Method and device for regulating and controlling broadband resonance damping characteristics

Abstract

The invention discloses a broadband resonance damping characteristic regulation and control method and device, and the method comprises the steps: carrying out the broadband signal sampling of a target node in a power system, obtaining the instantaneous distortion of a target voltage and the cumulative deviation of a target current, and carrying out the calculation of an energy transmission delay factor and an energy balance correction factor; according to the energy transfer delay factor and the energy balance correction factor, calculating a local damping dynamic coefficient and a regulation and control precision real-time error so as to optimize target voltage instantaneous distortion and target current accumulation deviation; and updating a power flow distribution proportion according to the optimized target voltage instantaneous distortion, the optimized target current accumulation deviation and the local damping dynamic coefficient so as to improve the voltage stability of the target node. Transient characteristics of energy changes of the electric field and the magnetic field can be quantitatively analyzed through an energy transfer delay factor and a balance correction factor; and according to the transient characteristic, the broadband resonance damping characteristic is regulated and controlled in combination with the real-time operation state of the power system, so that the stability of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for regulating broadband resonance damping characteristics. Background Art

[0002] During the operation of the power system, broadband resonance occurs frequently, which is closely related to the local damping characteristics and has a significant impact on the stability of the system. When broadband resonance occurs, it will affect the normal operation of the system and threaten the stability of the system. To effectively solve this problem, it is particularly important to deeply analyze the interaction between electric and magnetic field energy during the resonance process. However, in actual application scenarios, the changes in electric and magnetic field energy are extremely complex and exhibit transient characteristics. How to accurately capture the transient characteristics of the changes in electric and magnetic field energy, and how to achieve precise control of the broadband resonance damping characteristics based on the transient characteristics of the changes in electric and magnetic field energy, remains a challenging technical problem. At the same time, how to combine this precise control with the real-time operating status of the power system to achieve dynamic optimization of local damping also requires further research. Summary of the Invention

[0003] The present invention provides a method and device for regulating broadband resonant damping characteristics. The method quantitatively analyzes the transient characteristics of electric and magnetic field energy changes by adopting an energy transfer delay factor and a balance correction factor. Based on the transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics are dynamically regulated through a two-dimensional dynamic control strategy of the coupling coefficient, thereby improving the stability of the power system.

[0004] To achieve the above objectives, an embodiment of the present invention provides a method for controlling broadband resonance damping characteristics, comprising:

[0005] Performing broadband signal sampling on a target node in the power system to obtain instantaneous voltage distortion and cumulative current deviation of the target node; determining a dominant broadband resonant frequency based on the instantaneous voltage distortion and cumulative current deviation to obtain a corresponding target instantaneous voltage distortion and target cumulative current deviation;

[0006] Calculating an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation;

[0007] Calculating the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy;

[0008] Optimizing the instantaneous distortion of the target voltage and the cumulative deviation of the target current according to the real-time error of the control accuracy and the damping decay time constant;

[0009] The power flow distribution ratio of the power system is updated according to the optimized target voltage instantaneous distortion and target current cumulative deviation, as well as the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

[0010] As an improvement to the above solution, the energy transfer delay factor and the energy balance correction factor are calculated based on the target voltage instantaneous distortion and the target current cumulative deviation, including:

[0011] Calculating the electric field energy density distribution according to the instantaneous distortion of the target voltage, and calculating the magnetic field energy periodic fluctuation according to the cumulative deviation of the target current;

[0012] The electric field energy density distribution and the magnetic field energy periodic fluctuation are normalized to obtain an energy transfer delay factor and an energy balance correction factor.

[0013] As an improvement to the above solution, the calculation of the electric field energy density distribution based on the instantaneous distortion of the target voltage and the calculation of the magnetic field energy periodic fluctuation based on the target current cumulative deviation include:

[0014] performing low-pass filtering and noise reduction processing on the instantaneous distortion of the target voltage and the cumulative deviation of the target current to obtain a voltage instantaneous characteristic and an offset cumulative characteristic;

[0015] Performing a fast Fourier transform on the voltage transient characteristic to calculate a frequency domain amplitude of the voltage transient characteristic, calculating an electric field energy value based on the frequency domain amplitude, and obtaining an electric field energy density distribution;

[0016] The offset accumulation feature is decomposed into a time series, and the magnetic field energy value of the offset accumulation feature is calculated to obtain the magnetic field energy periodic fluctuation.

[0017] As an improvement to the above solution, the normalization processing of the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain the energy transfer delay factor and the energy balance correction factor includes:

[0018] Normalizing the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain processed distribution characteristics and fluctuation amplitudes;

[0019] calculating an energy transfer delay factor according to the distribution characteristics and the fluctuation amplitude;

[0020] Analyzing the degree of matching between energy transfer and periodic fluctuation according to the energy transfer delay factor;

[0021] The energy balance is adjusted according to the matching degree to obtain an energy balance correction factor.

[0022] As an improvement to the above solution, the local damping dynamic coefficient and the resonant mode coupling coefficient are calculated based on the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy, including:

[0023] calculating a phase adjustment response speed of the power system based on the energy transfer delay factor and the energy balance correction factor; matching the phase adjustment response speed with the target voltage transient distortion to determine a local damping dynamic coefficient of the power system;

[0024] The resonant modal coupling coefficient of the power system is calculated according to the local damping dynamic coefficient and the power flow distribution ratio of the power system to obtain a real-time error of the control accuracy.

[0025] As an improvement to the above solution, the optimization of the target voltage instantaneous distortion and the target current cumulative deviation according to the control accuracy real-time error and the damping decay time constant includes:

[0026] Adjusting the broadband resonance peak frequency according to the real-time error of the control accuracy and the damping decay time constant;

[0027] The target voltage instantaneous distortion and the target current cumulative deviation are optimized according to the adjustment range of the broadband resonance peak frequency.

[0028] As an improvement to the above solution, updating the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient to improve the voltage stability of the target node includes:

[0029] According to the optimized target voltage instantaneous distortion, the optimized electric field energy density distribution is calculated; according to the optimized target current cumulative deviation, the optimized magnetic field energy periodic fluctuation is calculated;

[0030] The optimized magnetic field energy periodic fluctuation is calibrated using the magnetic field amplitude mutation rate, and the resonant frequency optimization step is determined based on the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution;

[0031] According to the resonant frequency optimization step size and the local damping dynamic coefficient, the power flow distribution ratio of the power system is updated to improve the voltage stability of the target node.

[0032] As an improvement to the above solution, determining the dominant broadband resonant frequency based on the voltage instantaneous distortion and the current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and target current cumulative deviation includes:

[0033] Performing waveform decomposition on the instantaneous voltage distortion and the accumulated current deviation to obtain key parameters of the target node; wherein the key parameters include broadband resonance peak frequency, damping decay time constant, electric field phase offset, and magnetic field amplitude mutation rate;

[0034] The broadband resonance peak frequencies corresponding to the magnetic field amplitude mutation rates greater than a preset amplitude threshold are sorted to determine the dominant broadband resonance frequency, and the target voltage instantaneous distortion and target current cumulative deviation corresponding to the dominant broadband resonance frequency are obtained.

[0035] As an improvement to the above solution, after obtaining the real-time error of the control accuracy, the method further includes:

[0036] If the resonant mode coupling coefficient is greater than zero, adjusting the electric field energy density distribution by the electric field phase offset, and calculating the corresponding magnetic field energy periodic fluctuation based on the adjusted electric field energy density distribution;

[0037] If the resonant modal coupling coefficient is less than zero, the energy balance correction factor is adjusted according to the power flow distribution ratio of the power system, and the corresponding electric field energy density distribution is calculated.

[0038] To achieve the above objectives, an embodiment of the present invention provides a device for controlling broadband resonance damping characteristics, comprising:

[0039] A target electrical data acquisition module is configured to perform broadband signal sampling on a target node in the power system to obtain the instantaneous voltage distortion and current cumulative deviation of the target node; determine a dominant broadband resonant frequency based on the instantaneous voltage distortion and current cumulative deviation to obtain the corresponding target instantaneous voltage distortion and target current cumulative deviation;

[0040] an energy correction factor calculation module, configured to calculate an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation;

[0041] A control accuracy error calculation module is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the control accuracy real-time error;

[0042] a target electrical data optimization module, configured to optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current according to the real-time error of the control accuracy and the damping decay time constant;

[0043] A power distribution ratio updating module is used to update the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, as well as the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

[0044] Compared with the prior art, the embodiment of the present invention discloses a method and device for controlling broadband resonant damping characteristics, which obtains the voltage instantaneous distortion and current cumulative deviation of the target node by performing broadband signal sampling on the target node in the power system; determines the dominant broadband resonant frequency based on the voltage instantaneous distortion and current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and target current cumulative deviation; calculates the energy transfer delay factor and energy balance correction factor based on the target voltage instantaneous distortion and target current cumulative deviation; calculates the local damping dynamic coefficient and the resonant modal coupling coefficient based on the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy; optimizes the target voltage instantaneous distortion and target current cumulative deviation based on the real-time error of the control accuracy and the damping attenuation time constant; updates the power flow distribution ratio of the power system based on the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient to improve the voltage stability of the target node. The transient characteristics of electric and magnetic field energy changes can be quantitatively analyzed by adopting energy transfer delay factors and balance correction factors. Based on the transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics can be dynamically controlled through a two-dimensional dynamic control strategy of the coupling coefficient, thereby improving the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 1 is a flow chart of a method for controlling broadband resonance damping characteristics provided by an embodiment of the present invention;

[0046] Figure 2 1 is a schematic structural diagram of a device for controlling broadband resonance damping characteristics provided by an embodiment of the present invention;

[0047] Figure 3 This is a structural block diagram of a device for controlling broadband resonance damping characteristics provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] It should be noted that the terms "comprises" and "specifically" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products or apparatuses.

[0050] See also Figure 1 , Figure 1 1 is a flow chart of a method for controlling broadband resonance damping characteristics provided by an embodiment of the present invention. The method for controlling broadband resonance damping characteristics includes:

[0051] S1, sampling a broadband signal of a target node in a power system to obtain a voltage instantaneous distortion and a current cumulative deviation of the target node; determining a dominant broadband resonant frequency based on the voltage instantaneous distortion and the current cumulative deviation to obtain a corresponding target voltage instantaneous distortion and target current cumulative deviation;

[0052] S2, calculating an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation;

[0053] S3, calculating the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy;

[0054] S4, optimizing the instantaneous distortion of the target voltage and the cumulative deviation of the target current according to the real-time error of the control accuracy and the damping decay time constant;

[0055] S5, updating the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

[0056] Exemplarily, the method for controlling the broadband resonant damping characteristics described in the embodiment of the present invention is implemented by a broadband resonant damping characteristics control server, and the broadband resonant damping characteristics control server can exchange information with the target user. The broadband resonant damping characteristics control server performs broadband signal sampling on the voltage signal distortion and current signal offset of the target node in the power system through a broadband resonant monitoring device to obtain the voltage instantaneous distortion and current cumulative deviation of the target node; wherein, the voltage instantaneous distortion refers to the degree to which the voltage of the target node in the power system deviates from its normal sinusoidal waveform at a certain moment; the current cumulative deviation refers to the cumulative value of the deviation between the actual value and the expected value of the current of the target node in the power system over a period of time; the Esprit algorithm is used to perform waveform decomposition on the voltage instantaneous distortion and current cumulative deviation, and the dominant broadband resonant frequency is determined to obtain the target voltage instantaneous distortion and target current cumulative deviation corresponding to the dominant broadband resonant frequency; according to the target The instantaneous voltage distortion and the cumulative deviation of the target current are used to calculate the electric field energy density distribution and the magnetic field energy periodic fluctuation, and the electric field energy density distribution and the magnetic field energy periodic fluctuation are normalized to obtain the energy transfer delay factor and the energy balance correction factor; based on the energy transfer delay factor and the energy balance correction factor, the local damping dynamic coefficient and the resonant mode coupling coefficient are calculated to obtain the real-time error of the control accuracy; based on the real-time error of the control accuracy and the damping decay time constant, the target voltage instantaneous distortion and the target current cumulative deviation are optimized; based on the optimized target voltage instantaneous distortion and the target current cumulative deviation, and the local damping dynamic coefficient, the power flow distribution ratio of the power system is updated to improve the voltage stability of the target node. The embodiment of the present invention analyzes the transient characteristics of the electric field and magnetic field energy changes by quantifying the energy transfer delay factor and the balance correction factor; based on the transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics are dynamically controlled through a two-dimensional dynamic control strategy of the coupling coefficient to improve the voltage stability of the target node, thereby improving the stability of the power system.

[0057] Specifically, step S2 includes:

[0058] S21, calculating the electric field energy density distribution according to the instantaneous distortion of the target voltage, and calculating the magnetic field energy periodic fluctuation according to the cumulative deviation of the target current;

[0059] S22, normalizing the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain an energy transfer delay factor and an energy balance correction factor.

[0060] More specifically, step S21 includes:

[0061] S211, performing low-pass filtering and noise reduction processing on the instantaneous distortion of the target voltage and the cumulative deviation of the target current to obtain a voltage instantaneous characteristic and a cumulative deviation characteristic;

[0062] S212, performing a fast Fourier transform on the voltage transient characteristic to calculate a frequency domain amplitude of the voltage transient characteristic, calculating an electric field energy value based on the frequency domain amplitude to obtain an electric field energy density distribution;

[0063] S213 , performing time series decomposition on the offset accumulation feature, calculating the magnetic field energy value of the offset accumulation feature, and obtaining a periodic fluctuation of the magnetic field energy.

[0064] Exemplarily, the target voltage instantaneous distortion and target current cumulative deviation are subjected to low-pass filtering and noise reduction processing to obtain voltage instantaneous characteristics and offset cumulative characteristics; the voltage instantaneous characteristics are subjected to fast Fourier transform to calculate the frequency domain amplitude of the voltage instantaneous characteristics, and the electric field energy value is calculated based on the frequency domain amplitude to obtain the electric field energy density distribution; the offset cumulative characteristics are subjected to time series decomposition to calculate the magnetic field energy value of the offset cumulative characteristics to obtain periodic fluctuations of magnetic field energy. If the electric field energy density distribution exceeds a preset energy density distribution threshold (which can be set as needed), a convolutional neural network is used to extract the distribution anomaly features and determine the location of the abnormal area; based on the periodic fluctuations of the magnetic field energy corresponding to the distribution anomaly features, a sliding window method is used to calculate the fluctuation frequency, assuming that the window size is 100 sampling points and the sliding step is 10 sampling points, the fluctuation period stability is determined; the abnormal area location and the fluctuation period stability data are feature fused and compared through principal component analysis to obtain the comprehensive energy (electric field energy and magnetic field energy) change trend; a linear regression fitting is performed on the comprehensive energy change trend to generate an energy fluctuation prediction result (electric field energy density distribution and magnetic field energy periodic fluctuation), and the prediction result is output.

[0065] For example, the resonant frequency is determined to be 45GHz through spectrum analysis. The corresponding node voltage signal is decomposed using the fast Fourier transform (FFT) algorithm, and the instantaneous distortion value (target voltage instantaneous distortion) is 15V. The current signal is processed using the Kalman filter algorithm, and the target current cumulative deviation is calculated to be 0.8A. When calculating the electric field energy density distribution through the target voltage instantaneous distortion, the finite element analysis (FEA) method is used to substitute the node voltage distortion value into the Maxwell equations, and the distribution of the electric field energy density in space is calculated to be 12J / m 3 When calculating the magnetic field energy periodic fluctuation by the target current cumulative deviation, the magnetic energy density formula B = μ0H is used, combined with the current offset value, to calculate the magnetic field energy periodic fluctuation to be 0.5 J / m 3The entire process ensures accurate calculation of electric and magnetic field energy distribution through numerical simulation and algorithm analysis, providing reliable data support for subsequent electromagnetic field optimization.

[0066] More specifically, step S22 includes:

[0067] S221, normalizing the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain processed distribution characteristics and fluctuation amplitude;

[0068] S222, calculating an energy transfer delay factor according to the distribution characteristics and the fluctuation amplitude;

[0069] S223, analyzing the matching degree between energy transfer and periodic fluctuation according to the energy transfer delay factor;

[0070] S224: Adjust the energy balance according to the matching degree to obtain an energy balance correction factor.

[0071] Exemplarily, the electric field energy density distribution and the magnetic field energy periodic fluctuation are normalized to obtain the processed distribution characteristics and fluctuation amplitude; based on the distribution characteristics and fluctuation amplitude, the energy transfer delay factor is calculated, and the energy transfer delay factor is used to describe the time characteristics of energy transfer; based on the energy transfer delay factor, the degree of matching between energy transfer and periodic fluctuation is analyzed to obtain a matching result; if the matching result exceeds a preset matching threshold (which can be set as needed), the least squares method is used to adjust the energy balance to obtain an energy balance correction factor; based on the energy balance correction factor, the electric field energy density distribution and the magnetic field energy periodic fluctuation are optimized to obtain an optimized description of the relationship between energy distribution and fluctuation; key parameters are extracted from the optimized description of the relationship between energy distribution and fluctuation, and a linear regression method is used to judge the dynamic change trend of energy transfer and balance.

[0072] For example, in the normalization of the electric field energy density distribution and the magnetic field energy periodic fluctuation, it is necessary to quantify the electric field energy density. Assume that in a certain spatial region, the spatial distribution function of the electric field energy density is (Joules per cubic meter), where (x, y, z) are spatial coordinates and e is a natural constant. By numerical integration, the total electric field energy in the area is calculated as W E =∫∫∫u E dxdydz≈57×10 -3 (Joule); Analyze the periodic fluctuation of magnetic field energy, assuming that the time function of magnetic field energy is u B (t) = 2 × 10 -6·sin(2π·50·t) (joules per cubic meter), where t is time. Through Fourier transform, the fundamental frequency of the magnetic field energy is extracted as 50 Hz, and the average energy within the period T is calculated as (Joule). For normalization, the electric field energy density and magnetic field energy periodic fluctuation are divided by their maximum values, and the normalized electric field energy density is obtained as and the magnetic field energy fluctuation is On this basis, the energy transfer delay factor τ is defined as the time difference between the electric field energy reaching its maximum value and the magnetic field energy reaching its maximum value. By calculation, τ = 0.05 seconds is obtained. At the same time, the energy balance correction factor η is defined as the ratio of the electric field energy to the magnetic field energy. By calculation, Through the above steps, the normalization of the electric field energy density distribution and the magnetic field energy periodic fluctuation was achieved, and the energy transfer delay factor and energy balance correction factor were obtained, providing a basis for further energy transfer and balance analysis.

[0073] Specifically, step S3 includes:

[0074] S31, calculating a phase adjustment response speed of the power system based on the energy transfer delay factor and the energy balance correction factor; matching the phase adjustment response speed with the target voltage transient distortion to determine a local damping dynamic coefficient of the power system;

[0075] S33, calculating the resonant modal coupling coefficient of the power system according to the local damping dynamic coefficient and the power flow distribution ratio of the power system to obtain a real-time error of the control accuracy.

[0076] Exemplarily, in step S31, an initial phase adjustment value is determined based on the energy transfer delay factor; an initial phase adjustment response speed is calculated based on the initial phase adjustment value; the initial phase adjustment value is corrected based on the energy balance correction factor to obtain a corrected phase adjustment response speed; the corresponding node voltage is obtained, and if the node voltage exceeds a preset voltage threshold (which can be set as needed), the corrected phase adjustment response speed is secondary adjusted based on the target voltage transient distortion obtained by transient distortion analysis to obtain an adjusted phase adjustment response speed, and the influence of local damping on the adjusted phase adjustment response speed is judged to obtain local damping and a dynamic coefficient, matching process parameters are obtained based on the local damping and the dynamic coefficient, and the adjusted phase adjustment response speed is determined based on the matching process parameters; the dynamic characteristics of the node voltage are calculated based on the adjusted phase adjustment response speed; the local damping is analyzed based on the dynamic characteristics and the matching process parameters to obtain a final local damping dynamic coefficient; the final local damping dynamic coefficient is verified using a support vector machine algorithm to obtain an optimized result of the phase adjustment.

[0077] For example, in a power system, to optimize phase adjustment response speed, assuming an energy transfer delay factor of 85 and an energy balance correction factor of 2, the phase adjustment response speed is calculated using the formula: Phase Adjustment Response Speed ​​= Energy Transfer Delay Factor × Energy Balance Correction Factor × Baseline Response Speed ​​(assuming a baseline response speed of 50 ms). The calculated phase adjustment response speed is 51 ms. By matching the phase adjustment response speed with the target voltage transient distortion, assuming a target voltage transient distortion rate of 3%, analysis shows that the distortion rate can be controlled within 3% when the phase adjustment response speed is between 50 ms and 55 ms. Therefore, a response speed of 51 ms meets the requirement. To determine the local damping dynamic coefficient, an optimization algorithm based on the phase adjustment response speed and target voltage distortion is employed. Assuming an initial local damping dynamic coefficient of 5, iterative calculations show that when the local damping dynamic coefficient is adjusted to 6, system stability is optimal, and the distortion rate is further reduced to 8%. Repeating this process achieves precise matching of the phase adjustment response speed with the target voltage transient distortion, optimizes the local damping dynamic coefficient, and thus improves the overall performance of the power system.

[0078] In step S32, based on the local damping dynamic coefficient, the relationship between the local damping and the dynamic coefficient is obtained, and the influence of the damping characteristics on the coupling coefficient (resonant modal coupling coefficient) is calculated to obtain modal analysis data; a finite element analysis tool is used to extract the power flow distribution characteristics from the modal analysis data; based on the power flow distribution characteristics, a dynamic adjustment scheme for the power flow distribution ratio is determined to obtain an optimized result of the power distribution; based on the optimized power distribution (power flow distribution ratio), the finite element analysis tool is used to calculate the response characteristics of the resonant mode to obtain the real-time change trend of the coupling coefficient; if the real-time change trend of the coupling coefficient exceeds a preset coupling coefficient threshold (which can be set as needed), the least squares method is used to adjust the dynamic coefficient to obtain a control accuracy correction value; based on the correlation between the correction value and the power distribution, the normalized mean square error is used to calculate the error source to obtain a preliminary distribution of the error calculation; by comparing the preliminary distribution of the error calculation with the modal analysis data, the final error value of the control accuracy is determined; based on the final error value and the response characteristics of the resonant mode, the stability of the system control is judged to obtain the optimized resonant modal coupling coefficient.

[0079] For example, when calculating the resonant modal coupling coefficient, it is first necessary to obtain the local damping dynamic coefficient and the power flow distribution ratio. Assume that the local damping dynamic coefficient of a resonant mode in the system is 0.12 and the power flow distribution ratio is 0.65. By introducing the modal coupling coefficient calculation algorithm, the local damping dynamic coefficient is multiplied by the power flow distribution ratio to obtain a modal coupling coefficient of 0.078. This modal coupling coefficient is used to calculate the real-time error of the control accuracy. Assuming that the current system's control accuracy target value is 0.05, by comparing the actual control accuracy with the target value, the real-time error is calculated to be 0.028. To further analyze the source of the error, the spectrum analysis method can be used to decompose the error signal and identify the main error frequency as 150Hz. Based on the system parameters and error frequency, the control strategy is adjusted to optimize the local damping dynamic coefficient and the power flow distribution ratio, thereby reducing the real-time error and improving the overall performance of the system. Through the optimization algorithm, the local damping dynamic coefficient is adjusted to 0.10, the power flow distribution ratio is adjusted to 0.70, the modal coupling coefficient is recalculated to 0.07, and the real-time error is reduced to 0.02, significantly improving the control accuracy.

[0080] Specifically, step S4 includes:

[0081] S41, adjusting the broadband resonance peak frequency according to the real-time error of the control accuracy and the damping decay time constant;

[0082] S42: Optimize the target voltage instantaneous distortion and the target current cumulative deviation according to the adjustment range of the broadband resonance peak frequency.

[0083] Exemplarily, a damping attenuation time constant is obtained, such as by calculating the relationship between the damping attenuation and the time constant through numerical simulation, to obtain an adjustment range of the broadband resonant peak frequency; frequency adjustment data is extracted from the adjustment range, and a fast Fourier transform is used to analyze the trend of instantaneous distortion change of the node voltage; based on the trend of instantaneous distortion change, the least squares method is used to fit the current offset data to obtain a preliminary estimate of the current cumulative deviation; based on the preliminary estimate of the current cumulative deviation, a linear regression model is used to calculate a correction coefficient for the real-time error; if the correction coefficient exceeds a preset correction threshold (which can be set as needed), the Newton iteration method is used to optimize the frequency adjustment data, and the broadband resonant peak frequency is adjusted based on the optimized frequency adjustment data, and the Euler method is used to solve the final deviation of the node voltage and current offset to obtain a result (optimization result of the target voltage instantaneous distortion and the target current cumulative deviation); the optimized values ​​of the target voltage instantaneous distortion and the target current cumulative deviation are extracted from the result, and the Lyapunov stability criterion method is used to determine the stable state of the system after adjustment.

[0084] For example, in power systems, the real-time error in regulation accuracy can be assessed by monitoring the instantaneous distortion of node voltages and the cumulative deviation of current offsets. Assuming a real-time error of 0.5 and a damping decay time constant of 2 seconds, the system first performs spectrum analysis of the node voltage signals using a fast Fourier transform (FFT) algorithm, identifying a broadband resonant peak frequency of 50 Hz. Next, a proportional-integral-derivative (PID) controller is employed with a proportional gain of 2, an integral time of 1 second, and a derivative time of 0.5 seconds to adjust the resonant peak frequency. Through iterative optimization, the resonant peak frequency is adjusted from 50 Hz to 48 Hz, reducing the instantaneous distortion of node voltages from 2% to 8% and the cumulative deviation of current offsets from 15 A to 1 A. This process combines real-time data acquisition, spectrum analysis, control algorithms, and optimization strategies to ensure stable and accurate system operation. Through continuous monitoring and dynamic adjustment, the system can effectively respond to power load fluctuations and external disturbances, improving overall performance.

[0085] Specifically, step S5 includes:

[0086] S51, calculating the optimized electric field energy density distribution based on the optimized target voltage instantaneous distortion, and calculating the optimized magnetic field energy periodic fluctuation based on the optimized target current cumulative deviation;

[0087] S52, calibrating the optimized magnetic field energy periodic fluctuation using the magnetic field amplitude mutation rate, and determining the resonant frequency optimization step size according to the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution;

[0088] S53 : updating the power flow distribution ratio of the power system according to the resonant frequency optimization step size and the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

[0089] Exemplarily, based on the optimized target voltage instantaneous distortion, the optimized electric field energy density distribution is calculated, and the kernel density estimation method is used to analyze the changing trend of the electric field energy density distribution, and the electric field energy fluctuation range is determined to be the difference between the maximum and minimum values ​​of the electric field energy; based on the optimized target current cumulative deviation, the optimized magnetic field energy periodic fluctuation is calculated, and the magnetic field amplitude mutation rate is calculated by the difference method, and the periodic characteristics of the magnetic field energy are judged by the fast Fourier transform; based on the magnetic field amplitude mutation rate and the periodic characteristics, the optimized magnetic field energy periodic fluctuation is calibrated by the least squares method to obtain a periodic fluctuation correction value; based on the periodic fluctuation correction value, the resonant frequency parameter is adjusted by the gradient descent method, and the resonant frequency optimization step iteration direction is determined to be the negative gradient direction; using the random forest algorithm, with the resonant frequency parameter and the resonant frequency optimization step iteration direction as input, the weighted average method is used to update the consistency of the electric field energy and the magnetic field energy, and the stable state of the density distribution is judged to be that the absolute value of the difference between the electric field energy and the magnetic field energy is less than a preset difference threshold (which can be set as needed).

[0090] For example, in the process of optimizing transient voltage distortion, the voltage waveform is first spectrally analyzed using a fast Fourier transform (FFT) to extract harmonic components. Assuming a fundamental frequency of 50 Hz, calculations show that the amplitude of the third harmonic is 5% of the fundamental, and the fifth harmonic is 3%. These distortion components are suppressed using an adaptive filter with a cutoff frequency of 250 Hz and an attenuation coefficient of 1, reducing the third harmonic to 1% and the fifth harmonic to 5%. Based on the optimized voltage waveform, the finite element method is used to calculate the electric field energy density distribution. The calculation area is divided into 1000 cells, and the electric field strength of each cell is solved using Maxwell's equations. The spatial distribution of the electric field energy density is determined, with the maximum energy density occurring at 8 joules per cubic meter 5 meters from the power supply. The magnetic field amplitude mutation rate is then analyzed. By collecting magnetic field sensor data, the amplitude change rate between adjacent sampling points is calculated. A mutation rate threshold of 2 Tesla per second is set to screen out mutation points and perform a secondary calibration to keep the error of the magnetic field energy periodic fluctuation within 1%. Finally, based on the calibrated magnetic field energy data, the Newton iteration method was used to determine the optimal resonant frequency step size. The initial frequency was set to 50 Hz, and the iteration step size was 1 Hz. After five iterations, the resonant frequency converged to 48 Hz, and the system reached its optimal resonant state. Throughout this process, the various parameters and algorithms were interconnected, ensuring the accuracy and stability of the optimization results.

[0091] In step S53, according to the resonant frequency optimization step, the local damping analysis dynamic coefficient in the local damping dynamic coefficient is used to obtain an updated dynamic coefficient; according to the updated dynamic coefficient, the power flow distribution ratio is calculated to determine the power distribution of the target node; the voltage signal of the target node is obtained, the change trend of the voltage signal is analyzed, and it is determined whether the signal stability is improved; if the signal stability is lower than the preset signal threshold (which can be set as needed), the dynamic coefficient is adjusted through a preset update mechanism to obtain the optimized power flow distribution ratio; according to the optimized power flow distribution ratio, the voltage signal of the target node is obtained to determine the stability change; the relationship between the resonant frequency and the power flow is verified through dynamic adjustment, and the final stability result is determined.

[0092] For example, in the power system, the resonant frequency optimization step size and the update of the local damping dynamic coefficient have an important influence on the power flow distribution ratio. First, by adopting the optimization method based on genetic algorithm, the initial population size is set to 100, the crossover probability is 8, the mutation probability is 0.1, and the number of iterations is 50, and the resonant frequency is optimized. During the optimization process, the local damping dynamic coefficient is introduced, the initial value is set to 0.2, and it is dynamically adjusted according to the real-time operating status of the system, and the adjustment range is 0.1 to 0.5. By calculating the power flow distribution ratio, the Newton-Raphson method is used for iterative solution, and the initial error tolerance is set to 1e-6 (1×10 -6 ), with a maximum number of iterations of 100. During the solution process, the voltage signal at the target node is monitored in real time, and spectrum analysis is performed using a fast Fourier transform to calculate the harmonic distortion rate of the voltage signal. When the harmonic distortion rate is below 3%, it is determined that the voltage signal stability has been significantly improved. Through this method, the system can effectively improve the voltage signal stability of the target node while ensuring reasonable power flow distribution.

[0093] Specifically, in step S1, determining the dominant broadband resonant frequency according to the voltage instantaneous distortion and the current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and target current cumulative deviation includes:

[0094] S11, performing waveform decomposition on the instantaneous voltage distortion and the accumulated current deviation to obtain key parameters of the target node; wherein the key parameters include broadband resonance peak frequency, damping decay time constant, electric field phase offset, and magnetic field amplitude mutation rate;

[0095] S12, sorting the broadband resonance peak frequencies corresponding to the magnetic field amplitude mutation rates greater than a preset amplitude threshold, determining a dominant broadband resonance frequency, and obtaining a target voltage instantaneous distortion and a target current cumulative deviation corresponding to the dominant broadband resonance frequency.

[0096] Exemplarily, a broadband resonant damping characteristic control server uses a broadband resonant monitoring device to perform broadband signal sampling on the voltage signal distortion and current signal offset at a target node in the power system, obtaining the instantaneous voltage distortion and cumulative current deviation at the target node. For example, an analog-to-digital converter with a sampling frequency of 100kHz is used to synchronously sample the voltage and current at the target node to ensure signal integrity and accuracy. During the sampling process, a fast Fourier transform (FFT) algorithm is used to perform spectral analysis on the sampled data to identify harmonic components in the voltage signal. For example, the fifth harmonic is found in the voltage signal, with an amplitude of 8% of the fundamental, and the seventh harmonic is 5% of the fundamental. These harmonic components cause voltage signal distortion. An adaptive filter is used to process the current signal in real time to eliminate current offset caused by load changes. The specific algorithm uses a minimum mean square error (LMS) algorithm with a filter order of 64 and a convergence factor of 0.1, which can effectively reduce the cumulative deviation of current offset. During the processing process, the cumulative deviation of the monitored current offset was reduced from an initial 2A to 3A, significantly improving the stability of the current signal. Wavelet transform was used to perform multi-resolution analysis on the voltage and current signals to extract the transient distortion characteristics of the signals. The analysis found that the voltage transient distortion reached its peak within 0.2 seconds with a distortion rate of 12%, while the current offset accumulated to 8A within 0.5 seconds.

[0097] In step S11, the Esprit algorithm is used to perform waveform decomposition on the instantaneous voltage distortion and the cumulative current deviation to obtain initial decomposition data; the broadband resonance peak frequency and the damping decay time constant are extracted from the initial decomposition data to determine the frequency distribution characteristics; the electric field phase offset is calculated based on the frequency distribution characteristics to obtain the dynamic change of the phase offset; the magnetic field amplitude mutation rate is calculated based on the dynamic change of the phase offset to obtain a time series of amplitude mutation; if the time series of the amplitude mutation exceeds a preset mutation threshold (which can be set as needed), the wavelet transform is used to accurately locate the mutation point and judge the mutation trend; the associated fluctuation pattern of the node voltage and current offset is determined by combining the mutation trend with the frequency distribution characteristics; after obtaining the associated fluctuation pattern, the Kalman filter is used to optimize the waveform decomposition result to obtain the final decomposition parameters (broadband resonance peak frequency, damping decay time constant, electric field phase offset and magnetic field amplitude mutation rate).

[0098] For example, when using the Esprit algorithm to perform waveform decomposition on the instantaneous voltage distortion and cumulative current deviation, the time-domain signal data of the voltage and current signals is collected at a sampling frequency of 10kHz to ensure that high-frequency resonant signals can be captured. The time-domain data is converted to frequency-domain signal data using a fast Fourier transform (FFT). If preliminary analysis reveals a significant resonant peak at 5kHz, the Esprit algorithm is applied to decompose the frequency-domain signal data. By constructing a Hankel matrix and performing singular value decomposition (SVD) on it, the subspace information of the signal is extracted. The broadband resonant peak frequency is calculated to be 48kHz, which is consistent with the preliminary FFT analysis results. The damping decay time constant of the resonant signal is calculated to be 12ms, indicating that the resonant energy decays rapidly in a short period of time. The electric field phase offset is analyzed. By calculating the phase difference of the signal at different frequencies, the phase offset is 17 degrees, indicating that the electric field has a significant phase lag at the resonant frequency. By analyzing the amplitude changes of the magnetic field signal, the magnetic field amplitude mutation rate is calculated to be 8%, indicating that the magnetic field energy fluctuates significantly during the resonance process. Through the above analysis, the power system can comprehensively evaluate the resonance characteristics in the power grid and provide data support for subsequent harmonic suppression and system optimization.

[0099] In step S12, the magnetic field amplitude mutation rate is compared with a preset amplitude threshold (which can be set as needed), and the broadband resonance peak frequencies corresponding to the magnetic field amplitude mutation rates exceeding the preset amplitude threshold are sorted to determine the dominant broadband resonance frequency. The magnetic field amplitude mutation rate is screened according to a preset amplitude threshold to obtain target broadband resonance data (broadband resonance peak frequency) corresponding to the magnetic field amplitude mutation rate exceeding the preset amplitude threshold; the peak frequency is extracted using a fast Fourier transform based on the target broadband resonance data to obtain a peak frequency set; the peak frequency set is processed according to a pre-established sorting rule to determine a dominant broadband resonance frequency result; if there are at least two dominant broadband resonance frequency results, their resonance characteristic data are obtained using a frequency distribution analysis method based on the dominant broadband resonance frequency results; based on the resonance characteristic data, the correlation between the magnetic field amplitude mutation rate and the corresponding broadband resonance is determined to obtain a final dominant broadband resonance frequency; after obtaining the final dominant broadband resonance frequency, the magnetic field amplitude mutation rate is compared with the final dominant broadband resonance frequency to verify the mutation trend of the magnetic field amplitude. The target voltage instantaneous distortion and target current cumulative deviation corresponding to the dominant broadband resonance frequency are obtained.

[0100] For example, in the analysis of the magnetic field amplitude mutation rate, the time domain signal is first converted into a frequency domain signal through fast Fourier transform (FFT) to obtain the magnetic field amplitude distribution in the frequency domain. Assuming that the preset amplitude threshold is 5 Tesla, the magnetic field amplitude at each frequency point is compared with the preset amplitude threshold. When the frequency is 50 Hz, the magnetic field amplitude is 6 Tesla, which exceeds the preset amplitude threshold. Therefore, this frequency point is marked as a potential broadband resonant frequency. The power system sorts all frequency points that exceed the preset amplitude threshold and uses a quick sorting algorithm to arrange them in order from large to small amplitude. Assuming the sorting results are 50 Hz (6 Tesla), 60 Hz (55 Tesla) and 70 Hz (52 Tesla), the system will further analyze the resonant characteristics of these frequency points. By calculating the resonant bandwidth at each frequency point, for example, the resonant bandwidth of 50 Hz is 10 Hz, 60 Hz is 8 Hz, and 70 Hz is 6 Hz, the system can determine the dominant broadband resonant frequency. For example, based on a comprehensive evaluation of the resonant bandwidth and amplitude, 50 Hz is determined to be the most important dominant broadband resonant frequency because it has the largest amplitude and a wide resonant bandwidth, and thus has the most significant impact on system stability.

[0101] Furthermore, after obtaining the real-time error of the control accuracy, the method further includes:

[0102] S401, if the resonant modal coupling coefficient is greater than zero, adjusting the electric field energy density distribution by the electric field phase offset, and calculating the corresponding magnetic field energy periodic fluctuation based on the adjusted electric field energy density distribution;

[0103] S402: If the resonant modal coupling coefficient is less than zero, the energy balance correction factor is adjusted according to the power flow distribution ratio of the power system, and the corresponding electric field energy density distribution is calculated.

[0104] Exemplarily, in step S401, resonant modal data within a preset frequency range is obtained, and whether the resonant modal coupling coefficient is greater than zero is determined based on the resonant modal data to obtain an initial judgment result; based on the initial judgment result, the electric field energy distribution is calculated using a finite element analysis method to obtain electric field energy data; a fast Fourier transform is performed on the electric field energy data to calculate the phase offset and obtain a phase offset parameter; using the phase offset parameter, the electric field energy distribution is adjusted to obtain an adjusted electric field energy distribution; based on the adjusted electric field energy distribution, the corresponding magnetic field energy distribution is calculated using a finite element analysis method to obtain magnetic field energy data; time-frequency analysis is performed on the magnetic field energy data to extract periodic fluctuation data; a wavelet transform method is used to process the periodic fluctuation data to obtain fluctuation characteristic parameters; the fluctuation characteristic parameters are fitted using a least squares method to obtain an attenuation curve of the magnetic field energy; based on the trend of the attenuation curve, the positivity of the resonant modal coupling coefficient is determined to obtain a sign determination result of the resonant modal coupling coefficient; based on the sign determination result of the resonant modal coupling coefficient, the phase offset parameter is optimized to obtain an optimized electromagnetic field energy distribution.

[0105] For example, assuming the resonant mode coupling coefficient is 8, the phase offset is By calculating the electric field energy density distribution function Among them, W e (x, y) represents the electric field energy density distribution, ε0 is the vacuum dielectric constant, |E(x, y)| is the modulus of the electric field intensity, ω is the angular frequency, and t is the time. It can be observed that the electric field energy density presents a non-uniform distribution in space. Analysis of magnetic field energy density Where W m (x,y) represents the magnetic field energy density distribution, μ0 is the vacuum permeability, and |H(x,y)| is the modulus of the magnetic field intensity. Fourier transform decomposition into frequency components reveals that the amplitude of its periodic fluctuations decays exponentially with time, with an attenuation coefficient of α = 0.2. Numerical simulation methods, such as finite element analysis, can accurately calculate the energy density distribution and decay trends, providing data support for system optimization. Adjusting the phase offset can redistribute the electric and magnetic field energies, thereby improving the overall system efficiency.

[0106] In step S402, the resonant modal data detected by the spectrum analyzer is obtained, the resonant modal coupling coefficient is calculated, and it is determined whether the resonant modal coupling coefficient is less than zero; if the resonant modal coupling coefficient is less than zero, the distribution ratio data is extracted from the pre-established power flow database, and the ratio adjustment direction is determined according to a preset power threshold (which can be set as needed); the distribution ratio is adjusted according to the ratio adjustment direction, and the energy balance parameter is calculated using the energy conservation formula to obtain a normalized correction factor; the electric field energy data is normalized using the normalized correction factor, and the density distribution characteristics are determined by kernel density estimation; the change trend data is extracted from the density distribution characteristics, and the growth state is determined according to the slope of the change trend data; if the growth state is positive growth, the resonant modal parameters are updated in combination with the least squares calculation results to obtain adjusted resonant modal data; based on the adjusted resonant modal data, the t-test is used to verify the change in the resonant modal coupling coefficient, and the electric field energy density distribution trend is determined by linear regression analysis.

[0107] For example, when the resonant mode coupling coefficient is less than zero, the energy balance correction factor is first adjusted by calculating the power flow distribution ratio; assuming that the resonant mode coupling coefficient is -0.2 and the power flow distribution ratio is 6, the energy balance correction factor can be adjusted to 2. The specific algorithm is: Energy balance correction factor = 1-(resonant mode coupling coefficient × power flow distribution ratio), that is, 1-(-0.2×6) = 2.2, which is rounded to 2. By analyzing the changing trend of the electric field energy density distribution, it is determined whether it is showing an increasing trend. Assume that at a certain moment, the electric field energy density distribution is 5J / m 3 After the correction factor is adjusted, the electric field energy density distribution becomes 6J / m 3 , the growth rate is 20%. If the electric field energy density distribution after the correction factor adjustment continues to grow, for example, it reaches 8J / m at the next moment 3 , indicating that the electric field energy density distribution is indeed increasing. Furthermore, combined with the changes in the magnetic field energy density distribution, if the magnetic field energy density distribution increases synchronously, the validity of the energy balance correction factor is verified. The above specific numerical values, algorithms, and analysis processes ensure the accuracy of the energy balance correction factor and the determination of the growth trend of the electric field energy density distribution.

[0108] A method for controlling broadband resonant damping characteristics disclosed in an embodiment of the present invention obtains the voltage instantaneous distortion and current cumulative deviation of the target node by performing broadband signal sampling on the target node in the power system; determines the dominant broadband resonant frequency based on the voltage instantaneous distortion and current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and target current cumulative deviation; calculates the energy transfer delay factor and the energy balance correction factor based on the target voltage instantaneous distortion and target current cumulative deviation; calculates the local damping dynamic coefficient and the resonant modal coupling coefficient based on the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy; optimizes the target voltage instantaneous distortion and target current cumulative deviation based on the real-time error of the control accuracy and the damping attenuation time constant; updates the power flow distribution ratio of the power system based on the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient to improve the voltage stability of the target node. The transient characteristics of electric and magnetic field energy changes can be quantitatively analyzed by adopting energy transfer delay factors and balance correction factors. Based on the transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics can be dynamically controlled through a two-dimensional dynamic control strategy of the coupling coefficient, thereby improving the stability of the power system.

[0109] See also Figure 2 , Figure 2 1 is a schematic structural diagram of a device 10 for controlling broadband resonance damping characteristics provided by an embodiment of the present invention. The device 10 for controlling broadband resonance damping characteristics includes:

[0110] The target electrical data acquisition module 11 is configured to perform broadband signal sampling on a target node in the power system to obtain the voltage instantaneous distortion and current cumulative deviation of the target node; determine the dominant broadband resonant frequency based on the voltage instantaneous distortion and current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and current cumulative deviation;

[0111] An energy correction factor calculation module 12 is configured to calculate an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation;

[0112] A control accuracy error calculation module 13 is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain a control accuracy real-time error;

[0113] a target electrical data optimization module 14, configured to optimize the target voltage instantaneous distortion and the target current cumulative deviation according to the control accuracy real-time error and the damping decay time constant;

[0114] The power distribution ratio updating module 15 is used to update the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

[0115] Furthermore, the control device 10 for broadband resonance damping characteristics further includes:

[0116] an electric field energy density adjustment module, configured to adjust the electric field energy density distribution by an electric field phase offset if the resonant modal coupling coefficient is greater than zero, and calculate the corresponding magnetic field energy periodic fluctuation based on the adjusted electric field energy density distribution;

[0117] An energy correction factor adjustment module is used to adjust the energy balance correction factor according to the power flow distribution ratio of the power system if the resonant mode coupling coefficient is less than zero, and calculate the corresponding electric field energy density distribution.

[0118] A device 10 for controlling wide-band resonant damping characteristics provided in an embodiment of the present invention can implement all processes of the method for controlling wide-band resonant damping characteristics in the above-mentioned embodiment. The functions of each module in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the method for controlling wide-band resonant damping characteristics in the above-mentioned embodiment, and will not be repeated here.

[0119] See also Figure 3 , Figure 3 1 is a schematic structural diagram of a device 20 for controlling broadband resonant damping characteristics, provided in an embodiment of the present invention. The device 20 for controlling broadband resonant damping characteristics in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps of the aforementioned method for controlling broadband resonant damping characteristics are implemented. Alternatively, when the processor 21 executes the computer program, the functions of the modules in the aforementioned embodiment of the device for controlling broadband resonant damping characteristics are implemented.

[0120] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device 20 for controlling broadband resonant damping characteristics.

[0121] The broadband resonant damping characteristic control device 20 may be a computing device such as a desktop computer, a notebook computer, a PDA, or a cloud server. The broadband resonant damping characteristic control device 20 may include, but is not limited to, a processor 21 and a memory 22. It will be understood by those skilled in the art that the schematic diagram is merely an example of the broadband resonant damping characteristic control device 20 and does not constitute a limitation on the broadband resonant damping characteristic control device 20. The broadband resonant damping characteristic control device 20 may include more or fewer components than shown in the figure, or may combine certain components, or different components. For example, the broadband resonant damping characteristic control device 20 may also include input and output devices, network access devices, buses, etc.

[0122] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the device 20 for controlling the broadband resonant damping characteristics, and utilizes various interfaces and lines to connect various parts of the device 20 for controlling the broadband resonant damping characteristics.

[0123] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the broadband resonant damping characteristic control device 20 by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include a high-speed random access memory and a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0124] Wherein, if the module integrated in the control device 20 for the broadband resonant damping characteristics is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0125] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0126] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for controlling the broadband resonant damping characteristics as in the above embodiment.

[0127] In addition, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the method for controlling the broadband resonance damping characteristics of the above embodiment.

[0128] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling broadband resonance damping characteristics, characterized in that: include: Performing broadband signal sampling on a target node in the power system to obtain instantaneous voltage distortion and current cumulative deviation of the target node; Determining a dominant broadband resonant frequency according to the voltage instantaneous distortion and the current cumulative deviation to obtain a corresponding target voltage instantaneous distortion and a target current cumulative deviation; Calculating an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation; Calculating the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy; Optimizing the instantaneous distortion of the target voltage and the cumulative deviation of the target current according to the real-time error of the control accuracy and the damping decay time constant; The power flow distribution ratio of the power system is updated according to the optimized target voltage instantaneous distortion and target current cumulative deviation, as well as the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

2. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: The energy transfer delay factor and the energy balance correction factor are calculated based on the target voltage instantaneous distortion and the target current cumulative deviation, including: Calculating the electric field energy density distribution according to the instantaneous distortion of the target voltage, and calculating the magnetic field energy periodic fluctuation according to the cumulative deviation of the target current; The electric field energy density distribution and the magnetic field energy periodic fluctuation are normalized to obtain an energy transfer delay factor and an energy balance correction factor.

3. The method for controlling broadband resonance damping characteristics according to claim 2, wherein: The calculating of the electric field energy density distribution according to the instantaneous distortion of the target voltage and the calculating of the magnetic field energy periodic fluctuation according to the cumulative deviation of the target current include: performing low-pass filtering and noise reduction processing on the instantaneous distortion of the target voltage and the cumulative deviation of the target current to obtain a voltage instantaneous characteristic and an offset cumulative characteristic; Performing a fast Fourier transform on the voltage transient characteristic to calculate a frequency domain amplitude of the voltage transient characteristic, calculating an electric field energy value based on the frequency domain amplitude, and obtaining an electric field energy density distribution; The offset accumulation feature is decomposed into a time series, and the magnetic field energy value of the offset accumulation feature is calculated to obtain the magnetic field energy periodic fluctuation.

4. The method for controlling broadband resonance damping characteristics according to claim 2, wherein: The normalizing of the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain the energy transfer delay factor and the energy balance correction factor includes: Normalizing the electric field energy density distribution and the magnetic field energy periodic fluctuation to obtain processed distribution characteristics and fluctuation amplitudes; calculating an energy transfer delay factor according to the distribution characteristics and the fluctuation amplitude; Analyzing the degree of matching between energy transfer and periodic fluctuation according to the energy transfer delay factor; The energy balance is adjusted according to the matching degree to obtain an energy balance correction factor.

5. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: The calculating of the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the real-time error of the control accuracy includes: calculating a phase adjustment response speed of the power system based on the energy transfer delay factor and the energy balance correction factor; matching the phase adjustment response speed with the target voltage transient distortion to determine a local damping dynamic coefficient of the power system; The resonant modal coupling coefficient of the power system is calculated according to the local damping dynamic coefficient and the power flow distribution ratio of the power system to obtain a real-time error of the control accuracy.

6. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: The optimizing of the target voltage instantaneous distortion and the target current cumulative deviation according to the control precision real-time error and the damping attenuation time constant includes: Adjusting the broadband resonance peak frequency according to the real-time error of the control accuracy and the damping decay time constant; The target voltage instantaneous distortion and the target current cumulative deviation are optimized according to the adjustment range of the broadband resonance peak frequency.

7. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: The updating of the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, and the local damping dynamic coefficient to improve the voltage stability of the target node includes: According to the optimized target voltage instantaneous distortion, the optimized electric field energy density distribution is calculated; according to the optimized target current cumulative deviation, the optimized magnetic field energy periodic fluctuation is calculated; The optimized magnetic field energy periodic fluctuation is calibrated using the magnetic field amplitude mutation rate, and the resonant frequency optimization step is determined based on the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution; The power flow distribution ratio of the power system is updated according to the resonant frequency optimization step size and the local damping dynamic coefficient to improve the voltage stability of the target node.

8. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: The determining of the dominant broadband resonant frequency according to the voltage instantaneous distortion and the current cumulative deviation to obtain the corresponding target voltage instantaneous distortion and target current cumulative deviation includes: Performing waveform decomposition on the instantaneous voltage distortion and the accumulated current deviation to obtain key parameters of the target node; wherein the key parameters include broadband resonance peak frequency, damping decay time constant, electric field phase offset, and magnetic field amplitude mutation rate; The broadband resonance peak frequencies corresponding to the magnetic field amplitude mutation rates greater than a preset amplitude threshold are sorted to determine the dominant broadband resonance frequency, and the target voltage instantaneous distortion and target current cumulative deviation corresponding to the dominant broadband resonance frequency are obtained.

9. The method for controlling broadband resonance damping characteristics according to claim 1, wherein: After obtaining the real-time error of the control accuracy, the method further includes: If the resonant mode coupling coefficient is greater than zero, adjusting the electric field energy density distribution by the electric field phase offset, and calculating the corresponding magnetic field energy periodic fluctuation based on the adjusted electric field energy density distribution; If the resonant modal coupling coefficient is less than zero, the energy balance correction factor is adjusted according to the power flow distribution ratio of the power system, and the corresponding electric field energy density distribution is calculated.

10. A device for controlling broadband resonance damping characteristics, characterized in that: include: A target electrical data acquisition module is configured to perform broadband signal sampling on a target node in the power system to obtain the instantaneous voltage distortion and current cumulative deviation of the target node; determine a dominant broadband resonant frequency based on the instantaneous voltage distortion and current cumulative deviation to obtain the corresponding target instantaneous voltage distortion and target current cumulative deviation; an energy correction factor calculation module, configured to calculate an energy transfer delay factor and an energy balance correction factor based on the target voltage instantaneous distortion and the target current cumulative deviation; A control accuracy error calculation module is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient according to the energy transfer delay factor and the energy balance correction factor to obtain the control accuracy real-time error; a target electrical data optimization module, configured to optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current according to the real-time error of the control accuracy and the damping decay time constant; A power distribution ratio updating module is used to update the power flow distribution ratio of the power system according to the optimized target voltage instantaneous distortion and target current cumulative deviation, as well as the local damping dynamic coefficient, so as to improve the voltage stability of the target node.

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