Harmonic suppression method and system for wind farm based on passive and active two-stage configuration

Abstract

The present disclosure provides a wind farm harmonic suppression method and system based on passive and active two-stage configuration, relates to the technical field of harmonic suppression, and comprises constructing a wind farm harmonic frequency domain power flow model to describe the propagation and response law of different harmonic components in the network; a distribution network sequential governance framework model is constructed based on the wind farm harmonic frequency domain power flow model to realize wind farm harmonic suppression; the governance framework model comprises a passive device governance optimization model and an active device governance optimization model. The method of the present disclosure solves the problem of harmonics and resonance caused by the output uncertainty of wind turbines in wind farms, and realizes the goal of the lowest governance cost under the condition of meeting the power quality constraints. It is not only suitable for wind farm harmonic governance scenarios, but also can be popularized to other distribution network harmonic governance projects containing a large number of nonlinear loads, and provides a new solution for comprehensive harmonic governance of power systems.

Description

Technical Field

[0001] This disclosure relates to the field of harmonic suppression technology, specifically to a method and system for suppressing harmonics in wind farms based on a two-stage configuration of passive and active stages. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Wind power has become a core support for the construction of new power systems, with its installed capacity continuously expanding. Modern wind farms rely heavily on power electronic converters for grid connection, transforming the power collection system into a high-proportion power electronic network, disrupting the steady-state power quality of traditional power grids. The high-frequency switching and nonlinear control of the converters inject a large number of broadband harmonics. When these harmonics propagate in the radial power collection network, they easily couple with line capacitance and transformer leakage inductance, causing harmonic cascade amplification and series-parallel resonance problems. This results in excessive voltage harmonic distortion rates, accelerated aging of equipment insulation, and serious threats to the safety of power grid equipment. Simultaneously, the random fluctuations in wind power output lead to dynamic changes in harmonic injection, system impedance, and resonant frequency bands, significantly increasing the difficulty of harmonic mitigation.

[0004] Currently, harmonic mitigation in wind farms primarily relies on the installation of filtering equipment. Passive filters are simple in structure and economical, suitable for low-frequency harmonic mitigation, but their fixed parameters and poor adaptability make them prone to inducing secondary resonances. Active filters can dynamically compensate for broadband harmonics and adapt to grid disturbances, but their high cost and operating losses hinder large-scale application. Therefore, multi-device collaborative mitigation and optimized filter configuration have become the mainstream solutions in the industry. Existing synchronous planning systems have significant theoretical flaws; conventional single-time-section planning methods cannot characterize the dynamic evolution of system impedance after the addition of filtering equipment. Passive filters installed early can reshape the frequency characteristics of grid impedance, causing resonance point shifts, harmonic impedance amplification, and the risk of secondary resonances. Existing models sever the physical causal relationship of equipment connection, failing to predict and suppress harmonic hazards arising from mitigation devices in advance, and thus failing to meet the requirements for safe and stable operation of wind farms.

[0005] Recent research has focused on the coordinated configuration of PPF (Power Pulse Filter) with other active pollution control devices. However, existing coordinated configuration methods still have the following limitations: (1) The inherent intermittent and random characteristics of WP output make the governance effect uncertain and it is difficult to ensure the reliability of harmonic suppression; (2) Minimizing the total harmonic distortion (THD) is often the primary decision-making criterion, and the installation location or capacity of the filter device is optimized to meet the power quality standards. However, using THD as the optimization target cannot reflect the harmonic voltage response level and safety margin of the node under harmonic disturbances, i.e., the problem of single harmonic exceedance, and it is not economical. (3) Existing harmonic control optimization models mostly adopt deterministic constraints and do not fully consider the harmonic uncertainty caused by the randomness of WP output, which can easily lead to the failure of the control scheme under extreme operating scenarios and lack of robustness. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a wind farm harmonic suppression method and system based on a two-stage configuration of passive and active filters. It presents a two-stage sequential collaborative optimization architecture for passive and active filtering devices, constructing a dual-objective hierarchical mechanism. The first stage aims to minimize the overall network THD to achieve basic harmonic mitigation. The second stage introduces AHZ (Advanced Harmonic Frequency) as the core evaluation criterion, establishing a chance-constrained programming model that considers the stochastic nature of wind power. The model fully considers the intermittent and fluctuating characteristics of wind power output, ensuring that the proposed mitigation scheme possesses good robustness and operational reliability under extreme operating scenarios and dynamic fluctuations in harmonic power flow.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: Wind farm harmonic suppression methods based on passive and active two-stage configurations include: Constructing a harmonic frequency domain power flow model for a wind farm; Based on the wind farm harmonic frequency domain power flow model, a sequential governance model of the distribution network with passive and active governance equipment is constructed, and wind farm harmonic suppression is performed based on the sequential governance model of the distribution network. The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

[0008] As one embodiment, the construction of the wind farm harmonic frequency domain power flow model includes: Analyze the frequency-dependent impedance characteristics of the system, for the first...h The equation for the voltage at the harmonic node of the second harmonic is as follows: in, I i ( h ) is a node i The first harmonic source h Subsequent injection current; Y ii ( h ) represents the self-admittance of the node. Y 1i ( h ), Y i1 ( h ) etc. represent the mutual admittance between nodes; U i ( h ) is a node i The h Secondary voltage.

[0009] As one example, based on the harmonic frequency domain power flow model of a wind farm, the voltage response of any node in the system at any harmonic frequency is analyzed, and the quantitative relationship between the node harmonic injection amount, network impedance characteristics and inter-node coupling effect is analyzed. If resonance exceeds the limit, the node self-impedance will be greater than the allowable equivalent harmonic impedance of the system. Therefore, the resonance amplification is judged by the allowable value of the equivalent harmonic impedance of the system, and the resonance harmonic impedance amplification index is defined.

[0010] As one example, the first stage aims to minimize THD and LCC. For low-order characteristic components such as the 5th, 7th, and 11th harmonics, which account for a high proportion in the system's harmonic composition, the installation location and rated capacity of the PPF are globally optimized to complete large-scale harmonic foundation management at a low cost.

[0011] As one example, the second phase uses node AHZ less than 1 as a constraint boundary, focusing on the harmonic amplification problem and secondary resonance risk of high-risk nodes. Based on the system operation conditions configured in the first phase, it optimizes the access nodes and capacity parameters of APF and combines the coordinated use of the remaining capacity of distributed resources to achieve dynamic compensation for non-characteristic frequency harmonics and various uncertain disturbances, forming a complementary two-layer architecture with the first phase governance path.

[0012] As an example, the first stage establishes a passive device governance optimization model with the objectives of minimizing THD and LCC: in, C CAPEX The initial installation cost of the treatment equipment; COPEX To manage equipment operation and maintenance costs; U h,i and U 1,i They are nodes i No. h Second harmonic voltage and fundamental voltage amplitude; c fix The fixed cost of the treatment equipment; c PPF Q represents the unit capacity cost of PPF; C,i,rate This refers to the rated capacity of the PPF; CRF This is the capital recovery coefficient, where r The discount rate is... Y For equipment lifespan; This represents the average annual operating and maintenance cost of the APF.

[0013] As one example, the second stage introduces AHZ to quantify the resonance severity of nodes and constructs a dual-objective optimization function for LCC and system AHZ. By balancing the effectiveness of active damping with economic costs, the active control equipment can suppress secondary resonance peaks at the lowest cost, ensuring that the AHZ of all nodes in the network is within the safe critical threshold.

[0014] As one embodiment, the first stage focuses on optimizing the installation location and capacity of the PPF (Power Pulse Filter). Candidate installation locations are selected from nodes near the harmonic source nodes, and the system is updated accordingly. h The node admittance matrix of the subharmonics is used to obtain the subharmonic voltage amplitude of each node through harmonic power flow calculation using the updated node admittance matrix, and the THD of all nodes in the network is calculated accordingly to evaluate the basic governance effect of the first phase.

[0015] As one example, in the first stage of optimization configuration, the core constraint is the system THD opportunity constraint, while the PPF device capacity constraint must also be met.

[0016] As one embodiment, the active control device in the second stage can be equivalent to a controllable harmonic current source in the harmonic frequency domain. By injecting a compensation current into the power grid with the same amplitude but opposite phase as the harmonic source current, the harmonic current can be canceled in real time, thus suppressing harmonic voltage distortion.

[0017] As one example, the core constraint of the second phase is AHZ compliance constraint, while also meeting the physical constraints of active governance equipment.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions: A wind farm harmonic suppression system based on a passive and active two-stage configuration includes: The model building module is used to build harmonic frequency domain power flow models for wind farms. The optimization and governance module is used to construct a sequential governance model of the distribution network for passive and active governance equipment based on the wind farm harmonic frequency domain power flow model, and to suppress wind farm harmonics based on the sequential governance model of the distribution network. The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the wind farm harmonic suppression method based on a passive and active two-stage configuration.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the wind farm harmonic suppression method based on a passive and active two-stage configuration.

[0021] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosure presents a wind farm harmonic suppression method based on a two-stage configuration of passive and active systems. It proposes a two-stage sequential collaborative planning architecture for passive and active filtering devices, overcoming the theoretical limitations of traditional synchronous planning of multiple governance devices. A dual-objective hierarchical mechanism is constructed to address the blind spot in existing research that uses only THD as a single governance indicator. The first stage aims to minimize the overall THD of the network to achieve basic harmonic governance. The second stage introduces AHZ as the core evaluation criterion, shifting the focus of governance from overall area control to local point-based prevention and control, achieving quantification and targeted blocking of high-impedance resonant peaks.

[0022] This disclosed wind farm harmonic suppression method, based on a two-stage configuration of passive and active phases, leverages the remaining capacity of the wind turbine combined grid inverters to participate in active damping, thus changing the traditional asset-heavy model that heavily relies on Active Damping Function (APF). Phase 2 utilizes the remaining capacity of the wind turbines to provide damping support, treating it as an active damping resource with zero additional cost to participate in resonance suppression, significantly expanding the optimization space without increasing initial investment.

[0023] This disclosure presents a wind farm harmonic suppression method based on passive and active two-stage configurations, establishing a chance-constrained programming model that takes into account the stochasticity of wind power. The model fully considers the intermittent and fluctuating characteristics of wind power output, and uses a 95% confidence level to construct the chance-constrained framework, ensuring that the proposed mitigation scheme has good robustness and operational reliability under extreme operating scenarios and dynamic fluctuations in harmonic power flow.

[0024] This disclosure presents a wind farm harmonic suppression method based on a two-stage configuration of passive and active equipment, significantly reducing reliance on active equipment and achieving a substantial reduction in LCC (Local Cost of Harmonics). Employing a hierarchical optimization architecture with PPF (Power Pulse Filter) as the primary governance component, remaining WP (Power Plant) capacity as a collaborative damping resource, and small-capacity active power filters as a fallback supplement, the proposed solution achieves an LCC of only RMB 108,500. Compared to traditional solution 1, the total cost is reduced by 48.36%. Compared to traditional solution 2, under the premise of identical basic investment in the first stage, through collaborative scheduling of remaining capacity, the capacity reduction of the dedicated APF configuration in the second stage reaches 64.3%, further reducing the total cost by 27.18%.

[0025] This disclosure presents a wind farm harmonic suppression method based on a two-stage configuration of passive and active technologies. This method suppresses the risk of secondary resonance in the system and improves the safety margin of the distribution network. Addressing the system impedance drift problem caused by the grid connection of large-capacity capacitive equipment, the second-stage optimization of site selection and capacity setting, along with active damping current injection, strictly controls the harmonic impedance amplification index of high-risk nodes across the entire network within the safe critical value of AHz=1, thus fundamentally avoiding harmonic risks within the wind farm.

[0026] This disclosed wind farm harmonic suppression method, based on passive and active two-stage configuration, effectively enhances the robustness of harmonic mitigation strategies under complex operating conditions. The sequential configuration scheme derived from chance-constrained programming is no longer limited to a single typical static operating section, but can cover over 95% of wind farm stochastic operating scenarios. Even under extreme conditions of significant fluctuations in wind turbine output and strong background harmonic disturbances, it can still ensure that the total harmonic distortion rate of the entire network meets standard requirements, overcoming the shortcomings of traditional schemes where mitigation effectiveness decreases when operating boundaries change.

[0027] This disclosure presents a wind farm harmonic suppression method based on a two-stage configuration of passive and active power generation, maximizing the utilization efficiency of power plants (WPs) within the wind farm. This method breaks through the traditional engineering approach of requiring a large number of dedicated equipment for power quality management, enabling wind turbines to simultaneously perform active power generation and harmonic damping support. While ensuring normal fundamental active power output, it allows free use of the remaining inverter capacity of 7 nodes, totaling 0.58 MVA, improving the return on investment for individual equipment and providing a valuable engineering path for large-scale, low-cost harmonic management in new power systems. Attached Figure Description

[0028] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0029] Figure 1 This is a schematic diagram of a wind farm harmonic suppression method strategy based on passive and active two-stage configuration according to an embodiment of this disclosure; Figure 2 This is a topology diagram of the wind farm site disclosed in this publication; Figure 3 This is a comparison diagram of the THD distribution of distribution network nodes under each scheme in the first phase of this disclosure; Figure 4 A comparison of the Pareto fronts of THD and LCC in the first stage of this disclosure embodiment; Figure 5 A comparison of node AHZ before and after the second stage of Scheme 1 in this embodiment of the present disclosure; Figure 6 A comparison of node AHZ before and after the second stage of Scheme 1 and Scheme 3 in the embodiments of this disclosure; Figure 7 This describes the capacity configuration distribution of passive and active devices across the entire network under various schemes in the embodiments of this disclosure. Detailed Implementation

[0030] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0032] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0033] Example 1 One embodiment of this disclosure provides a wind farm harmonic suppression method based on a passive and active two-stage configuration, the method steps of which include: Step 1: Construct a harmonic frequency domain power flow model for the wind farm; Step 2: Construct a sequential governance model for the distribution network with passive and active governance equipment based on the wind farm harmonic frequency domain power flow model, and perform wind farm harmonic suppression based on the sequential governance model of the distribution network; The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

[0034] As one embodiment, the wind farm harmonic suppression method disclosed herein, based on a two-stage configuration of passive and active systems, firstly constructs a wind turbine harmonic emission model and a wind farm harmonic power flow model adapted to the stochastic output characteristics of wind power, to characterize the propagation law of harmonics in the wind farm network; secondly, it establishes a distribution network sequential governance model with the synergistic optimization objectives of minimizing governance costs and optimizing power quality. In the first stage, aiming to minimize THD and LCC, a passive equipment governance optimization model is used to optimize the location and capacity of the power distribution network power supply (PPF), achieving low-cost, full-area governance; in the second stage, active governance equipment is introduced. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model is used to optimize the access nodes and capacity parameters of the power distribution network power supply (APF), while simultaneously introducing AHZ compliance constraints to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes. The specific implementation process is as follows: Step 1: Construct a harmonic frequency domain power flow model for the wind farm; New power distribution networks generally adopt a radial topology, with significant heterogeneity in the electrical parameters of each branch and a highly uneven distribution of short-circuit capacity among nodes within the network. With the highly decentralized access of diverse distributed harmonic sources, the spatial propagation, interactive coupling, and dynamic amplification effects of harmonic energy in complex network topologies become increasingly intricate. To accurately quantify and characterize the voltage response behavior of such power networks in the full frequency domain to harmonic current disturbances, this disclosure provides an in-depth analysis of the system's frequency-dependent impedance characteristics and constructs a corresponding mathematical model for harmonic power flow analysis. The aim is to systematically reveal the evolution and response mechanisms of different frequency harmonic components in multi-node topologies.

[0035] Based on the analysis of the system's frequency-dependent impedance characteristics, for the first... h The voltage equation for the harmonic node of the second harmonic can be expressed as: Wherein: I i ( h ) is a node i The first harmonic source h Subsequent injection current; Y ii ( h ) represents the self-admittance of the node. Y 1i ( h ), Y i1 ( h ) etc. represent the mutual admittance between nodes; U i ( h ) is a node i The h Secondary voltage.

[0036] Based on the above model, nodes i In the h Node self-impedance at subharmonic frequencies Z ii ( h This can be represented as: The node self-impedance directly reflects the harmonic voltage amplitude generated by a unit harmonic current injection at the corresponding harmonic frequency, and is the core parameter for characterizing the harmonic amplification characteristics of the node.

[0037] Furthermore, based on the above harmonic frequency domain power flow model, the voltage response of any node in the system at any harmonic frequency can be further analyzed and derived, and the quantitative relationship between the node harmonic injection amount, network impedance characteristics and inter-node coupling effect can be obtained. This provides a solid theoretical support for the construction of a quantitative evaluation index for node harmonic risk.

[0038] Assuming a single harmonic source is injected at the node within the allowable current limit, a simplified first harmonic source can be established according to national standard GB / T 14549. h The limit of the second harmonic voltage content and the first harmonic voltage content limit h The relationship between the second harmonic impedance. The following expression can be defined as a node. i place h Permissible value of subharmonic impedance Z h,max; In the formula: U N The node's rated voltage; HRU h For nodes h Limits on the content of subharmonic voltage; I h For the inflow node h Secondary harmonic current.

[0039] If resonance exceeds the limit, the node self-impedance will exceed the allowable system equivalent harmonic impedance. Therefore, resonance amplification can be judged by the allowable value of the system equivalent harmonic impedance, and the amplification index of harmonic impedance (AHZ) is defined as follows: when AHZ A value greater than 1 indicates that the distribution network is at the busbar i When a harmonic source is injected, local harmonic amplification occurs. h When the frequency is the same as the resonant frequency, local resonance occurs; otherwise, it indicates that the distribution network is at the busbar. i No local resonance phenomenon occurred when the injected harmonic source was used. AHZ It reflects the degree of danger of harmonic voltage amplification at the harmonic source.

[0040] Step 2: Construct a sequential governance model for the distribution network with passive and active governance equipment based on the wind farm harmonic frequency domain power flow model, and suppress wind farm harmonics based on the sequential governance model of the distribution network; This disclosure proposes a two-stage sequential governance strategy, such as Figure 1 As shown. The phased harmonic mitigation system constructed in this disclosure follows a unified planning principle. The configuration of passive and active filtering equipment is implemented step by step according to the sequence of stages. Each stage utilizes a multi-objective optimization framework, with the collaborative goals of minimizing mitigation costs and optimizing power quality, ultimately achieving optimal overall benefits for the entire mitigation system. Specific details are as follows: The sequential governance model for the distribution network includes a passive equipment governance optimization model and an active equipment governance optimization model. It aims to minimize governance costs and optimize power quality as a synergistic optimization objective, ultimately achieving optimal overall benefits for the entire governance system. Specific content includes: (I) Phase 1: PPF Low-Cost Global Governance.

[0041] The core optimization objective of this phase is to ensure that the THD of all nodes in the new distribution network meets the limits stipulated by national standards at a 95% confidence level, while minimizing the investment in harmonic mitigation. For low-order characteristic components such as the 5th, 7th, and 11th harmonics, which constitute a relatively high proportion of the system's harmonic composition, global optimization of the installation location and rated capacity of the power supply filter (PPF) enables large-scale harmonic mitigation at a lower cost, thus laying a solid foundation for stable operation of the active harmonic mitigation equipment in Phase 2.

[0042] (ii) Second stage: Precise resonance blocking of active treatment equipment.

[0043] The second phase uses a node AHZ less than 1 as a constraint boundary, focusing on preventing harmonic amplification and secondary resonance risks at high-risk nodes. Based on the system operating conditions after the PPF configuration in the first phase, by optimizing the access nodes and capacity parameters of the APF and combining it with the coordinated use of the remaining capacity of distributed resources, dynamic compensation can be achieved for non-characteristic frequency harmonics and various uncertain disturbances, forming a complementary two-layer governance architecture with the first phase governance path.

[0044] In the proposed sequential governance framework for distribution networks, the configuration of passive and active filtering equipment is implemented step-by-step according to a specific sequence, with each stage solved using a multi-objective optimization framework. The goal is to minimize governance costs and optimize power quality, ultimately achieving the optimal overall benefit of the entire governance system. Based on this holistic optimization logic, this disclosure constructs the corresponding objective function.

[0045] (1) Objective function of the first stage Harmonic mitigation devices are typical engineering assets characterized by large initial investment and long service life. If only the one-time purchase and installation cost of the equipment is used as the core basis for economic evaluation, it often leads to a decision-making bias where short-term costs are optimal but the overall life-cycle benefits are low, failing to truly reflect the long-term economic value of the mitigation solution. Therefore, a dual-objective optimization model is established with the goals of minimizing THD and LCC as objectives: in, C CAPEX The initial installation cost of the treatment equipment; COPEX To manage equipment operation and maintenance costs; U h,i and U 1,i They are nodes i No. h The amplitudes of the second harmonic voltage and the fundamental voltage. c fix The fixed cost of the treatment equipment; c PPF Q represents the unit capacity cost of PPF; C,i,rate This refers to the rated capacity of the PPF; CRF This is the capital recovery coefficient, where r The discount rate is... Y For equipment lifespan; c O&M This refers to the average annual operating and maintenance cost of the APF. Z PPF,i Indicates whether node i is configured with PPF; is a variable of 0–1. Q C,i This indicates the PPF filter capacity.

[0046] (2) Objective function of the second stage This phase introduces AHZ (Active Harmonic Zone) to quantify the severity of node resonance and constructs a dual-objective optimization function for LCC (Limited Collision Control) and system AHZ. This objective function aims to balance active damping effectiveness with economic cost, enabling active damping devices to suppress secondary resonance peaks at the lowest possible cost, ensuring that the AHZ of all nodes in the network remains within a safe critical threshold. in, c APF Cost per unit capacity of APF power modules; I P,i,rate Z is the rated capacity of the APF. APF,i For nodes i Whether to configure APF, representing a 0–1 variable; I P,i This represents the APF filter capacity.

[0047] The specific parameters of equipment cost are shown in Table 1.

[0048] Table 1 Equipment Cost Parameters The overall objective function described above constitutes the global economic indicators. Both stages of the sequential solution are guided by minimizing this objective function, achieving the optimal allocation of governance resources globally.

[0049] Furthermore, in the first stage, the PPF filters out the corresponding harmonic currents by establishing a low-impedance path at specific harmonic frequencies. Mitigation typically employs a single-tuned filter to control characteristic frequency harmonics. The first stage focuses on optimizing the installation location and capacity of the PPF, with candidate installation locations selected from nodes near the harmonic source nodes.

[0050] Set up PPF access node i Then the system's first h The nodal admittance matrix of the second harmonic is updated as follows: In the formula: Y h,i,0 This is the admittance of the original system; Y h,i,ctrl Additional admittance term for the treatment device; Z f The equivalent impedance of PPF; R The equivalent resistance of PPF; The fundamental angular frequency; L The inductance value is PPF; C This is the capacitance value in PPF.

[0051] Using the updated node admittance matrix, harmonic power flow calculations are employed to obtain the amplitude of each node's harmonic voltage, and based on this, the total harmonic load (THD) of all nodes in the network is calculated to evaluate the effectiveness of Phase 1's basic mitigation measures. i The definition of THD is as follows: In the formula: Z eq1 The parallel equivalent impedance of the system and the PPF; I s ( h ) represents the background harmonic current flowing into the node.

[0052] At the tuning frequency of PPF h r At this point, the internal inductive and capacitive reactances cancel each other out, and the branch exhibits low resistance characteristics. At this time, the equivalent impedance of the node is significantly reduced, the harmonic current is effectively diverted to the PPF branch, and the node harmonic voltage drops to a level close to zero, thereby achieving the filtering of the target harmonic.

[0053] The system impedance is typically inductive, while the power filter (PPF) exhibits capacitive behavior below its tuning frequency. The inductive mains circuit and the capacitive PPF will inevitably interact at a certain frequency below the tuning frequency.h p Parallel resonance occurs. That is: In the formula: X s ( h p ) for the power grid h p The equivalent inductive impedance presented to the outside at the next frequency; X c ( h p ) for PPF in h p The equivalent capacitive reactance presented externally at this frequency; At this point, the denominator of equation (16) approaches zero, and the equivalent node impedance increases sharply, leading to a shift in the resonant point. Under these circumstances, the non-characteristic frequency harmonic voltages are severely amplified, increasing the harmonic degradation of the system.

[0054] In the first phase of PPF optimization configuration, the core constraint is the system THD opportunity constraint, while the PPF device capacity constraint must also be met. (1) System THD opportunity constraints To address the random fluctuations in harmonic distortion caused by the uncertainty of DERs output, this disclosure employs a chance constraint approach to ensure that the THD of all nodes in the network meets the specified limits in 95% of operating scenarios. The chance constraint quantifies the uncertainty risk through a probabilistic framework, effectively avoiding the failure of traditional deterministic methods in extreme scenarios, while balancing the conservatism of the constraints with the economy of the solution. Its mathematical expression is as follows: In the formula, It is a probability function; THD i For nodes i THD value; THD lim The specified limit for harmonic voltage distortion rate; For confidence levels, this disclosure takes This means that the system's THD must meet the standard in more than 95% of operating scenarios.

[0055] This disclosure employs a scenario-based approach to solve chance constraints and utilizes Latin hypercube sampling to generate... S An initial scenario. Based on this, at least... Each scenario satisfies the THD constraint. By statistically analyzing the distribution of THD in each scenario, we ensure that the THD is satisfied. i ≤ THD lim The required proportion of scenarios is met, thereby ensuring the robustness of the governance solution.

[0056] (2) Capacity constraints of PPF devices The compensation capacity of the PPF should be limited to the design limits: In the formula: Q C,i,max This is the maximum capacity of a single PPF unit.

[0057] Furthermore, the second stage: resonant blocking model of active control equipment. Active harmonic mitigation equipment can be equivalent to a controllable harmonic current source in the harmonic frequency domain. By injecting a compensation current into the power grid with the same amplitude but opposite phase to the harmonic source current, it achieves real-time cancellation of harmonic currents and suppresses harmonic voltage distortion. After the active harmonic mitigation equipment is put into operation, the nodes... i The h The subharmonic voltage can be expressed as: In the formula, I P,i ( h (This refers to the active power treatment equipment for nodes) i The injected compensation harmonic current column vector is only applied at the active power control device installation node. i There is a non-zero value at this point, and the rest of the nodes are 0.

[0058] Through voltage-feedforward-based active damping control, the active control device can be equivalent to a purely resistive virtual harmonic resistor at the node. R v ( h This achieves damping and suppression of harmonic currents. Substituting the control law into the voltage equation and simplifying it, we can obtain the equivalent node impedance after introducing the active control device. Z eq2 : This disclosure reveals the mechanism by which active suppression devices actively suppress secondary resonances. In the case of a stage 1 PPF connection, when the disturbance frequency falls into the parallel resonance point... h p hour, Z eq1 A sharp increase in voltage can easily induce severe voltage distortion. However, the second stage, with the introduction of active harmonic mitigation equipment, effectively disrupts the original resonance conditions through the active damping and parallel shunting effect generated by the created virtual harmonic resistance. Active harmonic suppression devices limit harmonic impedance, which could otherwise tend towards infinity, to a finite value. R v ( hp Within a certain range, the active power treatment device only requires a very small output current to eliminate the secondary resonance generated by the PPF.

[0059] The core constraint at this stage is AHZ compliance, while also meeting the physical constraints of active pollution control equipment. (1) Opportunity constraints for AHZ compliance Compared to the THD index, which focuses on the overall degree of distortion, this constraint can accurately suppress the risk of secondary resonance of non-characteristic subharmonics, and is the core basis for realizing the refined management of active power control equipment.

[0060] (2) APF capacity constraint To ensure the feasibility of the optimization results in a real system, the combined thermal effect of all compensation currents of the APF must not exceed its rated current. In the formula: I P,j,max For installation on the node j The maximum capacity of a single APF unit.

[0061] (3) WP capacity constraints The inverter (WP) needs to utilize its remaining capacity to output harmonic compensation current while maintaining the fundamental power output. Because node voltages exhibit time-varying characteristics, the upper limit of the inverter's available current is not constant. The system must adhere to the physical boundaries of the combined thermal effects of the fundamental and harmonic currents. In the formula: I WP,max This is the maximum allowable current limit for WP; S WP,rate The rated apparent capacity of WP; I WP (1) and I WP ( h ) are the fundamental current injected by WP and the first wave current, respectively. h Subharmonic compensation current; I WP,rate This represents the maximum rated current that WP is allowed to output.

[0062] In addition to the constraints of the above stages, to ensure the safety and feasibility of the system solution results, the model must simultaneously satisfy the following basic operational boundary conditions: (1) Fundamental power balance and voltage deviation constraint The fundamental power flow is verified using the worst-case deterministic scenario, taking the maximum rated output of WP to ensure that the voltage does not exceed the limit even when WP is operating at full capacity and reactive power demand is at its highest.

[0063] Fundamental power balance equation: In the formula: P i ,、 Q i They are nodes i The fundamental active and reactive power injection; P L,i , Q L,i They are nodes i The fundamental active and reactive loads; P WP,i This is the maximum output of WP; V i , V j They are nodes i and nodes j The amplitude of the fundamental voltage; G ij , B ij They are nodes i and nodes j The nodal conductance and nodal susceptance between nodes; q ij For nodes i With nodes j The fundamental voltage phase angle difference between them.

[0064] The fundamental voltage amplitude at the node must meet the following requirements: (2) Line transmission capacity constraints The apparent power of any distribution line must not exceed the line's rated transmission capacity. S ij,max : In the formula: S ij This refers to the line's transmission capacity.

[0065] Simulation Experiment To verify the applicability of the proposed collaborative governance strategy to systems of different scales, this disclosure constructs two computational example systems with different network sizes and complexities, used for mechanism verification and efficiency evaluation, respectively. By comparing and analyzing the governance process and optimization performance under different schemes, the comprehensive advantages of the proposed method in terms of harmonic suppression effect and computational efficiency are demonstrated.

[0066] The system background harmonics and the harmonics injected by WP are set to frequencies from 2 to 50. For low-order characteristic harmonics such as the 5th, 7th, 11th, and 13th, the injection amplitude accounts for 2% to 10% of the fundamental current of the corresponding node, and the values ​​are randomly selected within the range to characterize the core characteristic emission of the converter. For the remaining non-characteristic harmonics and high-frequency components in the range of 2 to 50, the injection amplitude accounts for 0.5% to 2% of the fundamental current to simulate the harmonics generated by WP in the system. Both the two-stage location and capacity stabilization models call MOPSO, and the key parameters of the algorithm are set as follows: population size K=100, maximum number of iterations Tmax=100, acceleration coefficients c1=c2=2, and the inertial weight w adopts a linear decreasing strategy.

[0067] To simulate the power station topology, a total of 8 WP units with a rated capacity of 0.6MW were connected within the power station, and their installation locations are as follows: Figure 2 As shown.

[0068] To quantitatively verify the comprehensive advantages of the method proposed in this disclosure, three sets of comparative schemes were designed. All schemes have completely identical constraints, cost parameters, and simulation scenarios, with differences only in the configuration strategy of the treatment device.

[0069] Option 1: Traditional network-wide APF optimization solution, which simultaneously optimizes the installation location and capacity of APFs across the entire network, represents a refined management model for single devices.

[0070] Option 2: A two-stage optimization scheme using PPF+APF, which performs site selection and capacity determination of PPF and APF in stages across the entire network, without WP participating in the governance. Its optimization objectives and constraints are completely consistent with Option 1. This scheme is currently the hybrid filtering governance model.

[0071] Option 3: The two-stage sequential and coordinated governance strategy of passive and active governance devices proposed in this disclosure.

[0072] Based on the proposed optimization model method, the first stage of site selection and capacity optimization was completed. The governance effect of the scheme and the Pareto front of LCC and THD are as follows: Figure 3 , Figure 4 As shown.

[0073] Figure 3The distribution of node THD before and after the passive filtering strategy in Phase 1. Under untreated conditions, the system suffers from severe harmonic pollution, with most nodes exhibiting THD far exceeding the national standard limit of 4%, and particularly significant harmonic distortion fluctuations at the end nodes. After optimization in Phase 1, all schemes can effectively suppress harmonics across the entire network, but the effectiveness varies. Scheme 1, using only APF, can control the THD of all nodes within the limit, but the maximum THD after treatment is close to 4%, indicating limited harmonic suppression. Schemes 2 and 3, due to the identical PPF optimization strategy used in Phase 1, have completely overlapping THD curves, resulting in a significant reduction in the distortion rate of all nodes across the network, demonstrating significantly better treatment effects than Scheme 1.

[0074] From the perspective of compensation characteristics, active devices generally have better dynamic tracking and suppression capabilities for complex harmonics than passive devices. However, the basic mitigation effects of schemes 2 and 3 in this paper are superior, primarily due to the constraint effect of LCC in the multi-objective optimization model. In the optimization process aimed at minimizing both the maximum THD and LCC of the system, there is a trade-off between the depth of mitigation and investment cost. Because the unit capacity cost and installation cost of APF are relatively high, achieving a suppression level comparable to schemes 2 and 3 in scheme 1 would require configuring large-capacity active devices, leading to a significant increase in LCC. Therefore, due to economic constraints, the optimization result of scheme 1 can only converge to a configuration scheme that meets power quality constraints and is cost-controllable. Schemes 2 and 3, based on the cost advantage of PPF, can deploy large-capacity filter branches without significantly increasing LCC, achieving more thorough global harmonic filtering at a lower cost.

[0075] Figure 4 The Pareto front for multi-objective optimization of THD and LCC in Stage 1 is presented. As shown in the figure, the Pareto front of Scheme 1 is generally located in the upper right of the coordinate system, and the LCC corresponding to its non-dominated solution set is generally high. Furthermore, to control total cost, the maximum THD of the selected optimal configuration point approaches the 4% limit. The Pareto front shared by Schemes 2 and 3 shifts to the lower left, forming a comprehensive non-dominated advantage over Scheme 1. This indicates that using PPF as the main governance device in Stage 1 can significantly reduce the total life cycle cost and achieve a higher power quality safety margin.

[0076] After the completion of Phase 1 treatment, the second phase focused on optimizing the configuration of active pollution control equipment, with the following results: Figure 5 , Figure 6 As shown.

[0077] Regarding the effectiveness of Phase 2 local resonance mitigation, the AHZ variation pattern of Scheme 1 was first compared and analyzed. After the initial mitigation, nodes 3, 5, 8, 10, and 13 in Scheme 1 exhibited abrupt changes in equivalent impedance, with AHZ values ​​far exceeding the safety threshold, indicating a significant risk of local harmonic resonance amplification in the system. After Phase 2 mitigation, Scheme 1, based on the full-network configuration of APF injection of active damping current, suppressed the AHZ of all exceeding the standard nodes to within the safety threshold, effectively eliminating the potential for local harmonic resonance.

[0078] Further comparison of the active damping performance of Scheme 2 and Scheme 3 in Stage 2 reveals that Scheme 3 demonstrates superior performance. The AHZ distribution in Stage 1 is consistent for both schemes, with significant high-impedance resonance peaks observed at nodes 3, 5, 6, 9, 10, 12, and 14. After Stage 2 mitigation, both schemes meet the network-wide AHZ compliance requirements, but Scheme 3 exhibits significantly better suppression of harmonic impedance than Scheme 2, providing a greater safety margin. The mechanism behind this difference can be explained from technical and economic perspectives: Scheme 2 relies entirely on adding a dedicated APF to achieve resonance blocking, and due to LCC constraints, the multi-objective optimization results can only converge to a conservative scheme close to the safety threshold; Scheme 3 utilizes the remaining capacity of the WP for collaborative damping, allowing direct use of existing inverter hardware without additional initial investment. The active damping of the WP expands the optimization space, enabling secondary resonance suppression without incurring additional costs.

[0079] Comparing the AHZ distributions after Phase 1 mitigation under different schemes reveals differences in the location of resonant over-limit nodes and impedance amplification factors, reflecting the secondary impact of PPF integration on system impedance characteristics. Schemes 2 and 3 configured large-capacity PPFs in Phase 1 to filter low-order harmonics. The integration of numerous capacitive branches fundamentally altered the frequency impedance characteristics of the distribution network. The coupling effect between the original inductive reactance and the newly added capacitive reactance caused a shift in the parallel resonant point, leading to the formation of new secondary high-impedance resonant points. This phenomenon explains the mechanism of impedance distribution differences between Schemes 1 and Schemes 2 and 3, confirming the necessity of the active damping strategy in Phase 2 after PPF integration.

[0080] Based on the quantitative comparison results of the network-wide governance equipment capacity configuration and LCC, it can be seen that the proposed solution 3 has significant economic advantages while ensuring power quality compliance, effectively breaking through the technical and economic bottlenecks of the traditional harmonic governance model.

[0081] Compared to Scheme 1, Scheme 3 significantly reduces reliance on active power devices, resulting in substantial cost reductions. Scheme 1 employs an Active Power Filter (APF) to handle harmonic mitigation across the entire frequency band, placing a high demand on power electronic devices. The two phases require a total of 10.2 MVA of APF, corresponding to a cost-to-value (LCC) of 210,100 yuan. In contrast, Scheme 3 uses a low-cost Power Power Filter (PPF) as the core mitigation method in Phase 1, with Phase 2 utilizing the remaining capacity of the Power Power Filter (WP). Ultimately, only a 0.5 MVA APF is needed as a supplement, resulting in an LCC of only 108,500 yuan, a 48.36% reduction compared to Scheme 1. This translates to a cumulative cost saving of 101,600 yuan, significantly improving the engineering feasibility of the scheme.

[0082] Compared to Scheme 2, Scheme 3 further reduces costs by utilizing the remaining capacity of the WP (Power Controller). Scheme 2 reduces the LCC to RMB 149,000 by introducing a PPF (Power Feeder), a 29.1% reduction compared to Scheme 1. However, to address the secondary resonance issue in Phase 2, an additional 1.4 MVA APF (Active Damping Factor) is still needed for active damping, limiting the cost reduction. The innovation of Scheme 3 lies in fully utilizing the remaining capacity potential of the WP. A total of 0.58 MVA of the remaining capacity of the WP grid-connected inverters across seven nodes participates in collaborative management, reducing the configuration capacity of the dedicated APF in Phase 2 from 1.4 MVA to 0.5 MVA, a reduction of 64.3%. With identical Phase 1 investment, through the collaborative scheduling of redundant capacity, Scheme 3 ultimately reduces the LCC by 27.18% compared to Scheme 2, resulting in a cumulative cost saving of RMB 40,500.

[0083] In summary, Scheme 3 not only achieves effective decoupling of stage 1 (full-range, low-cost harmonic suppression) and stage 2 (precise secondary resonance blocking) at the technical level, but also demonstrates excellent economic performance. Based on a layered governance architecture—with PPF (Power Pulse Filter) leading the basic governance, WP (Power Buffer) providing damping support, and a small amount of APF (Active Power Pulse Filter) providing a safety net—its governance costs are reduced by nearly 50% compared to Scheme 1 and by nearly 30% compared to Scheme 2. While ensuring the safe and stable operation of the distribution network under wide-frequency harmonic disturbances, it achieves a dual optimization of governance efficiency and life-cycle economic benefits.

[0084] Example 2 One embodiment of this disclosure provides a wind farm harmonic suppression system based on a passive and active two-stage configuration, including: The model building module is used to build harmonic frequency domain power flow models for wind farms. The optimization and governance module is used to construct a sequential governance model of the distribution network for passive and active governance equipment based on the wind farm harmonic frequency domain power flow model, and to suppress wind farm harmonics based on the sequential governance model of the distribution network. The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

[0085] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the wind farm harmonic suppression method based on a passive and active two-stage configuration.

[0086] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement the wind farm harmonic suppression method based on a passive and active two-stage configuration.

[0087] Example 5 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the wind farm harmonic suppression method based on a passive and active two-stage configuration.

[0088] In further embodiments, the following is also provided: A server that can be used to execute the methods provided in the above embodiments. Specifically: A server includes a Central Processing Unit (CPU), system memory comprising Random Access Memory (RAM) and Read Only Memory (ROM), and a system bus connecting the system memory and the CPU. The server also includes a basic input / output system (I / O system) to facilitate information transfer between various components within the computer, and mass storage devices for storing the operating system, applications, and other program modules.

[0089] A basic input / output system includes a display for showing information and input devices such as a mouse and keyboard for user input. Both the display and the input devices are connected to the central processing unit via an input / output controller connected to the system bus. The basic input / output system may also include an input / output controller for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller also provides output to a display screen, printer, or other types of output devices.

[0090] Mass storage devices are connected to the central processing unit via a mass storage controller (not shown) connected to the system bus. The mass storage devices and their associated computer-readable media provide non-volatile storage for the server. That is, mass storage devices may include computer-readable media (not shown) such as hard disks or CD-ROM (CompactDisc Read-Only Memory) drives.

[0091] Computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory or other solid-state storage technologies, CD-ROM, DVD (Digital Versatile Disc) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will understand that computer storage media are not limited to the above-mentioned types. The aforementioned system memories and mass storage devices can be collectively referred to as memory.

[0092] According to various embodiments of this disclosure, the server can also connect to and operate on a remote computer on a network, such as the Internet. That is, the server can connect to a network via a network interface unit connected to the system bus, or it can use a network interface unit to connect to other types of networks or remote computer systems (not shown).

[0093] The aforementioned memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU.

[0094] One embodiment provides a terminal that can be used to perform the methods provided in the above embodiments. The terminal may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal may also be referred to by other names such as user terminal, portable terminal, laptop terminal, desktop terminal, etc.

[0095] Typically, a terminal includes a processor and memory.

[0096] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented using at least one of the following hardware forms: DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which handles computational operations related to machine learning.

[0097] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory are used to store at least one instruction, which is executed by a processor to implement the sound reverberation method provided in the method embodiments of this application.

[0098] In some embodiments, the terminal may also optionally include: a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of: a radio frequency circuit, a display screen, a camera assembly, an audio circuit, a positioning assembly, or a power supply.

[0099] Peripheral device interfaces can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory. In some embodiments, the processor, memory, and peripheral device interface are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor, memory, and peripheral device interface can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0100] Radio frequency (RF) circuits are used to receive and transmit RF signals, also known as electromagnetic signals. RF circuits communicate with communication networks and other communication devices via electromagnetic signals. RF circuits convert electrical signals into electromagnetic signals for transmission, or convert received electromagnetic signals back into electrical signals. Optionally, RF circuits include: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, user identity module cards, etc. RF circuits can communicate with other terminals through at least one wireless communication protocol. These wireless communication protocols include, but are not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0101] The display screen is used to display the UI (User Interface). This UI can include graphics, text, icons, videos, and any combination thereof. When the display screen is a touch screen, it also has the ability to collect touch signals on or above the surface of the display. These touch signals can be input as control signals to a processor for processing. In this case, the display screen can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be one display screen, which serves as the front panel of the terminal; in other embodiments, there can be at least two display screens, respectively disposed on different surfaces of the terminal or in a folded design; in still other embodiments, the display screen can be a flexible display screen, disposed on a curved or folded surface of the terminal. Furthermore, the display screen can be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0102] A camera assembly is used to capture images or videos. Optionally, the camera assembly includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0103] The audio circuitry may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to a processor for processing, or to radio frequency (RF) circuitry for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, positioned at different locations on the terminal. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor or RF circuitry into sound waves. The speaker may be a traditional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuitry may also include a headphone jack.

[0104] The positioning component is used to determine the current geographical location of the terminal to enable navigation or LBS (Location Based Service). The positioning component can be based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0105] The power supply is used to power the various components in the terminal. The power supply can be alternating current (AC), direct current (DC), a disposable battery, or a rechargeable battery. When the power supply includes a rechargeable battery, it can be a wired or wirelessly rechargeable battery. A wired rechargeable battery is charged via a wired connection, while a wirelessly rechargeable battery is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0106] In some embodiments, the terminal further includes one or more sensors. These one or more sensors include, but are not limited to, accelerometers, gyroscopes, pressure sensors, fingerprint sensors, optical sensors, and proximity sensors.

[0107] An accelerometer can detect the magnitude of acceleration along the three axes of a coordinate system established by the terminal. For example, an accelerometer can be used to detect the components of gravitational acceleration along the three axes. The processor can then control the touchscreen to display the user interface in either landscape or portrait view based on the gravitational acceleration signals acquired by the accelerometer. Accelerometers can also be used for collecting motion data in games or for other applications.

[0108] The gyroscope sensor can detect the terminal's orientation and rotation angle. It can work in conjunction with an accelerometer to capture the user's 3D movements on the terminal. Based on the data collected by the gyroscope sensor, the processor can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0109] The pressure sensor can be located on the side bezel of the terminal and / or under the touchscreen display. When the pressure sensor is located on the side bezel, it can detect the user's grip signal on the terminal, and the processor can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor. When the pressure sensor is located under the touchscreen display, the processor can control the operable controls on the UI interface based on the user's pressure on the touchscreen display. Operable controls include at least one of button controls, scroll bar controls, icon controls, or menu controls.

[0110] A fingerprint sensor is used to collect a user's fingerprint. The processor identifies the user based on the fingerprint collected by the sensor, or vice versa. When the user's identity is verified as trusted, the processor authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor can be located on the front, back, or side of the terminal. When the terminal has physical buttons or a manufacturer's logo, the fingerprint sensor can be integrated with those buttons or the logo.

[0111] An optical sensor is used to collect ambient light intensity. In one embodiment, the processor can control the display brightness of the touch screen based on the ambient light intensity collected by the optical sensor. Specifically, when the ambient light intensity is high, the display brightness of the touch screen is increased; when the ambient light intensity is low, the display brightness of the touch screen is decreased. In another embodiment, the processor can also dynamically adjust the shooting parameters of the camera assembly based on the ambient light intensity collected by the optical sensor.

[0112] A proximity sensor, also known as a distance sensor, is typically located on the front panel of a terminal. It is used to detect the distance between the user and the front of the terminal. In one embodiment, when the proximity sensor detects that the distance between the user and the front of the terminal is gradually decreasing, the processor controls the touchscreen display to switch from a screen-on state to a screen-off state; conversely, when the proximity sensor detects that the distance between the user and the front of the terminal is gradually increasing, the processor controls the touchscreen display to switch from a screen-off state to a screen-on state.

[0113] Those skilled in the art will understand that the structure shown does not constitute a limitation on the terminal, and may include more or fewer components than shown, or combine certain components, or employ different component arrangements.

[0114] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0115] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0116] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0117] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0118] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0119] The computer storage medium of this embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0120] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0121] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0122] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0124] This disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0125] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0126] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0127] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A wind farm harmonic suppression method based on a two-stage configuration of passive and active stages, characterized in that, include: Constructing a harmonic frequency domain power flow model for a wind farm; Based on the wind farm harmonic frequency domain power flow model, a sequential governance model of the distribution network with passive and active governance equipment is constructed, and wind farm harmonic suppression is performed based on the sequential governance model of the distribution network. The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

2. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The construction of the wind farm harmonic frequency domain power flow model includes: Analyze the frequency-dependent impedance characteristics of the system, for the first... h The equation for the voltage at the harmonic node of the second harmonic is as follows: in, I i ( h ) is a node i The first harmonic source h Subsequent injection current; Y ii ( h ) represents the self-admittance of the node. Y 1i ( h ), Y i1 ( h ) etc. represent the mutual admittance between nodes; U i ( h ) is a node i The h Secondary voltage.

3. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 2, characterized in that, Based on the harmonic frequency domain power flow model of a wind farm, the voltage response of any node in the system at any harmonic frequency is analyzed, and the quantitative relationship between the node harmonic injection, network impedance characteristics and inter-node coupling effect is analyzed. If resonance exceeds the limit, the node self-impedance will be greater than the allowable equivalent harmonic impedance of the system. Therefore, the resonance amplification is judged by the allowable value of the equivalent harmonic impedance of the system, and the resonance harmonic impedance amplification index is defined.

4. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The first phase aims to minimize THD and LCC. It targets low-order characteristic components such as the 5th, 7th, and 11th harmonics, which account for a high proportion of system harmonics. By globally optimizing the installation location and rated capacity of PPFs, it achieves large-scale harmonic mitigation at low cost.

5. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The second phase uses node AHZ less than 1 as a constraint boundary, focusing on the harmonic amplification problem and secondary resonance risk of high-risk nodes. Based on the system operation conditions configured in the first phase, it optimizes the access nodes and capacity parameters of APF and combines the coordinated use of the remaining capacity of distributed resources to achieve dynamic compensation for non-characteristic frequency harmonics and various uncertain disturbances, forming a complementary two-layer architecture with the first phase governance path.

6. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The first phase establishes a passive device governance optimization model with the goals of minimizing THD and LCC: in, C CAPEX The initial installation cost of the treatment equipment; C OPEX To manage equipment operation and maintenance costs; U h,i and U 1,i They are nodes i No. h Second harmonic voltage and fundamental voltage amplitude; c fix The fixed cost of the treatment equipment; c PPF Q represents the unit capacity cost of PPF; C,i,rate This refers to the rated capacity of the PPF; CRF This is the capital recovery coefficient, where r The discount rate is... Y For equipment lifespan; This represents the average annual operating and maintenance cost of the APF.

7. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The second stage introduces AHZ to quantify the resonance severity of nodes and constructs a dual-objective optimization function for LCC and system AHZ. By balancing the effectiveness of active damping with economic costs, active control devices can suppress secondary resonance peaks at the lowest cost, ensuring that the AHZ of all nodes in the network is within the safe critical threshold.

8. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The first phase focuses on optimizing the installation location and capacity of the PPF (Power Pulse Filter). Candidate installation locations are selected from nodes near the harmonic source nodes, and the system is updated accordingly. h The node admittance matrix of the subharmonics is used to obtain the subharmonic voltage amplitude of each node through harmonic power flow calculation using the updated node admittance matrix, and the THD of all nodes in the network is calculated accordingly to evaluate the basic governance effect of the first phase.

9. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, In the first phase of optimization configuration, the core constraint is the system THD opportunity constraint, while the PPF device capacity constraint must also be met.

10. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The active control equipment in the second stage can be equivalent to a controllable harmonic current source in the harmonic frequency domain. By injecting a compensation current into the power grid with the same amplitude but opposite phase as the harmonic source current, it can achieve real-time cancellation of the harmonic current and suppress harmonic voltage distortion.

11. The wind farm harmonic suppression method based on passive and active two-stage configuration as described in claim 1, characterized in that, The core constraint of the second phase is AHZ compliance constraint, while also meeting the physical constraints of active governance equipment.

12. A wind farm harmonic suppression system based on a passive and active two-stage configuration, characterized in that, include: The model building module is used to build harmonic frequency domain power flow models for wind farms. The optimization and governance module is used to construct a sequential governance model of the distribution network for passive and active governance equipment based on the wind farm harmonic frequency domain power flow model, and to suppress wind farm harmonics based on the sequential governance model of the distribution network. The distribution network sequential governance model includes a passive equipment governance optimization model and an active equipment governance optimization model. With the goal of minimizing governance costs and optimizing power quality, a two-stage optimization is performed based on these two models. The first stage aims to minimize THD and LCC by optimizing the location and capacity of the Power Distribution Filter (PPF) using the passive equipment governance optimization model, achieving low-cost, comprehensive governance. The second stage introduces active governance equipment. For nodes still exceeding single harmonic limits after the first stage of governance, the active equipment governance optimization model optimizes the access nodes and capacity parameters of the Power Distribution Filter (APF). Simultaneously, AHZ compliance constraints are introduced to address the uncertainty of wind power output, suppressing harmonic amplification and resonance risks at high-risk nodes.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the wind farm harmonic suppression method based on passive and active two-stage configuration as described in any one of claims 1-11.

14. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the wind farm harmonic suppression method based on a passive and active two-stage configuration as described in any one of claims 1-11.

15. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the wind farm harmonic suppression method based on a passive and active two-stage configuration as described in any one of claims 1-11.

Images