Wind power plant unit configuration method based on voltage rigidity coefficient and switching variable quantity

Abstract

The invention provides a wind power plant unit configuration method based on a voltage rigidity coefficient and a switching variable quantity, which 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, and determining a dynamic coupling short-circuit ratio under a target configuration ratio; calculating a reactive compensation gap parameter of the wind power plant based on the maximum reactive power of the network-forming unit; determining a voltage rigidity coefficient based on the dynamic coupling short-circuit ratio and the reactive compensation gap parameter; determining a switching variable quantity parameter; and constructing a multi-target optimal proportion configuration model by taking maximization of the voltage rigidity coefficient and minimization of the switching variable quantity parameter as target functions, and configuring the wind power plant unit. According to the method, the voltage rigidity coefficient of the system can be remarkably improved, and then the stability of the system voltage in a normal operation state and the rapid recovery capability after disturbance are greatly enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of doubly-fed wind farms, and is used to improve the voltage active support capability of doubly-fed wind farms, in particular to provide an optimal proportional configuration method for wind farm grid-type units based on voltage rigidity coefficient and switching variation. Background Art

[0002] With the deepening implementation of the "dual carbon" strategic goals, wind power, as a core component of the clean energy system, has ushered in a historic development opportunity. Doubly-fed wind turbines, with their excellent power regulation, mature manufacturing processes, and high cost-performance, have become the mainstream model in the onshore wind power sector. In recent years, my country has planned and constructed numerous wind power bases with a capacity of tens of millions of kilowatts across its vast desert and Gobi regions. These large-scale renewable energy clusters, enabled by ultra-high voltage direct current (UHVDC) transmission channels, play a key role in the development of a new power system. However, these wind farms are often located at the end of the grid, and the network structure, lacking synchronous power support, exhibits significant weak grid characteristics. The system's equivalent short-circuit ratio continues to decline, leading to a serious lack of voltage support capacity. In particular, the risk of voltage instability increases significantly under transient disturbance conditions, becoming a major technical bottleneck hindering the efficient integration of renewable energy and the safe operation of the power system.

[0003] Traditional doubly-fed wind turbines generally adopt a grid-following control strategy, and their operating characteristics are highly dependent on the stability of the grid voltage. With the continuous increase in wind power penetration, the system's equivalent short-circuit ratio has shown a continuous downward trend. Research data shows that when the short-circuit ratio of renewable energy at a site falls below 2.0, the system's small-disturbance stability margin decreases sharply, easily inducing stability issues such as subsynchronous oscillations. To this end, academia has recently proposed grid-following control technology, which enhances the system's voltage support capability by simulating the external characteristics of synchronous generators. However, engineering practice has found that the configuration ratio of grid-following units presents a significant dilemma: if the configuration ratio is too low, it will not effectively increase the short-circuit capacity; if it is too high, it may lead to a surge in equipment investment, increased operating losses, and complex control interactions. Therefore, determining the optimal configuration ratio of grid-following units under multi-objective constraints has become a key breakthrough in improving the active voltage support capabilities of renewable energy sites.

[0004] The focus of existing research is mainly on the two core areas of short-circuit ratio improvement and economic optimization, aiming to build 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 suitable for 100% renewable energy independent power supply systems" calculates and compares the short-circuit ratio and critical short-circuit ratio SCR-0 of the step-up and low-voltage side of the grid-type new energy station, ensuring that the former is not less than the latter to meet the requirements of stable system operation, and then calculates the optimal proportion configuration of the grid-type converter. However, although this method performs well in improving the short-circuit ratio, it fails to fully take into account key dynamic performance indicators such as voltage recovery speed, fails to deeply explore the synergistic mechanism between the unit's own dead power capacity and the external compensation device, and does not touch on the impact of this synergistic mechanism on the rapid voltage recovery performance. In addition, Chinese patent CN118263920A "New Energy or Energy Storage Station Networking / Grid-following Unit Ratio Configuration Method and System" establishes a small signal state space model for the grid connection of new energy or energy storage stations, calculates key indicators of stability and dynamic characteristics under different grid-connected unit ratios, and uses this to construct constraints to solve the cost function, thereby optimizing the unit ratio configuration and significantly reducing unit costs. However, while pursuing cost optimization, it ignores the potential impact of unit switching changes on operation and maintenance costs and unit life. In practical applications, in order to optimize the objective function, it often leads to frequent switching of units close to the grid connection point, which not only significantly increases operation and maintenance costs, but also poses a serious threat to equipment life. More importantly, the above invention still relies too much on static parameters in the planning stage in model construction, lacks comprehensive adaptation to the dynamic characteristics of actual operating conditions, and cannot achieve real-time dynamic optimization and adjustment of the grid-connected unit ratio. In summary, current research results have significant limitations in terms of evaluation index system, consideration of unit switching changes, 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 not only effectively improve the strength and speed of system voltage recovery on the basis of fully considering the coordinated mechanism of reactive compensation between the units and reactive equipment, but also take into account problems such as frequent unit switching to reduce operation and maintenance costs and extend unit life, while simplifying the complexity of the optimization model, and ultimately achieving a significant improvement in the active voltage support capability of the doubly fed wind farm is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0006] In view of this, the present invention proposes a wind farm unit configuration method based on voltage rigidity coefficient and switching variation. By flexibly adjusting the configuration ratio of grid-type units in a doubly fed wind farm, the voltage rigidity 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 disturbances.

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

[0008] The present invention first discloses a method for configuring wind farm units based on voltage rigidity coefficient and switching variation. A plurality of units are configured in a wind farm, comprising the following steps:

[0009] S1: Calculating the contribution efficiency factor of the unit based on the electrical distance between the bus node where the unit is located and the grid-connected node, and determining the dynamic coupling short-circuit ratio under the target configuration ratio using the contribution efficiency factor;

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

[0011] S3: determining a voltage rigidity coefficient based on the dynamic coupling short-circuit ratio and the reactive compensation gap parameter;

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

[0013] S5: constructing a multi-objective optimal proportion configuration model with the maximization of the voltage rigidity coefficient and the minimization of the switching variation parameter as objective functions, solving the multi-objective optimal proportion configuration model to obtain the optimized target configuration ratio, and configuring the wind farm units.

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

[0015] ;

[0016] Where, is the virtual impedance of the node i where the unit is located; is the electrical distance parameter between the 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 Expressed as:

[0018] ;

[0019] Where, is the self-impedance of node i, and is the self-impedance of the point of common coupling PCC, is the mutual impedance between node i and the point of common coupling PCC, is the impedance from the point of common coupling PCC to node i.

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

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

[0022] Based on the virtual impedance of the unit, the grid-type unit set G0 and the grid-type unit set G0 are calculated respectively. Contributed short-circuit capacity ;

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

[0024] Based on short-circuit capacity The change increment , calculate the dynamic coupling short-circuit ratio under the target configuration ratio .

[0025] Preferably, S2 comprises the following steps:

[0026] Calculate the theoretical reactive power output by the grid-connected units based on the reactive voltage characteristics;

[0027] Based on the reactive power upper limit and electrical distance parameters of the grid-forming units, the actual reactive power output by the grid-forming units is determined;

[0028] Determine the reactive power output by the grid-following units;

[0029] For the real-time reactive load, the reactive compensation gap parameters that need to be compensated in addition to the actual reactive power output by the grid-forming units and the reactive power output by the grid-following units are calculated.

[0030] Preferably, S2 comprises the following steps:

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

[0032] ;

[0033] ;

[0034] Where, is the attenuation coefficient; is the electrical distance parameter between the node i where the unit is located and the common coupling point PCC; The theoretical reactive power output by the grid-connected unit; It is the upper limit of reactive power of grid-type units; is the maximum reactive power that can be generated by node i where the unit is located;

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

[0036] ;

[0037] Where, is the reactive load; The reactive power output by the grid-following unit; is the reactive loss of the wind farm, The unit located at node i belongs to the set of networked units ; The unit located at node i does not belong to the set of networked units .

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

[0039] ;

[0040] Where, Configure the scale for the target Dynamic coupling short-circuit ratio under ; Configure the scale for the target The reactive power compensation gap parameter under .

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

[0042] ;

[0043] Where, Configure the scale for the target; The current configuration ratio.

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

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

[0046] The actual reactive power output by the grid-type units meets the maximum reactive output limit;

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

[0048] The actual active power and reactive power output of all wind turbines meet 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 proportional configuration model.

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

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

[0053] (2) Innovation in dynamic reactive power coordination mechanism: Traditional configuration methods focus on improving the short-circuit ratio and optimizing static economic efficiency, but fail to reveal the dynamic interaction mechanism between the reactive power capacity of the grid-type unit itself and the external compensation equipment. This invention, through the voltage rigidity coefficient, achieves 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 paper innovatively constructs a dual-parameter collaborative driving mechanism. By mapping the equipment switching loss and operation and maintenance cost equivalently into switching variable parameters, and coupling the voltage rigidity 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 optimization model dimension, thereby improving the solution speed, taking into account the comprehensiveness of engineering decision-making and the need for real-time dynamic adjustment.

[0055] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only embodiments of the present invention. Those skilled in the art can also derive other drawings based on the provided drawings without inventive effort.

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

[0058] Figure 2 A connection topology diagram of the doubly-fed wind farm units provided in an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of the number of network-type units at each node provided in an embodiment of the present invention;

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

[0061] Figure 5 A diagram showing the voltage fluctuation amplitude of each node provided by an embodiment of the present invention;

[0062] Figure 6 A waveform diagram showing the voltage change at node 1 according to an embodiment of the present invention;

[0063] Figure 7 A voltage change waveform diagram of node 4 provided by an embodiment of the present invention;

[0064] Figure 8 A waveform diagram showing the voltage variation at node 7 according to an embodiment of the present invention;

[0065] Figure 9 A waveform diagram of the voltage change at the PCC point provided by an embodiment of the present invention;

[0066] Figure 10 A short-circuit capacity diagram of each node provided by an embodiment of the present invention;

[0067] Figure 11 A diagram of unavoidable reactive power compensation gaps at each node provided by an embodiment of the present invention;

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

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] During the real-time operation of a doubly-fed wind farm, this paper proposes a method for optimally allocating wind farm grid-type units based on voltage rigidity coefficient and switching variation. This method leverages the advantages of the parameters and optimization model defined in this paper, providing an effective solution to the limitations of existing methods for optimally allocating wind farm grid-type units, such as insufficient dynamic characterization, inadequate consideration of reactive power compensation coordination mechanisms, and a lack of assessment of the impact of operation and maintenance costs on unit lifespan. This method successfully enhances the active voltage support capability of the doubly-fed wind farm, achieving both improved voltage stability and faster recovery speed under complex operating conditions.

[0071] like Figure 1 As shown, the wind farm unit configuration method based on the voltage rigidity coefficient and the switching variation provided by the embodiment of the present invention is a solution for optimizing the configuration of multiple units in a wind farm, and mainly includes the following steps:

[0072] 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-connected node, and use the contribution efficiency factor to determine the dynamic coupling short-circuit ratio under the target configuration ratio;

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

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

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

[0076] S5: Construct a multi-objective optimal proportion configuration model with the objective function of maximizing the voltage rigidity coefficient and minimizing the switching change parameter, solve the multi-objective optimal proportion configuration model to obtain the optimized target configuration ratio, and configure the wind farm units.

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

[0078] The optimal configuration ratio solved by the optimal ratio configuration model for grid-type units constructed by the embodiment of the present invention has the ability to dynamically respond to the system operating status 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, while taking into account multi-dimensional indicators, this method maintains the relative simplicity of the model and successfully achieves a significant improvement in the active voltage support capability of the doubly fed wind farm.

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

[0080] ;

[0081] Where, is the virtual impedance of the node i where the unit is located; is the electrical distance parameter between the node i where the unit is located and the common coupling point PCC.

[0082] In this embodiment, the electrical distance parameters are calculated using the node impedance matrix of the network. Expressed as:

[0083] ;

[0084] Where, is the self-impedance of node i, and is the self-impedance of the point of common coupling PCC, is the mutual impedance between node i and the point of common coupling PCC.

[0085] In one embodiment, for step S1, in a doubly fed wind farm, the grid-type units can provide the necessary short-circuit capacity support to the system by virtue of their virtual impedance control capability, thereby having a significant impact on the short-circuit ratio of the system. Specifically, the configuration ratio of different grid-type units in the wind farm will directly determine the strength of the system short-circuit ratio. In view of this close relationship, it is particularly important to establish a mathematical model between the system short-circuit ratio and the target configuration ratio to be adjusted. Since the short-circuit ratio in this process will change dynamically with the change of the unit configuration ratio and the different unit switching positions, the concept of "dynamically coupled short-circuit ratio" is hereby introduced to fully reflect this dynamic characteristic.

[0086] To accurately quantify the impact of the 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 in conjunction with the node impedance matrix. Furthermore, considering that different units have different effects on improving the dynamic coupling short-circuit ratio due to differences in virtual impedance and electrical distance, a unit contribution efficiency factor was defined to comprehensively evaluate its potential contribution to improving the system short-circuit ratio.

[0087] Guided by the principle of improving the system's dynamic coupled short-circuit ratio, priority should be given to switching units with larger contribution efficiency factors. Therefore, when sorting units, we use descending contribution efficiency factors to determine the optimal set of grid-connected units when adjusting the current configuration toward the target configuration ratio. Combined with the inherent characteristics of doubly-fed generators, we can determine the incremental short-circuit capacity after adjusting the wind farm configuration ratio, and thus the dynamic coupled short-circuit ratio at the target configuration ratio.

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

[0089] If there are N wind turbines in the wind farm, the current configuration ratio of grid-type turbines is , and the target allocation ratio of the planned adjustment is In order to reflect the influence of the electrical coupling strength between the doubly-fed generator bus node and the grid-connected node on the dynamic coupling short-circuit ratio, the electrical distance parameter is introduced. , which represents the electrical distance between the i-node where the unit is located and the point of common coupling (PCC), can be specifically expressed using the node impedance matrix of the network as:

[0090] ; (1)

[0091] Where, is the self-impedance of node i, and is the self-impedance of the point of common coupling PCC, is the mutual impedance between node i and the point of common coupling PCC.

[0092] Considering that different units have different virtual impedances and electrical distances that have different effects on improving the short-circuit ratio, in order to comprehensively consider these two factors and to regularly sort the units for subsequent modeling, the contribution efficiency factor is specially defined. , such as the contribution efficiency factor of unit i It can be expressed as:

[0093] ; (2)

[0094] Where, is the virtual impedance of unit i.

[0095] In order to effectively improve the short-circuit ratio of the system, in principle, the units with large contribution efficiency factors should be switched first. When sorting the units, they can be sorted in descending order of contribution efficiency factors. Then, the set of grid-type units under the current configuration ratio and the target configuration ratio can be obtained. :

[0096] ; (3)

[0097] ; (4)

[0098] Where, Indicates rounding down to ensure the number of units is an integer.

[0099] So we can get:

[0100] ; (5)

[0101] ; (6)

[0102] Each grid-type unit provides short-circuit current through its own virtual impedance. Considering the influence of electrical distance, its contribution to short-circuit capacity is It can be expressed as:

[0103] ; (7)

[0104] Where, is the rated voltage.

[0105] The configuration ratio is determined by the current Adjust to When the system short-circuit capacity changes for:

[0106] ; (8)

[0107] in, For The short-circuit capacity increment of all grid-type units under the ratio For The short-circuit capacity increment of all grid-type units under the same ratio.

[0108] Specifically, it can be expressed as:

[0109] ; (9)

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

[0111] ; (10)

[0112] Where, Total active power output of the wind farm.

[0113] So It can also be expressed as:

[0114] ; (11)

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

[0116] (12)

[0117] In one embodiment, S2 proposes an unavoidable reactive power compensation gap, which is used to measure the speed of reactive power regulation and indirectly reflect the speed of voltage recovery. In a doubly-fed wind farm, increasing the proportion of grid-type units can significantly enhance the reactive power supply to the system from these units. Compared to traditional reactive power compensation equipment, the reactive power compensation provided by grid-type units offers advantages such as rapid response and high flexibility. Therefore, effectively enhancing the reactive power output of the units to the system is crucial for rapidly maintaining voltage stability and reducing voltage fluctuations.

[0118] However, under certain configuration ratios, when reactive loads reach high levels, even if all wind turbines compensate at their maximum reactive output capacity while maintaining the desired voltage, they may still not be able to meet full demand. In this case, the remaining reactive power shortfall must be filled by reactive power compensation equipment. This shortfall is referred to as the "unavoidable reactive power compensation gap" in this disclosure. The size of this gap directly reflects the system's ability to quickly maintain voltage stability and effectively mitigate voltage fluctuations.

[0119] To accurately assess this gap, we first calculate the theoretical maximum reactive power output of each unit, combining the reactive voltage characteristics of the grid-connected units and their reactive output limits. We then further consider the impact of electrical distance on the actual reactive power provided by the units and adjust the effective maximum reactive output power of each unit. Finally, by combining the set of units with the determined target ratio, we can calculate the final unavoidable reactive compensation gap at the target ratio.

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

[0121] The reactive power output of a grid-type unit can be determined by the reactive voltage characteristic and the upper limit of the reactive power that can be generated. According to the basic principle of reactive voltage characteristic, the reactive power that a grid-type unit can theoretically generate is:

[0122] ; (13)

[0123] Where, is the droop coefficient of unit i, is the expected voltage value, is the actual voltage of the bus node where unit i is located.

[0124] Whether the grid-type unit can generate corresponding reactive power according to the reactive voltage characteristics is also limited by the unit output power limit. At this time, the output active power is In the case of for:

[0125] ; (14)

[0126] Where, is the rated capacity of the i-th unit.

[0127] Combining 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 restrictions. This means that if the reactive power calculated according to the reactive voltage characteristics exceeds the hardware capability of the unit (that is, exceeds its maximum reactive output capability), then the unit will not be able to provide reactive support according to the theoretical value; vice versa, if the maximum reactive power allowed by the hardware is less than the theoretical 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 that the grid-type unit has active control characteristics, it is necessary to consider the impact of electrical distance on the actual reactive power provided by the grid-type unit. The maximum reactive power that the grid-type unit can effectively generate for the PCC point is Expressed as:

[0130] ; (16)

[0131] Where, is the attenuation coefficient, usually 0.2-0.5.

[0132] As for the grid-following type unit, since the grid-following type unit usually operates according to the constant power factor, the reactive power of the grid-following type unit is The active power that can be generated by this unit and power factor angle Directly obtain:

[0133] ; (17)

[0134] However, in order to obtain the unavoidable reactive power compensation gap, all grid-connected units should be calculated according to their maximum reactive power operating capacity, that is:

[0135] ; (18)

[0136] Since grid-following units do not have the ability to actively regulate voltage, the effect of their reactive output on reactive power compensation of the grid is completely determined by the natural distribution of grid currents, rather than active correction through control strategies. Therefore, the maximum reactive power they effectively generate does not need to be explicitly converted.

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

[0138] ; (19)

[0139] Where, is the reactive load, and is the system reactive power loss.

[0140] For the convenience 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 evaluating voltage support capability are mostly limited to isolated indicators such as short-circuit ratio and voltage deviation, making it difficult to fully quantify the complex impact of active control of new energy generators 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, strategic conflicts often arise. Furthermore, traditional multi-objective modeling methods significantly increase computational complexity and solution difficulty.

[0143] In light of this, this paper pioneered the concept of voltage rigidity coefficient. By constructing a dynamic coupling ratio that combines the dynamic coupling short-circuit ratio and the unavoidable reactive compensation gap, it aims to integrate grid strength indicators and reactive power balance requirements into a unified evaluation system. This innovation not only effectively avoids the curse of dimensionality that can arise from multi-objective optimization, but also, through the use of a nonlinear mapping mechanism, reveals the synergistic optimization principle between the configuration ratio of grid-forming units and the capacity of compensation equipment.

[0144] The voltage rigidity coefficient is intuitively defined as the ratio of the coupled short-circuit ratio to the unavoidable reactive compensation gap at the target ratio. It comprehensively quantifies the grid's ability to withstand voltage sags and maintain steady-state voltage levels. Specifically, a larger value indicates a stronger system's ability to actively support voltage and superior performance.

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

[0146] ; (twenty one)

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

[0148] In one embodiment, for step S4, conventional optimal ratio configuration methods often focus on maximizing the system short-circuit ratio, tending to prioritize units close to the grid connection point as grid-forming units. However, this approach can lead to frequent switching of units near the grid connection point during dynamic grid operation, which in turn increases operation and maintenance costs and shortens unit lifespans.

[0149] In contrast, the dynamic optimal proportion configuration method proposed in the present invention innovatively defines the switching change parameter by introducing the concepts of the current configuration ratio and the target configuration ratio. This parameter, that is, the difference between the target ratio and the current ratio, can indirectly reflect key performance indicators such as operation and maintenance costs and unit life. In the process of seeking the optimal configuration, the present invention is not only committed to improving the voltage rigidity coefficient to the target level, but also places special emphasis on reducing 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 the voltage support capability, but also take into account the operation and maintenance costs and life of the unit, achieving 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 current configuration ratio and 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 the search for the optimal configuration, this invention not only strives to increase the voltage rigidity coefficient to the target level, but also places particular emphasis on 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 the unit's operation and maintenance costs and lifespan, achieving dual optimization of economic benefits and operational reliability.

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

[0154] . (twenty three)

[0155] On the other hand, given the potential damage to unit life caused by frequent unit switching and the resulting increase in maintenance costs, it is hoped that the difference between the optimal configuration ratio and the current configuration ratio will be as small as possible, in order to minimize the number of unit switching while achieving a significant improvement in the voltage rigidity coefficient. This consideration constitutes the second optimization objective function in the proportional configuration optimization model, namely minimizing the switching change, that is:

[0156] . (twenty four)

[0157] In this embodiment, to ensure the feasibility of the optimization solution in actual engineering applications, the established proportional configuration optimization model also fully incorporates dynamic constraints in multiple dimensions, such as voltage safety constraints, equipment regulation 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 operations. Among them, the constraints of the multi-objective optimal proportional configuration model include at least:

[0158] Voltage safety constraints:

[0159] Voltages of all nodes in the system The voltage must always be kept within the permitted range to prevent equipment damage or protective action caused by voltage exceeding the limit.

[0160] (25)

[0161] Actual reactive output limit of the unit:

[0162] When the model is established, the calculation of the unavoidable reactive compensation gap is based on the maximum possible reactive output of the unit. Therefore, the actual reactive output of the unit under the optimal proportional configuration must also meet the maximum reactive output limit. That is:

[0163] (26)

[0164] Unavoidable limitations of reactive power compensation gap:

[0165] Since unavoidable reactive power is ultimately compensated by reactive power equipment, its value must be less than the maximum reactive power that the reactive power equipment can generate; in addition, to avoid the possibility of overcompensation, unavoidable reactive power compensation should be non-negative. Combining the above two aspects, we can get:

[0166] (27)

[0167] Power flow equilibrium equation constraints:

[0168] During the entire operation process, the active and reactive power of the system must be balanced, that is:

[0169] ; (28)

[0170] Where, The reactive power generated by reactive equipment.

[0171] Configure the ratio limit:

[0172] According to IEEE standards, the proportion of grid-type units should be ensured to be above 20%, which can ensure a short-circuit ratio of ≥2. On the other hand, if the proportion of grid-type units exceeds 70%, it may cause the risk of circulating current resonance and increase the probability of transient overvoltage. Therefore, the final optimized configuration ratio should be controlled within the range of (20-70)%, that is:

[0173] (29)

[0174] In one embodiment, an adaptive reference vector guided multi-objective evolutionary algorithm (AR-MOEA) is utilized for model solving. This algorithm dynamically adjusts the distribution density and direction of the reference vector to adaptively balance the "exploration-exploitation" capabilities, effectively solving the problems encountered by traditional methods in solving high-dimensional optimization problems with strong conflicting objectives and complex Pareto front shapes, such as uneven distribution of solution sets and deviation of the convergence direction from the true frontier.

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

[0176] In order to verify the effect of the optimal ratio configuration method proposed in this invention on improving voltage support capability, etc., this invention is based on the Northwest China region. Figure 2 A simulation model of a typical doubly-fed wind farm architecture, shown in Figure 2, was constructed using the MATLAB / Simulink platform. This model closely mirrors the actual wind farm topology, with eight wind turbine collection nodes, numbered 1-8 according to the length of the collection lines, to facilitate subsequent description and analysis. The number of wind turbines at each node is identical to the actual site: Node 1 (12 units), Node 2 (11 units), Node 3 (8 units), Node 4 (8 units), Node 5 (7 units), Node 6 (6 units), Node 7 (6 units), and Node 8 (6 units), for a total of 64 1.5 MW doubly-fed wind turbines. Key parameters for the simulation model are derived from actual operational data from the wind farm. See Table 1 for detailed parameter settings.

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

[0178]

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

[0180] ① Case 1 (traditional optimization solution): This solution adopts the conventional short-circuit ratio improvement strategy of the power grid, with the optimization goal of minimizing the losses of the entire network. 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 and off, reflecting the baseline operating condition without optimization strategy.

[0182] ③ Case 3 (design solution of the present invention): Apply the optimal proportion configuration method proposed in the present invention to achieve multi-objective collaborative optimization while ensuring voltage support capability.

[0183] Subsequently, rigorous simulation and comparative experiments will be conducted to quantitatively analyze key indicators such as voltage stability, rigidity coefficient, and unit switching changes, and the experimental data will be used to intuitively verify the significant advantages of this invention over other methods.

[0184] A. Comparison of unit switching changes:

[0185] Figure 3 The quantitative distribution of grid-type units at each node under the three schemes is intuitively displayed, supplemented by the original status of the grid-type units at each node before the implementation of the scheme, so as to clearly compare the changes in unit switching caused by each scheme. Figure 4The results show the probability distribution of the percentage of grid-type units at each node relative to the total number of units at that node, under multiple experimental conditions. Clearly, Scheme 1 focuses on reducing losses and maximizing the short-circuit ratio. Therefore, it focuses on increasing the switching of grid-type units near nodes (especially the first three nodes). This is because switching remote node units to grid-type to increase reactive power generation will increase losses. Furthermore, remote node units are less effective at improving the short-circuit ratio than near nodes. However, this scheme ignores the issue of frequent unit switching and over-reliance on near-node units for switching, which not only increases the operating and maintenance costs of near-node units but also potentially shortens their service life.

[0186] In contrast, Option 2 adopts a more randomized strategy, selectively switching units at each node to ensure that the system short-circuit ratio meets the specified threshold. This approach ensures that each node has units switched to a grid-connected configuration, which, to a certain extent, alleviates the pressure of frequent switching of units near the node and reduces the overall system operation and maintenance costs. However, the additional reactive power generated by units at distant nodes after switching to a grid-connected configuration increases losses, and the effective improvement in the system short-circuit ratio is not significant.

[0187] Solution 3, proposed in this invention, comprehensively examines and effectively addresses many of the issues present in the aforementioned solutions. By introducing an objective function that minimizes switching variations, this solution addresses the issues of decreased operating efficiency and increased losses caused by frequent unit switching. Furthermore, the objective function employed in Solution 3, which maximizes the voltage rigidity coefficient, ensures that near-node units are prioritized when switching units. This not only effectively increases the system's short-circuit ratio but also reduces unavoidable reactive power losses, thereby significantly improving the voltage rigidity coefficient.

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

[0189] Under normal system operation, the voltage fluctuation amplitude of 8 nodes under three different schemes is analyzed as follows: Figure 5As shown, the three adjacent bar graphs in the figure are the voltage stability maintenance capabilities of Scheme 1, Scheme 2, and Scheme 3 from left to right. Scheme 1 shows the strongest voltage stability maintenance capability at the near node, however, its performance is the worst at the far node. This is attributed to the objective function of Scheme 1, which aims to minimize network loss. This objective prompts the system to increase the short-circuit ratio as much as possible, resulting in most grid-type units being concentrated at the near node, while almost no units at the far node are converted to grid-type, thereby weakening the voltage stability capability of the far node. In contrast, Scheme 2 randomly selects units at different nodes for switching, so that the difference in maintaining voltage stability between near nodes and far nodes is not as significant as that of Scheme 1, which is more in line with the inherent characteristics of the power system: the voltage level gradually decreases from near to far, and the amplitude of change increases accordingly. As for Scheme 3 proposed in the present invention, it not only incorporates the improvement of the overall voltage rigidity coefficient into the objective function, but also takes into account the problem of frequent switching of units, that is, it integrates the objective function of minimizing the amount of change in unit switching. Therefore, under Scheme 3, generator switching at each node is neither heavily biased toward neighboring nodes as in Scheme 1, nor distributed nearly randomly across nodes as in Scheme 2. Instead, it strives to maximize overall system voltage stability while remaining consistent with node demand. This strategy makes Scheme 3 superior to the other two in maintaining voltage stability at each node.

[0190] In order to comprehensively evaluate the dynamic voltage support performance of the three schemes, a specific test condition is set in the simulation process: a 10% voltage drop disturbance is applied when the system runs to the second second, and the disturbance lasts for 0.5 seconds and is eliminated at 2.5 seconds. Due to space limitations, this embodiment only shows the voltage change waveforms of several key nodes, including node 1 representing the near node, node 4 representing the medium-distance node, node 7 representing the long-distance node, and the PCC node reflecting the overall performance of the system, such as Figures 6 to 9 Through careful comparative analysis, we can draw the following conclusions:

[0191] At near-node locations, Scheme 1 exhibits the best voltage recovery capability and speed due to its high proportion of grid-connected units. However, Scheme 3, proposed in this invention, is slightly less capable and efficient than Scheme 1 due to its design considerations for minimizing unit switching variations. As for Scheme 2, its voltage recovery capability and speed are the worst of the three due to its low proportion of grid-connected units near the node.

[0192] For medium-distance nodes, Solution 3 proposed in the present invention is almost on par with Solution 2 in terms of voltage recovery performance and speed, while Solution 1 performs the worst.

[0193] As for remote nodes, Scheme 1 has almost no units switched to the grid type, so its voltage recovery performance and speed continue to remain at the worst level. For Scheme 2 and Scheme 3 proposed in this invention, although Scheme 2 has a slight advantage in the number of units at remote nodes, the performance gap between the two is not significantly widened. This is because Scheme 3 proposed in this invention focuses on maximizing the voltage rigidity coefficient in the objective function, ensuring that the system as a whole has excellent voltage support capabilities, thereby making up for the lack of units to a certain extent.

[0194] A comprehensive analysis of the near, mid, and far nodes clearly shows that the three solutions have different voltage recovery capabilities and speed performance at different nodes. However, from a global perspective, the proposed solution 3 is superior to the other two solutions. This can be seen from the PCC voltage change diagram ( Figure 9 ) to support this. Figure 9 It is not difficult to find that Solution 3 of the present invention surpasses the other two solutions in terms of the strength and speed of increasing the PCC node voltage. This result is highly consistent with the previous analysis conclusions for each node and confirms each other.

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

[0196] To thoroughly compare the three schemes' effectiveness in improving the voltage rigidity coefficient, it's necessary to examine their performance in improving the short-circuit ratio and the unavoidable reactive compensation gap. This analysis is based on the definition of the voltage rigidity coefficient. Because directly describing the short-circuit ratio at 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 compensation gap of each node under the three schemes are shown respectively. Figure 10 The three adjacent bar graphs in the figure are the short-circuit capacity improvement effects of Scheme 1, Scheme 2, and Scheme 3 from left to right. Figure 11 The three bar graphs in the figure represent the unavoidable reactive power compensation gaps of Scheme 1, Scheme 2, and Scheme 3 from left to right.

[0197] observe Figure 10It can be found that since Scheme 1 mainly concentrates the grid-forming units at near nodes, it significantly improves the short-circuit capacity of these nodes, while the improvement effect on far nodes is relatively limited. In contrast, Scheme 2 randomly selects units on different nodes for switching, so that the improvement of the short-circuit capacity of each node shows a trend of gradually weakening from near to far, which is in line with the basic principle characteristics of the previous model analysis. As for Scheme 3 proposed in the present invention, it combines the advantages of the above two schemes, taking into account the effective enhancement of the voltage rigidity coefficient by the improvement of the short-circuit capacity of the near nodes, and taking into account the frequency of switching of the units at each node. Therefore, on the whole, Scheme 3 is better than the previous two schemes in improving the short-circuit capacity.

[0198] Through analysis Figure 11 , we can further draw the following conclusions. Since almost no units in Scheme 1 switch to the grid-forming type at the far nodes, this leads to an abnormally high value of the unavoidable reactive compensation gap at the far nodes. This extreme situation weakens the advantages brought by the improvement of the short-circuit capacity of the near nodes, and ultimately affects the improvement of the voltage rigidity coefficient of the entire system. Although the unavoidable reactive power at each node in Scheme 2 is relatively average, this approach does not effectively improve the overall voltage rigidity coefficient of the system. Scheme 3 proposed in the present invention avoids the shortcomings of the above two schemes. It does not take an extreme approach like Scheme 1, nor does it simply imitate the random average distribution strategy of Scheme 2. On the contrary, Scheme 3 adopts a more compromising approach, that is, maintaining a relatively low unavoidable reactive compensation gap at the near nodes and appropriately increasing it at the far nodes. This approach can fully utilize the advantages of the near-node units in improving the voltage rigidity coefficient, and also take into account the loss problem that may be caused by excessive reactive power transmission of the far-node units. Therefore, Scheme 3 performs best in improving the overall voltage rigidity coefficient of the system.

[0199] Based on the detailed analysis of the short-circuit capacity of each node and the unavoidable reactive power compensation gap, it is clear that Solution 3, proposed in this invention, significantly outperforms the other two solutions in terms of improving the voltage rigidity coefficient, a comprehensive performance indicator, thereby enhancing voltage support capability. Table 2 provides a detailed comparison of the three solutions in terms of short-circuit ratio, voltage rigidity coefficient, maximum and minimum node voltages, and total system losses.

[0200] Table 2 Comparison of key indicators

[0201]

[0202] While Scheme 1 demonstrates good performance in reducing grid losses and improving the system's short-circuit ratio, it fails to fully consider the minimization of overall unavoidable reactive power and the optimization of switching variations. Therefore, when it comes to ultimately improving the voltage rigidity coefficient, Scheme 1 underperforms Scheme 3 proposed in this invention. As for Scheme 2, it employs a random averaging strategy across all aspects, lacking targeted optimization considerations. This results in relatively poor performance in improving the short-circuit ratio, voltage rigidity coefficient, and reducing total system losses.

[0203] After comprehensive consideration of the comparative analysis of various solutions on multiple performance indicators, the following Figure 12 The radar chart shown here visually demonstrates the comprehensive capabilities of the three solutions across six key dimensions. These dimensions, labeled A to F, represent: A—the ability to suppress voltage fluctuations and maintain voltage stability; B—the rapid recovery of voltage to the preset value after a disturbance; C—the ability to mitigate the impact of unavoidable reactive power compensation gaps; D—the ability to enhance the system short-circuit ratio; E—the ability to minimize switching variations; and F—the efficiency of reducing overall system losses.

[0204] The radar chart clearly reveals that Option 1 demonstrates superior performance in improving the system short-circuit ratio and reducing losses, directly attributable to its optimization strategy's emphasis on these specific objectives. In contrast, Option 2's performance across various performance indicators is relatively balanced but less impressive, reflecting the limitations of its strategy, which randomly selects units and lacks a clear optimization focus.

[0205] It is noteworthy that Scheme 3, proposed in this invention, is only slightly inferior to Scheme 1 in reducing total system losses, while achieving the best performance across all other performance indicators. From a global perspective, while Scheme 3 makes a slight sacrifice in reducing losses, it achieves significant improvements in maintaining voltage stability, accelerating voltage recovery, increasing short-circuit ratio, reducing frequent unit switching, and extending equipment life. These achievements fully demonstrate 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 solution worthy of promotion.

[0206] The above is a detailed introduction to the wind farm unit configuration method based on the voltage rigidity coefficient and the switching change amount provided by the present invention. In this embodiment, specific examples are used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

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

Claims

1. A method for configuring wind farm units based on voltage rigidity coefficient and switching variation, characterized in that: There are multiple turbines in a wind farm, and the following steps are involved: S1: Calculating the contribution efficiency factor of the unit based on the electrical distance between the bus node where the unit is located and the grid-connected node, and determining the dynamic coupling short-circuit ratio under the target configuration ratio using the contribution efficiency factor; S2: Calculate the reactive power compensation gap parameters of the wind farm based on the maximum reactive power of the grid-connected units; S3: determining a voltage rigidity coefficient based on the dynamic coupling short-circuit ratio and the reactive compensation gap parameter; S4: Determine a switching change parameter based on the change from the current configuration ratio to the target configuration ratio; S5: constructing a multi-objective optimal proportion configuration model with the maximization of the voltage rigidity coefficient and the minimization of the switching variation parameter as objective functions, solving the multi-objective optimal proportion configuration model to obtain the optimized target configuration ratio, and configuring the wind farm units.

2. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: The contribution efficiency factor of unit i in S1 is Expressed as: ； Where, is the virtual impedance of the node i where the unit is located; is the electrical distance parameter between the node i where the unit is located and the common coupling point PCC.

3. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 2, characterized in that: Calculate electrical distance parameters using the network's node impedance matrix Expressed as: ； Where, is the self-impedance of node i, and is the self-impedance of the point of common coupling PCC, is the impedance from node i to the point of common coupling PCC, is the impedance from the point of common coupling PCC to node i.

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

5. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: The S2 comprises the following steps: Calculate the theoretical reactive power output by the grid-connected units based on the reactive voltage characteristics; Based on the reactive power upper limit and electrical distance parameters of the grid-forming units, the actual reactive power output by the grid-forming units is determined; Determine the reactive power output by the grid-following units; For the real-time reactive load, the reactive compensation gap parameters that need to be compensated in addition to the actual reactive power output by the grid-forming units and the reactive power output by the grid-following units are calculated.

6. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 5, characterized in that: The S2 comprises the following steps: Determine the actual reactive power output of the grid-connected units Expressed as: ； ； Where, is the attenuation coefficient; is the electrical distance parameter between the node i where the unit is located and the common coupling point PCC; The theoretical reactive power output by the grid-connected unit; It is the upper limit of reactive power of grid-type units; is the maximum reactive power that can be generated by node i where the unit is located; Calculate reactive power compensation gap parameters Expressed as: ； Where, is the reactive load; The reactive power output by the grid-following unit; is the reactive loss of the wind farm, The unit located at node i belongs to the set of networked units ; The unit located at node i does not belong to the set of networked units .

7. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: The voltage rigidity coefficient in S3 is expressed as: ； Where, Configure the scale for the target Dynamic coupling short-circuit ratio under ; Configure the scale for the target Reactive power compensation gap parameters under .

8. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: The switching variation parameter is expressed as: ； Where, Configure the scale for the target; The current configuration ratio.

9. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: The constraints of the multi-objective optimal proportion configuration model include at least: The voltage of all nodes is maintained within the set range; The actual reactive power output by the grid-type units meets the maximum reactive output limit; The reactive compensation gap parameter is a non-negative value; The actual active power and reactive power output of all wind turbines meet the constraints of the power flow balance equation; The target configuration ratio is controlled within the set range.

10. The method for configuring wind farm units based on voltage rigidity coefficient and switching variation according to claim 1, characterized in that: A multi-objective evolutionary algorithm guided by adaptive reference vectors is used to solve the multi-objective optimal proportional configuration model.

Images