Wind farm unit configuration method based on voltage stiffness coefficient and switching variation

Abstract

The application provides a wind farm unit configuration method based on a voltage rigidity coefficient and a switching change amount, and comprises the following steps: calculating a contribution efficiency factor of a unit based on an electrical distance between a bus node where the unit is located and a grid-connected node, determining a dynamic coupling short-circuit ratio under a target configuration ratio; calculating a reactive power compensation gap parameter of a wind farm based on a maximum reactive power of a network-constructing unit; determining a voltage rigidity coefficient based on the dynamic coupling short-circuit ratio and the reactive power compensation gap parameter; determining a switching change amount parameter; constructing a multi-objective optimal ratio configuration model with the maximum voltage rigidity coefficient and the minimum switching change amount parameter as objective functions, and configuring the wind farm units. The application can significantly improve the voltage rigidity coefficient of the system, and further greatly enhance the stability of the system voltage under a normal operating state and the rapid recovery capability after suffering from a disturbance.

Description

Technical Field

[0001] This invention belongs to the field of doubly fed wind farm technology and is used to improve the active voltage support capability of doubly fed wind farms. In particular, it is a method for optimal ratio configuration of wind farm grid-type units based on voltage rigidity coefficient and switching variation. Background Technology

[0002] With the deepening of the "dual-carbon" strategic goals, wind power, as a core component of the clean energy system, has ushered in a historic development opportunity. Among them, doubly-fed induction generator (DFIG) wind turbines, with their excellent power regulation performance, mature manufacturing processes, and high cost-effectiveness, have become the mainstream model in the onshore wind power sector. In recent years, my country has planned and constructed multiple 10-million-kilowatt-level wind power bases in the vast desert and Gobi regions. These large-scale new energy clusters are transmitted across regions through ultra-high-voltage direct current (UHVDC) transmission channels, playing a crucial role in the construction of the new power system. However, these wind farms are mostly located at the end of the power grid, and the network structure lacking synchronous power supply support exhibits significant weak grid characteristics. The continuously decreasing equivalent short-circuit ratio of the system leads to a serious deficiency in voltage support capacity. Especially under transient disturbance conditions, the risk of voltage instability increases significantly, becoming a major technical bottleneck restricting the efficient consumption of new energy and the safe operation of the power system.

[0003] Traditional doubly-fed induction generator (DFIG) wind turbines generally employ grid-following control strategies, making their operating characteristics highly dependent on the stability of the grid voltage. With the continuous increase in wind power penetration, the system's equivalent short-circuit ratio (EMR) shows a sustained downward trend. Research data indicates that when the EMR of multiple renewable energy sources at a power plant falls below 2.0, the system's small-disturbance stability margin decays sharply, easily inducing stability problems such as subsynchronous oscillations. To address this, the academic community has proposed grid-based control technology in recent years, which enhances the system's voltage support capability by simulating the external characteristics of synchronous generators. However, in engineering practice, a significant dilemma has been found regarding the configuration ratio of grid-based units: too low a configuration ratio fails to effectively improve short-circuit capacity, while too high a ratio may lead to a surge in equipment investment, increased operating losses, and more complex control interactions. Therefore, determining the optimal configuration ratio of grid-based units under multi-objective constraints has become a key breakthrough in improving the active voltage support capability of renewable energy power plants.

[0004] Existing research primarily focuses on two core areas: improving the short-circuit ratio and optimizing economic efficiency, aiming to construct a more efficient and stable power grid architecture. For example, Chinese patent CN119891367A, "A Method and System for Configuring the Proportion of Grid-Type Converters Applicable to 100% Independent Power Supply Systems of New Energy," calculates and compares the short-circuit ratio and critical short-circuit ratio (SCR-0) on the low-voltage side of the step-up transformer in a grid-connected new energy power station to ensure that the former is not less than the latter to meet the requirements for stable system operation, thereby calculating the optimal proportion configuration of grid-type converters. However, while this method performs well in improving the short-circuit ratio, it fails to fully consider key dynamic performance indicators such as voltage recovery speed, fails to deeply explore the synergistic mechanism between the unit's own non-functional capacity and external compensation devices, and even fails to address the impact of this synergistic mechanism on the rapid voltage recovery performance. Furthermore, Chinese patent CN118263920A, "Method and System for Configuring the Proportion of Grid-Connected / Grid-Connected Units in New Energy or Energy Storage Stations," establishes a small-signal state-space model for grid-connected new energy or energy storage stations. It calculates key stability and dynamic characteristics indicators under different grid-connected unit proportions and uses this to construct constraints to solve the cost function, thus optimizing the unit proportion configuration and significantly reducing unit costs. However, while pursuing cost optimization, it neglects the potential impact of unit switching variations on operation and maintenance costs and unit lifespan. In practical applications, optimizing the objective function often leads to frequent switching of units near the grid connection point, which not only significantly increases operation and maintenance costs but may also pose a serious threat to equipment lifespan. More importantly, the above inventions still rely excessively on static parameters from the planning stage in model construction, lacking a comprehensive adaptation to the dynamic characteristics of actual operating conditions, and cannot achieve real-time dynamic optimization and adjustment of the grid-connected unit proportion. In summary, current research results have significant limitations in terms of evaluation index systems, consideration of unit switching variations, and applicability under dynamic operating conditions.

[0005] Therefore, how to provide a wind farm unit configuration method that can be dynamically adjusted in real time, which can effectively improve the strength and speed of system voltage recovery based on fully considering the reactive power compensation coordination mechanism of the units and reactive power equipment, while also taking into account issues such as frequent unit switching to reduce operation and maintenance costs and extend unit life, and simplifying the complexity of the optimization model, ultimately achieving a significant improvement in the active voltage support capability of the doubly-fed wind farm, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention proposes a wind farm turbine configuration method based on voltage stiffness coefficient and switching variation. By flexibly adjusting the configuration ratio of grid-type turbines in a doubly fed wind farm, the voltage stiffness coefficient of the system can be significantly improved, thereby greatly enhancing the stability of the system voltage under normal operating conditions and its rapid recovery capability after being disturbed.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention first discloses a wind farm turbine configuration method based on voltage stiffness coefficient and switching variation, wherein multiple turbines are configured in the wind farm, and includes the following steps:

[0009] S1: Calculate the contribution efficiency factor of the unit based on the electrical distance between the bus node where the unit is located and the grid connection node, and use the contribution efficiency factor to determine the dynamic coupling short-circuit ratio under the target configuration ratio;

[0010] S2: Calculate the reactive power compensation gap parameters of wind farms based on the maximum reactive power of grid-connected units;

[0011] S3: Determine the voltage stiffness coefficient based on the dynamic coupling short-circuit ratio and the reactive power compensation gap parameter;

[0012] S4: Determine the switching change amount parameter based on the amount of change from the current configuration ratio to the target configuration ratio;

[0013] S5: Construct a multi-objective optimal proportional configuration model with the objective functions of maximizing the voltage stiffness coefficient and minimizing the switching change parameter, solve the multi-objective optimal proportional configuration model to obtain the optimized target configuration ratio, and configure the wind farm units.

[0014] Preferably, the contribution efficiency factor of unit i in S1 Represented as:

[0015] ;

[0016] In the formula, This represents the virtual impedance of node i where the generator unit is located; This refers to the electrical distance parameter between node i, where the unit is located, and the common coupling point PCC.

[0017] Preferably, the electrical distance parameters are calculated using the node impedance matrix of the network. Represented as:

[0018] ;

[0019] In the formula, Let be the self-impedance of node i, and The self-impedance of the common coupling point PCC, Let be the mutual impedance between node i and the common coupling point PCC. Let be the impedance from the common coupling point PCC to node i.

[0020] Preferably, the step in S1 of determining the dynamic coupling short-circuit ratio increment of the grid-type unit set under the target configuration ratio compared to the current configuration ratio using the contribution efficiency factor includes:

[0021] According to contribution efficiency factor Sort the units in descending order to obtain the set G0 of grid-connected units under the current configuration ratio and the set of grid-connected units under the target configuration ratio. ;

[0022] Based on the virtual impedance of the units, calculate the set of grid-connected units G0 and the set of grid-connected units respectively. Contributed short-circuit capacity ;

[0023] Calculate the short-circuit capacity when adjusting from the current configuration ratio to the target configuration ratio. incremental change ;

[0024] Based on short-circuit capacity incremental change Calculate the dynamic coupling short-circuit ratio under the target configuration ratio. .

[0025] Preferably, step S2 includes the following steps:

[0026] Calculate the theoretical reactive power output of the grid-type generator unit based on the reactive voltage characteristics;

[0027] Based on the upper limit of reactive power and electrical distance parameters of grid-type units, determine the actual reactive power output of grid-type units;

[0028] Determine the reactive power output of the grid-connected generator units;

[0029] For real-time reactive load, calculate the reactive power compensation gap parameter that needs to be compensated in addition to the actual reactive power output of grid-type units and the reactive power output of grid-connected units.

[0030] Preferably, step S2 includes the following steps:

[0031] Determine the actual reactive power output of grid-connected units Represented as:

[0032] ;

[0033] ;

[0034] In the formula, The attenuation coefficient; This refers to the electrical distance parameter between node i, where the unit is located, and the common coupling point PCC; The theoretical reactive power output of grid-connected generating units; This refers to the upper limit of reactive power for grid-connected generating units; This represents the maximum reactive power that node i, where the generator unit is located, can generate.

[0035] Calculate reactive power compensation gap parameters Represented as:

[0036] ;

[0037] In the formula, This is a reactive load; This refers to the reactive power output of the grid-connected generator unit; For reactive power losses in wind farms, The unit located at node i belongs to the set of network-type units. ; The unit located at node i does not belong to the set of network-type units. .

[0038] Preferably, the voltage stiffness coefficient in S3 is expressed as:

[0039] ;

[0040] In the formula, Target allocation ratio The dynamic coupling short-circuit ratio under the following conditions; Target allocation ratio The reactive power compensation gap parameter.

[0041] Preferably, the switching change parameter is expressed as follows:

[0042] ;

[0043] In the formula, Configure the target ratio; This represents the current configuration ratio.

[0044] Preferably, the constraints of the multi-objective optimal ratio configuration model include at least:

[0045] The voltage of all nodes is maintained within the set range;

[0046] The actual reactive power output of grid-type generating units meets the maximum reactive power output limit.

[0047] The reactive power compensation gap parameter is non-negative;

[0048] The actual active and reactive power outputs of all wind turbine units satisfy the constraints of the power flow balance equation.

[0049] The target configuration ratio is controlled within the set range.

[0050] Preferably, a multi-objective evolutionary algorithm guided by an adaptive reference vector is used to solve the multi-objective optimal ratio configuration model.

[0051] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention include:

[0052] (1) Breakthrough in dynamic adaptability: Unlike the traditional static planning-oriented configuration method, the real-time dynamic optimization framework constructed in this invention can respond to changes in the grid operation status. Through the rolling optimization mechanism, the proportion of grid-connected units can be dynamically adjusted according to system strength, load fluctuations and new energy output characteristics, ensuring that the configuration ratio is always at the optimal Pareto front under the current operating conditions.

[0053] (2) Innovation of dynamic reactive power coordination mechanism: Traditional configuration methods focus on improving the short-circuit ratio and optimizing static economy, but fail to reveal the dynamic interaction mechanism between the reactive power capacity of the grid-connected unit and the external compensation equipment. This invention, through the voltage stiffness coefficient, realizes for the first time a multi-dimensional coupled characterization of steady-state short-circuit capacity, transient reactive power support rate and dynamic voltage recovery strength.

[0054] (3) Multi-objective dimensionality reduction and computational efficiency improvement: To address the modeling complexity problem caused by the superposition of multi-dimensional indicators in traditional multi-objective optimization, this invention innovatively constructs a dual-parameter collaborative driving mechanism. By equivalently mapping equipment switching losses and operation and maintenance costs to switching change parameters, and coupling them with a voltage stiffness coefficient that can comprehensively characterize the short-circuit ratio and reactive power dynamic characteristics, a collaborative dimensionality reduction expression of complex economic and technical objectives is achieved. While retaining the core elements of multi-objective decision-making, this model effectively reduces the dimensionality of the optimization model, thereby improving the solution speed, and taking into account both the comprehensiveness of engineering decision-making and the need for real-time dynamic adjustment.

[0055] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0057] Figure 1 A schematic diagram illustrating the logical relationship of the wind farm turbine configuration method based on voltage rigidity coefficient and switching variation provided in an embodiment of the present invention;

[0058] Figure 2 This is a connection topology diagram of a doubly fed wind farm unit provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram showing the number of units in each node network configuration provided in an embodiment of the present invention;

[0060] Figure 4 This is a probability distribution diagram of the proportion of network units at each node provided in an embodiment of the present invention;

[0061] Figure 5 This is a diagram showing the voltage fluctuation amplitude at each node provided in an embodiment of the present invention.

[0062] Figure 6 The voltage change waveform of node 1 is provided in an embodiment of the present invention;

[0063] Figure 7 The voltage change waveform of node 4 is provided in an embodiment of the present invention;

[0064] Figure 8 The voltage change waveform of node 7 is provided in an embodiment of the present invention;

[0065] Figure 9 This is a waveform diagram of the voltage change at the PCC point provided in an embodiment of the present invention;

[0066] Figure 10 This is a diagram showing the short-circuit capacity of each node in an embodiment of the present invention.

[0067] Figure 11 This is a diagram showing the unavoidable reactive power compensation gap at each node, provided in the embodiments of the present invention.

[0068] Figure 12 This is a comprehensive comparison and evaluation chart of the various solutions provided in the embodiments of the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] In the real-time operation of doubly-fed induction generator (DFIG) wind farms, this invention proposes an optimal ratio configuration method for grid-connected wind farm units based on voltage stiffness coefficient and switching variation. This method fully leverages the advantages of the parameters and optimization model defined in this invention, and provides an effective solution to the limitations of existing optimal ratio configuration methods for grid-connected wind farms, such as insufficient dynamic characteristic representation, inadequate consideration of reactive power compensation coordination mechanisms, and lack of assessment of the impact on operation and maintenance costs and unit lifespan. It successfully enhances the active voltage support capability of DFIG wind farms, achieving a dual improvement in voltage stability and recovery speed under complex operating environments.

[0071] like Figure 1 As shown in the figure, the wind farm turbine configuration method based on voltage stiffness coefficient and switching variation provided in this embodiment of the invention is a scheme for optimizing the configuration of multiple turbines in a wind farm, mainly including the following steps:

[0072] S1: Based on the electrical distance between the bus node where the unit is located and the grid connection node, the contribution efficiency factor of the computer group is used to determine the dynamic coupling short-circuit ratio under the target configuration ratio.

[0073] S2: Calculate the reactive power compensation gap parameters of wind farms based on the maximum reactive power of grid-connected units;

[0074] S3: Determine the voltage stiffness coefficient based on the dynamic coupling short-circuit ratio and reactive power compensation gap parameter;

[0075] S4: Determine the switching change amount parameter based on the amount of change from the current configuration ratio to the target configuration ratio;

[0076] S5: Construct a multi-objective optimal proportional configuration model with the objective functions of maximizing the voltage stiffness coefficient and minimizing the switching change parameter. Solve the multi-objective optimal proportional configuration model to obtain the optimized target configuration ratio, and configure the wind farm units accordingly.

[0077] The embodiments of the present invention are used to effectively solve the problems of insufficient dynamic characteristic characterization, lack of reactive power coordination mechanism, and lack of consideration for operation and maintenance costs and unit life in the existing optimal ratio configuration method of grid-type units in doubly-fed wind farms, thereby effectively improving the voltage active support capability of doubly-fed wind farms.

[0078] The optimal configuration ratio obtained by the grid-connected wind farm configuration model constructed in this embodiment of the invention has the ability to dynamically respond to system operating conditions in real time and can closely match the system operating status. This not only significantly improves the voltage rigidity coefficient of the system, but also effectively solves the problems of increased operation and maintenance costs and shortened unit life caused by frequent adjustments to the configuration ratio in existing methods. Therefore, this method, while taking into account multiple dimensions of indicators, maintains the relative simplicity of the model and successfully achieves a significant improvement in the active voltage support capability of doubly-fed wind farms.

[0079] In one embodiment, the contribution efficiency factor of unit i in S1 Represented as:

[0080] ;

[0081] In the formula, This represents the virtual impedance of node i where the generator unit is located; This refers to the electrical distance parameter between node i, where the unit is located, and the common coupling point PCC.

[0082] In this embodiment, the electrical distance parameter is calculated using the node impedance matrix of the network. Represented as:

[0083] ;

[0084] In the formula, Let be the self-impedance of node i, and The self-impedance of the common coupling point PCC, Let be the mutual impedance between node i and the common coupling point PCC.

[0085] In one embodiment, for step S1, in a doubly-fed induction generator (DFIG) wind farm, grid-connected turbines, with their virtual impedance regulation capabilities, can provide the necessary short-circuit capacity support to the system, thus significantly affecting the system's short-circuit ratio. Specifically, the configuration ratio of different grid-connected turbines within the wind farm directly determines the strength of the system's short-circuit ratio. Given this close relationship, establishing a mathematical model between the system's short-circuit ratio and the target configuration ratio to be adjusted is particularly important. Since the short-circuit ratio in this process dynamically changes with variations in the turbine configuration ratio and the different turbine switching positions, the concept of "dynamically coupled short-circuit ratio" is introduced to comprehensively reflect this dynamic characteristic.

[0086] To accurately quantify the impact of electrical coupling strength between the bus node where the doubly-fed generator is located and the grid connection point on the dynamic coupling short-circuit ratio, an electrical distance parameter was constructed based on the node impedance matrix. Furthermore, considering that different units have varying effects on improving the dynamic coupling short-circuit ratio due to differences in virtual impedance and electrical distance, a contribution efficiency factor for each unit was defined to comprehensively evaluate its potential contribution to improving the system short-circuit ratio.

[0087] Guided by the principle of improving the dynamic coupling short-circuit ratio of the system, priority should be given to switching units with higher contribution efficiency factors. Therefore, when ranking units, a descending order of contribution efficiency factors is adopted to determine the optimal set of grid-connected units when adjusting from the current configuration to the target configuration ratio. Combined with the inherent characteristics of doubly-fed induction generators, the short-circuit capacity increment after the wind farm configuration ratio adjustment can be obtained, thus deriving the dynamic coupling short-circuit ratio under the target configuration ratio.

[0088] The specific execution process of step S1 is as follows:

[0089] If a wind farm has a total of N turbines, the current grid-connected turbine configuration ratio is: The planned adjustment target allocation ratio is To reflect the impact of electrical coupling strength between the doubly-fed generator bus node and the grid-connected node on the dynamic coupling short-circuit ratio, an electrical distance parameter is introduced. This represents the electrical distance between the i-node where the generator unit is located and the point of common coupling (PCC), which can be specifically represented using the network's node impedance matrix as follows:

[0090] (1)

[0091] In the formula, Let be the self-impedance of node i, and The self-impedance of the common coupling point PCC, Let be the mutual impedance between node i and the common coupling point PCC.

[0092] Considering that different generating units have different virtual impedances and electrical distances, which have varying effects on improving the short-circuit ratio, a contribution efficiency factor is defined to comprehensively consider these two factors and to allow for a systematic ranking of generating units during subsequent modeling. Such as the contribution efficiency factor of unit i It can be represented as:

[0093] (2)

[0094] In the formula, Let be the virtual impedance of unit i.

[0095] To effectively improve the system's short-circuit ratio, units with higher contribution efficiency factors should be prioritized for switching. Therefore, when sorting units, they can be ordered in descending order of contribution efficiency factors. This will yield the set of network-configured units under the current and target configuration ratios. :

[0096] (3)

[0097] (4)

[0098] In the formula, This indicates rounding down to the nearest integer to ensure the number of units is an integer.

[0099] Therefore, we can conclude that:

[0100] (5)

[0101] (6)

[0102] Each grid-connected unit provides short-circuit current through its own virtual impedance. Taking into account the influence of electrical distance, its contributed short-circuit capacity is... It can be represented as:

[0103] (7)

[0104] In the formula, This is the rated voltage.

[0105] The configuration ratio will be changed from the current Adjust to At that time, the change in the system short-circuit capacity increment for:

[0106] (8)

[0107] in, In order to be in The increase in short-circuit capacity for all grid-type generating units under the proportional ratio, while In order to be in The increase in short-circuit capacity for all grid-type generating units under the proportional ratio.

[0108] Specifically, it can be expressed as:

[0109] (9)

[0110] The increment of the dynamic coupling short-circuit ratio after switching for:

[0111] (10)

[0112] In the formula, They always contribute to the wind farm.

[0113] So It can also be expressed as:

[0114] (11)

[0115] Then in The dynamic coupling short-circuit ratio under proportional conditions is:

[0116] (12)

[0117] In one embodiment, S2 proposes an unavoidable reactive power compensation gap to measure the reactive power regulation speed and thus indirectly reflect the voltage recovery speed. In doubly-fed induction generator (DFIG) wind farms, increasing the configuration ratio of grid-connected turbines can significantly enhance the reactive power supply obtained by the system from the turbines. Compared with traditional reactive power compensation equipment, the reactive power compensation provided by grid-connected turbines has advantages such as rapid response and high flexibility. Therefore, effectively enhancing the reactive power output of the turbines to the system plays a crucial role in rapidly maintaining voltage stability and reducing voltage fluctuations.

[0118] However, under certain configuration ratios, when reactive load reaches a high level, even if all wind turbines compensate at their maximum reactive power output capacity while maintaining the voltage at the desired value, it may still be insufficient to meet all demands. In this case, the remaining reactive power deficit must be filled by reactive power compensation equipment; this deficit is referred to in this invention as the "unavoidable reactive power compensation gap." The size of this gap directly reflects the system's ability to quickly maintain voltage stability and effectively reduce voltage fluctuations.

[0119] To accurately assess this gap, the theoretical maximum reactive power output of each unit is first calculated by considering its reactive voltage characteristics and upper limit of reactive power output. Then, the impact of electrical distance on the actual reactive power provided by the units is further considered, and the effective maximum reactive power output of each unit is corrected. Finally, by combining this with the determined target proportion of units, the final unavoidable reactive power compensation gap under the target proportion can be calculated.

[0120] The specific execution process of step S2 is as follows:

[0121] The reactive power output of a grid-connected generating unit can be determined by both its reactive voltage characteristics and the upper limit of reactive power it can generate. Based on the fundamental principles of reactive voltage characteristics, the theoretical reactive power that a grid-connected generating unit can generate is:

[0122] (13)

[0123] In the formula, Let be the droop coefficient of unit i. For the desired voltage value, This is the actual voltage at the bus node where unit i is located.

[0124] Whether this grid-connected unit can generate corresponding reactive power according to its reactive voltage characteristics is also limited by the unit's output power limit. Therefore, the active power output at this time is... Under these circumstances, the upper limit of reactive power that the unit can generate is... for:

[0125] (14)

[0126] In the formula, Let be the rated capacity of the i-th unit.

[0127] Therefore, considering the above two aspects, the maximum reactive power that the unit can actually generate should be the maximum value that can be achieved without violating any of the limitations. This means that if the reactive power calculated based on the reactive voltage characteristics exceeds the unit's hardware capabilities (i.e., exceeds its maximum reactive power output capacity), then the unit cannot provide reactive power support according to the theoretical value; conversely, if the maximum reactive power allowed by the hardware is less than the theoretically calculated value, then the unit can only operate within the range allowed by its hardware. Therefore, the maximum reactive power that unit i can generate is... for:

[0128] (15)

[0129] Considering the active control characteristics of grid-connected generating units, the impact of electrical distance on the actual reactive power provided by these units must be taken into account. Therefore, the maximum effective reactive power that a grid-connected generating unit can generate at the PCC point is... Represented as:

[0130] (16)

[0131] In the formula, This is the attenuation coefficient, which is usually taken as 0.2-0.5.

[0132] For grid-connected units, since they typically operate at a constant power factor, their reactive power... The active power generated by this unit With power factor angle It can be obtained directly, that is:

[0133] (17)

[0134] However, in order to account for the unavoidable reactive power compensation gap, all grid-connected units should be calculated based on their maximum reactive power operating capacity, i.e.:

[0135] (18)

[0136] Since grid-connected generating units do not have active voltage regulation capabilities, the effect of their reactive power output on grid reactive power compensation is entirely determined by the natural distribution of grid power flow, rather than by active correction through control strategies. Therefore, their maximum effective reactive power output does not require explicit calculation.

[0137] The final unavoidable reactive power compensation gap It can be represented as:

[0138] (19)

[0139] In the formula, For reactive load, and This refers to the system's reactive power loss.

[0140] For ease of description, the maximum reactive power that all wind turbines can generate is... It can be described as:

[0141] (20)

[0142] In one embodiment, for step S3, current methods for assessing voltage support capability are mostly limited to isolated indicators such as short-circuit ratio and voltage deviation, making it difficult to comprehensively quantify the complex impact of active control of new energy units on the dynamic characteristics of the power grid. In particular, when the short-circuit ratio (a direct reflection of grid strength) and the unavoidable reactive power compensation gap (a key indicator reflecting rigid compensation requirements) are optimized independently, it often leads to policy conflicts. Furthermore, traditional multi-objective modeling methods significantly increase computational complexity and the difficulty of solving the problem.

[0143] In light of this, this invention innovatively proposes the concept of a voltage stiffness coefficient, aiming to integrate grid strength indicators and reactive power balance requirements into a unified evaluation system by constructing a dynamic coupling ratio that combines the dynamic coupling short-circuit ratio and the unavoidable reactive power compensation gap. This innovation not only effectively avoids the dimensionality curse that may arise from multi-objective optimization, but also, through a nonlinear mapping mechanism, deeply reveals the synergistic optimization law between the configuration ratio of grid-connected units and the capacity of compensation equipment.

[0144] The voltage stiffness coefficient is intuitively expressed as the ratio of the coupled short-circuit ratio to the unavoidable reactive power compensation gap under the target ratio. It comprehensively quantifies the power grid's "ability to withstand voltage drops" and "ability to maintain a steady-state voltage level." Specifically, the larger the coefficient value, the stronger the system's active voltage support capability and the better its performance.

[0145] The definition of the voltage stiffness coefficient in step S3 can be expressed as follows:

[0146] ; (twenty one)

[0147] The voltage stiffness coefficient comprehensively quantifies the power grid's "ability to resist voltage drops" and "ability to maintain a steady-state voltage level." The larger the value, the better the system's active voltage support capability.

[0148] In one embodiment, for step S4, the traditional optimal ratio configuration method often focuses on maximizing the system short-circuit ratio, tending to prioritize configuring units near the grid connection point as grid-connected units. However, this approach may lead to frequent switching of units near the grid connection point during dynamic grid operation, resulting in increased operation and maintenance costs and reduced unit lifespan.

[0149] In contrast, the dynamic optimal ratio configuration method proposed in this invention innovatively defines a switching change parameter by introducing the concepts of the current configuration ratio and the target configuration ratio. This parameter, the difference between the target ratio and the current ratio, can indirectly reflect key performance indicators such as operation and maintenance costs and unit lifespan. In seeking the optimal configuration, this invention not only strives to increase the voltage rigidity coefficient to the target level, but also emphasizes minimizing the switching change as much as possible while achieving this goal. This strategy can not only effectively improve the voltage rigidity coefficient and comprehensively enhance voltage support capabilities, but also take into account the unit's operation and maintenance costs and lifespan, achieving a dual optimization of economic benefits and operational reliability.

[0150] The dynamic optimal ratio configuration strategy proposed in this embodiment innovatively defines the switching change parameter by introducing the concepts of the current configuration ratio and the target configuration ratio. ,Right now:

[0151] ; (twenty two)

[0152] This parameter indirectly reflects key performance indicators such as operation and maintenance costs and unit lifespan. In seeking the optimal configuration, this invention not only aims to increase the voltage rigidity coefficient to the target level, but also emphasizes minimizing switching variations while achieving this goal. This strategy not only effectively improves the voltage rigidity coefficient and comprehensively enhances voltage support capabilities, but also balances unit operation and maintenance costs and lifespan, achieving a dual optimization of economic benefits and operational reliability.

[0153] In one embodiment, to ensure that the final configuration ratio can synchronously improve the system's dynamic coupling short-circuit ratio and minimize unavoidable reactive power compensation gaps, thereby effectively enhancing voltage support capability and response speed, a voltage stiffness coefficient is introduced as a core indicator. Therefore, in the proportional configuration optimization model, the primary and crucial optimization objective function is to maximize the voltage stiffness coefficient, i.e.:

[0154] . (twenty three)

[0155] On the other hand, given the potential damage to unit lifespan caused by frequent unit switching and the resulting increase in maintenance costs, it is desirable to minimize the difference between the final optimal configuration ratio and the current configuration ratio, in order to achieve a significant improvement in the voltage stiffness coefficient while minimizing the number of unit switching operations. This consideration constitutes the second optimization objective function in the proportional configuration optimization model, namely, minimizing the switching variation, i.e.:

[0156] . (twenty four)

[0157] In this embodiment, to ensure the feasibility of the optimization scheme in practical engineering applications, the established proportional configuration optimization model also comprehensively incorporates dynamic constraints from multiple dimensions, including voltage safety constraints, equipment adjustment limits, and network topology constraints. The introduction of these constraints ensures that the optimization results are not only theoretically feasible but also highly practical and reliable in actual operation. Specifically, the constraints of the multi-objective optimal proportional configuration model include at least the following:

[0158] Voltage safety constraints:

[0159] Voltage of all nodes in the system Voltage must always be maintained within the permissible range to prevent equipment damage or protection activation caused by exceeding voltage limits. That is:

[0160] (25)

[0161] Actual reactive power output limit of the unit:

[0162] When establishing the model, the calculation of unavoidable reactive power compensation gaps is based on the maximum possible reactive power output of the generating units. Therefore, the actual reactive power output of the generating units under the optimal proportional configuration must also meet the constraint of the maximum reactive power output. That is:

[0163] (26)

[0164] The limitation of reactive power compensation gap cannot be avoided:

[0165] Since unavoidable reactive power is ultimately compensated by reactive power equipment, its value must be less than the maximum reactive power that the equipment can generate. Furthermore, to avoid potential overcompensation, unavoidable reactive power compensation should be a non-negative value. Considering both of these factors, we can conclude that:

[0166] (27)

[0167] Power flow equilibrium equation constraints:

[0168] Throughout the entire operation, the system's active and reactive power must remain in balance, that is:

[0169] (28)

[0170] In the formula, The reactive power generated by reactive power equipment.

[0171] Configuration ratio limit:

[0172] According to IEEE standards, the proportion of grid-connected units should be at least 20% to ensure a short-circuit ratio ≥2. However, if the proportion exceeds 70%, it may lead to circulating current resonance risk and increase the probability of transient overvoltage. Therefore, the optimal configuration proportion should be controlled within the range of (20-70)%, i.e.:

[0173] (29)

[0174] In one embodiment, the adaptive reference vector-guided multi-objective evolutionary algorithm (AR-MOEA) is used for model solving. This algorithm dynamically adjusts the distribution density and direction of the reference vector to adaptively balance the "exploration-development" capability, effectively solving the problems of uneven solution set distribution and convergence direction deviation from the true front when traditional methods solve high-dimensional optimization problems with strong objective conflict and complex Pareto front shapes.

[0175] The following is an application example of this invention:

[0176] To verify the effectiveness of the optimal ratio configuration method proposed in this invention in improving voltage support capability, this invention is based on the Northwest region of China, such as... Figure 2 The actual architecture of a typical doubly-fed induction generator (DFIG) wind farm, as shown, was simulated using the MATLAB / Simulink platform. This model strictly adheres to the actual wind farm topology, setting up eight turbine collection nodes. The nodes are numbered 1-8 sequentially from nearest to farthest along the collector line length for ease of subsequent description and analysis. The number of turbines at each node is identical to the actual farm: Node 1 (12 turbines), Node 2 (11 turbines), Node 3 (8 turbines), Node 4 (8 turbines), Node 5 (7 turbines), Node 6 (6 turbines), Node 7 (6 turbines), and Node 8 (6 turbines), totaling 64 1.5MW DFIG wind turbine units. Key parameters of the simulation model are derived from the actual operating data of this wind farm; detailed parameter settings are shown in Table 1.

[0177] Table 1. Main simulation parameters of the doubly-fed motor

[0178]

[0179] To comprehensively evaluate the overall performance advantages of the proposed solution, three sets of comparative experiments were designed in the embodiments of the present invention to conduct systematic verification from different dimensions:

[0180] ① Case 1 (Traditional Optimization Scheme): This scheme adopts the conventional short-circuit ratio improvement strategy of the power grid, with the goal of minimizing the overall network loss. It represents the technical route commonly used in current engineering practice.

[0181] ② Case 2 (Random Switching Scheme): Under the premise of meeting the preset short-circuit ratio threshold, the target is achieved by randomly selecting units to switch on or off, reflecting the baseline operating condition without optimization strategy.

[0182] ③ Case 3 (Design Scheme of the Invention): The optimal ratio configuration method proposed in this invention is applied to achieve multi-objective collaborative optimization while ensuring voltage support capability.

[0183] Subsequent rigorous simulation and comparison experiments will be conducted to quantitatively analyze key indicators such as voltage stability, rigidity coefficient, and unit switching changes, so as to intuitively verify the significant advantages of this invention compared with other methods using experimental data.

[0184] A. Comparison of changes in unit switching:

[0185] Figure 3 The diagram visually illustrates the distribution of the number of grid-type generating units at each node under the three schemes, supplemented by the original state of the grid-type generating units at each node before the implementation of the schemes, so as to clearly compare the changes in the number of generating units switched over caused by each scheme. Figure 4The diagram illustrates the probability distribution of the percentage of grid-type generating units at each node relative to the total number of generating units at that node under various experimental conditions. Clearly, Scheme 1 focuses on reducing losses and maximizing the short-circuit ratio. Therefore, it concentrates on increasing the switching of grid-type generating units near the nodes (especially the first three nodes). This is because switching far-node units to grid-type configurations to generate additional reactive power would lead to increased losses. Furthermore, compared to near-node units, far-node units are less effective at improving the short-circuit ratio. However, this scheme overlooks the problem of frequent unit switching, over-relying on near-node units for switching. This not only increases the operation and maintenance costs of near-node units but may also shorten their service life.

[0186] In contrast, Scheme 2 adopts a more random strategy, selectively switching units at each node to ensure that the system short-circuit ratio reaches the predetermined threshold. This approach ensures that each node has units switched to network configuration, thereby alleviating the pressure of frequent switching of units near the nodes to some extent and reducing the overall operation and maintenance costs of the system. However, the increased reactive power generation of units at distant nodes after switching to network configuration increases losses, and the effective improvement of the system short-circuit ratio is not significant.

[0187] Scheme 3 proposed in this invention comprehensively examines and effectively addresses many problems existing in the aforementioned schemes. This scheme solves the problem of decreased operating efficiency and increased losses caused by frequent unit switching by introducing an objective function that minimizes the switching variation. Simultaneously, Scheme 3 employs an objective function that maximizes the voltage stiffness coefficient, ensuring that near-node units are preferentially selected during unit switching. This not only efficiently increases the system's short-circuit ratio but also reduces unavoidable reactive power losses, thereby achieving a significant improvement in the voltage stiffness coefficient.

[0188] B. Comparison of voltage stability and support performance:

[0189] Under normal system operation, the analysis of voltage fluctuation amplitude at eight nodes under three different schemes is as follows: Figure 5As shown in the figure, the three adjacent bars, from left to right, represent the voltage stability maintenance capabilities of Scheme 1, Scheme 2, and Scheme 3, respectively. Scheme 1 exhibits the strongest voltage stability maintenance capability at near nodes; however, its performance is the weakest at far nodes. This is attributed to the objective function of Scheme 1, which aims to minimize network losses. This objective prompts the system to maximize the short-circuit ratio, resulting in most grid-connected units being concentrated at near nodes, while almost no units at far nodes are converted to grid-connected configurations, thus weakening the voltage stability capability at far nodes. In contrast, Scheme 2, by randomly selecting units at different nodes for switching, makes the difference in voltage stability maintenance between near and far nodes less significant than that of Scheme 1, which is more in line with the inherent characteristics of the power system: voltage levels gradually decrease from near to far, and the magnitude of change increases accordingly. As for Scheme 3 proposed in this invention, it not only incorporates the improvement of the overall voltage stiffness coefficient into its objective function but also takes into account the problem of frequent unit switching, that is, it integrates the objective function of minimizing the change in unit switching. Therefore, under Scheme 3, the switching of units at each node is neither as heavily biased towards neighboring nodes as in Scheme 1, nor as almost randomly distributed across nodes as in Scheme 2. Instead, it strives to maximize the overall voltage stability of the system while closely meeting the needs of each node. This strategy makes Scheme 3 superior to the other two schemes in maintaining voltage stability at each node.

[0190] To comprehensively evaluate the dynamic voltage support performance of the three schemes, a specific test condition was set during the simulation: a 10% voltage drop disturbance was applied at the 2nd second of system operation, lasting for 0.5 seconds and then eliminated at 2.5 seconds. Due to space limitations, this embodiment only shows the voltage change waveforms of a few key nodes, specifically including node 1 representing the near node, node 4 representing the mid-distance node, node 7 representing the far-distance node, and the PCC node reflecting the overall system performance, such as... Figures 6 to 9 As shown. Through detailed comparative analysis, the following conclusions can be drawn:

[0191] At the near-node, Scheme 1 exhibits the best voltage recovery capability and speed due to its higher proportion of grid-connected units. Scheme 3, proposed in this invention, suffers from slightly weaker voltage recovery capability and speed compared to Scheme 1 because its design minimizes unit switching variations. Scheme 2, with its smaller proportion of grid-connected units near the near-node, has the worst voltage recovery capability and speed among the three.

[0192] For mid-distance nodes, the proposed scheme 3 and scheme 2 are almost on par in terms of voltage recovery performance and speed, while the performance of scheme 1 falls to the worst.

[0193] Regarding remote nodes, Scheme 1 continues to exhibit the worst voltage recovery performance and speed due to the near absence of units switching to the grid-connected configuration. For Scheme 2 and the proposed Scheme 3, although Scheme 2 has a slight advantage in the number of units at remote nodes, the performance gap between the two is not significant. This is because Scheme 3 emphasizes maximizing the voltage stiffness coefficient in its objective function, ensuring excellent overall system voltage support capabilities, thus compensating to some extent for the insufficient number of units.

[0194] A comprehensive analysis of near-node, mid-node, and far-node data clearly reveals differences in voltage recovery capability and speed performance among the three schemes at different nodes. However, from a global perspective, Scheme 3 proposed in this invention demonstrates superiority over the other two schemes. This can be illustrated by the PCC voltage variation graph, which reflects the overall performance. Figure 9 To provide further evidence. Observation Figure 9 It is easy to see that Scheme 3 of the present invention surpasses the other two schemes in both the strength and speed of increasing the voltage of the PCC node. This result is highly consistent with the previous analysis conclusions for each node and mutually corroborates each other.

[0195] C. Comparison of voltage stiffness coefficient improvement:

[0196] To compare the effectiveness of the three schemes in improving the voltage stiffness coefficient, it is necessary to examine their performance in improving the short-circuit ratio and the unavoidable reactive power compensation gap. This analysis is based on the definition of the voltage stiffness coefficient. Since directly describing the short-circuit ratio of a node is not sufficiently clear, the short-circuit capacity of each node is used as an indicator to indirectly reflect changes in the short-circuit ratio. Figure 10 and Figure 11 The comparison results of the short-circuit capacity and unavoidable reactive power compensation gap of each node under the three schemes are presented respectively. Figure 10 The three adjacent bar graphs in the middle, from left to right, represent the short-circuit capacity improvement effects of Scheme 1, Scheme 2, and Scheme 3, respectively. Figure 11 The three bar graphs in the figure, from left to right, represent the unavoidable reactive power compensation gaps of Scheme 1, Scheme 2, and Scheme 3, respectively.

[0197] observe Figure 10It can be observed that Scheme 1, by concentrating grid-connected units near the nodes, significantly improves the short-circuit capacity of these nodes, while its effect on distant nodes is relatively limited. In contrast, Scheme 2, by randomly selecting units on different nodes for switching, results in a gradual decrease in the improvement of short-circuit capacity from near to far, which aligns with the basic principles and characteristics analyzed in the previous model. As for Scheme 3 proposed in this invention, it combines the advantages of the above two schemes, considering both the effective enhancement of voltage stiffness coefficient by improving the short-circuit capacity of near nodes and the frequency of unit switching at each node. Therefore, overall, Scheme 3 outperforms the previous two schemes in improving short-circuit capacity.

[0198] Through analysis Figure 11 Further conclusions can be drawn. Since Scheme 1 has almost no units switching to a grid-like configuration at far nodes, this results in an abnormally high unavoidable reactive power compensation gap at far nodes. This extreme situation weakens the advantages brought by the increased short-circuit capacity at near nodes, ultimately affecting the improvement of the overall system voltage rigidity coefficient. Although Scheme 2 has a relatively even distribution of unavoidable reactive power at each node, this approach does not effectively improve the overall system voltage rigidity coefficient. Scheme 3 proposed in this invention avoids the shortcomings of the above two schemes. It does not adopt the extreme approach of Scheme 1, nor does it simply imitate the random average distribution strategy of Scheme 2. Instead, Scheme 3 adopts a more moderate approach, maintaining a relatively low unavoidable reactive power compensation gap at near nodes and appropriately increasing it at far nodes. This approach fully utilizes the advantages of near-node units in improving the voltage rigidity coefficient while considering the potential losses caused by excessive reactive power transmission from far-node units. Therefore, Scheme 3 performs best in improving the overall system voltage rigidity coefficient.

[0199] Based on the detailed analysis above regarding the short-circuit capacity of each node and the unavoidable reactive power compensation gap, it is easy to conclude that Scheme 3 proposed in this invention is significantly superior to the other two schemes in improving the voltage stiffness coefficient, a comprehensive performance indicator, to enhance voltage support capability. Table 2 details the data comparison of the three schemes in terms of short-circuit ratio, voltage stiffness coefficient, maximum and minimum node voltages, and total system losses.

[0200] Table 2 Comparison of Key Indicators

[0201]

[0202] Scheme 1 demonstrates good performance in reducing grid losses and improving the system short-circuit ratio. However, it fails to fully consider the minimization of unavoidable reactive power and the optimization of switching variations. Therefore, when ultimately aiming to improve the voltage stiffness coefficient, Scheme 1 performs worse than Scheme 3 proposed in this invention. As for Scheme 2, it adopts a random averaging strategy in all aspects, lacking targeted optimization considerations. This results in its relatively inferior performance in improving the short-circuit ratio, voltage stiffness coefficient, and reducing total system losses.

[0203] Taking into account the comparative analysis of various solutions on multiple performance indicators, the following approach is adopted: Figure 12 The radar chart shown visually illustrates the comprehensive capabilities of the three schemes across six key dimensions. These dimensions are labeled A through F, representing: A—the ability to suppress voltage fluctuations and maintain voltage stability; B—the speed at which voltage recovers to a preset value after being disturbed; C—the performance to mitigate the impact of unavoidable reactive power compensation gaps; D—the ability to enhance the system's short-circuit ratio; E—the performance to minimize switching variations; and F—the efficiency to reduce overall system losses.

[0204] The radar chart clearly reveals that Option 1 demonstrates superior capabilities in improving the system short-circuit ratio and reducing losses, directly attributable to its optimization strategy's focus on these specific objectives. Conversely, Option 2's performance across various performance metrics is relatively balanced but not outstanding, reflecting the limitations of its strategy of randomly selecting units and lacking a clear optimization focus.

[0205] It is worth noting that Scheme 3 proposed in this invention is only slightly inferior to Scheme 1 in reducing total system losses, while performing optimally in all other performance indicators. From a global perspective, although Scheme 3 makes a slight sacrifice in reducing losses, it achieves significant improvements in maintaining voltage stability, accelerating voltage recovery, increasing the short-circuit ratio, reducing the frequency of unit switching, and extending equipment lifespan. These achievements fully verify the superiority and effectiveness of Scheme 3 designed in this invention in enhancing the active voltage support capability of doubly-fed wind farms, making it a preferred scheme worthy of promotion.

[0206] The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation provided by the present invention has been described in detail above. Specific examples have been used in this embodiment to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0207] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined in these embodiments may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring wind farm turbine units based on voltage stiffness coefficient and switching variation, characterized in that, A wind farm is equipped with multiple turbines, including the following steps: S1: Calculate the contribution efficiency factor of the unit based on the electrical distance between the bus node where the unit is located and the grid connection node, and use the contribution efficiency factor to determine the dynamic coupling short-circuit ratio of the grid-type units under the target configuration ratio in the wind farm. S2: Calculate the reactive power compensation gap parameters of wind farms based on the maximum reactive power of grid-connected units; S3: Based on the dynamic coupling short-circuit ratio and the reactive power compensation gap parameter, the voltage stiffness coefficient is determined as follows: ； In the formula, Target allocation ratio The dynamic coupling short-circuit ratio under the following conditions; Target allocation ratio The reactive power compensation gap parameter is as follows; S4: Determine the switching change parameter based on the change in the current configuration ratio of grid-connected units in the wind farm to the target configuration ratio; S5: Construct a multi-objective optimal proportional configuration model with the objective functions of maximizing the voltage stiffness coefficient and minimizing the switching change parameter, solve the multi-objective optimal proportional configuration model to obtain the optimized target configuration ratio, and configure the wind farm units.

2. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, The unit in S1 i Contribution efficiency factor Represented as: ； In the formula, The node where the unit is located i The virtual impedance; The node where the unit is located i Electrical distance parameters between the common coupling point (PCC) and the common coupling point (PCC).

3. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 2, characterized in that, Calculating electrical distance parameters using the node impedance matrix of the network Represented as: ； In the formula, For nodes i The self-impedance, and The self-impedance of the common coupling point PCC, For nodes i The impedance to the common coupling point PCC, For the common coupling point PCC to the node i The impedance.

4. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, The step in S1 of determining the dynamic coupling short-circuit ratio increment of the grid-type unit set under the target configuration ratio compared to the grid-type unit set under the current configuration ratio using the contribution efficiency factor includes: According to contribution efficiency factor Sort the units in descending order to obtain the set of grid-connected units G0 under the current configuration ratio and the set of grid-connected units under the target configuration ratio. ; Based on the virtual impedance of the units, the grid-connected unit set G0 and the grid-connected unit set are calculated respectively. Contributed short-circuit capacity ; Calculate the short-circuit capacity when adjusting from the current configuration ratio to the target configuration ratio. incremental change ; Based on short-circuit capacity incremental change Calculate the dynamic coupling short-circuit ratio under the target configuration ratio. .

5. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, S2 includes the following steps: Calculate the theoretical reactive power output of the grid-type generator unit based on the reactive voltage characteristics; Based on the upper limit of reactive power and electrical distance parameters of grid-type units, determine the actual reactive power output of grid-type units; Determine the reactive power output of the grid-connected generator units; For real-time reactive load, calculate the reactive power compensation gap parameter that needs to be compensated in addition to the actual reactive power output of grid-type units and the reactive power output of grid-connected units.

6. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 5, characterized in that, S2 includes the following steps: Determine the actual reactive power output of grid-connected units Represented as: ； ； In the formula, The attenuation coefficient; The node where the unit is located i Electrical distance parameters between the common coupling point (PCC) and the PCC; The theoretical reactive power output of grid-connected generating units; This refers to the upper limit of reactive power for grid-connected generating units; The node where the unit is located i The maximum reactive power that can be generated; Calculate reactive power compensation gap parameters Represented as: ； In the formula, This is a reactive load; This refers to the reactive power output of the grid-connected generator unit; For reactive power losses in wind farms, For the node i The units belong to the network-type unit collection. ; For the node i The units do not belong to the grid-type unit set. .

7. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, The switching change parameter is expressed as follows: ； In the formula, Configure the target ratio; This represents the current configuration ratio.

8. The wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, The constraints of the multi-objective optimal ratio configuration model include at least the following: The voltage of all nodes is maintained within the set range; The actual reactive power output of grid-type generating units meets the maximum reactive power output limit. The reactive power compensation gap parameter is non-negative; The actual active and reactive power outputs of all wind turbine units satisfy the constraints of the power flow balance equation. The target configuration ratio is controlled within the set range.

9. A wind farm turbine configuration method based on voltage stiffness coefficient and switching variation according to claim 1, characterized in that, A multi-objective optimal ratio configuration model is solved using a multi-objective evolutionary algorithm guided by adaptive reference vectors.

Images