Control parameter calibration method for over-voltage of sending end of wind-solar-thermal bundled direct-current delivery system

Abstract

The invention discloses a control parameter calibration method for overvoltage of a sending end of a wind-solar-thermal bundled direct-current delivery system, and belongs to the technical field of inverter parameter setting, and the method comprises the steps: determining a to-be-selected range of control parameters affecting the overvoltage level based on current output characteristic expressions in the fault ride-through process of a wind turbine generator and a photovoltaic device inverter; in combination with typical scene calculation, an overvoltage sensitivity factor is obtained, and corresponding to-be-optimized control parameters are screened out; a control parameter calibration optimization model for reducing the overvoltage level of the wind-solar-thermal bundled direct-current delivery system is constructed, an improved genetic optimization algorithm more suitable for solving multiple parameters is adopted to solve the control parameter calibration optimization model, and a control parameter fixed value is obtained in combination with an electromechanical-numerical optimization combined solving method. According to the invention, by setting the control parameters of the inverter, the active support capability of the new energy station in the energy base is improved, and a reference basis is provided for the construction of a direct-current high-voltage delivery system of the new energy station.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter parameter setting, and in particular to a control parameter calibration method for overvoltage at a sending end of a wind-solar-thermal bundled direct current transmission system. Background Art

[0002] With the rapid development of my country's wind power, photovoltaic and thermal power energy bases, power resources and load centers show obvious inverse distribution characteristics. The western and northern regions are rich in resources, but the load is concentrated in the east and south. This distribution makes the cross-regional ultra-high voltage direct current transmission system a key technology to solve the long-distance and large-capacity transmission needs. Through this system, wind, light and thermal power resources can be effectively bundled and transmitted to the load center over long distances, which not only optimizes energy allocation, but also promotes large-scale grid connection of new energy.

[0003] However, existing technologies have shown some obvious technical deficiencies when facing the volatility of renewable energy power generation:

[0004] UHV DC transmission systems face the risk of commutation failure during operation, especially when the output of renewable energy fluctuates greatly. Commutation failure will cause the inverter controller to fail to coordinate with other parts of the system, further causing overvoltage. Especially in wind-solar-thermal bundling systems, this overvoltage phenomenon not only affects system stability, but may also damage the equipment of the power grid at the sending end. Although existing inverter control strategies can alleviate the overvoltage problem to a certain extent, these strategies are often based on fixed parameter settings and fail to fully consider the dynamic changes in the system operating environment. For example, under different weather conditions, the output power of wind and photovoltaic power generation varies greatly, and the existing control methods cannot respond to these changes quickly, resulting in an increased possibility of commutation failure. In the prior art, strategies for suppressing overvoltage by adjusting inverter control parameters have been proposed, but their optimization effects are limited. Most strategies rely on theoretical calculations and ignore the actual operating conditions in different scenarios. Therefore, overvoltage cannot be effectively suppressed in multiple typical scenarios. When the overvoltage level is high, it not only endangers the safety of power grid equipment, but also significantly increases the operating risk of the system.

[0005] Existing technologies have significant deficiencies in dealing with overvoltage problems in wind-solar-thermal bundling systems, especially in dealing with dynamic environments and improving control accuracy. To solve these problems, it is urgent to develop an optimization method that can dynamically adjust control parameters to effectively reduce overvoltage levels and improve system safety and stability. Summary of the invention

[0006] In response to the problems of increased risk of commutation failure, inadequate control strategy and limited overvoltage suppression effect, the present invention provides a control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system. By optimizing the control parameters of inverters such as wind turbines and photovoltaic devices, the overvoltage level during commutation failure caused by a receiving-end power grid fault is reduced.

[0007] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0008] The control parameter calibration method of the overvoltage at the sending end of the wind-solar-thermal bundled DC transmission system includes the following steps:

[0009] Based on the current output characteristic expressions of wind turbines and photovoltaic device inverters during fault ride-through, the candidate ranges of control parameters that affect the overvoltage level are determined;

[0010] Combined with typical scenario calculations, the overvoltage sensitivity factor is obtained, and the corresponding control parameters to be optimized are screened out; the typical scenarios include typical fault conditions of receiving-end power grid failure or inverter-side commutation failure;

[0011] A control parameter calibration optimization model for reducing the overvoltage level of the wind-solar-thermal bundled DC transmission system is constructed, wherein the objective function of the control parameter calibration optimization model is the minimum peak value of the commutation bus voltage at the sending end during the commutation failure period, and the constraint condition is the upper and lower limits of the control parameter to be optimized;

[0012] An improved genetic optimization algorithm that is more suitable for solving multiple parameters is used to solve the constructed control parameter calibration optimization model. Combined with the electromechanical-numerical optimization joint solution method, the control parameter constants for improving the overvoltage level of the wind-solar-thermal bundled DC transmission system are obtained.

[0013] Preferably, it is concluded that the inverter side commutation failure caused by the receiving-end power grid fault, the switching of discrete reactive compensation devices such as parallel capacitors and filters, the start and stop of conventional units, and the large-scale industrial load drop are the main influencing factors causing the overvoltage phenomenon in the sending-end power grid. The corresponding fault sources of different degrees are set to form a fault set Ψ to represent all overvoltage conditions caused by the receiving-end power grid fault, which is expressed as:

[0014] ;

[0015] in, Indicates the overvoltage phenomenon of the sending-end power grid caused by the first receiving-end power grid fault; Indicates the overvoltage phenomenon of the sending-end power grid caused by the second receiving-end power grid fault; It indicates the overvoltage phenomenon of the sending-end power grid caused by the commutation failure caused by the Nth receiving-end power grid fault.

[0016] Preferably, although the wind turbines and photovoltaic grid-connected devices in the wind-solar-thermal bundled power grid are all grid-connected inverters, there are significant differences in the operating principles and control methods of the wind turbine inverters and photovoltaic inverters. In particular, the voltage crossing control parameters in the transient process are constructed using two different mathematical analytical formulas. The adjustment effects of different control instructions in the transient process are intertwined and coupled, which aggravates the overvoltage phenomenon in this form. It is necessary to conduct comprehensive optimization and identification of different types of inverter control parameters. Among them, the range of control parameters to be selected includes the high voltage crossing active current control instructions of the wind turbine inverter and reactive current control command , the expression is:

[0017] ;

[0018] in: and The high voltage ride through active current calculation coefficient representing the active current control instruction expression; Indicates grid-related voltage; Indicates the initial value of active current; Indicates the high voltage ride through active current setting value; and Represents the high voltage ride-through reactive current calculation coefficient of the reactive current control instruction expression; 1.1 is the voltage threshold for entering high voltage ride-through; Indicates the initial reactive current value; Indicates the high voltage ride-through reactive current setting value;

[0019] Active current command during high voltage ride through control of photovoltaic inverter and reactive current command The expression is:

[0020] ;

[0021] in: Indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates taking a smaller value; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter; Indicates the sudden rise of grid voltage; It indicates the reactive current command value of the high voltage ride-through of the photovoltaic inverter when the detected grid connection point voltage U∈(1,1.1]; It represents the correction value of the reactive current command value of the PV inverter high voltage ride through when the detected grid connection point voltage U∈(1.1,∞).

[0022] Preferably, the range of control parameters affecting the overvoltage level includes: , , , , , , , , , , There are 11 parameters in total, and the values ​​of the parameters directly affect the overvoltage level at the sending end caused by the failure of commutation of wind, solar and thermal bundles through the DC transmission system.

[0023] Preferably, the overvoltage sensitivity factor is calculated in combination with a typical scenario, and the specific steps include:

[0024] Based on the control variable method, under full load operation, partial load operation and inverter commutation failure operation conditions, ensure that the target control parameter is at the maximum value of the operation range, and other control parameters are at the initial value, and record the overvoltage peak value under this condition;

[0025] Scan all elements in the fault set Ψ and take the maximum value of the overvoltage peak value under each working condition as the overvoltage sensitivity factor;

[0026] Each target control parameter uses the control variable method to calculate the overvoltage sensitivity factor, and generates a set of overvoltage sensitivity factors corresponding to all target control parameters. , the expression is:

[0027] ;

[0028] in, Indicates the overvoltage sensitivity factor corresponding to the first target control parameter; represents the overvoltage sensitivity factor corresponding to the second target control parameter; represents the overvoltage sensitivity factor corresponding to the Kth target control parameter; K is the total number of target control parameters for calculating the overvoltage sensitivity factor, and K=11.

[0029] Preferably, the corresponding control parameters to be optimized are screened out, and the method includes:

[0030] The set of overvoltage sensitivity factors corresponding to the target control parameters The elements in are arranged in descending order, and the control parameters corresponding to the first 30% of the elements are selected as the control parameters to be optimized.

[0031] Preferably, the objective function of the control parameter calibration optimization model is expressed as:

[0032] ;

[0033] in, It indicates the peak value of overvoltage at the wind, solar and thermal energy base at the sending end due to commutation failure; is the objective function value.

[0034] Preferably, the constraint expression of the upper and lower limits of the control parameters to be optimized is:

[0035] ;

[0036] in, For the The control parameters to be optimized are For the The lower limit of the control parameter to be optimized, For the The upper limit value of the control parameter to be optimized.

[0037] Preferably, the electromechanical-numerical optimization joint solution method specifically includes:

[0038] Establish an electromechanical-numerical optimization joint simulation platform, set a typical fault of overvoltage at the sending end caused by commutation failure on the electromechanical transient simulation platform, and input the initial value of the control parameter to be optimized into the corresponding electromechanical transient simulation platform tab to form an electromechanical-numerical optimization joint simulation platform;

[0039] The control parameter calibration optimization model is solved by using an improved genetic optimization algorithm on the numerical simulation platform, which is more suitable for solving multiple parameters. The simulation results of the electromechanical simulation model are input into the numerical simulation model. The overvoltage peak value of the sending-end power grid under the current operating conditions is recorded and input into the improved genetic algorithm as a fitness function. The control parameters to be optimized are assigned to the input card of the electromechanical transient model by iterations until the optimal convergence conditions are met, and the final control parameter constant of the overvoltage level at the sending end of the wind-solar-thermal bundled DC transmission system is obtained.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention constructs an optimization model and combines an improved genetic algorithm to dynamically calibrate the control parameters of the inverter, effectively suppressing the overvoltage level of the power grid at the sending end during the phase change failure. According to the actual output characteristics of the wind turbine and photovoltaic device, the optimization range of the key control parameters is determined by calculating the overvoltage sensitivity factor, overcoming the defect of the prior art that it cannot dynamically respond to complex operating conditions, and realizing accurate control for different scenarios. Especially in the wind-solar-fire bundling system, the present invention can dynamically optimize the control parameters according to the real-time changing new energy output characteristics to ensure the stable operation of the system. By solving the optimization model, the present invention effectively reduces the peak value of the sending end voltage when a phase change failure occurs, reducing the system risk caused by the phase change failure. This not only improves the protection level of the equipment, but also reduces the probability of the system shutting down due to overvoltage. The present invention improves the power grid support capacity of the new energy station by reasonably setting the inverter control parameters. The existing inverter control strategy is difficult to provide sufficient support in the process of new energy grid connection, and the present invention has made effective improvements on this. This plays a key role in the large-scale grid connection of new energy sources such as wind and solar, especially in the cross-regional ultra-high voltage direct current transmission system, which can better bear the large-scale new energy transmission needs. The present invention adopts an improved genetic algorithm, combined with an electromechanical-numerical optimization joint solution platform, which not only improves the speed and accuracy of the optimization solution, but also reduces the instability factors caused by parameter mismatch in the actual operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0042] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 This is a topology diagram of a wind, solar and thermal bundling system for DC transmission in an embodiment of the present invention; Figure 3 It is a schematic diagram of the solution process of an improved genetic optimization algorithm that is more suitable for solving multiple parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 1 FIG. 1 is an embodiment of the present invention, which provides a control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system, including:

[0046] Step S1. Determine the range of control parameters that affect the overvoltage level based on the current output characteristic expressions of the wind turbine and photovoltaic device inverter during the fault ride-through process;

[0047] Step S2. Calculate in combination with typical scenarios to obtain an overvoltage sensitivity factor and select corresponding control parameters to be optimized; the typical scenarios include typical fault conditions of receiving-end power grid failure or inverter-side commutation failure;

[0048] Step S3. Construct a control parameter calibration optimization model for reducing the overvoltage level of the wind-solar-thermal bundled DC transmission system, wherein the objective function of the control parameter calibration optimization model is the minimum peak value of the commutation bus voltage at the sending end during the commutation failure, and the constraint condition is the upper and lower limits of the control parameter to be optimized;

[0049] Step S4. Use an improved genetic optimization algorithm that is more suitable for solving multiple parameters to solve the constructed control parameter calibration optimization model, and combine it with the electromechanical-numerical optimization joint solution method to obtain the control parameter constants for improving the overvoltage level of the wind-solar-thermal bundling system through DC transmission.

[0050] Specifically, in this embodiment, it is concluded that the inverter-side commutation failure caused by the receiving-end power grid fault, the switching of discrete reactive compensation devices such as parallel capacitors and filters, the start and stop of conventional units, and the large-scale industrial load drop are the main influencing factors causing the overvoltage phenomenon in the sending-end power grid. Corresponding fault sources of different degrees are set to form a fault set Ψ to represent all overvoltage conditions caused by the receiving-end power grid fault, which is expressed as:

[0051] ;

[0052] in, Indicates the overvoltage phenomenon of the sending-end power grid caused by the first receiving-end power grid fault; Indicates the overvoltage phenomenon of the sending-end power grid caused by the second receiving-end power grid fault; It indicates the overvoltage phenomenon of the sending-end power grid caused by the commutation failure caused by the Nth receiving-end power grid fault.

[0053] like Figure 2 The figure shows the topological structure of the wind-solar-thermal bundled high-voltage direct current transmission system, which shows the system structure diagram of the phenomenon of overvoltage in the sending-end grid caused by the failure of the receiving-end commutation after the sending-end grid failure of the wind-solar-thermal bundled high-voltage direct current transmission system. In the figure, the output power of the wind turbine, photovoltaic power generation system and thermal power generation unit is expressed in active power. , and reactive power , It indicates that after passing through the sending-end inverter, it is input into the DC transmission channel. The figure contains multiple transmission channels, which are marked as transmission channel 1 to transmission channel , which is used to represent the parallel transmission status of different transmission paths in the system.

[0054] In the sending-end power grid, the commutation failure caused by the receiving-end power grid fault causes the sending-end commutation bus voltage to The voltage of the output ... It is used to indicate the overvoltage conditions caused by different faults. The voltage fluctuations of multiple parallel transmission channels are directly related to the phase change failure at the receiving end. The electrical components and power flow direction in the attached figure clearly show the operation of each power component and its impact on the system voltage. By controlling the active and reactive currents of the inverter, the system overvoltage can be suppressed.

[0055] In this embodiment, although the wind turbines and photovoltaic grid-connected devices in the wind-solar-thermal bundled power grid are both grid-connected inverters, there are significant differences in the operating principles and control methods of the wind turbine inverters and photovoltaic inverters. In particular, the voltage crossing control parameters in the transient process are constructed using two different mathematical analytical formulas. The regulation effects of different control instructions in the transient process are intertwined and coupled, which aggravates the overvoltage phenomenon in this form. It is necessary to conduct comprehensive optimization and identification of different types of inverter control parameters. The range of control parameters to be selected includes the high voltage crossing active current control instructions of the wind turbine inverter. and reactive current control command , the expression is:

[0056] ;

[0057] in: and The high voltage ride through active current calculation coefficient representing the active current control instruction expression; Indicates grid-related voltage; Indicates the initial value of active current; Indicates the high voltage ride through active current setting value; and Represents the high voltage ride-through reactive current calculation coefficient of the reactive current control instruction expression; 1.1 is the voltage threshold for entering high voltage ride-through; Indicates the initial reactive current value; Indicates the high voltage ride-through reactive current setting value;

[0058] Active current command during high voltage ride through control of photovoltaic inverter and reactive current command The expression is:

[0059] ;

[0060] in: Indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates taking a smaller value; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter; Indicates the sudden rise of grid voltage; It indicates the reactive current command value of the high voltage ride-through of the photovoltaic inverter when the detected grid connection point voltage U∈(1,1.1]; It represents the correction value of the reactive current command value of the PV inverter high voltage ride through when the detected grid connection point voltage U∈(1.1,∞).

[0061] In this embodiment, preferably, the range of control parameters affecting the overvoltage level includes: , , , , , , , , , , There are 11 parameters in total, and the values ​​of the parameters directly affect the overvoltage level at the sending end caused by the failure of commutation of wind, solar and thermal bundles through the DC transmission system.

[0062] This embodiment further calculates the overvoltage sensitivity factor in combination with a typical scenario, and the specific steps include:

[0063] Based on the control variable method, under full load operation, partial load operation and inverter commutation failure operation conditions, ensure that the target control parameter is at the maximum value of the operation range, and other control parameters are at the initial value, and record the overvoltage peak value under this condition;

[0064] Scan all elements in the fault set Ψ and take the maximum value of the overvoltage peak value under each working condition as the overvoltage sensitivity factor;

[0065] Each target control parameter uses the control variable method to calculate the overvoltage sensitivity factor, and generates a set of overvoltage sensitivity factors corresponding to all target control parameters. , the expression is:

[0066] ;

[0067] in, Indicates the overvoltage sensitivity factor corresponding to the first target control parameter; represents the overvoltage sensitivity factor corresponding to the second target control parameter; represents the overvoltage sensitivity factor corresponding to the Kth target control parameter; K is the total number of target control parameters for calculating the overvoltage sensitivity factor, and K=11.

[0068] Specifically, in this embodiment, the corresponding control parameters to be optimized are screened out, and the method includes:

[0069] The set of overvoltage sensitivity factors corresponding to the target control parameters The elements in are arranged in descending order, and the control parameters corresponding to the first 30% of the elements are selected as the control parameters to be optimized.

[0070] Specifically, in this embodiment, the objective function of the control parameter calibration optimization model is expressed as:

[0071] ;

[0072] in, It indicates the peak value of overvoltage at the wind, solar and thermal energy base at the sending end due to commutation failure; is the objective function value.

[0073] In this embodiment, preferably, the constraint condition expression of the upper and lower limits of the operation of the control parameter to be optimized is:

[0074] ;

[0075] in, For the The control parameters to be optimized are For the The lower limit of the control parameter to be optimized, For the The upper limit value of the control parameter to be optimized.

[0076] In this embodiment, preferably, an electromechanical-numerical optimization joint simulation platform is established, a typical fault of overvoltage at the sending end caused by commutation failure is set on the electromechanical transient simulation platform, and the initial value of the control parameter to be optimized is input into the corresponding electromechanical transient simulation platform tab to form an electromechanical-numerical optimization joint simulation platform;

[0077] Specifically, in this embodiment, an improved genetic optimization algorithm that is more suitable for solving multiple parameters is used on a numerical simulation platform to solve the control parameter calibration optimization model, the simulation results of the electromechanical simulation model are input into the numerical simulation model, the overvoltage peak value of the sending-end power grid under the current operating conditions is recorded, and it is input into the improved genetic algorithm as a fitness function, and the values ​​of the control parameters to be optimized are assigned to the input card of the electromechanical transient model by iterations successively until the optimal convergence conditions are met, so as to obtain the final control parameter constant of the overvoltage level at the sending end of the wind-solar-thermal bundled DC transmission system.

[0078] Example 2

[0079] like Figure 3 As shown, this embodiment optimizes the overvoltage control parameters of the sending end of the wind-solar-fired bundling system through a genetic algorithm.

[0080] The operational stability of the wind-solar-thermal bundled DC transmission system is crucial, especially in the case of commutation failure, the overvoltage level of the sending-end power grid may seriously affect the safety of the equipment. This embodiment optimizes the control parameters through a genetic algorithm to reduce the overvoltage peak and improve the reliability of the system.

[0081] Implementation steps:

[0082] 1. Generate the initial population

[0083] First, determine the control parameters to be optimized, including the active current and reactive current control instructions of the wind turbine, the active current and reactive current control instructions of the photovoltaic inverter, etc. Based on these parameters, generate the initial population. Each individual consists of a set of control parameters, expressed as:

[0084] ;

[0085] in, Indicates The jth control parameter of each individual, the size of the initial population is set to N, which means it contains N groups of different control parameter combinations.

[0086] 2. Fitness function calculation

[0087] Each individual is evaluated using the electromechanical simulation platform to calculate its overvoltage peak value in the case of commutation failure. The fitness function is defined as the objective function that minimizes the overvoltage peak value:

[0088] ;

[0089] in, is the peak value of the sending-end voltage during the simulation process. The performance of each individual is evaluated by calculating its fitness value under the fault scenario.

[0090] 3. Selection, crossover and mutation operations

[0091] Based on the fitness value, the roulette algorithm is used to select excellent individuals to enter the next generation. The individuals with lower fitness values ​​(i.e., smaller overvoltage peak values) have higher probability of being selected. The crossover operation generates new individuals by exchanging some control parameters of different individuals; the mutation operation randomly adjusts some parameters in the individuals to ensure population diversity.

[0092] 4. Joint simulation platform optimization

[0093] In each generation, the newly generated control parameter combinations are simulated and evaluated using the electromechanical simulation platform to simulate the voltage response of the system under fault scenarios. The simulation results will be fed back into the fitness evaluation process of the genetic algorithm to ensure that the optimization is not only effective in theory, but also improves system stability under actual working conditions.

[0094] 5. Iterative optimization and termination conditions

[0095] When the set number of iterations is reached or the fitness value no longer changes significantly, the genetic algorithm is terminated, and the optimal control parameter combination is output as the final optimization result to reduce the overvoltage level at the sending end.

[0096] The control parameters optimized by the genetic algorithm in this embodiment can effectively reduce the voltage peak of the power grid at the sending end during the commutation failure, thereby significantly reducing the risk of the system being subjected to overvoltage shock. Simulation verification shows that the optimized control parameters exhibit strong dynamic response capabilities under various fault scenarios, improving the overall stability and safety of the system.

[0097] In summary, the advantages of the present invention are mainly reflected in the following aspects:

[0098] Dynamic calibration of control parameters: This invention proposes a technology for real-time dynamic calibration of inverter control parameters by building an optimization model and combining it with an improved genetic algorithm. This solves the limitation of the prior art that it cannot deal with the overvoltage problem during commutation failure in the wind-solar-thermal bundling system, effectively reduces the overvoltage level of the sending-end power grid, and improves system stability.

[0099] Optimization control based on overvoltage sensitivity: This invention screens out key control parameters by calculating the overvoltage sensitivity factor, and optimizes the parameters in different typical scenarios. This optimization control method based on actual scenarios significantly improves the adaptability of the system, enabling the system to maintain a good overvoltage suppression effect under variable new energy output conditions, overcoming the defect that fixed control parameters in the prior art are difficult to adapt to dynamic changes.

[0100] Application of improved genetic optimization algorithm: This invention uses an improved genetic algorithm and combines it with a joint electromechanical-numerical optimization solution platform to achieve efficient solution of control parameters. Compared with traditional algorithms, the optimization solution of this invention is not only faster and more accurate, but also can dynamically adapt to a variety of operating scenarios, ensuring the stability and reliability of the system in practical applications.

[0101] Improve the support capacity of new energy stations: Through the reasonable optimization of inverter control parameters, the present invention improves the grid support capacity of wind, light, thermal and other new energy stations in the large-scale grid connection process, especially the support for cross-regional ultra-high voltage direct current transmission systems. It provides strong technical support for large-scale new energy grid connection and solves the problem of insufficient new energy support capacity in the existing technology.

[0102] Application of joint simulation platform: This invention introduces the joint solution method of electromechanical simulation and numerical optimization into the control parameter calibration process for the first time, and ensures the rationality and optimality of the control parameters through multiple iterations and simulations. This joint simulation technology significantly improves the calibration efficiency and effect of the control parameters, and provides strong technical support for optimizing system operation.

[0103] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "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. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0104] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system, characterized in that: The following steps are involved: Based on the current output characteristic expressions of wind turbines and photovoltaic device inverters during fault ride-through, the candidate ranges of control parameters that affect the overvoltage level are determined; Combined with typical scenario calculations, the overvoltage sensitivity factor is obtained, and the corresponding control parameters to be optimized are screened out; the typical scenarios include: typical fault conditions of receiving-end power grid failure or inverter-side commutation failure; A control parameter calibration optimization model for reducing the overvoltage level of the wind-solar-thermal bundled DC transmission system is constructed, wherein the objective function of the control parameter calibration optimization model is the minimum peak value of the commutation bus voltage at the sending end during the commutation failure period, and the constraint condition is the upper and lower limits of the control parameter to be optimized; The control parameter calibration optimization model constructed by solving the improved genetic optimization algorithm suitable for solving multiple parameters is adopted, and the control parameter constants for improving the overvoltage level of the wind-solar-thermal bundled DC transmission system are obtained by combining the electromechanical-numerical optimization joint solution method. The control parameter selection range includes the high voltage ride through active current control instruction of the wind turbine inverter and reactive current control command , the expression is: ； in: and The high voltage ride through active current calculation coefficient representing the active current control instruction expression; Indicates grid-related voltage; Indicates the initial value of active current; Indicates the high voltage ride through active current setting value; and Represents the high voltage ride-through reactive current calculation coefficient of the reactive current control instruction expression; 1.1 is the voltage threshold for entering high voltage ride-through; Indicates the initial reactive current value; Indicates the high voltage ride-through reactive current setting value; Active current command during high voltage ride through control of photovoltaic inverter and reactive current command The expression is: ； in: Indicates the reactive current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates taking a smaller value; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter; Indicates the sudden rise of grid voltage; It indicates the reactive current command value of the high voltage ride-through of the photovoltaic inverter when the detected grid connection point voltage U∈(1,1.1]; It represents the correction value of the reactive current command value of the PV inverter high voltage ride through when the detected grid connection point voltage U∈(1.1,∞).

2. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 1 is characterized in that: When determining the range of control parameters that affect the overvoltage level, the operating state of the wind-solar-thermal bundled high-voltage direct current transmission system is further analyzed, and it is concluded that the inverter-side commutation failure caused by the receiving-end power grid fault, the switching of the shunt capacitor and the filter discrete reactive compensation device, the start and stop of the conventional unit, and the large-scale industrial load drop are the main influencing factors causing the overvoltage phenomenon in the sending-end power grid. The corresponding fault sources of different degrees are set to form a fault set Ψ for representing all overvoltage conditions caused by the receiving-end power grid fault, which is expressed as: ； in, Indicates the overvoltage phenomenon of the sending-end power grid caused by the first receiving-end power grid fault; Indicates the overvoltage phenomenon of the sending-end power grid caused by the second receiving-end power grid fault; It indicates the overvoltage phenomenon of the sending-end power grid caused by the commutation failure caused by the Nth receiving-end power grid fault.

3. The control parameter calibration method for overvoltage at the sending end of the wind-solar-thermal bundled DC transmission system according to claim 1 is characterized in that: The control parameter selection range affecting the overvoltage level includes: , , , , , , , , , , There are 11 parameters in total, and the values ​​of the parameters directly affect the overvoltage level at the sending end caused by the failure of commutation of wind, solar and thermal bundles through the DC transmission system.

4. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 1 is characterized in that: The overvoltage sensitivity factor is calculated by combining typical scenarios, and the specific steps include: Based on the control variable method, under full load operation, partial load operation and inverter commutation failure operation conditions, ensure that the target control parameter is at the maximum value of the operation range, and other control parameters are at the initial value, and record the overvoltage peak value under this condition; Scan all elements in the fault set Ψ and take the maximum value of the overvoltage peak value under each working condition as the overvoltage sensitivity factor; Each target control parameter uses the control variable method to calculate the overvoltage sensitivity factor, and generates a set of overvoltage sensitivity factors corresponding to all target control parameters. , the expression is: ； in, Indicates the overvoltage sensitivity factor corresponding to the first target control parameter; represents the overvoltage sensitivity factor corresponding to the second target control parameter; represents the overvoltage sensitivity factor corresponding to the Kth target control parameter; K is the total number of target control parameters for calculating the overvoltage sensitivity factor, and K=11.

5. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 4 is characterized in that: The method for screening out the corresponding control parameters to be optimized includes: The set of overvoltage sensitivity factors corresponding to the target control parameters The elements in are arranged in descending order, and the control parameters corresponding to the first 30% of the elements are selected as the control parameters to be optimized.

6. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 1 is characterized in that: The objective function of the control parameter calibration optimization model is expressed as: ； in, It indicates the peak value of overvoltage at the wind, solar and thermal energy base at the sending end due to commutation failure; is the objective function value.

7. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 6 is characterized in that: The constraint condition expression of the upper and lower limits of the control parameters to be optimized is: ； in, For the The control parameters to be optimized are For the The lower limit of the control parameter to be optimized, For the The upper limit value of the control parameter to be optimized.

8. The control parameter calibration method for overvoltage at the sending end of a wind-solar-thermal bundled DC transmission system according to claim 1 is characterized in that: The electromechanical-numerical optimization joint solution method specifically includes: Establish an electromechanical-numerical optimization joint simulation platform, set a typical fault of overvoltage at the sending end caused by commutation failure on the electromechanical transient simulation platform, and input the initial value of the control parameter to be optimized into the corresponding electromechanical transient simulation platform tab to form an electromechanical-numerical optimization joint simulation platform; The control parameter calibration optimization model is solved by using an improved genetic optimization algorithm suitable for solving multiple parameters on the numerical simulation platform. The simulation results of the electromechanical simulation model are input into the numerical simulation model. The overvoltage peak value of the sending-end power grid under the current operating conditions is recorded and input into the improved genetic algorithm as a fitness function. The control parameters to be optimized are assigned to the input card of the electromechanical transient model by iterations until the optimal convergence conditions are met, and the final control parameter constant of the overvoltage level at the sending end of the wind-solar-thermal bundled DC transmission system is obtained.

Images