC Thevenin equivalent model, a through-state loss model of the sub-module is established by discretizing the sub-module capacitor using the numerical integration method.

[0078] In this embodiment, the submodule conduction loss model established based on the Thevenin equivalent model of MMC discretizes the submodule capacitor using numerical integration. The resulting equivalent resistance of the capacitor is only used to ensure the numerical stability of the capacitor state variables in the discrete domain and does not represent the ohmic characteristics of the device. Within the same simulation step, the instantaneous power flowing through the equivalent resistance of the capacitor will be entirely absorbed or released by the series controlled voltage source with opposite signs to maintain energy conservation. Therefore, this part is not included in the conduction loss. Thus, based on the Thevenin equivalent model, the average conduction loss power of the bridge arm during normal operation can be expressed as:

[0079]

[0080] in: Let be the throughput loss at time t, specifically the average throughput loss of the bridge arm at time t. For the number of bridge arm submodules, The on-state resistance of the switch group in the Thevenin equivalent is given. Let be the current flowing through the bridge arm at time t. This is the on-state bias voltage.

[0081] The on-state resistance and on-state bias voltage of the switch group in the Thevenin equivalent can be obtained by fitting based on the datasheet provided by the manufacturer.

[0082] The calculation method is simple and does not require consideration of the switching state of the sub-modules within the bridge arm. However, since the IGBT and the anti-parallel freewheeling diode are treated as a single switch group, the conduction losses of the two are not considered separately, which may reduce the accuracy of the loss calculation. This is verified by experiments in this embodiment.

[0083] First, based on the conduction states of the submodules, the on-state loss models of the IGBT and diode can be expressed as the product of the on-state voltage drop and the current flowing through the device:

[0084]

[0085]

[0086] in: The on-state power loss of the IGBT. This represents the on-state power loss of the diode. , These are the on-state resistances of the IGBT and the diode, respectively. , These are the bias voltages of the IGBT and diode, respectively, and their data can be obtained by fitting the datasheets provided by the manufacturers. , These represent the currents flowing through the IGBT and the diode, respectively.

[0087] When considering the conduction losses of different switching devices, the average conduction loss power of the bridge arm is related to the number of sub-modules connected in the bridge arm at each moment and the direction of the bridge arm current. If T1, T2, D1, and D2 represent the upper and lower IGBTs and the anti-parallel freewheeling diodes in the sub-module, respectively, the current flow paths of the sub-module under different conditions are shown in Table 1.

[0088] Table 1

[0089] ,

[0090] As can be seen from the table, the current flows through only one switching device at any given time. Combining this with the current flow paths of the submodules under different conditions, the average on-state power loss of the bridge arm can be expressed as:

[0091]

[0092] in:

[0093]

[0094]

[0095] in: For the bridge arm current, This represents the total number of bridge arm sub-modules. Let t be the number of submodules deployed in the bridge arm at time t. This represents the number of sub-modules through which current flows from the IGBT. This represents the number of sub-modules through which current flows.

[0096] Step S3: Based on the datasheet curves, obtain the IGBT's turn-on loss, turn-off loss, and diode's reverse recovery loss. Since datasheets often only provide curves for specific terminal voltages, the voltage between the collector and emitter during simulation may not be as specified in the datasheet; therefore, voltage correction is required. The voltage-corrected IGBT turn-on loss, turn-off loss, and diode reverse recovery loss are as follows:

[0097]

[0098]

[0099]

[0100] in: For IGBT turn-on losses, The fitting coefficients for the first and second quadratic terms are... The fitting coefficients for the first-order term are... The current flowing through the IGBT, The fitting coefficients for the first 0th term are...U This is the actual voltage across the device. The voltage specified in the datasheet. For IGBT turn-off losses, The fitting coefficients are the second and quadratic terms. The fitting coefficients for the second-order term are... The fitting coefficients for the 20th term are... This refers to the reverse recovery loss of the diode. The fitting coefficients for the third and second quadratic terms are... The fitting coefficients for the third-order term are... c 3 is the fitting coefficient for the third term of degree 0. This represents the current flowing through the diode.

[0101] Step S4: Within one simulation step, the conduction loss is calculated based on the conduction loss model, and the total number of switching operations is calculated based on the port data of the Thevenin equivalent model. The switching loss is obtained by combining the turn-on loss and turn-off loss of the IGBT and the reverse recovery loss and switching operations of the diode.

[0102] Current methods mostly rely on obtaining submodule trigger signals directly from detailed models, or constructing switching frequency surfaces through two-dimensional interpolation based on simulation results from detailed models. When there are many submodules, detailed modeling becomes too time-consuming, and constructing switching frequency surfaces through two-dimensional interpolation is quite complex. The switching state of submodules is affected by the nearest voltage level approximation and capacitor voltage equalization control algorithms.

[0103] The process of calculating the total number of switches based on the port data from the Thevenin equivalent model includes:

[0104] Step S4-1: Obtain the port data of the Thevenin equivalent model at the current time t and the previous time, respectively;

[0105] Step S4-2: Based on port data parsing, obtain the switching status of each submodule at time t and the previous time, specifically including:

[0106] Step S4-2-1: Based on port data parsing, obtain the upper IGBT trigger signals of each submodule at time t and the previous time.

[0107] Step S4-2-2: Based on the IGBT trigger signal of each submodule, obtain the switching state of each submodule at time t and the previous time.

[0108] Step S4-3: Calculate the action count value of each submodule based on the switching state of each submodule at time t:

[0109]

[0110] in: This is the action count value for submodule k. Let represent the switching state of submodule k at time t. Let Δt be the switching state of submodule k at the previous time step, and Δt be the simulation step size.

[0111] Step S4-4: Sum the count values ​​of all sub-modules to obtain the total number of switches.

[0112] The final switching losses are as follows:

[0113]

[0114] in: The total number of switches is the simulation step corresponding to time t.

[0115] Specifically,

[0116] Step S5: The sum of the conduction loss and the switching loss is taken as the total loss.

[0117] This method determines whether a switching action has occurred by only checking the switching state of the submodules before and after sorting. It does not require obtaining PWM pulse or IGBT trigger signal waveforms, and only uses the Thevenin port signals for counting. It does not change the order of the node admittance matrix in the model simulation. Compared to detailed modeling, it can quickly calculate the average switching frequency even with a high number of submodules, and the computational load is very small, requiring only N XOR operations and (N-1) addition operations within each step. The flowchart for calculating the average switching frequency of the bridge arm submodules is shown below. Figure 3 As shown. This method is universal, as the counter only compares the states of the sub-modules before and after sorting. It is compatible with various modulation algorithms such as carrier phase-shift modulation, carrier stacking modulation, and nearest-level approximation, as well as models such as detailed electromagnetic transient models and MMC models based on Thevenin equivalents.

[0118] Taking a typical 27-level two-ended MMC-HVDC simulation system as an example, a detailed model and a Thevenin equivalent model are built respectively, and the MMC conduction loss and switching loss are calculated. The system structure diagram is shown below. Figure 4 As shown in Table 2.

[0119] Table 2

[0120] ,

[0121] The program used was built in the electromagnetic transient program, and the simulation step size was set to 50 microseconds.

[0122] Figure 4 middle, u 1 represents the phase voltage of AC system 1. L T The leakage inductance of the converter transformer, P1 represents the active power flowing into MMC1 from AC system 1. Q 1 represents the reactive power flowing into MMC1 from AC system 1. MMC1 is the first modular multilevel converter, and MMC2 is the second modular multilevel converter. L A reactor connected in series on the AC side of the MMC. v 1 represents the AC side port voltage of MMC1. v 2 represents the AC side port voltage of MMC2. u 2 represents the phase voltage of AC system 2. P 2 represents the active power flowing into MMC2 from AC system 2. Q 2 represents the reactive power flowing into MMC2 from AC system 2.

[0123] Under different operating conditions, the IGBT trigger signals were recorded using a detailed model, and the average switching frequency of the upper arm of phase A of MMC1 was calculated. The switching frequency obtained by the method proposed in this application based on a custom valve control model is... By comparing the results and then using the Thevenin equivalent model, the switching frequencies calculated using the method proposed in this application are obtained under the corresponding identical operating conditions. Compare, calculate and The error between them is shown in Table 3.

[0124] Table 3

[0125] ,

[0126] As shown in Table 3, the detailed model obtains the average switching frequency by recording the IGBT trigger signals. The average switching frequency calculated by the method proposed in this application The complete consistency demonstrates the accuracy of the switching frequency calculation method proposed in this application. The average error between the average switching frequency calculated using the Thevenin equivalent model and the average switching frequency calculated using the detailed model is 1.62%, and the error is mainly caused by the simplification of the Thevenin equivalent model.

[0127] Verification of the accuracy of conduction loss calculation. When considering the switching losses of different devices separately, data is obtained from the manufacturer's datasheet and fitted, such as... Figure 5 and Figure 6 As shown, the results are as follows:

[0128]

[0129] The four operating conditions in Table 3 are still used. The method for calculating the on-state loss of the submodule based on the MMC Thevenin equivalent model is denoted as Method 1, and the method for establishing and calculating the on-state loss models of IGBTs and diodes according to the conduction state of the submodules is denoted as Method 2. The on-state loss of the simulation system based on the Thevenin equivalent model is calculated using Method 1 and Method 2 respectively, and compared with the on-state loss calculated by the simulation system based on the detailed electromagnetic transient model. The results are as follows: Figure 7 As shown in the figure, the error between the results of Method 1 and Method 2 is calculated and labeled, using the on-state loss calculated based on the detailed electromagnetic transient model as the benchmark.

[0130] Depend on Figure 7 The calculation results show that, for conduction loss, the average error of Method 1 is 3.55% and the average error of Method 2 is 2.88% under the four operating conditions. This result verifies the effectiveness of the conduction loss calculation method proposed in this application, and also shows that treating the IGBT and the anti-parallel freewheeling diode as a whole for equivalent calculation will reduce the accuracy of loss calculation.

[0131] Verify the accuracy of switching loss calculations. Obtain data from the manufacturer's datasheet and perform a secondary fitting, such as... Figures 8 to 10 As shown, the results are as follows:

[0132]

[0133] The four operating conditions in Table 3 are still used. Switching losses are calculated using the Thevenin equivalent model and the detailed model, respectively, and the results are as follows. Figure 11 As shown. The switching loss calculated based on the detailed electromagnetic transient model is used as the benchmark for the calculation error. Figure 11 The calculation results show that the average error of the switching loss based on the Thevenin equivalent model under the four operating conditions is 1.17%, which is within the allowable error range, thus verifying the effectiveness of the switching loss calculation method proposed in this application.

[0134] The modulation method used is nearest-level approximation, employing an average-value-based capacitor voltage equalization control strategy and a circulating current suppression strategy. The IGBT module used is an ABB 5SNE1000E330300.

[0135] Example 2

[0136] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0137] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0138] The processing unit executes the various methods and processes described above, such as methods S1 to S5. For example, in some embodiments, methods S1 to S5 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S5 by any other suitable means (e.g., by means of firmware).

[0139] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0140] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0142] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 described in the various embodiments of this 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.

Claims

1. A fast estimation method for MMC losses in an electromagnetic transient model, characterized in that, include: Step S1: Construct the Thevenin equivalent model of MMC; Step S2: Based on the MMC Thevenin equivalent model, a through-state loss model of the sub-module is established by discretizing the sub-module capacitor using the numerical integration method. Step S3: Obtain the IGBT's turn-on loss, turn-off loss, and diode's reverse recovery loss by correcting the curves according to the device datasheet. Step S4: Within one simulation step, the on-state loss is calculated based on the on-state loss model, and the total number of switching operations is calculated based on the port data of the Thevenin equivalent model. The switching loss is obtained by combining the IGBT's turn-on loss, turn-off loss, and the diode's reverse recovery loss with the number of switching operations. Step S5: Take the sum of the conduction loss and the switching loss as the total loss; The on-state loss model does not include losses caused by the equivalent resistance of capacitance, and the mathematical expression of the on-state loss model is: in: Let be the flux loss at time t. For the number of bridge arm submodules, The on-state resistance of the switch group in the Thevenin equivalent is given. Let be the current flowing through the bridge arm at time t. This is the on-state bias voltage; The process of calculating the total number of switches based on the port data from the Thevenin equivalent model includes: Step S4-1: Obtain the port data of the Thevenin equivalent model at the current time t and the previous time, respectively; Step S4-2: Based on port data parsing, obtain the switching status of each submodule at time t and the previous time. Step S4-3: Calculate the action count value of each submodule based on the switching state of each submodule at time t: in: This is the action count value for submodule k. Let represent the switching state of submodule k at time t. Let Δt be the switching state of submodule k at the previous time step, and Δt be the simulation step size. Step S4-4: Sum the count values ​​of all sub-modules to obtain the total number of switches; Step S4-2 includes: Step S4-2-1: Based on port data parsing, obtain the upper IGBT trigger signals of each submodule at time t and the previous time. Step S4-2-2: Based on the IGBT trigger signal of each submodule, obtain the switching state of each submodule at time t and the previous time.

2. The method for rapid estimation of MMC loss in an electromagnetic transient model according to claim 1, characterized in that, Step S1 includes: The overall structure consisting of an IGBT and a corresponding anti-parallel freewheeling diode is equivalent to a switch group. The switching state of the submodule determines whether the switch group is on or off. When on, the equivalent resistance of the switch group is 0.01 ohms, and when off, the equivalent resistance of the switch group is 100 kiloohms.

3. The method for rapid estimation of MMC loss in an electromagnetic transient model according to claim 1, characterized in that, The turn-on loss, turn-off loss, and reverse recovery loss of the IGBT are as follows: in: For IGBT turn-on losses, The fitting coefficients for the first and second quadratic terms are... The fitting coefficients for the first-order term are... The current flowing through the IGBT, The fitting coefficients for the first 0th term are... U This is the actual voltage across the device. The voltage specified in the datasheet. For IGBT turn-off losses, The fitting coefficients are the second and quadratic terms. The fitting coefficients for the second-order term are... The fitting coefficients for the 20th term are... This refers to the reverse recovery loss of the diode. The fitting coefficients for the third and second quadratic terms are... The fitting coefficients for the third-order term are... c 3 is the fitting coefficient for the third term of degree 0. This represents the current flowing through the diode.

4. The method for rapid estimation of MMC loss in an electromagnetic transient model according to claim 3, characterized in that, The switching loss is: in: The total number of switches is the simulation step corresponding to time t.

5. A device for rapidly estimating MMC losses in an electromagnetic transient model, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-4.

6. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-4.

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