A high-proportion new energy transmission system stability evaluation method

Abstract

The application discloses a high-proportion new energy through extra-high voltage transmission system stability evaluation method and relates to the technical field of power system stability evaluation. In view of the problems that the new energy through extra-high voltage transmission system is prone to multi-frequency band resonance due to the lack of rotational inertia and damping of the new energy, the existing method is difficult to adapt to dynamic scenes, only single-frequency band analysis and lacks engineering quantitative indexes, and the scheme points are as follows: an extensible system node impedance matrix is established, multi-frequency band characteristics are extracted by deducing frequency domain response, a composite evaluation index system is formed, comprehensive risk is calculated by frequency weighting integral, and visual results are output. The application is mainly used for new energy station parameter setting, extra-high voltage channel operation optimization and power grid regulation and control, and guarantees the safety of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power system stability assessment technology, and in particular to a method for assessing the stability of a system with a high proportion of new energy transmitted via ultra-high voltage. Background Technology

[0002] In recent years, with the rapid increase in the proportion of installed new energy capacity, large-scale transmission of new energy has become an important way for my country's energy structure transformation and regional resource optimization. Ultra-high voltage (UHV) transmission channels, as a key support for large-scale inter-regional consumption of new energy, have played a core role in promoting a high proportion of clean energy grid connection. However, most new energy power sources rely on power electronic interfaces to connect to the grid, lacking the rotational inertia and damping characteristics of traditional synchronous machines. This leads to low-frequency oscillations, subsynchronous resonances, and even high-frequency instability in the system under long-distance, high-power transmission conditions. These resonant stability problems seriously threaten the safe operation of the power grid. Currently, various stability analysis and evaluation methods exist both domestically and internationally, such as stability analysis based on impedance ratio criteria, modal damping analysis, and frequency domain stability discrimination methods using the Nyquist criterion. These methods can theoretically identify stability risks in single components, but they still have the following shortcomings: 1. Most studies are based on fixed grid equivalent models, which are difficult to adapt to the complex scenarios of dynamic changes in new energy access points and transmission channels; 2. Existing methods mostly focus on the analysis of a certain frequency band, and cannot comprehensively quantify the risks of different resonant modes across a wide frequency range; 3. Most methods are biased towards qualitative judgment and lack a multi-dimensional and highly operable quantitative indicator system for engineering practice.

[0003] Therefore, based on the dynamic characteristics of new energy access points and UHV transmission channels, the multi-mode characteristics of wide-band resonance risks, and the quantitative requirements of engineering practice, it is necessary to develop a stability assessment method that integrates dynamically expandable impedance modeling, multi-band risk quantification, and composite index evaluation. This method will enable adaptation to complex scenarios, full-band risk coverage, and engineering control support, ensuring the safe and stable operation of a high proportion of new energy transmission systems via UHV. Summary of the Invention

[0004] This invention provides a stability assessment method for high-proportion renewable energy transmission systems via UHVDC, quantifying multi-band resonance risk. Based on the topology of renewable energy power plants, power grid channels, and UHVDC converter stations, this invention establishes a system node impedance matrix that can flexibly expand with changes in the number of renewable energy access points or the scale of the transmission channel, while maintaining consistent global impedance characteristics. Combining the impedance matrix model with system operating parameters, the frequency domain response characteristics of each node and region are derived, and key resonance points and mutual coupling relationships in different frequency bands are extracted. Integrating different levels of operational objectives, a composite evaluation index system is formed through index normalization and weight allocation. Taking into account multi-band resonance risk and calculating its contribution, a comprehensive risk value is obtained using a frequency-weighted integral method. Sensitivity analysis is performed on the comprehensive risk value to calculate the rate of change of the comprehensive risk value under different parameter disturbances, identifying key factors of resonance risk and directions for improvement, as detailed below. Firstly, a stability assessment method for high-proportion new energy transmission systems via ultra-high voltage (UHV) transmission lines, oriented towards quantifying multi-band resonance risks, is provided, the method comprising: Based on the topology of new energy power plants, power grid channels and UHV converter stations, a system node impedance matrix model is established that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics. By combining the impedance matrix model with system operating parameters, the frequency domain response characteristics of each node and region are derived, and the impedance characteristics and mutual coupling relationships of different frequency bands are extracted. Considering the impedance characteristics and mutual coupling relationships of different frequency bands, and integrating the operational objectives at different levels, a composite evaluation index system is formed through index normalization and weight allocation. Based on the composite evaluation index system, the risk of multi-frequency band resonance is comprehensively considered and its contribution is calculated. The comprehensive risk value is obtained by the frequency weighted integral method. Sensitivity analysis was performed on the comprehensive risk value to identify key factors of resonance risk and directions for improvement.

[0005] Frequency domain modeling of new energy power plants; although the detailed state-space models of new energy power generation equipment vary, each piece of new energy power generation equipment is connected to the grid through a single node at the grid connection point, and the processing method is the same. Under the two-phase rotating dq coordinate system determined by its own phase-locked loop, the state-space model of the new energy power generation equipment can be expressed as: In the formula: s is the Laplace operator, ΔX S For the state variables of new energy power generation equipment; For new energy power generation equipment in its own dq Voltage vector at grid connection point in coordinate system For new energy power generation equipment in its own dq Output current vector in coordinate system; A1 This is the first coefficient matrix. B 1 This is the second coefficient matrix. C 1 This is the third coefficient matrix; The transformation relationship between the dq rotating coordinate system of the new energy power generation equipment and the two-phase synchronous rotating xy coordinate system of the power grid is as follows: In the formula: A x Representing physical quantities A exist xy x-axis components in the coordinate system A y Representing physical quantities A exist xy y-axis components in the coordinate system A d Representing physical quantities A exist dq d-axis components in the coordinate system A q Representing physical quantities A exist dq q-axis components in the coordinate system; δ Let be the angle between the x-axis and the q-axis; by linearizing this transformation relationship and substituting the grid connection voltage and output current of the new energy power generation equipment into it, we can obtain: In the formula: To rotate synchronously with the power grid xy Voltage vector at grid connection point in coordinate system To rotate synchronously with the power grid xy Output current vector at grid connection point in coordinate system; Let be the coordinate transformation matrix from the dq coordinate system to the xy synchronously rotating coordinate system. This is the fifth coefficient matrix. The sixth coefficient matrix is ​​expressed as follows: In the formula: δ 0 represents the steady-state angle between the x-axis of the xy coordinate system and the q-axis of the dq coordinate system; Represents the steady-state value of the grid connection point voltage. d Axial components, Represents the steady-state value of the grid connection point voltage. q Axial components; Represents the steady-state value of the output current d Axial components, These represent the steady-state values ​​of the output current, respectively. q Axial components; Substituting the coordinate transformation relationship between the grid connection point voltage and the output current into the state space model of the new energy power generation equipment in its own dq coordinate system, the state space model of the new energy power generation equipment in the grid synchronously rotating xy coordinate system can be expressed as: In the formula: , The seventh coefficient matrix, The eighth coefficient matrix, Ninth coefficient matrix, Furthermore, the frequency domain port impedance model of new energy power generation equipment in the xy coordinate system can be obtained: In the formula: For the frequency domain port impedance of new energy power generation equipment This is the inverse matrix of the frequency domain port admittance of new energy power generation equipment. It is the identity matrix. For modeling power grid channels, transmission lines adopt a distributed parameter model, and their frequency domain impedance expression is as follows: Where r is the resistance per unit length, L is the inductance per unit length, and C is the capacitance per unit length. The imaginary unit is used, and it is converted to admittance: For frequency domain admittance, The transformer adopts a π-type equivalent model, taking into account the frequency domain characteristics of leakage reactance and excitation impedance, and constructs the admittance. ; For frequency domain modeling of UHV converter stations, a thyristor converter model is used for AC UHV converter stations, and a modular multilevel converter model is used for DC UHV converter stations. Considering commutation reactance, trigger delay angle, and control system dynamics, the port frequency domain admittance is derived. Y HVDC ( s ); Next, the entire system's node admittance matrix is ​​constructed using the branch addition method. New energy grid-connected nodes, UHV converter station nodes, and synchronous machine nodes are designated as critical nodes, while tie nodes and load nodes are designated as non-critical nodes. The frequency domain admittance parameters of each component are connected to the corresponding nodes according to the topology, forming a complete system frequency domain node admittance matrix encompassing all nodes. Y full ( s The matrix has dimensions N×N, where N is the total number of nodes in the system. Then, matrix order reduction is performed, followed by Gaussian elimination. Yfull ( s From the non-critical nodes in the array, we obtain the equivalent node admittance matrix containing only the critical nodes (new energy grid-connected nodes, UHV converter station nodes, and synchronous machine nodes). Y eq ( s For the subsynchronous (5-30Hz), supersynchronous (50-100Hz), and high-frequency (100-200Hz) bands, the discrete frequency point intervals are set according to the "frequency band risk sensitivity": the subsynchronous band (high resonance zone) is selected at 0.5Hz intervals, the supersynchronous band at 1Hz intervals, and the high-frequency band at 2Hz intervals. The above order reduction process is performed on the discrete frequency points of each preset interval to obtain the equivalent admittance matrix of the corresponding frequency point. Then, the equivalent nodal impedance matrix is ​​obtained by matrix inversion. When the number of new energy access points increases or decreases, or the scale of the transmission channel changes, in new scenarios, only the frequency domain admittance parameters of the new components need to be connected. Y full ( s To add new nodes and supplement the connections between nodes, in the exit scenario, simply set the frequency domain admittance parameter of the exiting element to zero and remove the corresponding node connections. Then repeat the order reduction process to obtain the updated three-band impedance matrix. Z eq ( s This eliminates the need to reconstruct the entire system model and ensures the consistency of global impedance characteristics before and after dynamic changes.

[0006] System operating parameters include the real-time output of renewable energy power plants, the transmission power of ultra-high voltage converter stations, and the voltage amplitude at each node; firstly, the renewable energy power plants... dq Impedance in coordinate system via coordinate transformation matrix: T0 represents the coordinate transformation matrix from the dq coordinate system to the xy synchronously rotating coordinate system. This represents the steady-state value of the angle between the x-axis of the xy coordinate system and the q-axis of the dq coordinate system; Transform to the xy coordinate system that rotates synchronously with the power grid, and then perform a positive-to-negative sequence transformation: in: Represents the equivalent positive sequence impedance. express xy Impedance matrix in coordinate system xx Axis element, express xy Impedance matrix in coordinate system yy Axis element; Represents the impedance matrix in the xy coordinate system xy Axial coupling element, Represent the impedance matrix in the xy coordinate system respectively. yx Axial coupling element, The imaginary unit; To obtain the equivalent positive sequence impedance, thus eliminating the coupling effect between voltage and current; Subsequently, for the three frequency bands of subsynchronous (5-30Hz), supersynchronous (50-100Hz), and high frequency (100-200Hz), the frequency domain response characteristics of each node and region were calculated in combination with the system node impedance matrix, and the key resonant points of each frequency band were extracted: for the subsynchronous frequency band, the subsynchronous resonant point under the negative resistance characteristics of the doubly-fed wind farm and the inductive coupling of the grid side resistance was extracted; for the supersynchronous frequency band, the supersynchronous resonant point under the capacitive impedance of the direct-drive wind farm and the frequency domain coupling effect of the UHV converter station was extracted; and for the high frequency band, the high frequency resonant point of the photovoltaic inverter was extracted. Finally, the mutual coupling relationship between power stations and between power stations and UHV converter stations is identified by calculating the magnitude of the mutual impedance between nodes. The calculation of the mutual coupling relationship includes the mutual coupling coefficient between node i and node j. in: Represents frequency f Next node i With nodes j The mutual coupling coefficients; Represents the nodes in the nodal impedance matrix i With nodes j The mutual impedance at angular frequency 2π f The value at; The self impedance of node i at angular frequency The value at that location, These represent the self-impedance of node j at angular frequencies of [missing information]. The value at that location, This indicates finding the maximum value; when A value greater than 0.8 indicates strong coupling (close electrical distance). When the value is less than 0.3, it indicates weak coupling (relatively long electrical distance), forming a multi-frequency impedance characteristic and mutual coupling relationship matrix.

[0007] The composite evaluation index system takes the quantification of multi-band resonance risk as its core, and integrates three types of objectives: system-level resonance amplitude matching risk, system-level resonance phase adaptation risk, and station-level resonance contribution and impact risk. The comprehensive evaluation is achieved through the normalization of the three-level indicators and the weight allocation. Resonance risk amplitude index (A): in: Represents frequency f The resonance risk amplitude index is normalized to [0,1]. The equivalent positive sequence impedance of the new energy power station; This is the equivalent positive sequence impedance on the grid side. To find the minimum value; Phase difference risk assessment index (B): in: Represents frequency f The phase difference risk index is normalized to [0,1]. The phase angle of the positive sequence impedance on the new energy side. The phase angle of the positive sequence impedance on the grid side; when the phase difference is greater than 180°, it is forced to be 1 (critical state of resonance); Station contribution factor index (C) It consists of an amplitude factor and a phase factor: in: For the first i The resonance participation of each new energy power station; The characteristic direction matrix of the back ratio matrix is ​​the first... k Line number i Column elements; The characteristic direction matrix of the back ratio matrix is ​​the first... k Line number m Column elements, in: For phase factor; Used to determine the nature of the influence, "+1" indicates "homogeneity" which helps to amplify resonance, and "-1" indicates "anti-homogeneity" which suppresses resonance; for The phase angle; Indicator normalization; linear mapping is applied to the above indicators, uniformly scaling them to the [0,1] interval: , Upper limit truncation normalization has been adopted for... Range normalization is used: in, The normalized station contribution factor index. The minimum value of the station's contribution factor index. The maximum value of the station's contribution factor index; Indicator layer weights: allocated according to the degree of impact on resonance risk, based on practical engineering experience: 50%, 30%, 20%; Composite index value calculation: The composite index value for a certain frequency band is: in: For frequency The following are composite risk indicators; For frequency f The weight corresponding to the frequency band k =1, 2, and 3 correspond to subsynchronous, supersynchronous, and high-frequency bands, respectively. The subsynchronous weight is determined based on risk level and practical engineering experience. =0.4, hypersynchronous weight =0.3, high-frequency weight =0.3; Through the above steps, a resonance risk quantification system covering multiple frequency bands and dimensions is formed.

[0008] Integration interval division: Based on the frequency band characteristics of resonance risk, the integration interval is divided into three key frequency bands: Subsynchronous frequency band: Supersynchronous band: High frequency band: Exclude the ±2Hz range around 50Hz power frequency to avoid power frequency interference; Frequency band weights: allocated based on risk level and practical engineering experience: subsynchronous band (5-30Hz) 0.4, supersynchronous band (50-100Hz) 0.3, high frequency band (100-200Hz) 0.3; Frequency-weighted integral method: Comprehensive risk value R Composite risk indicators for each frequency band The sum of the weighted integrals over frequencies is given by the following formula: In the formula: R This represents the overall multi-band resonance risk value of the entire system; For frequency f The weight corresponding to the frequency band; k =1,2,3 correspond to subsynchronous, supersynchronous, and high-frequency bands, respectively; For frequency The following are composite risk indicators; Let k be the starting frequency of the k-th frequency band. This is the termination frequency of the k-th frequency band; The calculated R value reflects the overall resonance risk level of the system: R<0.3, low risk, system stable, no intervention required; 0.3≤R<0.6, medium risk, key station operation parameters need to be monitored; R≥0.6: High risk, suppression measures are required.

[0009] Screening key analysis parameters: Focusing on the core parameters that affect the system impedance characteristics and resonance risk, covering three major aspects: new energy, ultra-high voltage converter stations, and power grid channels; New energy power plants: proportional coefficient of current controller Phase-locked loop time constant ; Ultra-high voltage converter station: Trigger delay angle commutation reactor ; Power grid channels: inductance L per unit length and capacitance C per unit length of transmission lines; Set the parameter perturbation amplitude, and use the small perturbation method to apply a perturbation of ±5% to a single parameter. The parameter value after perturbation is: in, The original value of the parameter. These are the parameter values ​​after the perturbation; "+5%" indicates that the parameter increases, and "-5%" indicates that the parameter decreases. Only one parameter is perturbed each time, while the other parameters remain unchanged. Sensitivity coefficient G The formula used to quantify the impact of relative changes in parameters on relative changes in the overall risk value is defined as follows: In the formula, R' This is the comprehensive risk value recalculated after parameter perturbation; G The larger the value, the stronger the influence of this parameter on the resonance risk; G When the value is greater than 0, as the parameter increases, R As the number of parameters increases, the parameters become positively correlated with the risk. G When <0, as the parameter increases, R As the parameter decreases, it becomes negatively correlated with risk. Key factors for screening resonance risk: Threshold filtering: Set a sensitivity threshold to filter out | G Parameters with a value ≥ 0.3 are defined as key parameters for resonance risk; small changes in these parameters can lead to significant changes in the risk value.

[0010] Analysis based on the mutual coupling coefficient matrix: If the node to which the key parameter belongs has strong coupling with other nodes (mutual coupling coefficient...) If the value is greater than 0.8, the associated parameters of strongly coupled nodes will be included in the key factors to avoid risk transfer caused by adjusting a single parameter.

[0011] Based on the sign of the sensitivity coefficient and the physical meaning of the key parameters, determine specific directions for improvement: like G If the value is >0, the improvement direction is to lower this parameter; like G If the value is less than 0, the improvement direction is to increase this parameter; If the nodes to which the key parameters belong are strongly coupled, the associated parameters need to be adjusted synchronously.

[0012] Secondly, a stability assessment device for a high-proportion new energy transmission system via ultra-high voltage for quantifying multi-band resonance risks, the device comprising: Node impedance matrix construction module: Based on the topology of new energy power plants, power grid channels and UHV converter stations, it establishes a system node impedance matrix model that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics. Specifically, it performs component frequency domain modeling, construction of the whole system node admittance matrix, matrix order reduction and dynamic expansion adaptation operations. Frequency domain response derivation module: It is used to combine the impedance matrix model with the system operating parameters to derive the frequency domain response characteristics of each node and region, extract the impedance characteristics and mutual coupling relationships of different frequency bands, and specifically perform impedance coordinate transformation, positive sequence impedance extraction, multi-band response calculation and mutual coupling relationship identification. Composite index system construction module: It is used to consider the impedance characteristics and mutual coupling relationship of different frequency bands, integrate the operational objectives of different levels, and form a composite evaluation index system through index normalization and weight allocation. Specifically, it defines, calculates and normalizes three core indicators: resonance risk amplitude, phase difference and station contribution factor, and completes the weight allocation between the index layer and the frequency band layer. The comprehensive risk calculation module is used to comprehensively consider the multi-band resonance risk and calculate its contribution based on the composite evaluation index system. It uses the frequency-weighted integral method to obtain the comprehensive risk value, and specifically performs the division of the integral interval, numerical integration calculation and risk level determination. Sensitivity Analysis and Key Factor Locator Module: This module is used to perform quantitative sensitivity analysis on the comprehensive risk value, accurately locate the key control parameters of resonance risk, and deduce the direction of engineering improvement, providing a targeted basis for subsequent regulation. Specifically, it performs operations such as setting parameter disturbance range, calculating sensitivity coefficient, screening key parameters, deducing improvement directions, and conducting collaborative analysis of strongly coupled nodes.

[0013] The beneficial effects of the technical solution provided by this invention are: 1. Dynamic adaptation and precise modeling: Construct an impedance matrix that can be expanded with the changes in new energy / ultra-high voltage channels. Adding or removing components only requires parameter adjustment without reconstruction. Establish a precise frequency domain model for each component to ensure reliable evaluation. 2. Full-band risk quantification: Covering three frequency bands, extracting impedance characteristics and mutual coupling relationships, establishing a composite index system, and combining weights and integrals to obtain risk classification, thus supplementing the shortcomings of single frequency bands and qualitative analysis. 3. Precisely locate and regulate control targets: Identify key parameters through sensitivity analysis and combine them with mutual coupling relationships to avoid risk transfer and support power grid regulation.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 Topology diagram of a high-proportion renewable energy transmission system via ultra-high voltage. Figure 2 A flowchart of a stability assessment method for high-proportion new energy transmission systems via UHV transmission, oriented towards quantifying multi-band resonance risks. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0018] To address the problems existing in the background technology, achieve adaptation to complex scenarios, full-band risk coverage and engineering control support, and ensure the safe and stable operation of high-proportion new energy transmission systems via UHV transmission, this invention takes high-proportion new energy transmission systems via UHV transmission as the research object and provides a stability assessment method for high-proportion new energy transmission systems via UHV transmission oriented towards multi-band resonance risk quantification.

[0019] Example 1 A stability assessment method for high-proportion new energy transmission systems via UHVDC, oriented towards multi-band resonance risk quantification, such as... Figure 2 As shown, the method includes the following steps: Step 101: Establish a system node impedance matrix that can be flexibly expanded while maintaining consistent global impedance characteristics; Step 102: Combine the impedance matrix model with the system operating parameters to extract the key resonant points and mutual coupling relationships in different frequency bands; Step 103: Integrate the operational objectives at different levels, and form a composite evaluation indicator system through indicator normalization and weight allocation; Step 104: Taking into account the risks of multi-band resonance and calculating their contributions, the comprehensive risk value is obtained by using the frequency-weighted integral method; Step 105: Perform sensitivity analysis on the comprehensive risk value to identify key factors of resonance risk and directions for improvement.

[0020] Example 2 The scheme in Example 1 will be further described below with specific calculation formulas and examples: Step 201: Based on the topology of new energy power plants, power grid channels and UHV converter stations, establish a system node impedance matrix model that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics.

[0021] The topology diagram of a high-proportion new energy transmission system via ultra-high voltage is shown below. Figure 1 As shown, the electricity from wind farms, photovoltaic power plants, and traditional energy power plants enters the power grid system through ultra-high voltage transmission terminals. For the modeling of new energy power plants, for doubly-fed induction generator (DFIG) wind farms (P1), direct-drive wind farms (P2), and photovoltaic power plants (P3), state-space models including phase-locked loops, current controllers, and power outer loops are established in the dq coordinate system. Taking a doubly-fed induction generator (DFIG) wind turbine as an example, its state equation is: (1) In the formula: =0.5, For current controller parameters, =0.01s, =0.02s, These are the parameters of the phase-locked loop. =10, The angular frequency of the power grid. =100 πrad / s ; for ΔX S The first derivative; The dq voltage and current are transformed to the xy coordinate system using a linearized coordinate transformation matrix. After substituting these values ​​into the state equation, the frequency domain port impedance is derived. , This model can accurately reflect the impedance characteristics of new energy power stations over a wide frequency band.

[0022] In the modeling of power grid channels, transmission lines adopt a distributed parameter model, considering the frequency dependence of resistance, inductance, and capacitance. Their unit length parameters are r = 0.01Ω / km, L = 1mH / km, and C = 0.01μF / km. The frequency domain impedance expression is: (2) The step-up transformer (connecting the renewable energy power plant and the power transmission channel) adopts a π-type equivalent model, taking into account leakage reactance. (Per-unit value, base capacity 100MVA) and excitation impedance Its admittance matrix The parameters were calculated through short-circuit and no-load tests to ensure the accuracy of the model during subsynchronization to the high-frequency band.

[0023] In the modeling of the UHV converter station, the AC side converter station (H1) adopts a thyristor converter model, considering commutation reactance. Trigger delay angle °, and the constant DC voltage control loop, yield the frequency domain admittance. The DC-side converter station (H2) adopts a modular multilevel converter model, simplifying the dynamics of submodule capacitors, ignoring high-frequency fluctuations, and only considering commutation reactance. Its frequency domain admittance The coupling characteristics of the 50-100Hz supersynchronous frequency band can be reflected by the harmonic linearization method.

[0024] The frequency domain node admittance matrix of the entire system is constructed using the branch addition method. Using the new energy grid connection points (P1-P3), UHV converter station nodes (H1-H2), and synchronous machine nodes (G1) as key nodes, and tie nodes and load nodes as non-key nodes, the admittance parameters of each component are connected to the corresponding nodes according to the topology connection relationship, forming an 8×8 dimension. .

[0025] Eliminate using Gaussian elimination. The non-critical nodes are used to obtain the 5×5 dimensional equivalent admittance matrix of the critical nodes. For each discrete frequency point in the subsynchronous (5-30Hz), supersynchronous (50-100Hz), and high-frequency (100-200Hz) bands, repeat the order reduction process and inverse it to obtain the equivalent nodal impedance matrix for each band. .

[0026] When adding a new 200MW photovoltaic power plant (grid connection point P4), only in A new node P4 is added, supplementing the connection admittance between P4 and tie node C1. Calculations are based on the newly added line parameters, eliminating the need to reconstruct other node relationships. When a 200km transmission line is taken out of service, the line will be... By setting the admittance parameter to zero and removing the corresponding node connections, and repeating the order reduction-inversion process, the value can be updated in a short time. This significantly improves the model's response speed when topology changes.

[0027] Step 202: Combining the impedance matrix model with the system operating parameters, derive the frequency domain response characteristics of each node and region, and extract the impedance characteristics and mutual coupling relationships of different frequency bands.

[0028] Key system data are collected through synchronous phasor measurement units, including real-time power output of new energy power plants, transmission power of ultra-high voltage converter stations, and voltage amplitude of each key node.

[0029] Through coordinate transformation matrix: (3) Transform the impedance of the new energy power station in the dq coordinate system to the grid synchronous rotating xy coordinate system; Then, using the positive / negative order transformation formula: (4) Calculate the equivalent positive sequence impedance Eliminate cross-coupling terms between voltage and current , This ensures the accuracy of impedance characteristic analysis.

[0030] Multi-band impedance feature extraction: Subsynchronous frequency band (5-30Hz): Focus on analyzing the negative resistance characteristics of doubly-fed wind farms (P1) and their resistivity and inductance on the grid side; Supersynchronous frequency band (50-100Hz): Focusing on the coupling effect between the capacitive impedance of direct-drive wind farms (P2) and ultra-high voltage converter stations; High frequency band (100-200Hz): Focus on the high frequency resonant point of the photovoltaic power station (P3).

[0031] According to the formula for the mutual coupling coefficient: (5) The mutual coupling strength between key nodes is calculated, and the calculation results are organized into a subsynchronous frequency band mutual coupling matrix, which intuitively reflects the electrical correlation strength between nodes and provides a basis for subsequent risk transmission analysis.

[0032] Step 203: Considering the impedance characteristics and mutual coupling relationships of different frequency bands, and integrating the operational objectives at different levels, a composite evaluation index system is formed through index normalization and weight allocation.

[0033] Resonance risk amplitude index A The formula reflects the degree of impedance matching between new energy sources and the power grid. (6) when near hour, A When the value approaches 1, the risk of resonance is high. Phase difference risk index BThe phase matching characteristics between the new energy source and the grid impedance are represented by the following formula: (7) When the phase difference is close to 180°, the value of B approaches 1, satisfying the resonant phase condition; Station contribution factor indicators C The impact of a single renewable energy power station on system resonance is quantified by an amplitude factor and a phase factor, as shown in the formula: (8) To eliminate differences in indicator dimensions, A and B use upper limit truncation normalization to ensure their physical meaning is the risk proportion; C uses range normalization. (9) Map it to the interval [0,1]; The weights are determined using the analytic hierarchy process (AHP). Indicator layer: Impedance amplitude matching is the core condition for resonance, accounting for 50%; Phase condition is a necessary condition for resonance, accounting for 30%; Station contribution reflects the control priority, accounting for 20%. Frequency band layer: subsynchronous band is 0.4, supersynchronous band is 0.3, and high frequency band is 0.3; Formula for calculating composite index: (10) in: For frequency The following are composite risk indicators; The weights for the corresponding frequency bands; Through the above steps, a resonance risk quantification system covering multiple frequency bands and dimensions is formed.

[0034] Step 204: Based on the composite evaluation index system, comprehensively consider the multi-band resonance risk and calculate its contribution, and use the frequency-weighted integral method to obtain the comprehensive risk value.

[0035] Based on the frequency band distribution characteristics of resonance risk in systems with a high proportion of new energy transmitted via ultra-high voltage, the assessment interval is divided into three core frequency bands, each corresponding to different types of resonance risk scenarios in the system: Subsynchronous frequency band: Covers the risk range that is prone to subsynchronous resonance in new energy power plants such as doubly-fed wind farms; Supersynchronous frequency band: covers the risk range where supersynchronous coupling effects are likely to occur between direct-drive wind farms and UHV converter stations; High frequency band: Covers the range where high frequency resonance is likely to occur in equipment such as photovoltaic inverters; To avoid interference from the fundamental frequency signal of the power grid on the resonance risk assessment results, it is necessary to specifically exclude the interference range near the power frequency (power frequency ±2Hz). The frequency signal in this range mainly originates from the fundamental frequency component of the power grid during normal operation and is not directly related to the resonance risk. Excluding it can ensure that the assessment results focus on the resonance risk itself.

[0036] The frequency-weighted integral of the composite risk index for each frequency band is performed using the interval averaging method to integrate the risk contribution of the entire frequency band. The calculation formula is as follows: (11) In the formula: R represents the comprehensive resonance risk value of the entire system across multiple frequency bands (normalized to [0,1]); k=1,2,3 correspond to subsynchronous, supersynchronous, and high-frequency bands, respectively; The weights for the k-th frequency band are: subsynchronous 0.4, supersynchronous 0.3, and high frequency 0.3, consistent with the frequency band weights in the composite index system. For frequency The following are composite risk indicators; Let k be the starting frequency of the k-th frequency band. This is the termination frequency of the k-th frequency band; During the calculation process, first, for each frequency band... Perform integration to obtain the risk integral result for that frequency band; then compare the integral result with the corresponding frequency band weight. Multiply the values ​​to obtain the risk-weighted contribution of each frequency band; finally, sum the weighted contributions of all frequency bands to obtain the comprehensive multi-band resonance risk value R of the whole system, and normalize it to the [0,1] interval so that the risk value has a unified quantitative standard.

[0037] Based on experience in stable system operation and the need for resonance risk management in engineering practice, three levels of risk thresholds are set, and corresponding handling strategies for different risk levels are defined: Low risk R<0.3: The system currently has a low resonance risk, the overall operation is stable, and the new energy power station can operate at its rated output without the need for additional intervention measures; Medium risk 0.3≤R<0.6: The system has potential resonance risk, and it is necessary to focus on monitoring the operating parameters of key new energy power plants and UHV converter stations, track the risk change trend in real time, and prevent the risk from escalating further. High risk R≥0.6: The system has a significant risk of resonance and has approached or reached the critical state of resonance. Targeted suppression measures should be taken immediately to avoid resonance accidents and ensure the safe and stable operation of the system.

[0038] Step 205: Perform sensitivity analysis on the comprehensive risk value to calculate the rate of change of the comprehensive risk value under different parameter disturbances, and find the key factors of resonance risk and its improvement direction.

[0039] proportional coefficient of current controller in new energy power plants =0.5, Phase-Locked Loop Time Constant =0.02s; Trigger delay angle of UHV converter station commutation reactor The inductance per unit length of the power grid transmission line is L = 1 mH / km, and the capacitance is C = 0.01 mH / km. μ F / km.

[0040] According to the disturbance formula , After perturbation, the values ​​are 0.525 (+5%) and 0.475 (-5%). In principle, only one parameter is perturbed at a time, while the remaining parameters remain in their original state to ensure that changes in risk are attributable to a cause; Based on the comprehensive risk value R = 0.45 (medium risk) in step 204, the formula is: (12) Calculate sensitivity coefficients, such as the proportional gain of current controllers in renewable energy power plants. =0.5, +5% =0.42, therefore we can get =-1.33; | S |≥0.3, For key parameters, adjust upwards. This reduces R to below 0.35, balancing stability with the consumption of new energy sources.

[0041] In summary, the advantages of this stability assessment method for high-proportion new energy transmission systems via UHV transmission, which focuses on quantifying multi-band resonance risks, are as follows: 1. The method proposed in this invention can construct a dynamically expanding node impedance matrix. When the access point of new energy sources or the ultra-high voltage channel changes, only the parameters need to be adjusted without reconstructing the model. Furthermore, it establishes an accurate frequency domain model for each component, ensuring that the evaluation matches the actual system.

[0042] 2. The method proposed in this invention can cover subsynchronous, supersynchronous, and high-frequency bands. It achieves full-band risk quantification through a composite index system and frequency-weighted integral, avoiding the limitations of single-band and qualitative analysis.

[0043] 3. The method proposed in this invention uses offline pre-storage + online correction to produce results in seconds, and is combined with visualization tools such as heat maps to improve the efficiency of engineering applications and the readability of results.

[0044] 4. The method proposed in this invention can be classified according to risk value, and provide targeted guidance for the setting of new energy parameters, optimization of ultra-high voltage channels and grid regulation, supporting the safe consumption of a high proportion of new energy.

[0045] A stability assessment device for high-proportion new energy transmission systems via ultra-high voltage (UHV) transmission lines, oriented towards multi-band resonance risk quantification, the device comprising: Node impedance matrix construction module: Based on the topology of new energy power plants, power grid channels and UHV converter stations, it establishes a system node impedance matrix model that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics. Specifically, it performs component frequency domain modeling, construction of the whole system node admittance matrix, matrix order reduction and dynamic expansion adaptation operations. Frequency domain response derivation module: It is used to combine the impedance matrix model with the system operating parameters to derive the frequency domain response characteristics of each node and region, extract the impedance characteristics and mutual coupling relationships of different frequency bands, and specifically perform impedance coordinate transformation, positive sequence impedance extraction, multi-band response calculation and mutual coupling relationship identification. Composite index system construction module: It is used to consider the impedance characteristics and mutual coupling relationship of different frequency bands, integrate the operational objectives of different levels, and form a composite evaluation index system through index normalization and weight allocation. Specifically, it defines, calculates and normalizes three core indicators: resonance risk amplitude, phase difference and station contribution factor, and completes the weight allocation between the index layer and the frequency band layer. The comprehensive risk calculation module is used to comprehensively consider the multi-band resonance risk and calculate its contribution based on the composite evaluation index system. It uses the frequency-weighted integral method to obtain the comprehensive risk value, and specifically performs the division of the integral interval, numerical integration calculation and risk level determination. Sensitivity Analysis and Key Factor Locator Module: This module is used to perform quantitative sensitivity analysis on the comprehensive risk value, accurately locate the key control parameters of resonance risk, and deduce the direction of engineering improvement, providing a targeted basis for subsequent regulation. Specifically, it performs operations such as setting parameter disturbance range, calculating sensitivity coefficient, screening key parameters, deducing improvement directions, and conducting collaborative analysis of strongly coupled nodes.

[0046] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0047] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the stability of a high-proportion new energy transmission system via ultra-high voltage, characterized in that, The method includes: Based on the topology of new energy power plants, power grid channels and UHV converter stations, a system node impedance matrix model is established that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics. By combining the impedance matrix model with system operating parameters, the frequency domain response characteristics of each node and region are derived, and the impedance characteristics and mutual coupling relationships of different frequency bands are extracted. Considering the impedance characteristics and mutual coupling relationships of different frequency bands, and integrating the operational objectives at different levels, a composite evaluation index system is formed through index normalization and weight allocation. Based on the composite evaluation index system, the risk of multi-frequency band resonance is comprehensively considered and its contribution is calculated. The comprehensive risk value is obtained by the frequency weighted integral method. Sensitivity analysis was performed on the comprehensive risk value to identify key factors of resonance risk and directions for improvement.

2. The stability assessment method for a high-proportion new energy transmission system via ultra-high voltage as described in claim 1, characterized in that, The system node impedance matrix model, which is based on the topology of new energy power plants, power grid channels, and ultra-high voltage converter stations, and which can be flexibly expanded with changes in the number of new energy access points or the scale of transmission channels while maintaining consistency in global impedance characteristics, is specifically as follows: Frequency domain modeling of new energy power plants; each individual new energy power generation unit is connected to the grid through a single node at the grid connection point, and the processing method is the same, with the two-phase rotation determined by its own phase-locked loop. dq In the coordinate system, the state-space model of new energy power generation equipment is represented as follows: In the formula: s is the Laplace operator, ΔX S For the state variables of new energy power generation equipment; For new energy power generation equipment in its own dq Voltage vector at grid connection point in coordinate system For new energy power generation equipment in its own dq Output current vector in coordinate system; A 1 This is the first coefficient matrix. B 1 This is the second coefficient matrix. C 1 This is the third coefficient matrix; New energy power generation equipment itself dq The rotating coordinate system rotates synchronously with the power grid in two phases. xy The coordinate system transformation relationship is as follows: In the formula: A x Representing physical quantities A exist xy x-axis components in the coordinate system A y Representing physical quantities A exist xy y-axis components in the coordinate system A d Representing physical quantities A exist dq d-axis components in the coordinate system A q Representing physical quantities A exist dq q-axis components in the coordinate system; δ for x shaft and q The included angle of the axes; by linearizing this transformation relationship and substituting the grid connection voltage and output current of the new energy power generation equipment into it, we can obtain: In the formula: To rotate synchronously with the power grid xy Voltage vector at grid connection point in coordinate system To rotate synchronously with the power grid xy Output current vector at grid connection point in coordinate system; Let be the coordinate transformation matrix from the dq coordinate system to the xy synchronously rotating coordinate system. This is the fifth coefficient matrix. The sixth coefficient matrix is ​​expressed as follows: In the formula: δ 0 represents the steady-state angle between the x-axis of the xy coordinate system and the q-axis of the dq coordinate system; Represents the steady-state value of the grid connection point voltage. d Axial components, Represents the steady-state value of the grid connection point voltage. q Axial components; Represents the steady-state value of the output current d Axial components, These represent the steady-state values ​​of the output current, respectively. q Axial components; Substituting the coordinate transformation relationship between the grid connection point voltage and the output current into the new energy power generation equipment itself... dq In the state-space model of the coordinate system, the new energy power generation equipment rotates synchronously with the power grid. xy The state-space model in the coordinate system is represented as follows: In the formula: , The seventh coefficient matrix, The eighth coefficient matrix, Ninth coefficient matrix, Further, we can obtain xy Frequency domain port impedance model of new energy power generation equipment in coordinate system: In the formula: For the frequency domain port impedance of new energy power generation equipment This is the inverse matrix of the frequency domain port admittance of new energy power generation equipment. It is the identity matrix. For modeling power grid channels, transmission lines adopt a distributed parameter model, and their frequency domain impedance... The expression is: Where r is the resistance per unit length, L is the inductance per unit length, and C is the capacitance per unit length. The imaginary unit is used, and it is converted to admittance: For frequency domain admittance, The transformer adopts a π-type equivalent model, taking into account the frequency domain characteristics of leakage reactance and excitation impedance, and constructs the admittance. ; For frequency domain modeling of UHV converter stations, a thyristor converter model is used for AC UHV converter stations, and a modular multilevel converter model is used for DC UHV converter stations. Considering commutation reactance, trigger delay angle, and control system dynamics, the port frequency domain admittance is derived. Y HVDC (s) ; Next, the entire system's node admittance matrix is ​​constructed using the branch addition method. New energy grid-connected nodes, UHV converter station nodes, and synchronous machine nodes are designated as critical nodes, while tie nodes and load nodes are designated as non-critical nodes. The frequency domain admittance parameters of each component are connected to the corresponding nodes according to the topology, forming a complete system frequency domain node admittance matrix encompassing all nodes. Y full (s) The matrix has an dimension of N×N, where N is the total number of nodes in the system; Then, matrix reduction is performed, followed by Gaussian elimination. Y full (s) From the non-critical nodes, we obtain the equivalent node admittance matrix containing only the critical nodes. Y eq (s) ; For each discrete frequency point in the subsynchronous, supersynchronous, and high-frequency bands, the above order reduction process is performed to obtain the equivalent admittance matrix of the corresponding frequency point, and then the equivalent nodal impedance matrix is ​​obtained by matrix inversion. When the number of new energy access points increases or decreases, or the scale of the transmission channel changes, in new scenarios, only the frequency domain admittance parameters of the new components need to be connected. Y full (s) Add corresponding new nodes and supplement the connection relationships between nodes. In the exit scenario, simply set the frequency domain admittance parameter of the exiting element to zero and remove the corresponding node connection relationship. Then repeat the order reduction process to obtain the updated three-band impedance matrix. Z eq (s) It eliminates the need to reconstruct the entire system model and ensures the consistency of global impedance characteristics before and after dynamic changes.

3. The stability assessment method for a high-proportion new energy transmission system via ultra-high voltage as described in claim 1, characterized in that, The process of combining the impedance matrix model with system operating parameters to derive the frequency domain response characteristics of each node and region, and extracting the impedance characteristics and mutual coupling relationships of different frequency bands, is as follows: System operating parameters include the real-time output of the renewable energy power plants, the transmission power of the UHV converter stations, and the voltage amplitude at each node; firstly, the impedance of the renewable energy power plants in the dq coordinate system is transformed using a coordinate transformation matrix. T Transform to the xy coordinate system of the power grid rotating synchronously, and then obtain the positive sequence impedance using the following formula: in: Represents the equivalent positive sequence impedance. express xy Impedance matrix in coordinate system xx Axis element, express xy Impedance matrix in coordinate system yy Axis element; Represents the impedance matrix in the xy coordinate system xy Axial coupling element, Represent the impedance matrix in the xy coordinate system respectively. yx Axial coupling element, The imaginary unit; To obtain the equivalent positive sequence impedance, thus eliminating the coupling effect between voltage and current; Subsequently, for the three frequency bands of subsynchronous, supersynchronous, and high frequency, the frequency domain response characteristics of each node and region were calculated by combining the system node impedance matrix model, and the impedance characteristics of each frequency band were extracted: for the subsynchronous frequency band, the negative resistance characteristics of the doubly-fed wind farm and the inductive resistance of the grid side were extracted; for the supersynchronous frequency band, the capacitive impedance of the direct-drive wind farm and the frequency domain coupling effect of the UHV converter station were extracted; and for the high frequency band, the high frequency resonance point of the photovoltaic inverter was extracted. Finally, the mutual coupling relationship between power stations and between power stations and UHV converter stations is identified by calculating the magnitude of the mutual impedance between nodes. The calculation of the mutual coupling relationship includes the mutual coupling coefficient between node i and node j. in: Represents frequency f Next node i With nodes j The mutual coupling coefficients; Represents the nodes in the nodal impedance matrix i With nodes j The mutual impedance at angular frequency The value at; This represents the self-impedance of node i at angular frequency. The value at that location, These represent the self-impedance of node j at angular frequencies of [missing information]. The value at that location, This indicates finding the maximum value; when A value greater than 0.8 indicates strong coupling. When the value is less than 0.3, it is considered weak coupling, forming a multi-band impedance characteristic and mutual coupling relationship matrix.

4. The stability assessment method for a high-proportion new energy transmission system via ultra-high voltage as described in claim 1, characterized in that, The composite evaluation index system, which considers the impedance characteristics and mutual coupling relationships of different frequency bands and integrates operational objectives at different levels, is formed through index normalization and weight allocation as follows: The composite evaluation index system takes multi-band resonance risk quantification as its core, integrates three types of objectives: system-level stability, site-level operational safety, and equipment-level tolerance, and achieves comprehensive evaluation through three-level index normalization and dynamic weight allocation. Resonance risk amplitude index: in: Represents frequency f The resonance risk amplitude index is normalized to [0,1]. The equivalent positive sequence impedance of the new energy power station; This is the equivalent positive sequence impedance on the grid side. To find the minimum value; Phase difference risk assessment indicators: in: This represents the phase difference risk index at frequency f, normalized to [0,1]. The phase angle of the positive sequence impedance on the new energy side. This is the phase angle of the positive sequence impedance on the grid side; when the phase difference is greater than 180°, it is forced to be 1. Station contribution factor indicators It consists of an amplitude factor and a phase factor: in: Let be the resonance participation degree of the i-th renewable energy power station, normalized to [0,1]. The characteristic direction matrix of the back ratio matrix is ​​the first... k Line number i Column elements; The characteristic direction matrix of the back ratio matrix is ​​the first... k Line number m Column element, where N is the total number of nodes in the system; in: Let be the phase factor of the i-th renewable energy power station; Used to determine the nature of the influence, +1 indicates that the resonance is amplified by the same modulation, and -1 indicates that the resonance is suppressed by the opposite modulation. for The phase angle; Indicator normalization; linear mapping is applied to the above indicators, uniformly scaling them to the [0,1] interval: , Using upper limit truncation normalization, for Range normalization is used: in, The normalized station contribution factor index. The minimum value of the station's contribution factor index. The maximum value of the station's contribution factor index; Weighting rules: Indicator layer weights: allocated according to the degree of impact on resonance risk. 50%, 30%, 20%; Frequency band weights: allocated according to risk level: subsynchronous band 0.4, supersynchronous band 0.3, high frequency band 0.3; Composite index value calculation: The composite index value for a certain frequency band is: in: For frequency The following are composite risk indicators; The weights for the corresponding frequency bands; A resonance risk quantification system covering multiple frequency bands and dimensions has been formed.

5. The stability assessment method for a high-proportion new energy transmission system via ultra-high voltage as described in claim 1, characterized in that, The comprehensive risk value obtained by the frequency-weighted integral method based on the composite evaluation index system, which comprehensively considers the multi-band resonance risk and calculates its contribution, is as follows: Integration interval division: Based on the frequency band characteristics of resonance risk, the integration interval is divided into three key frequency bands: Subsynchronous frequency band: Supersynchronous band: High frequency band: Exclude the area within ±2Hz of the power frequency range of 50Hz to avoid power frequency interference. f For frequency; Frequency-weighted integral method: The comprehensive risk value R is the composite risk index within each frequency band. The sum of the weighted integrals over frequencies is given by the following formula: Where: R represents the comprehensive resonance risk value of the entire system across multiple frequency bands, normalized to [0,1]; k=1,2,3 correspond to subsynchronous, supersynchronous, and high-frequency bands, respectively; Let be the weight of the k-th frequency band; For frequency The following are composite risk indicators; Let k be the starting frequency of the k-th frequency band. This is the termination frequency of the k-th frequency band; The calculated R value reflects the overall resonance risk level of the system: R<0.3, low risk, system stable, no intervention required; 0.3≤R<0.6, medium risk, key station operation parameters need to be monitored; R≥0.6: High risk, suppression measures are required.

6. The stability assessment method for a high-proportion new energy transmission system via ultra-high voltage as described in claim 1, characterized in that, Sensitivity analysis of the comprehensive risk value was conducted to identify key factors contributing to resonance risk and directions for improvement. Identify key factors for resonance risk and screen key analysis parameters: Focus on the core parameters that affect the system impedance characteristics and resonance risk, covering three major aspects: new energy, ultra-high voltage converter stations, and power grid channels; New energy power plants: proportional coefficient of current controller Phase-locked loop time constant ; Ultra-high voltage converter station: Trigger delay angle commutation reactor ; Power grid channels: inductance L per unit length and capacitance C per unit length of transmission lines; Set the parameter perturbation amplitude, and use the small perturbation method to apply a perturbation of ±5% to a single parameter. The parameter value after perturbation is: in, The original value of the parameter. These are the parameter values ​​after the disturbance; Sensitivity coefficient G The formula used to quantify the impact of relative changes in parameters on relative changes in the overall risk value is defined as follows: In the formula, R is the comprehensive resonance risk value of the entire system across multiple frequency bands. R' This is the comprehensive risk value recalculated after parameter perturbation; Key factors for screening resonance risk: Threshold filtering: Set a sensitivity threshold to filter out | G Parameters with a value ≥ 0.3, where even small changes in these parameters can lead to significant changes in the risk value, are defined as key parameters for resonance risk. Based on the analysis of the mutual coupling coefficient matrix: if the node to which the key parameter belongs is strongly coupled with other nodes, the associated parameters of the strongly coupled nodes will be included in the key factors in a synchronized manner to avoid risk transfer caused by the adjustment of a single parameter; Based on the sign of the sensitivity coefficient and the physical meaning of the key parameters, determine specific directions for improvement: like G If the value is >0, the improvement direction is to lower this parameter; like G If the value is less than 0, the improvement direction is to increase this parameter; If the nodes to which the key parameters belong are strongly coupled, the associated parameters need to be adjusted synchronously.

7. A stability assessment device for a high-proportion new energy transmission system via ultra-high voltage, characterized in that, The device includes: Node impedance matrix construction module: Based on the topology of new energy power plants, power grid channels and UHV converter stations, it establishes a system node impedance matrix model that can be flexibly expanded with the number of new energy access points or the scale of transmission channels while maintaining the consistency of global impedance characteristics. Specifically, it performs component frequency domain modeling, construction of the whole system node admittance matrix, matrix order reduction and dynamic expansion adaptation operations. Frequency domain response derivation module: It is used to combine the impedance matrix model with the system operating parameters to derive the frequency domain response characteristics of each node and region, extract the impedance characteristics and mutual coupling relationships of different frequency bands, and specifically perform impedance coordinate transformation, positive sequence impedance extraction, multi-band response calculation and mutual coupling relationship identification. Composite index system construction module: It is used to consider the impedance characteristics and mutual coupling relationship of different frequency bands, integrate the operational objectives of different levels, and form a composite evaluation index system through index normalization and weight allocation. Specifically, it defines, calculates and normalizes three core indicators: resonance risk amplitude, phase difference and station contribution factor, and completes the weight allocation between the index layer and the frequency band layer. The comprehensive risk calculation module is used to comprehensively consider the multi-band resonance risk and calculate its contribution based on the composite evaluation index system. It uses the frequency-weighted integral method to obtain the comprehensive risk value, and specifically performs the division of the integral interval, numerical integration calculation and risk level determination. Sensitivity Analysis and Key Factor Locator Module: This module is used to perform quantitative sensitivity analysis on the comprehensive risk value, accurately locate the key control parameters of resonance risk, and deduce the direction of engineering improvement, providing a targeted basis for subsequent regulation. Specifically, it performs operations such as setting parameter disturbance range, calculating sensitivity coefficient, screening key parameters, deducing improvement directions, and conducting collaborative analysis of strongly coupled nodes.

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