Offshore wind plant station harmonic resonance suppression method, system, equipment and medium

Abstract

The invention belongs to the technical field of regional power grid electric energy quality management, and discloses an offshore wind plant station harmonic resonance suppression method, system and device and a medium, and the method comprises the steps: calling the residual capacity of an offshore wind power access node, carrying out the primary harmonic compensation based on the residual capacity, and carrying out the primary evaluation of a regional power grid after the primary harmonic compensation; if the initial evaluation does not meet the preset suppression requirement, establishing a collaborative suppression mechanism by combining an active filter, performing secondary harmonic compensation by applying the collaborative suppression mechanism, and performing secondary evaluation on the regional power grid after the secondary harmonic compensation; and if the secondary evaluation result still does not meet the preset suppression requirement, performing iterative optimization on the parameter configuration of the active filter and the utilization configuration of the residual capacity by using an improved particle swarm optimization algorithm to obtain an optimal configuration scheme. According to the invention, through cooperative utilization of the residual capacity of the wind power, the number of active filters is reduced, the treatment cost is reduced, and the wind power compensation economy is optimized.

Description

Technical Field

[0001] This invention relates to the field of regional power grid power quality management technology, and in particular to a method, system, equipment and medium for suppressing harmonic resonances in offshore wind farms. Background Technology

[0002] With the acceleration of the global energy transition, especially the increasing penetration of wind power into the power system, the utilization of renewable energy is becoming increasingly widespread. According to a report by the International Energy Agency, global wind power capacity is expected to exceed 1000 GW by 2025, bringing significant benefits to clean energy. However, after wind power is connected to the grid, its generating equipment uses nonlinear components such as doubly-fed induction generators (DFIGs) or permanent magnet synchronous generators (PMSGs) combined with power electronic converters. These devices generate characteristic harmonics (such as the 5th, 7th, and 11th harmonics), which are injected into the grid through the point of common coupling (PCC), causing voltage and current distortion and thus affecting power quality. Especially with increased wind speed fluctuations and power output uncertainty, traditional grid harmonic mitigation methods are difficult to adapt to dynamic wind power integration scenarios, and harmonic pollution problems are becoming increasingly prominent.

[0003] Traditional harmonic mitigation methods typically rely on passive power filters (PPFs) such as LC tuned filters or high-pass filters. These devices suppress harmonics by providing a low-impedance path to bypass harmonic currents. However, the performance of PPFs is highly dependent on system impedance and load variations. When wind farms are integrated into the system, especially in offshore wind farms, the presence of distributed parameter networks such as cable capacitance and transformer inductance often prevents these traditional passive filters from effectively covering the entire frequency band of harmonics. Furthermore, once the system impedance drifts, parallel resonance may occur, leading to harmonic amplification and thus failing to achieve the desired mitigation effect.

[0004] To overcome the limitations of traditional passive filters, active power filters (APFs) have been widely studied and applied in wind farms as a dynamic compensation technique. An APF can be considered equivalent to a controlled current source, detecting harmonics in the system in real time and injecting compensation currents of equal amplitude but opposite phase, thereby achieving precise harmonic suppression. Existing research shows that APFs can significantly reduce total harmonic distortion (THD) in wind power integration systems; however, optimizing them remains a challenge, particularly in terms of the uncertainty of wind power resources, equipment coordination optimization, and system-level overall planning.

[0005] Currently, traditional APF optimization methods are mainly based on deterministic frameworks, such as genetic algorithms (GA) or particle swarm optimization (PSO), with the objective function of minimizing governance costs or total harmonic distortion (THD). The installation location of the APF is typically determined through power flow calculations and sensitivity analysis. However, these methods fail to fully consider the volatility and uncertainty of wind power resources. Random variations in wind speed will lead to deviations in wind power output, making it difficult for the optimized scheme to adapt to changing load conditions in actual operation, thus increasing the risk of harmonic exceedances. Furthermore, the remaining capacity of wind power resources can be used for harmonic compensation by generating reverse current through modifications to the control strategy of wind inverters, avoiding additional hardware investment. However, current research on the synergistic mechanism between APF and wind power resources is insufficient, and most studies focus on the optimization of individual devices, lacking system-level coordination and resource sharing. This limitation leads to problems such as low governance efficiency, resource waste, and insufficient economic viability.

[0006] Therefore, how to provide a method, system, equipment, and medium for suppressing harmonic resonances in offshore wind farms is an urgent problem to be solved. Summary of the Invention

[0007] This invention provides a method, system, equipment, and medium for suppressing harmonic resonances in offshore wind farms to solve the aforementioned technical problems in the prior art.

[0008] According to a first aspect of the present invention, a method for suppressing harmonic resonances in offshore wind farms is provided.

[0009] In one embodiment, a method for suppressing harmonic resonances at an offshore wind farm includes:

[0010] The remaining capacity of the offshore wind power access node is used to perform initial harmonic compensation based on the remaining capacity, and an initial assessment of the regional power grid after the initial harmonic compensation is conducted.

[0011] If the initial assessment does not meet the preset suppression requirements, a collaborative suppression mechanism is established in conjunction with the active filter. The collaborative suppression mechanism is used to perform second harmonic compensation, and a second assessment is conducted on the regional power grid after the second harmonic compensation.

[0012] If the second evaluation result still does not meet the preset suppression requirements, the parameter configuration and remaining capacity utilization configuration of the active filter are iteratively optimized using an improved particle swarm optimization algorithm until the preset suppression requirements are met and the optimal configuration scheme is obtained.

[0013] According to a second aspect of the present invention, a harmonic resonance suppression system for offshore wind farms is provided.

[0014] In one embodiment, the offshore wind farm harmonic resonance suppression system includes:

[0015] The initial assessment unit is used to call up the remaining capacity of the offshore wind power access node, perform initial harmonic compensation based on the remaining capacity, and conduct an initial assessment of the regional power grid after the initial harmonic compensation.

[0016] The secondary evaluation unit is used to establish a cooperative suppression mechanism in conjunction with the active filter if the initial evaluation fails to meet the preset suppression requirements. The cooperative suppression mechanism is then used to perform secondary harmonic compensation, and a secondary evaluation is performed on the regional power grid after secondary harmonic compensation.

[0017] The configuration scheme acquisition unit is used to iteratively optimize the parameter configuration and remaining capacity utilization configuration of the active filter using an improved particle swarm optimization algorithm if the secondary evaluation result still does not meet the preset suppression requirements, until the preset suppression requirements are met and the optimal configuration scheme is obtained.

[0018] According to a third aspect of the present invention, a computer device is provided.

[0019] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0021] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0023] 1. This invention improves power quality by steadily reducing the total harmonic distortion rate of the entire network under uncertain scenarios, suppressing the distortion of harmonic voltage and current, thereby improving the operational stability of the regional power grid. At the same time, by synergistically utilizing the remaining capacity of wind power, it reduces the number of active filters, lowers governance costs, and optimizes the economics of wind power compensation.

[0024] 2. This invention enhances the robustness and reliability of the system. By employing chance constraints, it ensures that harmonic mitigation requirements can be met with a high probability even under fluctuating wind power output. This avoids the failure of traditional deterministic methods in real-world environments and improves the system's ability to cope with random factors. Furthermore, this invention fully utilizes the remaining capacity of wind power inverters, avoids resource waste, achieves distributed local compensation, expands the coverage of mitigation, and reduces hardware investment.

[0025] 3. This invention accelerates the solution process, shortens the governance cycle, and improves the overall governance efficiency through an improved particle swarm optimization algorithm, providing a sustainable solution for clean energy integration and power quality optimization for regional power grids with a large amount of wind power.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0028] Figure 1 This is a flowchart illustrating an exemplary embodiment;

[0029] Figure 2 This is a principle block diagram illustrated according to an exemplary embodiment;

[0030] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0031] Figure 4 This is a schematic diagram illustrating the working principle of a classic harmonic mitigation device according to an exemplary embodiment;

[0032] Figure 5 This is a wind speed probability curve diagram illustrated according to an exemplary embodiment;

[0033] Figure 6 This is an active power output probability density curve diagram illustrated according to an exemplary embodiment;

[0034] Figure 7 This is a flowchart illustrating a program according to an exemplary embodiment;

[0035] Figure 8 This is a node circuit diagram illustrated according to an exemplary embodiment;

[0036] Figure 9 This is a probability diagram of wind power resource output at node 5, as illustrated in an exemplary embodiment.

[0037] Figure 10 This is a comparison chart of THD before and after optimization at different confidence levels, according to an exemplary embodiment.

[0038] Figure 11 This is a comparison graph of harmonic currents before and after optimization at different orders, according to an exemplary embodiment.

[0039] Figure 12This is a comparison graph of harmonic voltages before and after optimization at different orders, according to an exemplary embodiment. Detailed Implementation

[0040] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0041] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0042] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0043] Figure 1 An embodiment of a harmonic resonance suppression method for offshore wind farms according to the present invention is shown.

[0044] In this optional embodiment, the method for suppressing harmonic resonances in an offshore wind farm includes:

[0045] Step S101: Call the remaining capacity of the offshore wind power access node, perform initial harmonic compensation based on the remaining capacity, and conduct an initial assessment of the regional power grid after the initial harmonic compensation.

[0046] Step S102: If the initial assessment does not meet the preset suppression requirements, a collaborative suppression mechanism is established in conjunction with the active filter, and the collaborative suppression mechanism is used to perform second harmonic compensation. A second assessment is then performed on the regional power grid after the second harmonic compensation.

[0047] Step S103: If the second evaluation result still does not meet the preset suppression requirements, the parameter configuration and remaining capacity utilization configuration of the active filter are iteratively optimized using the improved particle swarm optimization algorithm until the preset suppression requirements are met and the optimal configuration scheme is obtained.

[0048] In this optional embodiment, the process of calling upon the remaining capacity of the offshore wind power access node, performing initial harmonic compensation based on the remaining capacity, and conducting an initial assessment of the regional power grid after the initial harmonic compensation includes:

[0049] By performing power flow calculations and initial harmonic analysis, harmonic source nodes in the power grid are identified, and the sensitivity of harmonic voltage at each harmonic source node to the compensation current of the active filter is calculated using the harmonic propagation equation.

[0050] Based on the node and branch characteristics of the regional power grid, a harmonic propagation matrix is ​​constructed, and a harmonic control sensitivity matrix is ​​constructed with sensitivity as the matrix element. Combined with the multi-harmonic source scenario, the comprehensive sensitivity of the node is obtained.

[0051] All harmonic source nodes are sorted from high to low based on the obtained comprehensive sensitivity, and active filters are installed preferentially at nodes with high comprehensive sensitivity.

[0052] In this optional embodiment, the expression for the sensitivity is:

[0053]

[0054] In the formula, S h,ij Indicates sensitivity; U h,j I represents the h-th harmonic voltage at node j; h,i This represents the active filter compensation current at node i.

[0055] In this optional embodiment, the expression for calling the remaining capacity of the offshore wind power access node and performing first harmonic compensation based on the remaining capacity is:

[0056]

[0057] In the formula, Indicates the remaining available capacity of wind power resources; Indicates the remaining wind power capacity; This indicates that wind power resources can be used for harmonic compensation current; U B This represents the reference voltage.

[0058] In this optional embodiment, the iterative optimization of the active filter's parameter configuration and remaining capacity utilization using an improved particle swarm optimization algorithm until a preset suppression requirement is met, thereby obtaining the optimal configuration scheme, includes:

[0059] Based on the relationship between wind speed and power generation, and combined with the harmonic control requirements of the regional power grid, dual optimization objectives are set and constraints are determined. The constraints include active filter capacity constraints and wind power resource compensation constraints.

[0060] Based on the uncertainty of wind speed, and combining dual optimization objectives and constraints, a harmonic mitigation optimization model is constructed.

[0061] An improved particle swarm optimization algorithm is used to solve the harmonic control optimization model, and the optimal configuration scheme is obtained by using the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables.

[0062] In this alternative embodiment, the dual optimization objectives include minimizing the maximum total harmonic distortion rate of the regional power grid and minimizing the total governance cost.

[0063] In this optional embodiment, the expression for the maximum total harmonic distortion rate is:

[0064]

[0065] In the formula, maxTHD represents the maximum total harmonic distortion (THD); V h,i V1 represents the effective value of each harmonic voltage; V2 represents the effective value of the fundamental voltage.

[0066] In this optional embodiment, the expression for the total governance cost is:

[0067] C total =C APF +N time (C capacity +k wp C wp );

[0068] C APF =N APF ·c fixed ;

[0069]

[0070] In the formula, C total Indicates total governance cost; C APF Indicates the installation cost of the active filter; N time Indicates equipment uptime; C capacity This represents the operation and maintenance cost of an active filter; k wp C represents the wind power resource cost penalty coefficient; wp Indicates the operation and maintenance cost of harmonic mitigation for wind power resources; N APF Indicates the number of active filters; c fixed This indicates the fixed installation cost of each active filter; c unit This represents the maintenance cost of the active filter; i represents the index value; S i Indicates the governance capacity of the APF; c wp This represents the operation and maintenance cost of wind power resource harmonic mitigation; k represents the wind power resource node; WP_nodes represents the set of wind power resource nodes; This represents the harmonic compensation current at wind power resource node k.

[0071] In this optional embodiment, the construction of the harmonic mitigation optimization model based on the uncertainty of wind speed, combined with dual optimization objectives and constraints, includes:

[0072] Based on the relationship between wind speed and power generation, the uncertainty of wind speed is identified, and a wind speed probability distribution model is constructed using the Weibull distribution.

[0073] Based on dual optimization objectives and constraints, the probabilistic constraints corresponding to the wind speed probability distribution model are transformed into deterministic constraints through chance constraints, and a harmonic governance optimization model is constructed.

[0074] In this optional embodiment, the step of solving the harmonic mitigation optimization model using an improved particle swarm optimization algorithm, and obtaining the optimal configuration scheme by using the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables, includes:

[0075] Initialize the parameters of the particle swarm optimization algorithm, and define the position of each particle as a combination of the parameter configuration of the active filter and the remaining capacity utilization configuration. Based on the initialization results, determine whether the current iteration number exceeds the preset maximum iteration number.

[0076] If the current iteration count exceeds the maximum iteration count, the active filter parameter configuration and remaining capacity utilization scheme corresponding to the global optimal solution are output as the optimal configuration scheme.

[0077] If the current iteration count does not exceed the maximum iteration count, then according to the dual optimization objective, calculate the fitness of each particle in the population, update the individual optimal solution and global optimal solution of each particle based on the calculation results, update the velocity and position of each particle, adjust the inertia weight, and continue iterating until the current iteration count exceeds the maximum iteration count. Output the active filter parameter configuration and remaining capacity utilization scheme corresponding to the global optimal solution as the optimal configuration scheme.

[0078] Figure 2 An embodiment of a harmonic resonance suppression system for an offshore wind farm according to the present invention is shown.

[0079] In this optional embodiment, the offshore wind farm harmonic resonance suppression system includes:

[0080] The initial assessment unit 201 is used to call up the remaining capacity of the offshore wind power access node, perform initial harmonic compensation based on the remaining capacity, and perform an initial assessment of the regional power grid after the initial harmonic compensation.

[0081] The secondary evaluation unit 202 is used to establish a cooperative suppression mechanism in conjunction with the active filter if the initial evaluation fails to meet the preset suppression requirements, and to use the cooperative suppression mechanism to perform secondary harmonic compensation, and to perform a secondary evaluation on the regional power grid after secondary harmonic compensation.

[0082] The configuration scheme acquisition unit 203 is used to iteratively optimize the parameter configuration and remaining capacity utilization configuration of the active filter using an improved particle swarm optimization algorithm if the secondary evaluation result still does not meet the preset suppression requirements, until the preset suppression requirements are met and the optimal configuration scheme is obtained.

[0083] In this optional embodiment, the initial evaluation unit 201 is preceded by:

[0084] The sensitivity calculation module is used to identify harmonic source nodes in the power grid through power flow calculation and initial harmonic analysis, and to calculate the sensitivity of the harmonic voltage in each harmonic source node to the compensation current of the active filter using the harmonic propagation equation.

[0085] The comprehensive sensitivity acquisition module is used to construct a harmonic propagation matrix based on the node and branch characteristics of the regional power grid, and to construct a harmonic control sensitivity matrix with sensitivity as matrix elements. Combined with multi-harmonic source scenarios, the comprehensive sensitivity of the nodes is obtained.

[0086] The active filter installation module is used to sort all harmonic source nodes from high to low based on the obtained comprehensive sensitivity, and prioritize the installation of active filters on nodes with high comprehensive sensitivity.

[0087] In this optional embodiment, the configuration scheme acquisition unit 203 includes:

[0088] The target and constraint setting module is used to set dual optimization targets based on the relationship between wind speed and power generation, combined with the harmonic control requirements of the regional power grid, and to determine the constraints, including active filter capacity constraints and wind power resource compensation constraints.

[0089] The optimization model building module is used to construct a harmonic mitigation optimization model based on the uncertainty of wind speed and by combining dual optimization objectives and constraints.

[0090] The configuration optimization module is used to solve the harmonic control optimization model using an improved particle swarm optimization algorithm, and uses the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables to obtain the optimal configuration scheme.

[0091] In this optional embodiment, the optimization model construction module includes:

[0092] The probability distribution model construction submodule is used to identify the uncertainty of wind speed based on the relationship between wind speed and power generation, and to construct a wind speed probability distribution model using the Weibull distribution.

[0093] The constraint transformation submodule is used to transform the probabilistic constraints corresponding to the wind speed probability distribution model into deterministic constraints through chance constraints, based on the dual optimization objectives and constraints, and to construct a harmonic governance optimization model.

[0094] To facilitate understanding of the above technical solutions of the present invention, the following further describes the above technical solutions of the present invention from the perspectives of architecture and principle, as follows:

[0095] I. Working Principle:

[0096] 1. Working principle of harmonic mitigation devices: such as Figure 4 As shown, the regional power grid is connected to a large number of nonlinear loads, forming a dense source of harmonics. To address this, parallel harmonic mitigation devices are commonly used to manage the nodes. These devices can be equivalent to controlled current sources: they detect the load harmonic current in real time and inject compensation currents with equal amplitude and opposite phase to cancel the harmonic components at the PCC, thus restoring the total current at that point to a sinusoidal waveform.

[0097] 2. Working principle of collaborative governance of wind power resources:

[0098] Based on its remaining capacity, the wind power resources within the regional power grid are monitored in real time, and a reverse compensation current is generated and injected in conjunction with an active power filter (APF) to effectively suppress harmonic pollution, reduce pollution control costs, and improve power quality. The principle is as follows:

[0099] Apparent power S of wind power resources wp (In volt-amperes) is usually greater than its actual output active power P. wp (in watts); remaining capacity is determined by the formula. The calculation shows that, among which The remaining capacity of wind power resources; The active power generated by wind power resources; the wind power resources utilize their remaining capacity, and the control system collects the output power and grid connection point voltage, and extracts harmonic components in real time; through pulse width modulation (PWM), a compensation current with equal amplitude and opposite phase is generated, which can be injected into the grid without adding new hardware, only software rewriting, to cancel out the harmonic current, reduce pollution, and achieve economical and efficient suppression.

[0100] Wind power resources and APFs form a collaborative governance framework: APFs inject reverse current in parallel to achieve node governance, while wind power resources rely on their decentralized grid connection advantages to share the harmonic suppression task locally and expand governance coverage; by optimizing the allocation of compensation amounts, the number and capacity of APFs can be reduced, thereby lowering the overall governance cost.

[0101] II. Governance Method: This invention proposes an improved APF (Adaptive Particle Swarm Optimization) and Chance-Constrained Programming (CCP) site selection and capacity determination strategy, which utilizes the remaining wind power capacity for harmonic compensation to efficiently govern the regional power grid. This method uses the power grid topology as a benchmark, collaboratively optimizes the allocation of APF and wind power resources, and incorporates the uncertainty of wind power output into the constraints. The goal is to reduce THD (Total Harmonic Discharge) to below 4% and minimize governance costs. The governance principles and basis are described below.

[0102] 1. Governance Principles: The governance strategy of this invention is based on the characteristics of regional power grid nodes and branches. It combines the local compensation function of parallel APF with the remaining capacity of wind power inverters to form a unified and coordinated harmonic suppression mode. The APF injects reverse current at the harmonic source node to achieve local governance. The wind power resources output additional compensation current through the inverter to share part of the suppression task and reduce the configuration requirements of APF.

[0103] (1) The governance principles are as follows: give priority to leveraging the local governance advantages of APF, carry out targeted compensation for harmonic source nodes, and reduce node THD; utilize the remaining capacity of wind power access nodes to participate in harmonic suppression, and use opportunity constraints to ensure compensation reliability when there is uncertainty in wind power output; optimize the installation location and capacity of APF globally through APSO algorithm, coordinate wind power compensation capabilities, and achieve the dual goals of reducing the total network THD to below 4% and minimizing governance costs.

[0104] (2) The specific process is as follows: First, the remaining wind power capacity is used for compensation; if the compensation is insufficient, the second step is for APF to intervene and coordinate the governance; if the requirements are still not met, the third step is to adjust the APF configuration and optimize iteratively to the global optimum through APSO.

[0105] This model fully utilizes the distribution characteristics of wind power resources and the compensation performance of APF, and is suitable for complex structures such as radial regional power grids, achieving efficient and economical harmonic control.

[0106] 2. Basis for distributed governance:

[0107] (1) Sensitive area analysis of harmonic control:

[0108] Harmonic source location: Through power flow calculation and initial harmonic analysis, the amplitude and phase of harmonic currents at harmonic source nodes in the regional power grid are determined.

[0109] Sensitivity Calculation: Using the harmonic propagation equation, the sensitivity S of the harmonic voltage at each node to the APF compensation current is calculated. h,ij The formula is as follows;

[0110]

[0111] In the formula, S h,ij Indicates sensitivity; U h,j I represents the h-th harmonic voltage at node j; h,i This represents the active filter compensation current at node i.

[0112] Sensitivity Matrix: Based on the regional power grid, a harmonic control sensitivity matrix is ​​constructed. The matrix elements represent the response degree of each node to the harmonic control equipment. If node j is not affected by the control of node i, the sensitivity is 0. For multi-harmonic source scenarios, the corresponding columns of the matrix are summed to obtain the comprehensive sensitivity.

[0113] In a power system, assuming a regional power grid has n nodes, the harmonic propagation equation can be written in matrix form:

[0114]

[0115] In the formula, Y h,ii Y is the self-admittance of node i (including the admittance of all branches connected to node i and the load admittance); h,ij (i≠j) represents the mutual admittance between nodes i and j (usually negative, equal to the admittance of the connecting branch); U h,i Let I be the h-th harmonic voltage at node j; h,i The injected harmonic current at node i is 0 (or 0 if there is no harmonic source or mitigation device).

[0116] By inverting the matrix, the harmonic voltage can be expressed as:

[0117]

[0118] Let Z h =Y h -1 Let be the harmonic impedance matrix, then:

[0119]

[0120] Among them, Z h,jk These are elements of the impedance matrix, representing the effect of the injected current at node k on the harmonic voltage at node j.

[0121] according to to I h,i Find the partial derivative:

[0122] S h,ij =Z h,ji ;

[0123] That is, the sensitivity is equal to the harmonic impedance matrix Z. hThe element in the j-th row and i-th column reflects the degree of influence of the compensation current of node i on the harmonic voltage of node j.

[0124] For n nodes, the harmonic mitigation sensitivity matrix S is an n×n matrix with the following elements:

[0125] S h =[S h,ij ] = [Z h,ji ];

[0126] Diagonal element S h,ii =Z h,ii Indicates the effectiveness of local governance, typically large (close to 1); off-diagonal element S h,ij (i≠j) represents the governance influence of node i on node j, which decreases with increasing electrical distance; if nodes i and j are not directly connected, Z h,ji It is usually 0 or close to 0.

[0127] Site selection criteria: Based on the overall sensitivity from high to low, APFs are installed first at nodes with high sensitivity to ensure maximum governance effectiveness.

[0128] (2) Modeling of wind power output uncertainty:

[0129] Random fluctuations in wind speed and weather lead to significant uncertainties in wind power output, posing new challenges to regional power grid harmonic mitigation strategies. To address this, this invention introduces CCP to explicitly model the randomness of wind power output, requiring, in probabilistic terms, that the total harmonic distortion rate of the voltage after mitigation not exceed 4%. This ensures that mitigation targets are met under uncertain scenarios while improving the robustness and economy of the solution.

[0130] 1) Relationship between wind speed and power generation: The actual power output of a wind turbine is usually represented by a piecewise function based on a wind speed range;

[0131]

[0132] In the formula, P represents the power output of the wind turbine; v represents the wind speed; r represents the air density; A represents the swept area of ​​the wind turbine; C p Indicates the power coefficient; v cut-in Indicates the cut-in wind speed; v rate Indicates the rated wind speed; v cut-out Indicates the cut-out wind speed; P rate Indicates the rated power.

[0133] The uncertainty of wind speed is usually modeled using probability distributions. This invention chooses the Weibull distribution, whose probability density function is:

[0134]

[0135] In the formula, v represents wind speed; k represents shape parameter; and c represents scale parameter.

[0136] The cumulative distribution function of the Weibull distribution is:

[0137]

[0138] The probability distribution diagram of wind speed is as follows: Figure 5 As shown; the probability density curve of the active power output of the wind turbine, calculated from the wind turbine speed, is as follows. Figure 6 As shown.

[0139] 2) Chance Constraint Modeling Principle: Chance constraints express inequalities containing random variables in probabilistic form, allowing constraints to be satisfied under a set confidence level. Based on this, this invention models the stochasticity of wind power output, ensuring compliance with power quality standards (GB / T 14549) with a probability not lower than the specified level in fluctuating scenarios. Its mathematical expression is as follows:

[0140] P(THD U ≤THD GB )≥α;

[0141] In the formula, THD GB The upper limit of THD in the national standard; α is the confidence level, if taken as 0.95, it means that the system THD does not exceed the specified THD in 95% of scenarios. GB By introducing opportunity constraints, this invention can balance governance effectiveness and risk control under conditions of uncertainty.

[0142] 3) Deterministic transformation of chance constraints: To integrate probabilistic constraints into the particle swarm optimization model, the uncertainty of wind power output needs to be transformed into deterministic constraints; the goal of chance constraints is to find a reliable output P. min This ensures that the wind power output P satisfies:

[0143] P(P≥P min )≥α;

[0144] Where P is the wind power resource output, and a is the confidence level of the opportunity constraint; and when P≥P min At that time, the active power output of wind power resources meets the needs of the power grid.

[0145] Assuming reliable output P min The corresponding wind speed is v min Considering the piecewise nature of the power curve:

[0146] A. When P min ≤P rate At that time, P min With v min The relationship is:

[0147] but:

[0148] The required opportunity constraint can be changed to:

[0149] P(v≥v min And v < v cut-out )≥α;

[0150] P(v min ≤v<v cut-out )=F(v cut-out ;k,c)-F(v min ;k,c);

[0151] After sorting, we can obtain:

[0152] Normally v cut-out Larger, its CDF term It can be approximated as 0, and according to the above formula, we can obtain:

[0153] Solving for: v min Substituting back into the power formula, we get:

[0154] When P min ≥P rate At this point, the opportunity constraint here can be:

[0155]

[0156] This scenario can be disregarded because the rated wind speed is very high, and the minimum wind speed required to meet the power grid requirements is even higher than the rated wind speed, which is unrealistic.

[0157] B, when P min When the value is 0, the minimum wind speed is too conservative and does not reflect reality.

[0158] 4) Calculation of Remaining Wind Power Capacity: Remaining wind power capacity 80% of the remaining capacity is used for harmonic compensation, and the compensation current (in A) is:

[0159]

[0160] in, This refers to the remaining available capacity of wind power resources; Wind power resources can be used as harmonic compensation current; U B This is the reference voltage.

[0161] Incorporate wind power resource compensation capacity into the optimization model and configure it in conjunction with APF.

[0162] 5) Comprehensive optimization and configuration:

[0163] Optimization objective: Minimize the system's maximum THD and total governance cost: F = min(maxTHD, C total );

[0164] Maximum THD of the computing system:

[0165] Calculate the total governance cost C total :C total =C APF +N time (C capacity +k wp C wp );

[0166] C APF =N APF ·c fixed ;

[0167]

[0168] In the formula, C total Indicates total governance cost; C APF Indicates the installation cost of the active filter; N time Indicates equipment uptime; C capacity This represents the operation and maintenance cost of an active filter; k wp C represents the wind power resource cost penalty coefficient; wp Indicates the operation and maintenance cost of harmonic mitigation for wind power resources; N APF Indicates the number of active filters; c fixed This indicates the fixed installation cost of each active filter; c unit This represents the maintenance cost of the active filter; i represents the index value; S i Indicates the governance capacity of the APF; c wp This represents the operation and maintenance cost of wind power resource harmonic mitigation; k represents the wind power resource node; WP_nodes represents the set of wind power resource nodes; This represents the harmonic compensation current at wind power resource node k.

[0169] III. Optimization Model and Solution:

[0170] The following describes the implementation steps of the present invention in detail, taking a 17-node regional power grid as an example. These steps include system parameter settings, APF site selection and capacity determination, collaborative governance of wind power resources and APF, and analysis of experimental results. The present invention achieves a reduction of total harmonic voltage distortion (THD) to below 4% by using APSO and opportunity constraint modeling, combined with the harmonic compensation capability of wind power resources, while optimizing governance costs.

[0171] 1. Objective function: The objective is to minimize the maximum THD and total governance cost of the system.

[0172] 2. Constraints:

[0173] APF Capacity Constraint: During harmonic compensation, the maximum compensation amount of the APF cannot exceed the capacity limit. The constraint formula is: 0 ≤ I APF,i ≤I maxAPF,i Among them, I APF,i I is the compensation current value of APF. maxAPF,i This represents the maximum compensation current value for each APF;

[0174] Wind power resource compensation constraints: During harmonic compensation, the maximum compensation amount of wind power resources cannot exceed the remaining capacity limit. The constraint formula is as follows: in, This is the compensation current value for wind power resources.

[0175] 3. Model solution: such as Figure 6 As shown, this invention uses the APSO algorithm to solve the optimization model and uniformly controls the harmonic current compensation values ​​of harmonic control equipment in all regions.

[0176] IV. Example Analysis:

[0177] 1. Regional power grid structure and line parameters used in the example: To verify the proposed strategy, this invention uses a 17-node regional power grid as a test example, and the simulation platform is Matlab; the network structure is as follows: Figure 7 As shown, the line and node parameters are listed in Tables 1 and 2 respectively; wind power access nodes 7–13 and 15–17 are shown in Tables 3 and 4; nonlinear load harmonic sources are located at nodes 9 and 15; harmonic currents at each node are randomly selected from 2% to 20% of the fundamental frequency; particle swarm size is 100, iteration upper limit is 200, inertia weight w is linearly reduced from 0.9 to 0.4, c1=c2=2; cost parameters: APF fixed installation cost is 100,000 yuan / unit, annual operation and maintenance cost is 150 yuan / A; annual operation and maintenance cost for wind power is 100 yuan / A; opportunity constraint penalty coefficients are set according to confidence level: 95% corresponds to 1.2, 90% corresponds to 1.5, 85% corresponds to 1.8, and 80% corresponds to 2.

[0178] Table 1 Line Parameter Table

[0179] starting node Termination Node Branch impedance / Ω Branch reactance / Ω 1 2 0.5 0.5 2 3 1.1 1.1 3 4 1.5 1.1 4 5 0.8 1.1 4 6 1.1 1.1 6 7 0.4 0.4 7 8 8 1.1 8 9 0.75 1 8 10 0.9 1.8 10 11 0.4 0.4 3 12 1.1 1.1 12 13 0.4 0.4 13 14 0.9 1.2 14 15 1.1 1.1 14 16 0.8 1.10 16 17 0.4 0.4

[0180] Table 2 Node Parameter Table

[0181] Node number Active power demand Pd / kW Reactive power demand Qd / kvar 1 0 0 2 8 6 3 8 6 4 8 6 5 8 6.4 6 12 1.6 7 8 1.6 8 6 4.8 9 16 10.8 10 20 7.2 11 4 3.6 12 2.4 2 13 18 8 14 4 3.6 15 4 4.4 16 4 3.6 17 8.4 3.2

[0182] Table 3 Harmonic Source Amplitude Data

[0183]

[0184]

[0185] Table 4 Harmonic Source Radius Data

[0186] Node\radian 5th harmonic 7th harmonic 11th harmonic 13th harmonic 17th harmonic 7 0.0031 0.0031 0.0031 0.0031 0.0031 8 0.0031 0.0031 0.0031 0.0031 0.0031 9 0.0031 0.0031 0.0031 0.0031 0.0031 10 0.0062 0.0062 0.0062 0.0062 0.0062 11 0.0031 0.0031 0.0031 0.0031 0.0031 12 0.0062 0.0062 0.0062 0.0062 0.0062 13 0.0062 0.0062 0.0062 0.0062 0.0062 15 0.0094 0.0094 0.0094 0.0094 0.0094 16 0.0062 0.0062 0.0062 0.0062 0.0062 17 0.0094 0.0094 0.0094 0.0094 0.0094

[0187] Taking the wind power grid connection at node 7 as an example, Figure 8 The available output power at different confidence levels is shown: 2.411MW at a confidence level of 95%; 2.301MW at a confidence level of 90%; 2.039MW at a confidence level of 85%; and 1.884MW at a confidence level of 80%. It can be seen that the available output power of wind power resources decreases as the confidence level decreases. The same method is applied to the remaining wind power access nodes to obtain the output power of each node at different confidence levels. The results are summarized in Table 5.

[0188] Table 5 Active Power Output of Wind Power Resources (MW)

[0189] Node / Confidence Level 95％ 90％ 85％ 80％ 7 2.411 2.301 2.039 1.884 8 2.411 2.301 2.039 1.884 9 2.411 2.301 2.039 1.884 10 2.411 2.301 2.039 1.884 11 2.411 2.301 2.039 1.884 12 1.987 1.912 1.774 1.593 13 1.987 1.912 1.774 1.593 15 1.987 1.912 1.774 1.593 16 1.987 1.912 1.774 1.593 17 1.987 1.912 1.774 1.593

[0190] After obtaining the output from various wind power resources, it can be used... The remaining capacity of each wind power resource is calculated, and 80% of the remaining capacity is taken as the maximum harmonic compensation capacity of that wind power resource. Figure 9 As shown; then through The maximum harmonic compensation capacity is converted into the maximum harmonic compensation current as shown in Table 6.

[0191] Table 6 Maximum Harmonic Compensation Current of Wind Power Resources at Different Confidence Levels

[0192]

[0193] 2. Results of example governance and optimization:

[0194] The method of this invention was implemented in a 17-node regional power grid, and the specific results are shown in Table 7:

[0195] Table 7 Comparison of effects before and after optimization

[0196]

[0197] (1) Before the collaborative governance of wind power resources:

[0198] APF configuration: 7 APFs are installed on nodes [7,910,11,15,16,17], with capacities of [11.26,19.40,56.53,63.33,94.87,19.83,39.08] A respectively; maximum THD: 3.51%, meeting the requirement of below 4%; governance cost: 745,648.94 yuan.

[0199] (2) After collaborative governance of wind power resources:

[0200] Unrestricted wind power resource collaborative governance: There are 2 APF units installed at nodes [5,15], with capacities of [98.325,96.457]A respectively, a maximum THD of 2.1%, and a total cost of RMB 257,015.07.

[0201] Opportunity-constrained wind power resource collaborative governance: Confidence levels are divided into [95%, 90%, 85%, 80%]. When the confidence level is 95% and 90%, the APF is installed at 5 nodes; when the confidence level is 85%, the APF is installed at 4 nodes; and when the confidence level is 80%, the APF is installed at 6 nodes. The scenario with the best THD governance effect is the scenario with a confidence level of 95%, with a THD of 1.31% after governance, and the governance cost is the lowest, lower than other scenarios, at 151,011.58 yuan.

[0202] like Figure 10 As shown, in the initial scenario, the THD of each node significantly exceeded the limit, with nodes 8-11 reaching over 12%, resulting in severe deterioration of power quality. When only APF was deployed, the THD was effectively suppressed, with all nodes dropping to below 4%, and the peak value reaching 3.1%, meeting the requirements of GB / T14549. After further introducing opportunity-constrained wind power and APF for coordinated governance, the THD of the entire network stabilized at approximately 2%, with a peak value of only 1.31% at a 95% confidence level, demonstrating even better governance results.

[0203] like Figure 11 As shown, under initial operating conditions, the harmonic current content of each branch of the regional power grid is generally high: the peak value of the fifth harmonic reaches 0.07 pu, and the other characteristic harmonics are also at a high level, especially in the 1-3 section of the branch, indicating that the system has serious harmonic pollution. After the APF is put into operation alone, the peak value of the fifth harmonic drops to about 0.02 pu, and the amplitude of each harmonic decreases synchronously, verifying the compensation efficiency of the APF, but there is still a compensation gap in some areas. After the introduction of wind power and APF for synergistic treatment, the suppression effect is further improved: under the 95% confidence level scenario, the peak value of the fifth harmonic is compressed to below 0.01 pu, the harmonic current content of the whole network is significantly reduced, and the compensation performance is better than that of the single APF scheme.

[0204] like Figure 12As shown, under initial operating conditions, the system harmonic voltage distortion is significant: the peak value of the fifth harmonic voltage at nodes 6–17 reaches 0.11 pu, and the seventeenth harmonic voltage reaches 0.014 pu, indicating severe multi-harmonic pollution. After the APF is put into operation, the node harmonic voltage is reduced, with the peak value of the fifth harmonic dropping to about 0.03 pu, and the amplitude of each harmonic decreasing synchronously, showing that the APF has a good suppression effect locally. After introducing the wind power and APF synergistic strategy, the harmonic voltage is further improved: at the 95% confidence level, the peak value of the third harmonic voltage drops to about 0.01 pu, and the harmonic voltage distribution of all nodes in the network is more balanced, with the mitigation effect being better than other confidence levels.

[0205] In summary, this invention addresses the uncertainty of wind power output by introducing opportunity-constrained programming (CCP), overcoming the shortcomings of traditional methods that ignore the random fluctuations of wind power. Specifically, it uses Weibull distribution to model wind speed uncertainty and transforms probabilistic constraints into deterministic constraints, ensuring that the harmonic mitigation scheme can meet the requirement that the total harmonic distortion (THD) does not exceed 4% at a preset confidence level, thereby improving the robustness and adaptability of the scheme. In addition, this invention proposes a collaborative governance mechanism between wind power resources and active power filters (APFs). This mechanism utilizes the remaining capacity of wind power resources to participate in harmonic suppression without additional hardware investment. It only generates reverse compensation current by modifying the PWM control strategy in software, complementing the APF and achieving on-site sharing of governance tasks, effectively reducing the configuration requirements of APFs. In terms of governance process design, a hierarchical governance strategy is adopted: the remaining capacity of wind power is used first for compensation. If the compensation is insufficient, the APF intervenes for collaborative governance. Finally, an improved particle swarm optimization algorithm (APSO) is used to adjust to the optimal configuration. This method is suitable for complex radial power grid structures, can coordinate system-level resource allocation, avoid resource waste, and improve governance efficiency.

[0206] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0207] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0208] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0209] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0210] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0211] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for suppressing harmonic resonances in offshore wind farms, characterized in that, The method includes: The remaining capacity of the offshore wind power access node is used to perform initial harmonic compensation based on the remaining capacity, and an initial assessment of the regional power grid after the initial harmonic compensation is conducted. If the initial assessment does not meet the preset suppression requirements, a collaborative suppression mechanism is established in conjunction with the active filter. The collaborative suppression mechanism is used to perform second harmonic compensation, and a second assessment is conducted on the regional power grid after the second harmonic compensation. If the second evaluation result still does not meet the preset suppression requirements, the parameter configuration and remaining capacity utilization configuration of the active filter are iteratively optimized using an improved particle swarm optimization algorithm until the preset suppression requirements are met and the optimal configuration scheme is obtained.

2. The method for suppressing harmonic resonance in an offshore wind farm according to claim 1, characterized in that, The process of accessing the remaining capacity of offshore wind power nodes, performing initial harmonic compensation based on the remaining capacity, and conducting an initial assessment of the regional power grid after initial harmonic compensation includes: By performing power flow calculations and initial harmonic analysis, harmonic source nodes in the power grid are identified, and the sensitivity of harmonic voltage at each harmonic source node to the compensation current of the active filter is calculated using the harmonic propagation equation. Based on the node and branch characteristics of the regional power grid, a harmonic propagation matrix is ​​constructed, and a harmonic control sensitivity matrix is ​​constructed with sensitivity as the matrix element. Combined with the multi-harmonic source scenario, the comprehensive sensitivity of the node is obtained. All harmonic source nodes are sorted from high to low based on the obtained comprehensive sensitivity, and active filters are installed preferentially at nodes with high comprehensive sensitivity.

3. The method for suppressing harmonic resonance in an offshore wind farm according to claim 2, characterized in that, The expression for the sensitivity is: In the formula, S h,ij Indicates sensitivity; U h,j I represents the h-th harmonic voltage at node j; h,i This represents the active filter compensation current at node i.

4. The method for suppressing harmonic resonance in an offshore wind farm according to claim 1, characterized in that, The expression for calling upon the remaining capacity of the offshore wind power access node and performing initial harmonic compensation based on the remaining capacity is as follows: In the formula, Indicates the remaining available capacity of wind power resources; Indicates the remaining wind power capacity; This indicates that wind power resources can be used for harmonic compensation current; U B This represents the reference voltage.

5. The method for suppressing harmonic resonances in an offshore wind farm according to claim 1, characterized in that, The step of iteratively optimizing the parameter configuration and remaining capacity utilization of the active filter using an improved particle swarm optimization algorithm until a preset suppression requirement is met, thereby obtaining the optimal configuration scheme, includes: Based on the relationship between wind speed and power generation, and combined with the harmonic control requirements of the regional power grid, dual optimization objectives are set and constraints are determined. The constraints include active filter capacity constraints and wind power resource compensation constraints. Based on the uncertainty of wind speed, and combining dual optimization objectives and constraints, a harmonic mitigation optimization model is constructed. An improved particle swarm optimization algorithm is used to solve the harmonic control optimization model, and the optimal configuration scheme is obtained by using the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables.

6. The method for suppressing harmonic resonance in an offshore wind farm according to claim 5, characterized in that, The dual optimization objectives include minimizing the maximum total harmonic distortion rate of the regional power grid and minimizing the total governance cost.

7. A method for suppressing harmonic resonances in an offshore wind farm according to claim 6, characterized in that, The expression for the maximum total harmonic distortion rate is: In the formula, maxTHD represents the maximum total harmonic distortion (THD); V h,i V1 represents the effective value of each harmonic voltage; V2 represents the effective value of the fundamental voltage.

8. A method for suppressing harmonic resonances in an offshore wind farm according to claim 7, characterized in that, The expression for the total governance cost is: C total =C APF +N time (C capacity +k wp C wp ); C APF =N APF ·c fixed ; In the formula, C total Indicates total governance cost; C APF Indicates the installation cost of the active filter; N time Indicates equipment uptime; C capacity This represents the operation and maintenance cost of an active filter; k wp C represents the wind power resource cost penalty coefficient; wp Indicates the operation and maintenance cost of harmonic mitigation for wind power resources; N APF Indicates the number of active filters; c fixed This indicates the fixed installation cost of each active filter; c unit This represents the maintenance cost of the active filter; i represents the index value; S i Indicates the governance capacity of the APF; c wp This represents the operation and maintenance cost of wind power resource harmonic mitigation; k represents the wind power resource node; WP_nodes represents the set of wind power resource nodes; This represents the harmonic compensation current at wind power resource node k.

9. A method for suppressing harmonic resonances in an offshore wind farm according to claim 8, characterized in that, The harmonic mitigation optimization model, based on the uncertainty of wind speed and combined with dual optimization objectives and constraints, includes: Based on the relationship between wind speed and power generation, the uncertainty of wind speed is identified, and a wind speed probability distribution model is constructed using the Weibull distribution. Based on dual optimization objectives and constraints, the probabilistic constraints corresponding to the wind speed probability distribution model are transformed into deterministic constraints through chance constraints, and a harmonic governance optimization model is constructed.

10. A method for suppressing harmonic resonances in an offshore wind farm according to claim 9, characterized in that, The method of solving the harmonic mitigation optimization model using an improved particle swarm optimization algorithm, and obtaining the optimal configuration scheme by using the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables, includes: Initialize the parameters of the particle swarm optimization algorithm, and define the position of each particle as a combination of the parameter configuration of the active filter and the remaining capacity utilization configuration. Based on the initialization results, determine whether the current iteration number exceeds the preset maximum iteration number. If the current iteration count exceeds the maximum iteration count, the active filter parameter configuration and remaining capacity utilization scheme corresponding to the global optimal solution are output as the optimal configuration scheme. If the current iteration count does not exceed the maximum iteration count, then according to the dual optimization objective, calculate the fitness of each particle in the population, update the individual optimal solution and global optimal solution of each particle based on the calculation results, update the velocity and position of each particle, adjust the inertia weight, and continue iterating until the current iteration count exceeds the maximum iteration count. Output the active filter parameter configuration and remaining capacity utilization scheme corresponding to the global optimal solution as the optimal configuration scheme.

11. A harmonic resonance suppression system for offshore wind farms, characterized in that, The system includes: The initial assessment unit is used to call up the remaining capacity of the offshore wind power access node, perform initial harmonic compensation based on the remaining capacity, and conduct an initial assessment of the regional power grid after the initial harmonic compensation. The secondary evaluation unit is used to establish a cooperative suppression mechanism in conjunction with the active filter if the initial evaluation fails to meet the preset suppression requirements. The cooperative suppression mechanism is then used to perform secondary harmonic compensation, and a secondary evaluation is performed on the regional power grid after secondary harmonic compensation. The configuration scheme acquisition unit is used to iteratively optimize the parameter configuration and remaining capacity utilization configuration of the active filter using an improved particle swarm optimization algorithm if the secondary evaluation result still does not meet the preset suppression requirements, until the preset suppression requirements are met and the optimal configuration scheme is obtained.

12. A harmonic resonance suppression system for offshore wind farms according to claim 11, characterized in that, Prior to the initial evaluation unit, the following were included: The sensitivity calculation module is used to identify harmonic source nodes in the power grid through power flow calculation and initial harmonic analysis, and to calculate the sensitivity of the harmonic voltage in each harmonic source node to the compensation current of the active filter using the harmonic propagation equation. The comprehensive sensitivity acquisition module is used to construct a harmonic propagation matrix based on the node and branch characteristics of the regional power grid, and to construct a harmonic control sensitivity matrix with sensitivity as matrix elements. Combined with multi-harmonic source scenarios, the comprehensive sensitivity of the nodes is obtained. The active filter installation module is used to sort all harmonic source nodes from high to low based on the obtained comprehensive sensitivity, and prioritize the installation of active filters on nodes with high comprehensive sensitivity.

13. A harmonic resonance suppression system for offshore wind farms according to claim 11, characterized in that, The configuration scheme acquisition unit includes: The target and constraint setting module is used to set dual optimization targets based on the relationship between wind speed and power generation, combined with the harmonic control requirements of the regional power grid, and to determine the constraints, including active filter capacity constraints and wind power resource compensation constraints. The optimization model building module is used to construct a harmonic mitigation optimization model based on the uncertainty of wind speed and by combining dual optimization objectives and constraints. The configuration optimization module is used to solve the harmonic control optimization model using an improved particle swarm optimization algorithm, and uses the parameter configuration of the active filter and the utilization configuration of the remaining capacity as iterative optimization variables to obtain the optimal configuration scheme.

14. A harmonic resonance suppression system for offshore wind farms according to claim 13, characterized in that, The optimization model construction module includes: The probability distribution model construction submodule is used to identify the uncertainty of wind speed based on the relationship between wind speed and power generation, and to construct a wind speed probability distribution model using the Weibull distribution. The constraint transformation submodule is used to transform the probabilistic constraints corresponding to the wind speed probability distribution model into deterministic constraints through chance constraints, based on the dual optimization objectives and constraints, and to construct a harmonic governance optimization model.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 10.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

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