A method and device for optimizing voltage level of offshore wind power flexible HVDC transmission system

Abstract

This invention relates to a method and apparatus for optimizing the voltage level of a flexible DC transmission system for offshore wind power. Based on a genetic algorithm, the method optimizes the DC voltage level design with the goal of achieving optimal economic efficiency. The method uses DC voltage and the average voltage of submodule capacitors as decision variables. Within the range of DC voltage and average voltage of submodule capacitors that meet the requirements for main equipment selection, it calculates the main equipment parameters and system cost, selecting the DC voltage level and average voltage of submodule capacitors that minimize system cost. This avoids the voltage level being overly dependent on DC cable parameters. The technical solution provided by this invention monitors the population aggregation degree during the genetic iteration process. At the end of the evolutionary stage, by reinitializing the population with high aggregation, it avoids excessive assimilation of population genes, improving the stability of the output results and the global optimization capability.

Description

Technical Field

[0001] This invention relates to the field of flexible DC transmission technology, and in particular to a method and apparatus for optimizing the voltage level of a flexible DC transmission system for offshore wind power. Background Technology

[0002] my country's southeastern coastal region boasts abundant wind resources and is also an industrially developed area with a massive electricity load. Offshore wind power technology can be fully utilized to meet this growing demand. Currently, nearshore wind power development is nearing saturation, wind farm site selection is becoming increasingly difficult, and wind farms have a significant impact on shipping lanes. Therefore, developing offshore wind power is emerging as a new development direction. When the distance from shore to offshore wind farms exceeds a critical value, high-voltage direct current (HVDC) grid connection is more economical than high-voltage alternating current (HVAC). Flexible DC converter stations can operate in islanded mode and control the AC voltage of wind farms; therefore, offshore wind power generally adopts flexible DC grid connection. The selection of the DC voltage level is fundamental to the planning and design of flexible DC transmission projects and has a significant impact on the reliability and economy of the project. Summary of the Invention

[0003] Based on the above-mentioned situation of the prior art, the purpose of the present invention is to provide a method and device for optimizing the voltage level of a flexible DC transmission system for offshore wind power. The method is based on a genetic algorithm, which overcomes the defect that the existing DC voltage level selection schemes rely heavily on the DC cable grades that have been standardized or have engineering experience, and searches for the most economically optimal voltage level while ensuring reliability.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing the voltage level of an offshore wind power flexible DC transmission system is provided. The offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station, which are connected via a DC bus. The method includes:

[0005] S1. Select the DC bus and submodule capacitor voltages as decision variables, and set the average voltage range of the decision variables;

[0006] S2, the initial population that generates decision variables;

[0007] S3. During the population evolution process, calculate the aggregation interval of the population;

[0008] S4. Determine whether the population has entered the final stage of evolution based on the aggregation interval; if yes, set the final stage flag to 1; if no, proceed to step S6 to perform crossover and mutation operations.

[0009] S5. Set the average voltage range of decision variables according to the boundary of the population aggregation area, and regenerate the initial population;

[0010] S6. Determine whether the number of iterations is greater than the iteration threshold or whether the iteration has converged. If yes, the optimal solution is obtained. If no, perform crossover and mutation operations, and merge the population after crossover and mutation operations with the previous population to form a new population. Calculate the main equipment parameters and bridge arm current of the individuals in the new population.

[0011] S7. Determine whether the bridge arm current meets the equipment selection requirements; if yes, proceed to the next step; if no, return to step S1.

[0012] S8. Calculate the system cost, select individuals with low cost, and generate a new generation of population.

[0013] Furthermore, it also includes the following steps:

[0014] S9. Determine if flag == 1; if yes, return to step S6; otherwise, return to step S3.

[0015] Furthermore, in step S3, calculating the clustering interval of the population includes using the following formula:

[0016]

[0017] in, This is the concentration range of the DC bus voltage. This represents the clustering range of the average capacitor voltage of the submodule. This is the maximum DC bus voltage. This is the minimum DC bus voltage. This represents the average voltage of the capacitors in the largest submodule. This represents the average voltage of the capacitor in the smallest submodule.

[0018] Furthermore, in step S4, determining whether the population has entered the final stage of evolution based on the aggregation interval includes:

[0019] like and If so, the current population has entered the final stage of evolution.

[0020] Furthermore, the main equipment parameters include converter valves, connecting transformers, and bridge arm reactors; the converter valve parameters include the number of bridge arm sub-modules and the sub-module capacitor capacity.

[0021] Furthermore, the main equipment parameters are calculated according to the following formula:

[0022]

[0023] in, For the capacitor capacity of the submodule, For converter capacity, For voltage modulation, The fundamental angular frequency of the power grid. This refers to the voltage fluctuation rate of the submodule capacitor. This is the DC bus voltage. This represents the average voltage of the capacitors in the submodule. Power factor;

[0024]

[0025] in, For the capacity of the bridge arm reactor, The time from the occurrence of the fault to the activation of the bridge arm overcurrent protection. This represents the change in bridge arm current.

[0026] Furthermore, the bridge arm current is calculated using the following formula:

[0027]

[0028] in, This represents the peak value of the bridge arm current. To connect the rated phase voltage on the valve side of the transformer, This represents the DC bus current.

[0029] Furthermore, it also includes calculating system losses based on the main equipment parameters:

[0030]

[0031] in, For the loss of the converter valve, For on-state losses, For cutoff loss, For switching losses; For DC submarine cable loss, For DC submarine cable resistance, This refers to the length of the DC submarine cable.

[0032] Furthermore, the system cost includes converter station cost, submarine cable cost, system loss cost, and operation and maintenance cost.

[0033] According to another aspect of the present invention, a voltage level optimization device for an offshore wind power flexible DC transmission system is provided. The offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station connected by a DC bus. The device includes a decision variable setting module, an initial population generation module, a clustering interval calculation module, an evolutionary end-stage judgment module, an evolutionary end-stage processing module, and a new population generation module.

[0034] The decision variable setting module is used to select the DC bus and submodule capacitor voltages as decision variables, and set the average voltage range of the decision variables;

[0035] The initial population generation module is used to generate an initial population of decision variables;

[0036] The aggregation interval calculation module is used to calculate the aggregation interval of the population during the population evolution process.

[0037] The evolutionary end-stage determination module is used to determine whether the population has entered the evolutionary end-stage based on the aggregation interval; if so, it enters the evolutionary end-stage processing module for processing; if not, it directly enters the new population generation module.

[0038] The late-stage evolution processing module is used to set the average voltage range of decision variables according to the boundary of the population aggregation area and regenerate the initial population.

[0039] The new population generation module is used to perform crossover and mutation operations when the conditions for new population generation are met, and to merge the population after crossover and mutation operations with the previous generation population to form a new population. It calculates the main equipment parameters and bridge arm current of individuals in the new population. When the bridge arm current meets the equipment selection requirements, it calculates the system cost, selects individuals with low cost, and generates a new generation population.

[0040] In summary, this invention provides a method and apparatus for optimizing the voltage level of a flexible DC transmission system for offshore wind power. Based on a genetic algorithm, it optimizes the DC voltage level design with economic optimization as the objective. This method uses DC voltage and the average voltage of submodule capacitors as decision variables. Within the range of DC voltage and average voltage of submodule capacitors that meet the requirements for main equipment selection, it calculates the main equipment parameters and system cost, selecting the DC voltage level and average voltage of submodule capacitors with the lowest system cost, thus avoiding excessive dependence of the voltage level on DC cable parameters. The technical solution provided by this invention monitors the population aggregation degree during the genetic iteration process. At the end of the evolution, by re-initializing the population with high aggregation degree, it avoids excessive assimilation of population genes, improving the stability of the output results and the global optimization capability. Attached Figure Description

[0041] Figure 1 (a) in the diagram is a typical topology diagram of an offshore wind power flexible DC transmission system;

[0042] Figure 1 (b) is the topology diagram of the converter in the offshore wind power flexible DC transmission system;

[0043] Figure 2 This is a flowchart of the voltage level optimization method for offshore wind power flexible DC transmission system according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0045] In existing technologies, the design of flexible DC voltage levels typically involves the following steps:

[0046] Step 1: Determine the transmission capacity and distance of the DC transmission system.

[0047] Step 2: Determine the feasible DC voltage range based on the Ullman empirical formula or the West German empirical formula. The Ullman empirical formula is:

[0048]

[0049] in, This is the line voltage to ground of a bipolar DC line. For bipolar DC transmission lines to transmit power, the DC voltage should be selected at... Within the range.

[0050] The West German empirical formula is:

[0051]

[0052] in, This represents the line length.

[0053] Step 3: Analyze the manufacturing level of DC cables and converter equipment to determine multiple candidate schemes for DC voltage levels.

[0054] Step 4: Conduct an economic evaluation of the candidate solutions.

[0055] Step 5: Select the DC voltage level with the best economic efficiency.

[0056] The basic steps of the corresponding genetic algorithm in the existing technology are as follows:

[0057] Step 1: Determine the decision variables and their range.

[0058] Step 2: Generate the initial population of decision variables.

[0059] Step 3: Generate a new population through crossover and mutation operations.

[0060] Step 4: Select individuals with high fitness to enter the next generation of the population by calculating fitness.

[0061] Step 5: When the population converges or the number of iterations reaches the threshold, the genetic iteration ends and the optimal solution is output.

[0062] In the aforementioned flexible DC voltage level design method, the selection of candidate voltage levels in step 3 largely depends on standardized or engineering-experienced DC cable voltage levels. For high-voltage flexible DC transmission applications that are not yet standardized or lack engineering experience, this method will struggle to select multiple candidate voltage levels. The economic evaluation method in step 4 typically relies on calculations based on typical parameters and formulas. For example, the converter station loss estimate is the product of the loss rate and the converter station capacity, making it difficult to guarantee the accuracy of the economic evaluation. Furthermore, in the aforementioned genetic algorithm, the degree of gene assimilation among individuals in the population gradually increases during the evolutionary process, leading to increasingly consistent fitness or a tendency towards local optima. This results in a certain degree of deviation in each output result or the algorithm getting trapped in a local optimum.

[0063] To address the shortcomings of existing DC voltage level selection schemes, which heavily rely on standardized or engineering-experienced DC cable grades, this invention provides a voltage level optimization method for offshore wind power flexible DC transmission systems. This method employs a genetic algorithm to search for the most economically optimal voltage level while ensuring reliability. The technical solution of this invention will be described in detail below with reference to the accompanying drawings. According to one embodiment of the invention, a voltage level optimization method for an offshore wind power flexible DC transmission system is provided, wherein the offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station, which are connected via a DC bus. Figure 1 Figure (a) shows a typical topology of an offshore wind power flexible DC transmission system. The offshore wind farm is connected to the offshore booster station via a 35kV AC submarine cable, and the offshore booster station is connected to the offshore converter station via a 220kV AC submarine cable. The offshore converter station is connected to the onshore converter station via a high-voltage DC submarine cable. Figure 1 Figure (b) shows the topology of the converter in the offshore wind power flexible DC transmission system. The offshore and onshore converter stations include modular multilevel converters, transformers, bridge arm reactors, and other equipment. The flowchart of the voltage level optimization method for the offshore wind power flexible DC transmission system according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following steps:

[0064] S1. Given the system capacity and the length of the DC submarine cable, the DC bus voltage and submodule capacitor voltage are selected as decision variables. Based on the system capacity and the current-carrying capacity and voltage withstand capability of the IGBTs used in the converter station, the average voltage range of the DC bus voltage and the submodule capacitor is initially set, i.e., the average voltage range of the decision variables is set. In this embodiment of the invention, the submodule capacitors of the offshore converter station and the onshore converter station are designed according to the same principle, that is, the submodule capacitors of the offshore converter station and the onshore converter station are equal.

[0065] S2. Generate the initial population of decision variables. The initial population of decision variables can be generated using binary encoding methods.

[0066] S3. During the population evolution process, calculate the clustering interval of the population. In this step, the binary code of the population is converted into decimal DC voltage and submodule capacitor average voltage, and the DC bus voltage interval and submodule capacitor average voltage interval are calculated respectively. The clustering interval of the population can be calculated using the following formula:

[0067]

[0068] in, This is the concentration range of the DC bus voltage. This represents the clustering range of the average capacitor voltage of the submodule. This is the maximum DC bus voltage. This is the minimum DC bus voltage. This represents the average voltage of the capacitors in the largest submodule. This represents the average voltage of the capacitor in the smallest submodule.

[0069] S4. Determine whether the population has entered the final stage of evolution based on the clustering interval; if yes, set the final stage flag (flag) to 1; otherwise, proceed to step S6 for crossover and mutation operations. The determination of whether the population has entered the final stage of evolution can be made by comparing the clustering interval with a threshold.

[0070] like and If so, it is determined that the current population has entered the final stage of evolution.

[0071] S5. Set the average voltage range of decision variables based on the population clustering zone boundary, and regenerate the initial population. When it is determined that the population has entered the late stage of evolution, the population clustering zone is no longer calculated. The optimal solution is output when the number of iterations reaches the threshold or the population converges.

[0072] S6. Determine if the number of iterations exceeds the iteration threshold or if the iteration has converged. If yes, the optimal solution is obtained; otherwise, perform crossover and mutation operations, and merge the resulting population with the previous population to form a new population. Calculate the main equipment parameters of individuals in the new population, as well as the arm currents of the offshore and onshore converter stations. The number of iterations in this step refers to the number of times the genetic algorithm iterates. Iteration convergence can be determined by whether the fitness of individuals in the population is the same. If the fitness of all individuals in the population is the same, then the iteration is considered to have converged. The main equipment parameters include converter valves, connecting transformers, and arm reactors; the converter valve parameters include the number of arm sub-modules and the sub-module capacitance. The main equipment parameters can be calculated using the following formula:

[0073]

[0074] in, For the number of bridge arm submodules, This is the DC bus voltage. The average voltage of the capacitors in the submodule;

[0075] Submodule capacitors, designed to limit capacitor voltage fluctuations, should meet the following requirements:

[0076]

[0077] in, For the capacitor capacity of the submodule, For converter capacity, For voltage modulation, The fundamental angular frequency of the power grid. This refers to the voltage fluctuation rate of the submodule capacitor. This is the DC bus voltage. This represents the average voltage of the capacitors in the submodule. The power factor; the bridge arm reactor is designed to limit the DC short-circuit fault current and should meet the following requirements:

[0078]

[0079] in, For the capacity of the bridge arm reactor, The time from the occurrence of the fault to the activation of the bridge arm overcurrent protection. This represents the change in bridge arm current.

[0080] The bridge arm current can be calculated using the following formula:

[0081]

[0082] in, This represents the peak value of the bridge arm current. To connect the rated phase voltage on the valve side of the transformer, This refers to the DC bus current. The capacity of the connecting transformer follows the converter valve capacity, and is generally taken as 1.1 times the rated capacity. The leakage reactance of the connecting transformer is generally taken as 0.16 pu. The rated phase voltage on the valve side of the connecting transformer is... Calculate using the following formula:

[0083]

[0084] in, To connect the per-unit reactance, This is the per-unit value of the rated reactive power.

[0085] After the main equipment parameters are determined, the system losses are calculated. The system losses mainly include converter valve losses and DC submarine cable losses.

[0086]

[0087] in, For the loss of the converter valve, For on-state losses, For cutoff loss, For switching losses; For DC submarine cable loss, For DC submarine cable resistance, The length of the DC submarine cable is given. The on-state loss of the converter valve refers to the losses generated by the IGBT and diode in the on-state, which are related to the bridge arm current and power device parameters. The off-state loss refers to the losses generated by the power devices under reverse voltage, which are related to the submodule capacitor voltage and power device parameters. Switching losses include IGBT turn-on losses, IGBT turn-off losses, and diode reverse recovery losses; switching losses are related to the switching frequency, bridge arm current, and power device parameters. The calculation method for converter valve losses is a conventional method in this field and will not be elaborated here.

[0088] S7. Determine if the bridge arm current meets the equipment selection requirements. If yes, proceed to the next step; otherwise, interrupt the transfer operation, return to step S1, and modify the range of the DC bus voltage. In this step, determining if the bridge arm current meets the equipment selection requirements can be done by comparing whether the bridge arm current is less than the IGBT's rated current: if the calculated bridge arm current is less than the IGBT's rated current, then the equipment selection requirements are met.

[0089] S8. Calculate the system cost and select individuals with low costs to generate a new generation of the population. The system cost includes the cost of the converter station, the cost of the submarine cable, the system loss costs, and the operation and maintenance costs. The calculation of the system cost is a standard method in this field and will not be elaborated here.

[0090] S9. Determine if flag == 1; if yes, it means the genetic algorithm has entered the final stage of evolution, then return to step S6; otherwise, return to step S3.

[0091] According to another embodiment of the present invention, a voltage level optimization device for an offshore wind power flexible DC transmission system is provided. The offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station, which are connected via a DC bus. The device includes a decision variable setting module, an initial population generation module, a clustering interval calculation module, an evolutionary end-stage judgment module, an evolutionary end-stage processing module, and a new population generation module.

[0092] The decision variable setting module is used to select the DC bus and submodule capacitor voltages as decision variables, and set the average voltage range of the decision variables;

[0093] The initial population generation module is used to generate an initial population of decision variables;

[0094] The aggregation interval calculation module is used to calculate the aggregation interval of the population during the population evolution process.

[0095] The evolutionary end-stage determination module is used to determine whether the population has entered the evolutionary end-stage based on the aggregation interval; if so, it enters the evolutionary end-stage processing module for processing; if not, it directly enters the new population generation module.

[0096] The late-stage evolution processing module is used to set the average voltage range of decision variables according to the boundary of the population aggregation area and regenerate the initial population.

[0097] The new population generation module is used to perform crossover and mutation operations when the conditions for new population generation are met, and to merge the population after crossover and mutation operations with the previous generation population to form a new population. It calculates the main equipment parameters and bridge arm current of individuals in the new population. When the bridge arm current meets the equipment selection requirements, it calculates the system cost, selects individuals with low cost, and generates a new generation population.

[0098] In this embodiment, the specific steps for each module of the device to implement its function are the same as those in the first embodiment of the present invention, and will not be repeated here.

[0099] According to a third embodiment of the present invention, a method for implementing the above-mentioned optimization method using a transfer algorithm is provided, specifically including the following steps:

[0100] Step S100: Write a population initialization program using binary encoding. Input parameters include: the number of individuals in the population m and the length of the binary code l; the output parameter is: a population matrix POP with m rows and l columns; the binary code is generated randomly.

[0101] Step S200: Write a binary-to-decimal conversion program. Input parameters include: an m x l population matrix POP, the minimum value of the decision variable min, and the maximum value of the decision variable max. Output parameter: an m x l decimal matrix of decision variables POPdec. Write the program based on the principles of binary-to-decimal conversion.

[0102] Step S300: Write a program to determine the population clustering interval. Input parameters include: the decimal matrix of decision variables, POPdec. Output parameters are: the minimum value of the clustering interval, Smin, and the maximum value of the clustering interval, Smax. Search for the minimum and maximum values ​​of POPdec to obtain the population clustering interval.

[0103] Step S400: Write the main equipment parameter calculation program. Input parameters include: system capacity, DC voltage, average voltage of submodule capacitors, voltage modulation, and power factor. Output parameters include: number of bridge arm submodules, submodule capacitance, bridge arm inductance, transformer valve-side rated voltage, and bridge arm peak current. Write the program based on the main equipment parameter calculation formula.

[0104] Step S500: Write the system loss calculation program. Input parameters include: system capacity, DC voltage, average voltage of submodule capacitors, bridge arm current, switching frequency, number of submodules, IGBT on-state resistance, diode on-state resistance, IGBT turn-off resistance, diode turn-off resistance, IGBT turn-on loss energy, IGBT turn-off loss energy, diode reverse recovery loss energy, DC submarine cable resistance, and DC submarine cable length. Output parameter: system loss.

[0105] Step S600: Write the economic cost calculation program. Input parameters include: system capacity, DC voltage, number of submodules, DC submarine cable length, converter station unit capacity price, DC submarine cable unit length price, grid connection electricity price, annual utilization hours, annual operating rate, annual interest rate, and life cycle. Output parameter: system cost.

[0106] Step S700: Write the crossover operation program. Input parameters include: an m-row, l-column matrix POPUdc of the DC voltage population, an m-row, l-column matrix POPUc of the submodule capacitor average voltage population, and the crossover probability. Output parameters include: an m-row, 2l-column matrix POPc of the crossover population. The DC voltage population matrix and the submodule capacitor average voltage population matrix are concatenated row-wise into an m-row, 2l-column matrix. During the program's loop operation, if a random number is less than the crossover probability, the binary codes of adjacent rows in the matrix are swapped at a random bit position. After m / 2 loops, the crossover operation is completed.

[0107] Step S800: Write the mutation operation program. Input parameters include: an m-row, l-column matrix POPudc of the DC voltage population, an m-row, l-column matrix POPuc of the average voltage population of the submodule capacitors, and the mutation probability. Output parameters include: an m-row, 2l-column matrix POPm of the mutated population. The DC voltage population matrix and the average voltage population matrix of the submodule capacitors are concatenated row-wise into an m-row, 2l-column matrix. During the program loop, if a random number is less than the mutation probability, the binary code of one row of the matrix is ​​inverted at a random bit position. After m iterations, the mutation operation is complete.

[0108] Step S900: Write the selection operation procedure. Input parameters include: the m-row 2l-column matrix POPc of the crossover population, the m-row 2l-column matrix POPm of the mutated population, the m-row 2l-column matrix POPp of the previous generation population, and the system cost of each individual. Output parameters include: the m-row 2l-column matrix POPn of the new generation population. Combine POPc, POPm, POPp, and the system cost into a 3m-row 2l+1-column matrix, sort them in ascending order of system cost, and find m low-cost individuals to form the new generation population.

[0109] Step S1000: Write the main program. Write the main program according to the method provided in the first embodiment of the present invention.

[0110] Step S1100: Testing. Testing includes subroutine testing and main program testing. First, input typical parameters to test the main equipment parameter calculation program, system loss calculation program, and economic cost calculation program to confirm the accuracy of the calculation programs. Next, input the DC voltage range, the average voltage range of the submodule capacitors, the number of individuals in the population, and the binary code length to test the remaining subroutines and the main program, and check the convergence of population evolution.

[0111] Step S1200: Output the optimal voltage level. After inputting the DC voltage and the average voltage range of the submodule capacitors, perform multiple calculations, statistically analyze the output results, analyze the deviation of the results, and select the voltage level with the lowest system cost as the optimal result.

[0112] This embodiment of the invention uses a binary encoding method to generate the initial population of decision variables. During the population evolution process, the population clustering interval is calculated to determine whether the population has entered the final stage of evolution. When the population clustering interval is greater than a threshold, it indicates that the population has not entered the final stage of evolution. Crossover, mutation, and selection operations are performed on the population to gradually cluster towards the optimal solution. When the population clustering interval is less than the threshold, it indicates that the population has entered the final stage of evolution. Based on the population clustering boundary, the DC voltage and the average voltage range of the submodule capacitor are set, the initial population is regenerated, genetic operations are performed, and the population clustering interval calculation function is disabled. At the final stage of evolution, the optimal solution is output when the number of iterations reaches the threshold or the population converges.

[0113] In summary, this invention relates to a method and apparatus for optimizing the voltage level of a flexible DC transmission system for offshore wind power. Based on a genetic algorithm, the method optimizes the DC voltage level design with the goal of achieving optimal economic efficiency. This method uses the DC voltage and the average voltage of the submodule capacitors as decision variables. Within the range of DC voltage and average voltage of the submodule capacitors that meet the requirements for main equipment selection, it calculates the main equipment parameters and system cost, selecting the DC voltage level and average voltage of the submodule capacitors that minimize system cost. This avoids the voltage level being overly dependent on DC cable parameters. The technical solution provided by this invention monitors the population aggregation degree during the genetic iteration process. At the end of the evolutionary stage, by re-initializing the population with a high aggregation degree, it avoids excessive assimilation of population genes, improving the stability of the output results and the global optimization capability.

[0114] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A method for optimizing the voltage level of an offshore wind power flexible DC transmission system, wherein the offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station, the offshore converter station and the onshore converter station being connected via a DC bus, characterized in that, include: S1. Select the DC bus and submodule capacitor voltages as decision variables, and set the average voltage range of the decision variables; S2, the initial population that generates decision variables; S3. During the population evolution process, the aggregation interval of the population is calculated using the following formula: in, This is the concentration range of the DC bus voltage. This represents the clustering range of the average capacitor voltage of the submodule. This is the maximum DC bus voltage. This is the minimum DC bus voltage. This represents the average voltage of the capacitors in the largest submodule. The average voltage of the capacitors in the smallest submodule; S4. Determine whether the population has entered the final stage of evolution based on the aggregation interval; if yes, set the final stage flag to 1; if no, proceed to step S6 to perform crossover and mutation operations. S5. Set the average voltage range of decision variables according to the boundary of the population aggregation area, and regenerate the initial population; S6. Determine if the number of iterations is greater than the iteration threshold or if the iteration has converged; if yes, the optimal solution is obtained; if no, perform crossover and mutation operations, and merge the population after crossover and mutation with the previous population to form a new population. Calculate the main equipment parameters and bridge arm currents of individuals in the new population; the main equipment parameters include converter valves, connecting transformers, and bridge arm reactors; calculate the main equipment parameters according to the following formula: in, For the capacitor capacity of the submodule, For converter capacity, For voltage modulation, The fundamental angular frequency of the power grid. This refers to the voltage fluctuation rate of the submodule capacitor. This is the DC bus voltage. This represents the average voltage of the capacitors in the submodule. Power factor; in, For the capacity of the bridge arm reactor, The time from the occurrence of the fault to the activation of the bridge arm overcurrent protection. The change in bridge arm current is given by the following formula: in, This represents the peak value of the bridge arm current. To connect the rated phase voltage on the valve side of the transformer, This refers to the DC bus current. S7. Determine whether the bridge arm current meets the equipment selection requirements; if yes, proceed to the next step; if no, return to step S1. S8. Calculate the system cost, select individuals with low cost, and generate a new generation of population.

2. The method according to claim 1, characterized in that, It also includes the following steps: S9, determine if flag==1; if yes, return to step S6, otherwise return to step S3.

3. The method according to claim 2, characterized in that, In step S4, determining whether the population has entered the final stage of evolution based on the aggregation interval includes: like and If so, the current population has entered the final stage of evolution.

4. The method according to claim 3, characterized in that, It also includes calculating system losses based on the main equipment parameters: in, For the loss of the converter valve, For on-state losses, For cutoff loss, For switching losses; For DC submarine cable loss, For DC submarine cable resistance, This refers to the length of the DC submarine cable.

5. The method according to claim 4, characterized in that, The system cost includes converter station cost, submarine cable cost, system loss cost, and operation and maintenance cost.

6. A voltage level optimization device for an offshore wind power flexible DC transmission system, wherein the offshore wind power flexible DC transmission system includes an offshore converter station and an onshore converter station, the offshore converter station and the onshore converter station being connected via a DC bus, characterized in that, It includes a decision variable setting module, an initial population generation module, a clustering interval calculation module, an evolutionary end-stage judgment module, an evolutionary end-stage processing module, and a new population generation module; among which, The decision variable setting module is used to select the DC bus and submodule capacitor voltages as decision variables, and set the average voltage range of the decision variables; The initial population generation module is used to generate an initial population of decision variables; The aggregation interval calculation module is used to calculate the aggregation interval of the population during the population evolution process using the following formula: in, This is the concentration range of the DC bus voltage. This represents the clustering range of the average capacitor voltage of the submodule. This is the maximum DC bus voltage. This is the minimum DC bus voltage. This represents the average voltage of the capacitors in the largest submodule. The average voltage of the capacitors in the smallest submodule; The evolutionary end-stage determination module is used to determine whether the population has entered the evolutionary end-stage based on the aggregation interval; if so, it enters the evolutionary end-stage processing module for processing; if not, it directly enters the new population generation module. The late-stage evolution processing module is used to set the average voltage range of decision variables according to the boundary of the population aggregation area and regenerate the initial population. The new population generation module is used to perform crossover and mutation operations when the conditions for new population generation are met, and to merge the population after crossover and mutation operations with the previous generation population to form a new population. It calculates the main equipment parameters and bridge arm currents of individuals in the new population. When the bridge arm current meets the equipment selection requirements, it calculates the system cost, selects individuals with low cost, and generates a new generation population. The main equipment parameters include converter valves, connecting transformers, and bridge arm reactors. The main equipment parameters are calculated according to the following formula: in, For the capacitor capacity of the submodule, For converter capacity, For voltage modulation, The fundamental angular frequency of the power grid. This refers to the voltage fluctuation rate of the submodule capacitor. This is the DC bus voltage. This represents the average voltage of the capacitors in the submodule. Power factor; in, For the capacity of the bridge arm reactor, The time from the occurrence of the fault to the activation of the bridge arm overcurrent protection. The change in bridge arm current is given by the following formula: in, This represents the peak value of the bridge arm current. To connect the rated phase voltage on the valve side of the transformer, This represents the DC bus current.

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