Method and apparatus for regulating broadband resonance damping characteristics

Abstract

The application discloses a kind of regulation and control method and device of broadband resonance damping characteristics, the method includes that target node in electric power system is sampled to broadband signal, target voltage instantaneous distortion and target current cumulative deviation are obtained, to calculate energy transmission delay factor and energy balance correction factor;According to energy transmission delay factor and energy balance correction factor, calculate local damping dynamic coefficient and control accuracy real-time error, to optimize target voltage instantaneous distortion and target current cumulative deviation;According to the target voltage instantaneous distortion and target current cumulative deviation after optimization, and local damping dynamic coefficient updates power flow distribution proportion, to improve the voltage stability of target node.Can be through energy transmission delay factor and balance correction factor quantitative analysis the transient characteristics of energy change of electric field and magnetic field;According to the transient characteristics in combination with the real-time running state of electric power system, regulate and control broadband resonance damping characteristics, to improve the stability of electric power system.

Description

Methods and devices for controlling broadband resonant damping characteristics Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and apparatus for regulating broadband resonant damping characteristics. Background Technology

[0002] Wideband resonance phenomena frequently occur during power system operation, which are closely related to local damping characteristics and have a significant impact on system stability. When wideband resonance occurs, it affects the normal operation of the system and threatens its stability. To effectively address this issue, a thorough analysis of the interaction between electric and magnetic field energies during the resonance process is crucial. However, in practical applications, the changes in electric and magnetic field energies are extremely complex and exhibit transient characteristics. Accurately capturing these transient characteristics and using them to precisely control the damping characteristics of wideband resonance remains a challenging technical problem. Furthermore, integrating 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] This invention provides a method and apparatus for controlling broadband resonant damping characteristics. It uses energy transfer delay factor and balance correction factor to quantitatively analyze the transient characteristics of electric and magnetic field energy changes. Based on these 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 coupling coefficient, thereby improving the stability of the power system.

[0004] To achieve the above objectives, embodiments of the present invention provide a method for controlling broadband resonant damping characteristics, comprising:

[0005] Wideband signal sampling is performed on the target node in the power system to obtain the instantaneous voltage distortion and cumulative current deviation of the target node; based on the instantaneous voltage distortion and cumulative current deviation, the dominant wideband resonant frequency is determined to obtain the corresponding instantaneous voltage distortion and cumulative current deviation of the target node.

[0006] Based on the instantaneous distortion of the target voltage and the cumulative deviation of the target current, the energy transfer delay factor and the energy balance correction factor are calculated.

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

[0008] Based on the real-time error of the control accuracy and the damping decay time constant, the instantaneous distortion of the target voltage and the cumulative deviation of the target current are optimized.

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

[0010] As an improvement to the above scheme, the calculation of the energy transfer delay factor and energy balance correction factor based on the instantaneous distortion of the target voltage and the cumulative deviation of the target current includes:

[0011] The electric field energy density distribution is calculated based on the instantaneous distortion of the target voltage, and the periodic fluctuation of the magnetic field energy is calculated based on the cumulative deviation of the target current.

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

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

[0014] The instantaneous distortion of the target voltage and the cumulative deviation of the target current are subjected to low-pass filtering and noise reduction processing to obtain the instantaneous voltage characteristics and the cumulative offset characteristics;

[0015] A fast Fourier transform is performed on the instantaneous voltage characteristics to calculate the frequency domain amplitude of the instantaneous voltage characteristics. The electric field energy value is then calculated based on the frequency domain amplitude to obtain the 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 periodic fluctuation of the magnetic field energy.

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

[0018] The electric field energy density distribution and the magnetic field energy periodic fluctuation are normalized to obtain the processed distribution characteristics and fluctuation amplitude.

[0019] Calculate the energy transfer delay factor based on the distribution characteristics and fluctuation amplitude;

[0020] Based on the energy transfer delay factor, analyze the degree of matching between energy transfer and periodic fluctuations;

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

[0022] As an improvement to the above scheme, the step of calculating the local damping dynamic coefficient and the resonant mode 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 includes:

[0023] Based on the energy transfer delay factor and energy balance correction factor, the phase adjustment response speed of the power system is calculated; the phase adjustment response speed and the instantaneous distortion of the target voltage are matched to determine the local damping dynamic coefficient of the power system.

[0024] Based on the local damping dynamic coefficient and the power flow distribution ratio of the power system, the resonant mode coupling coefficient of the power system is calculated to obtain the real-time error of the control accuracy.

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

[0026] Adjust the broadband resonant peak frequency based on the real-time error of the control precision and the damping attenuation time constant;

[0027] Based on the adjustment range of the wideband resonant peak frequency, the instantaneous distortion of the target voltage and the cumulative deviation of the target current are optimized.

[0028] As an improvement to the above scheme, the step of updating the power flow allocation ratio of the power system based on the optimized instantaneous distortion of the target voltage and the cumulative deviation of the target current, as well as the local damping dynamic coefficient, to improve the voltage stability of the target node, includes:

[0029] Based on the optimized instantaneous distortion of the target voltage, the optimized electric field energy density distribution is calculated, and based on the optimized cumulative deviation of the target current, the optimized magnetic field energy periodic fluctuation is calculated.

[0030] The magnetic field amplitude mutation rate is used to calibrate the optimized magnetic field energy periodic fluctuation. Based on the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution, the resonant frequency optimization step size is determined.

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

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

[0033] Waveform decomposition is performed on the instantaneous voltage distortion and cumulative current deviation to obtain the key parameters of the target node; wherein, the key parameters include wideband resonant peak frequency, damping decay time constant, electric field phase offset, and magnetic field amplitude abrupt change rate;

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

[0035] As an improvement to the above scheme, 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, the electric field energy density distribution is adjusted by the electric field phase offset, and the corresponding magnetic field energy periodic fluctuation is calculated based on the adjusted electric field energy density distribution.

[0037] If the resonant mode coupling coefficient is less than zero, the energy balance correction factor is adjusted by 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, embodiments of the present invention provide a device for adjusting broadband resonant damping characteristics, comprising:

[0039] The target electrical data acquisition module is used to sample broadband signals from target nodes in the power system to obtain the instantaneous voltage distortion and cumulative current deviation of the target nodes; based on the instantaneous voltage distortion and cumulative current deviation, the dominant broadband resonant frequency is determined to obtain the corresponding instantaneous voltage distortion and cumulative current deviation of the target nodes.

[0040] The energy correction factor calculation module is used to calculate the energy transfer delay factor and the energy balance correction factor based on the instantaneous distortion of the target voltage and the cumulative deviation of the target current.

[0041] The control precision error calculation module is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient based on the energy transfer delay factor and the energy balance correction factor, so as to obtain the real-time control precision error.

[0042] The target electrical data optimization module is used to optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current based on the real-time error of the control accuracy and the damping decay time constant.

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

[0044] Compared with existing technologies, the present invention discloses a method and apparatus for regulating broadband resonant damping characteristics. This method involves sampling a target node in a power system using broadband signals to obtain the instantaneous voltage distortion and cumulative current deviation of the target node; determining the dominant broadband resonant frequency based on the voltage distortion and current deviation to obtain the corresponding target voltage distortion and target current deviation; calculating an energy transfer delay factor and an energy balance correction factor based on the target voltage distortion and target current deviation; calculating a local damping dynamic coefficient and a resonant mode coupling coefficient based on the energy transfer delay factor and energy balance correction factor to obtain the real-time error of regulation accuracy; optimizing the target voltage distortion and target current deviation based on the real-time error of regulation accuracy and the damping decay time constant; and updating the power flow distribution ratio of the power system based on the optimized target voltage distortion and target current 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 using energy transfer delay factors and balance correction factors. Based on these transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics can be dynamically adjusted through a two-dimensional dynamic control strategy of coupling coefficient, thereby improving the stability of the power system. Attached Figure Description

[0045] Figure 1 is a schematic flowchart of a method for controlling broadband resonant damping characteristics provided in an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of the structure of a broadband resonant damping characteristic control device provided in an embodiment of the present invention;

[0047] Figure 3 is a structural block diagram of a broadband resonant damping characteristic control device provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] It should be noted that the terms "comprising" and "specific" in this invention, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0050] Please refer to Figure 1, which is a flowchart illustrating a method for controlling broadband resonant damping characteristics according to an embodiment of the present invention. The method for controlling broadband resonant damping characteristics includes:

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

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

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

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

[0055] S5. The power flow distribution ratio of the power system is updated based on the optimized instantaneous distortion of the target voltage and the cumulative deviation of the target current, as well as the local damping dynamic coefficient, in order to improve the voltage stability of the target node.

[0056] For example, the method for controlling the broadband resonant damping characteristics described in this embodiment of the invention is implemented by a broadband resonant damping characteristic control server, which is capable of interacting with the target user. The broadband resonant damping characteristic control server uses a broadband resonant monitoring device to sample the voltage signal distortion and current signal offset of the target node in the power system, obtaining the instantaneous voltage distortion and cumulative current deviation of the target node; wherein, the instantaneous voltage 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 cumulative current deviation refers to the cumulative value of the deviation between the actual current value and the expected current value of the target node in the power system over a period of time; the Esprit algorithm is used to perform waveform decomposition on the instantaneous voltage distortion and cumulative current 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 cumulative target current deviation are used to calculate the electric field energy density distribution and magnetic field energy periodic fluctuations. These are then normalized to obtain an energy transfer delay factor and an energy balance correction factor. Based on these factors, a local damping dynamic coefficient and a resonant mode coupling coefficient are calculated to obtain the real-time error of the control precision. The instantaneous voltage distortion and cumulative target current deviation are optimized based on the optimized coefficients and the local damping dynamic coefficient, thereby improving the voltage stability of the target node. This embodiment of the invention uses a quantitative analysis of the transient characteristics of electric and magnetic field energy changes using an energy transfer delay factor and a balance correction factor. Based on these transient characteristics and the real-time operating state of the power system, a two-dimensional dynamic control strategy using the coupling coefficient is employed to dynamically adjust the broadband resonant damping characteristics, thereby improving the voltage stability of the target node and ultimately enhancing the stability of the power system.

[0057] Specifically, step S2 includes:

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

[0059] S22, normalize 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.

[0060] More specifically, step S21 includes:

[0061] S211, perform low-pass filtering and noise reduction on the instantaneous distortion of the target voltage and the cumulative deviation of the target current to obtain the instantaneous voltage characteristics and the cumulative offset characteristics;

[0062] S212, Perform a fast Fourier transform on the instantaneous voltage characteristics to calculate the frequency domain amplitude of the instantaneous voltage characteristics, and calculate the electric field energy value based on the frequency domain amplitude to obtain the electric field energy density distribution;

[0063] S213, perform time series decomposition on the offset accumulation feature, calculate the magnetic field energy value of the offset accumulation feature, and obtain the periodic fluctuation of the magnetic field energy.

[0064] For example, the instantaneous distortion of the target voltage and the cumulative deviation of the target current are subjected to low-pass filtering and noise reduction processing to obtain instantaneous voltage features and cumulative offset features; the instantaneous voltage features are subjected to fast Fourier transform to calculate the frequency domain amplitude of the instantaneous voltage features, and the electric field energy value is calculated based on the frequency domain amplitude to obtain the electric field energy density distribution; the cumulative offset features are subjected to time series decomposition to calculate the magnetic field energy value of the cumulative offset features to obtain the periodic fluctuation of the 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 distribution anomaly features and determine the location of the anomaly region. 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 the window size is 100 sampling points and the sliding step size is 10 sampling points, the stability of the fluctuation period is determined. The location of the anomaly region and the fluctuation period stability data are fused together and compared using principal component analysis to obtain the overall energy (electric field energy and magnetic field energy) change trend. Linear regression fitting is performed on the overall energy change trend to generate energy fluctuation prediction results (electric field energy density distribution and magnetic field energy periodic fluctuations), and the prediction results are output.

[0065] For example, the resonant frequency is determined to be 45 GHz through spectral analysis. The corresponding node voltage signal is decomposed using the Fast Fourier Transform (FFT) algorithm, yielding an instantaneous distortion value (target voltage instantaneous distortion) of 15 V. The current signal is processed using a Kalman filter algorithm, calculating the target current cumulative deviation to be 0.8 A. When calculating the electric field energy density distribution using the target voltage instantaneous distortion, the finite element analysis (FEA) method is used. The node voltage distortion value is substituted into Maxwell's equations, and the spatial distribution of the electric field energy density is calculated to be 12 J / m³. When calculating the periodic fluctuation of the magnetic field energy using the target current cumulative deviation, the magnetic energy density formula is used. By combining the current offset value, the periodic fluctuation of the magnetic field energy was calculated to be 0.5 J / m³. The entire process, through numerical simulation and algorithm analysis, ensured the accurate calculation of the electric and magnetic field energy distributions, providing reliable data support for subsequent electromagnetic field optimization.

[0066] More specifically, step S22 includes:

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

[0068] S222, Calculate the energy transfer delay factor based on the distribution characteristics and fluctuation amplitude;

[0069] S223, Based on the energy transfer delay factor, analyze the degree of matching between energy transfer and periodic fluctuations;

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

[0071] For example, the electric field energy density distribution and the magnetic field energy periodic fluctuations are normalized to obtain the processed distribution characteristics and fluctuation amplitudes; based on the distribution characteristics and fluctuation amplitudes, an energy transfer delay factor is calculated, which 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 fluctuations 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 fluctuations are optimized to obtain an optimized description of the energy distribution and fluctuation relationship; key parameters are extracted from the optimized description of the energy distribution and fluctuation relationship, and a linear regression method is used to determine the dynamic trend of energy transfer and balance.

[0072] For example, in the normalization process of electric field energy density distribution and magnetic field energy periodic fluctuations, it is necessary to perform quantitative analysis of the electric field energy density. Assume that within a certain spatial region, the spatial distribution function of the electric field energy density is... (Joules per cubic meter), of which, For spatial coordinates, Let be a natural constant. The total electric field energy within this region is calculated using numerical integration. (Joule); Analyzing the periodic fluctuations of magnetic field energy, assuming the time function of magnetic field energy is... (Joules per cubic meter), of which, For time. Using Fourier transform, the fundamental frequency of the magnetic field energy is extracted as 50 Hz, and its average energy within a period T is calculated. (Joules). To normalize the data, the electric field energy density and the periodic fluctuation of the magnetic field energy are each divided by their maximum values ​​to obtain the normalized electric field energy density. and magnetic field energy fluctuations Based on this, an energy transfer delay factor is defined. The time difference between the electric field energy reaching its maximum value and the magnetic field energy reaching its maximum value is calculated. Seconds. Simultaneously, define the energy balance correction factor. The ratio of electric field energy to magnetic field energy is calculated. Through the above steps, the normalization of the electric field energy density distribution and the periodic fluctuation of the magnetic field energy was achieved, and the energy transfer delay factor and energy balance correction factor were obtained, providing a foundation for further energy transfer and balance analysis.

[0073] Specifically, step S3 includes:

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

[0075] S33. Based on the local damping dynamic coefficient and the power flow distribution ratio of the power system, calculate the resonant mode coupling coefficient of the power system to obtain the real-time error of the control accuracy.

[0076] For example, in step S31, an initial value for phase adjustment 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; if the node voltage exceeds a preset voltage threshold (which can be set as needed), the corrected phase adjustment response speed is adjusted a second time using the instantaneous distortion of the target voltage obtained through instantaneous distortion analysis to obtain an adjusted phase adjustment response speed; the influence of local damping on the adjusted phase adjustment response speed is determined to obtain local damping and dynamic coefficients; matching process parameters are obtained based on the local damping and dynamic coefficients; 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 and the instantaneous distortion of the target voltage; the local damping is analyzed based on the dynamic characteristics and the matching process parameters to obtain the final local damping dynamic coefficient; the final local damping dynamic coefficient is verified using a support vector machine algorithm to obtain the optimized phase adjustment result.

[0077] For example, in a power system, to optimize the 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 × Base Response Speed ​​(assuming a base response speed of 50ms). The calculated phase adjustment response speed is 51ms. Matching the phase adjustment response speed with the instantaneous distortion of the target voltage, assuming a target voltage instantaneous distortion rate of 3%, analysis shows that when the phase adjustment response speed is between 50ms and 55ms, the distortion rate can be controlled within 3%. Therefore, a response speed of 51ms meets the requirement. To determine the local damping dynamic coefficient, an optimization algorithm based on the phase adjustment response speed and the target voltage distortion is used. Assuming an initial local damping dynamic coefficient of 5, through iterative calculation, when the local damping dynamic coefficient is adjusted to 6, the system stability is optimal, and the distortion rate is further reduced to 8%. Repeating the above process achieves precise matching between the phase adjustment response speed and the instantaneous distortion of the target voltage and optimizes the local damping dynamic coefficient, thereby improving the overall performance of the power system.

[0078] In step S32, based on the local damping dynamic coefficient, the relationship between local damping and the dynamic coefficient is obtained, and the influence of damping characteristics on the coupling coefficient (resonant mode coupling coefficient) is calculated to obtain modal analysis data. A finite element analysis tool is used to extract power flow distribution characteristics from the modal analysis data. Based on the power flow distribution characteristics, a dynamic adjustment scheme for the power flow allocation ratio is determined to obtain the optimized power distribution result. Based on the optimized power distribution (power flow allocation ratio), a 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 and obtain the 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, and the optimized resonant mode coupling coefficient is obtained.

[0079] For example, when calculating the resonant mode coupling coefficient, it is first necessary to obtain the local damping dynamic coefficient and the power flow distribution ratio. Assume the local damping dynamic coefficient of a certain resonant mode in the system is 0.12, and the power flow distribution ratio is 0.65. By introducing a mode coupling coefficient calculation algorithm, multiplying the local damping dynamic coefficient by the power flow distribution ratio yields a mode coupling coefficient of 0.078. This mode coupling coefficient is then used to calculate the real-time error of the control accuracy. Assuming the current system's control accuracy target value is 0.05, by comparing the actual control accuracy with the target value, the calculated real-time error is 0.028. To further analyze the error sources, a spectral analysis method can be used to decompose the error signal, identifying the main error frequency as 150Hz. By combining system parameters and error frequency, the control strategy is adjusted, and the local damping dynamic coefficient and power flow distribution ratio are optimized, thereby reducing real-time error and improving the overall system performance. Through 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, adjust the broadband resonant peak frequency according to the real-time error of the control precision and the damping attenuation time constant;

[0082] S42, based on the adjustment range of the wideband resonant peak frequency, optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current.

[0083] For example, the damping decay time constant is obtained, such as by numerical simulation, calculating the relationship between the damping decay and the time constant to obtain the 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 instantaneous distortion trend of the node voltage; based on the instantaneous distortion trend, 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 the correction coefficient of 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; based on the optimized frequency adjustment data, the broadband resonant peak frequency is adjusted, and the Euler method is used to solve for the final deviation of the node voltage and current offset to obtain the results (optimized results 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 results, and the Lyapunov stability criterion is used to determine the stable state of the system after adjustment.

[0084] For example, in power systems, the real-time error of control precision can be assessed by monitoring the instantaneous distortion of node voltage and the cumulative deviation of current offset. Assume the current system has a real-time error of 0.5 and a damping decay time constant of 2 seconds. First, a Fast Fourier Transform (FFT) algorithm is used to perform spectral analysis on the node voltage signal, identifying a wideband resonant peak frequency of 50Hz. Next, a Proportional-Integral-Derivative (PID) controller is used, 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 50Hz to 48Hz, reducing the instantaneous distortion of node voltage from 2% to 8% and the cumulative deviation of current offset from 15A to 1A. This process combines real-time data acquisition, spectral analysis, control algorithms, and optimization strategies to ensure the stability and accuracy of system operation. Through continuous monitoring and dynamic adjustment, the system can effectively cope with changes in power load and external interference, improving overall performance.

[0085] Specifically, step S5 includes:

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

[0087] S52, the magnetic field amplitude mutation rate is used to calibrate the optimized magnetic field energy periodic fluctuation, and the resonant frequency optimization step size is determined based on the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution;

[0088] S53, based on the resonant frequency optimization step size and the local damping dynamic coefficient, update the power flow distribution ratio of the power system to improve the voltage stability of the target node.

[0089] For example, based on the optimized instantaneous distortion of the target voltage, the optimized electric field energy density distribution is calculated. The kernel density estimation method is used to analyze the changing trend of the electric field energy density distribution, and the range of electric field energy fluctuation is determined to be the difference between the maximum and minimum electric field energy values. Based on the optimized cumulative deviation of the target current, the optimized periodic fluctuation of the magnetic field energy is calculated. The difference method is used to calculate the magnetic field amplitude mutation rate, and the periodicity of the magnetic field energy is determined by the fast Fourier transform. Based on the magnetic field amplitude mutation rate and the periodicity, the least squares method is used to calibrate the optimized periodic fluctuation of the magnetic field energy to obtain the periodic fluctuation correction value. Based on the periodic fluctuation correction value, the gradient descent method is used to adjust the resonant frequency parameter, and the iteration direction of the resonant frequency optimization step size is determined to be the negative gradient direction. Using the random forest algorithm, with the resonant frequency parameter and the iteration direction of the resonant frequency optimization step size as input, the weighted average method is used to update the consistency of the electric field energy and the magnetic field energy. The stable state of the density distribution is determined when 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 optimizing instantaneous voltage distortion, a fast Fourier transform (FFT) is first used to perform spectral analysis on the voltage waveform to extract each harmonic component. Assuming the fundamental frequency is 50 Hz, calculations show that the third harmonic amplitude is 5% of the fundamental frequency, and the fifth harmonic is 3%. Adaptive filters are used to suppress these distortion components, 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 computational domain is divided into 1000 elements, and the electric field strength of each element is solved using Maxwell's equations, ultimately obtaining the spatial distribution of the electric field energy density. The maximum energy density occurs 5 meters from the power source, reaching 8 joules per cubic meter. Then, the magnetic field amplitude abrupt change rate is analyzed. By collecting data from a magnetic field sensor, the amplitude change rate between adjacent sampling points is calculated. A threshold of 2 Tesla per second is set, abrupt change points are screened, and secondary calibration is performed to control the error of the magnetic field energy periodic fluctuation within 1%. Finally, based on the calibrated magnetic field energy data, the Newton-Raphson iteration method was used to determine the optimal step size for the resonant frequency. The initial frequency was set to 50 Hz, and the iteration step size was 1 Hz. After 5 iterations, the resonant frequency converged to 48 Hz, and the system reached its optimal resonant state. Throughout the process, the various parameters and algorithms were interrelated, ensuring the accuracy and stability of the optimization results.

[0091] In step S53, based on the resonant frequency optimization step size, the local damping dynamic coefficient in the local damping dynamic coefficient is used to obtain the updated dynamic coefficient; based on the updated dynamic coefficient, the power flow allocation ratio is calculated to determine the power distribution of the target node; the voltage signal of the target node is obtained, the changing trend of the voltage signal is analyzed, and it is determined whether the signal stability has improved; if the signal stability is lower than a 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 allocation ratio; based on the optimized power flow allocation 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 to determine the final stability result.

[0092] For example, in power systems, the optimization step size of the resonant frequency and the updating of the local damping dynamic coefficient have a significant impact on the power flow allocation ratio. First, an optimization method based on a genetic algorithm is used, with an initial population size of 100, a crossover probability of 8, a mutation probability of 0.1, and 50 iterations to optimize the resonant frequency. During the optimization process, a local damping dynamic coefficient is introduced, initially set at 0.2, and dynamically adjusted according to the real-time operating status of the system, with an adjustment range of 0.1 to 0.5. The power flow allocation ratio is calculated using the Newton-Raphson method iteratively, with an initial error tolerance set to 1e-6 (…). The maximum number of iterations is 100. During the solution process, the voltage signal of the target node is monitored in real time, and a Fast Fourier Transform is used for spectral analysis to calculate the harmonic distortion rate of the voltage signal. When the harmonic distortion rate is below 3%, the stability of the voltage signal is considered to be significantly improved. Through the above method, the system can effectively improve the stability of the voltage signal of the target node while ensuring reasonable power flow distribution.

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

[0094] S11, Waveform decomposition is performed on the instantaneous voltage distortion and cumulative current deviation to obtain the key parameters of the target node; wherein, the key parameters include wideband resonant peak frequency, damping decay time constant, electric field phase offset and magnetic field amplitude abrupt change rate.

[0095] S12, sort the broadband resonant peak frequencies corresponding to the magnetic field amplitude mutation rate that is greater than the preset amplitude threshold, determine the dominant broadband resonant frequency, and obtain the target voltage instantaneous distortion and target current cumulative deviation corresponding to the dominant broadband resonant frequency.

[0096] For example, a broadband resonant damping characteristic control server uses a broadband resonant monitoring device to sample the voltage signal distortion and current signal offset of a target node in a power system, obtaining the instantaneous voltage distortion and cumulative current deviation of the target node. For instance, an analog-to-digital converter with a sampling frequency of 100kHz is used to synchronously sample the voltage and current of the target node, ensuring signal integrity and accuracy. During sampling, 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, a 5th harmonic with an amplitude of 8% of the fundamental frequency and a 7th harmonic with an amplitude of 5% of the fundamental frequency are found; 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. Specifically, the Least Mean Square (LMS) algorithm is used, with a filter order of 64 and a convergence factor of 0.1, effectively reducing the cumulative deviation of current offset. During processing, the cumulative current offset was monitored to decrease from the initial 2A to 3A, significantly improving the stability of the current signal. Wavelet transform was used to perform multi-resolution analysis on voltage and current signals to extract the instantaneous distortion characteristics of the signals. The analysis revealed that the instantaneous voltage distortion reached its peak within 0.2 seconds, with a distortion rate of 12%, while the current offset accumulated a deviation of 8A within 0.5 seconds.

[0097] In step S11, the Esprit algorithm is used to decompose the instantaneous voltage distortion and cumulative current deviation into waveforms to obtain initial decomposition data. The broadband resonant peak frequency and damping decay time constant are extracted from the initial decomposition data to determine the frequency distribution characteristics. The electric field phase shift is calculated based on the frequency distribution characteristics to obtain the dynamic changes in the phase shift. The magnetic field amplitude mutation rate is calculated based on the dynamic changes in the phase shift to obtain the time series of amplitude mutations. If the time series of amplitude mutations exceeds a preset mutation threshold (which can be set as needed), wavelet transform is used to accurately locate the mutation points and determine the mutation trend. The correlation fluctuation mode of the node voltage and current deviations is determined by combining the mutation trend with the frequency distribution characteristics. After obtaining the correlation fluctuation mode, Kalman filtering is used to optimize the waveform decomposition results to obtain the final decomposition parameters (broadband resonant peak frequency, damping decay time constant, electric field phase shift, and magnetic field amplitude mutation rate).

[0098] For example, when using the Esprit algorithm to decompose the instantaneous voltage distortion and cumulative current deviation into waveforms, time-domain signal data of the voltage and current signals are collected at a sampling frequency of 10kHz to ensure the capture of high-frequency resonant signals. The time-domain data is converted to frequency-domain signal data using 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), the subspace information of the signal is extracted, and the broadband resonant peak frequency is calculated to be 48kHz, which matches the preliminary FFT analysis results. Simultaneously, the damping decay time constant of the resonant signal is calculated to be 12ms, indicating that the resonant energy decays rapidly within a short time. Analyzing the electric field phase shift, the phase difference at different frequency points is calculated to be 17 degrees, indicating a significant phase lag at the resonant frequency. Analyzing the amplitude change of the magnetic field signal, the amplitude abrupt change rate is calculated to be 8%, indicating significant fluctuations in magnetic field energy during resonance. Through the above analysis, the power system can comprehensively assess the resonance characteristics in the power grid, providing data support for subsequent harmonic suppression and system optimization.

[0099] In step S12, the magnetic field amplitude abrupt change rate is compared with a preset amplitude threshold (which can be set as needed). The broadband resonant peak frequencies corresponding to magnetic field amplitude abrupt change rates exceeding the preset amplitude threshold are sorted to determine the dominant broadband resonant frequency. Based on the preset amplitude threshold, the magnetic field amplitude abrupt change rates are filtered to obtain target broadband resonant data (broadband resonant peak frequencies) corresponding to magnetic field amplitude abrupt change rates exceeding the preset amplitude threshold. Based on the target broadband resonant data, a fast Fourier transform is used to extract its peak frequencies, resulting in a set of peak frequencies. According to a pre-established sorting rule, the set of peak frequencies is processed to determine the dominant broadband resonant frequency result. If there are at least two dominant broadband resonant frequency results, their resonant characteristic data are obtained using a frequency distribution analysis method based on the dominant broadband resonant frequency results. Based on the resonant characteristic data, the correlation between the magnetic field amplitude abrupt change rate and the corresponding broadband resonant frequency is determined to obtain the final dominant broadband resonant frequency. After obtaining the final dominant broadband resonant frequency, the magnetic field amplitude abrupt change rate is compared with the final dominant broadband resonant frequency to verify the abrupt change trend of the magnetic field amplitude. Obtain the instantaneous distortion of the target voltage and the cumulative deviation of the target current corresponding to the dominant broadband resonant frequency.

[0100] For example, in the analysis of magnetic field amplitude abrupt change rate, the time-domain signal is first converted into a frequency-domain signal using a Fast Fourier Transform (FFT) to obtain the magnetic field amplitude distribution in the frequency domain. Assuming a preset amplitude threshold of 5 Tesla, the magnetic field amplitude at each frequency point is compared with the preset threshold. When the frequency is 50 Hz, the magnetic field amplitude is 6 Tesla, exceeding the preset threshold; therefore, this frequency point is marked as a potential broadband resonant frequency. The power system sorts all frequency points exceeding the preset amplitude threshold using a fast sorting algorithm, arranging them in descending order of amplitude. Assuming the sorting results are 50 Hz (6 Tesla), 60 Hz (55 Tesla), and 70 Hz (52 Tesla), the system further analyzes the resonant characteristics of these frequency points. By calculating the resonant bandwidth at each frequency point, such as 10 Hz for 50 Hz, 8 Hz for 60 Hz, and 6 Hz for 70 Hz, the dominant broadband resonant frequency of the system can be determined. 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 the widest resonant bandwidth, and thus has the most significant impact on the stability of the system.

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

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

[0103] S402, if the resonant mode coupling coefficient is less than zero, the energy balance correction factor is adjusted by the power flow distribution ratio of the power system, and the corresponding electric field energy density distribution is calculated.

[0104] For example, in step S401, resonant mode data within a preset frequency range is acquired; the resonant mode coupling coefficient is determined to be greater than zero based on the resonant mode data to obtain an initial judgment result; based on the initial judgment result, the electric field energy distribution is calculated using the 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 shift and obtain a phase shift parameter; the electric field energy distribution is adjusted using the phase shift parameter 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 the 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; the periodic fluctuation data is processed using the wavelet transform method to obtain fluctuation characteristic parameters; the fluctuation characteristic parameters are fitted using the least squares method to obtain a magnetic field energy attenuation curve; the sign of the resonant mode coupling coefficient is determined based on the trend of the attenuation curve to obtain a resonant mode coupling coefficient sign determination result; based on the resonant mode coupling coefficient sign determination result, the phase shift parameter is optimized to obtain an optimized electromagnetic field energy distribution.

[0105] For example, assuming the resonant mode coupling coefficient is 8, using the phase offset Adjustments are made by calculating the electric field energy density distribution function. ,in, Represents the distribution of electric field energy density. The vacuum permittivity, The magnitude of the electric field strength Angular frequency, Over time, it can be observed that the electric field energy density exhibits a non-uniform distribution in space. Analysis of the magnetic field energy density... ,in Indicates the distribution of magnetic field energy density. The permeability of free space, The magnitude of the magnetic field strength is decomposed into components of different frequencies using a Fourier transform. It is found that the amplitude of its periodic fluctuations decays exponentially with time, and the decay coefficient is... Numerical simulation methods, such as finite element analysis, can be used to accurately calculate energy density distribution and decay trends, providing data support for system optimization. By adjusting the phase shift, the energy of the electric and magnetic fields can be redistributed, thereby improving the overall efficiency of the system.

[0106] In step S402, resonant mode data detected by a spectrum analyzer is acquired, the resonant mode coupling coefficient is calculated, and it is determined whether the resonant mode coupling coefficient is less than zero. If the resonant mode coupling coefficient is less than zero, the allocation ratio data is extracted from a 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 allocation ratio is adjusted according to the ratio adjustment direction, and the energy balance parameters are calculated using the energy conservation formula to obtain a normalization correction factor. The electric field energy data is normalized using the normalization correction factor, and the density distribution characteristics are determined by kernel density estimation. Trend data is extracted from the density distribution characteristics, and the growth state is determined according to the slope of the trend data. If the growth state is positive, the resonant mode parameters are updated based on the least squares calculation results to obtain the adjusted resonant mode data. Based on the adjusted resonant mode data, the t-test is used to verify the change in the resonant mode 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 allocation ratio. Assuming the resonant mode coupling coefficient is -0.2 and the power flow allocation 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 allocation ratio), i.e., 1 - (-0.2 × 6) = 2.2, rounded to 2. The trend of the electric field energy density distribution is analyzed to determine if it shows an increasing trend. Assuming that at a certain moment the electric field energy density distribution is 5 J / m³, after adjustment by the correction factor, the electric field energy density distribution becomes 6 J / m³, an increase of 20%. If the electric field energy density distribution after the correction factor adjustment continues to increase, for example, reaching 8 J / m³ at the next moment, it indicates that the electric field energy density distribution does indeed show an increasing trend. Furthermore, combined with the change in the magnetic field energy density distribution, if the magnetic field energy density distribution increases synchronously, the effectiveness of the energy balance correction factor is verified. Through the above specific numerical values, algorithms, and analysis processes, the accuracy of the energy balance correction factor and the judgment of the growth trend of the electric field energy density distribution are ensured.

[0108] This invention discloses a method for regulating broadband resonant damping characteristics. The method involves sampling a target node in a power system using broadband signals to obtain the instantaneous voltage distortion and cumulative current deviation of the target node. Based on these voltage distortions and current deviations, a dominant broadband resonant frequency is determined to obtain the corresponding target voltage distortion and target current deviation. An energy transfer delay factor and an energy balance correction factor are calculated based on these target voltage distortions and current deviations. A local damping dynamic coefficient and a resonant mode coupling coefficient are calculated based on these energy transfer delay factors and energy balance correction factors to obtain the real-time regulation accuracy error. The target voltage distortion and target current deviation are optimized based on the real-time regulation accuracy error and the damping decay time constant. Finally, the power flow distribution ratio of the power system is updated based on the optimized target voltage distortion and target current deviation, as well as 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 using energy transfer delay factors and balance correction factors. Based on these transient characteristics and the real-time operating status of the power system, the broadband resonant damping characteristics can be dynamically adjusted through a two-dimensional dynamic control strategy of coupling coefficient, thereby improving the stability of the power system.

[0109] Referring to Figure 2, which is a schematic diagram of a broadband resonant damping characteristic control device 10 provided in an embodiment of the present invention, the broadband resonant damping characteristic control device 10 includes:

[0110] The target electrical data acquisition module 11 is used to sample broadband signals from target nodes in the power system to obtain the instantaneous voltage distortion and cumulative current deviation of the target nodes; and to determine the dominant broadband resonant frequency based on the instantaneous voltage distortion and cumulative current deviation to obtain the corresponding instantaneous voltage distortion and cumulative current deviation of the target nodes.

[0111] The energy correction factor calculation module 12 is used to calculate the energy transfer delay factor and the energy balance correction factor based on the instantaneous distortion of the target voltage and the cumulative deviation of the target current.

[0112] The control precision error calculation module 13 is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient based on the energy transfer delay factor and the energy balance correction factor, so as to obtain the real-time control precision error.

[0113] The target electrical data optimization module 14 is used to optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current based on the real-time error of the control accuracy and the damping decay time constant.

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

[0115] Furthermore, the broadband resonant damping characteristic control device 10 also includes:

[0116] An electric field energy density adjustment module is used to adjust the electric field energy density distribution by electric field phase offset if the coupling coefficient of the resonant mode is greater than zero, and to calculate the corresponding magnetic field energy periodic fluctuation based on the adjusted electric field energy density distribution.

[0117] The 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 to calculate the corresponding electric field energy density distribution.

[0118] The broadband resonant damping characteristic control device 10 provided in this embodiment of the invention can realize all the processes of the broadband resonant damping characteristic control method of the above embodiment. The functions and technical effects of each module in the device are the same as the functions and technical effects of the broadband resonant damping characteristic control method of the above embodiment, and will not be repeated here.

[0119] Referring to Figure 3, Figure 3 is a schematic diagram of the structure of a broadband resonant damping characteristic control device 20 provided in an embodiment of the present invention. The broadband resonant damping characteristic control device 20 of 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, it implements the steps in the above-described broadband resonant damping characteristic control method embodiment. Alternatively, when the processor 21 executes the computer program, it implements the functions of each module in the above-described broadband resonant damping characteristic control device embodiment.

[0120] For example, 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 complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the broadband resonant damping characteristic control device 20.

[0121] The broadband resonant damping characteristic control device 20 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The broadband resonant damping characteristic control device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand 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 device. It may include more or fewer components than shown, or combine certain components, or use different components. For example, the broadband resonant damping characteristic control device 20 may also include input / output devices, network access devices, buses, etc.

[0122] The processor 21 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the broadband resonant damping characteristic control device 20, connecting all parts of the device through various interfaces and lines.

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

[0124] The module integrated into the broadband resonant damping characteristic control device 20, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0125] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0126] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform a method for regulating the broadband resonant damping characteristics as described in the above embodiments.

[0127] Furthermore, embodiments of the present invention also provide 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 regulating the broadband resonant damping characteristics described in the above embodiments.

[0128] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and 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 resonant damping characteristics, characterized in that, include: Broadband signal sampling is performed on the target node in the power system to obtain the instantaneous voltage distortion and cumulative current deviation of the target node; Based on the instantaneous voltage distortion and cumulative current deviation, the dominant broadband resonant frequency is determined to obtain the corresponding instantaneous voltage distortion and cumulative current deviation; the instantaneous voltage distortion and cumulative current deviation are subjected to low-pass filtering and noise reduction processing to obtain the instantaneous voltage characteristics and cumulative offset characteristics. A Fast Fourier Transform (FFT) is performed on the instantaneous voltage characteristics to calculate their frequency domain amplitude. The electric field energy value is then calculated based on this amplitude to obtain the electric field energy density distribution. The cumulative offset characteristics are decomposed into a time series to calculate their magnetic field energy value, resulting in periodic fluctuations in the magnetic field energy. The electric field energy density distribution and the periodic magnetic field energy fluctuations are normalized to obtain the processed distribution characteristics and fluctuation amplitude. An energy transfer delay factor is calculated based on the distribution characteristics and fluctuation amplitude. The degree of matching between energy transfer and periodic fluctuations is analyzed based on the energy transfer delay factor. The energy balance is adjusted based on the degree of matching to obtain an energy balance correction factor. Based on the energy transfer delay factor and energy balance correction factor, the local damping dynamic coefficient and 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 instantaneous distortion of the target voltage and the cumulative deviation of the target current are optimized. Based on the optimized instantaneous distortion of the target voltage and the cumulative deviation of the target current, as well as 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.

2. The method for controlling the broadband resonant damping characteristics as described in claim 1, characterized in that, The step of calculating the local damping dynamic coefficient and the resonant mode 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 includes: calculating the 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 instantaneous distortion of the target voltage to determine the local damping dynamic coefficient of the power system; and calculating the resonant mode coupling coefficient of the power system based on the local damping dynamic coefficient and the power flow distribution ratio of the power system to obtain the real-time error of the control accuracy.

3. The method for controlling the broadband resonant damping characteristics as described in claim 1, characterized in that, The optimization of the instantaneous distortion of the target voltage and the cumulative deviation of the target current based on the real-time error of the control precision and the damping decay time constant includes: adjusting the broadband resonant peak frequency based on the real-time error of the control precision and the damping decay time constant; and optimizing the instantaneous distortion of the target voltage and the cumulative deviation of the target current based on the adjustment range of the broadband resonant peak frequency.

4. The method for adjusting the broadband resonant damping characteristics as described in claim 1, characterized in that, The step of updating the power flow allocation ratio of the power system based on the optimized instantaneous distortion of the target voltage and the cumulative deviation of the target current, as well as the local damping dynamic coefficient, to improve the voltage stability of the target node includes: calculating the optimized electric field energy density distribution based on the optimized instantaneous distortion of the target voltage; calculating the optimized magnetic field energy periodic fluctuation based on the optimized cumulative deviation of the target current; calibrating the optimized magnetic field energy periodic fluctuation using the magnetic field amplitude mutation rate; determining the resonant frequency optimization step size based on the calibrated magnetic field energy periodic fluctuation and the optimized electric field energy density distribution; and updating the power flow allocation ratio of the power system based on the resonant frequency optimization step size and the local damping dynamic coefficient to improve the voltage stability of the target node.

5. The method for adjusting the broadband resonant damping characteristics as described in claim 1, characterized in that, The step of determining the dominant broadband resonant frequency based on the instantaneous voltage distortion and cumulative current deviation to obtain the corresponding target instantaneous voltage distortion and target current deviation includes: performing waveform decomposition on the instantaneous voltage distortion and cumulative current deviation to obtain key parameters of the target node; wherein, the key parameters include broadband resonant peak frequency, damping decay time constant, electric field phase offset, and magnetic field amplitude abrupt change rate; sorting the broadband resonant peak frequencies corresponding to magnetic field amplitude abrupt change rates greater than a preset amplitude threshold to determine the dominant broadband resonant frequency, and obtaining the target instantaneous voltage distortion and target current cumulative deviation corresponding to the dominant broadband resonant frequency.

6. The method for controlling the broadband resonant damping characteristics as described in claim 1, characterized in that, 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 mode coupling coefficient is less than zero, adjusting the energy balance correction factor by the power flow allocation ratio of the power system, and calculating the corresponding electric field energy density distribution.

7. A device for regulating broadband resonant damping characteristics, characterized in that, include: The target electrical data acquisition module is used to sample broadband signals from target nodes in the power system to obtain the instantaneous voltage distortion and cumulative current deviation of the target nodes; based on the instantaneous voltage distortion and cumulative current deviation, the dominant broadband resonant frequency is determined to obtain the corresponding instantaneous voltage distortion and cumulative current deviation of the target nodes; the energy correction factor calculation module is used to perform low-pass filtering and noise reduction processing on the instantaneous voltage distortion and cumulative current deviation of the target nodes to obtain the instantaneous voltage characteristics and cumulative offset characteristics. A Fast Fourier Transform (FFT) is performed on the instantaneous voltage characteristics to calculate their frequency domain amplitude. The electric field energy value is then calculated based on this amplitude to obtain the electric field energy density distribution. The cumulative offset characteristics are decomposed into a time series to calculate their magnetic field energy value, resulting in periodic fluctuations in the magnetic field energy. The electric field energy density distribution and the periodic magnetic field energy fluctuations are normalized to obtain the processed distribution characteristics and fluctuation amplitude. An energy transfer delay factor is calculated based on the distribution characteristics and fluctuation amplitude. The degree of matching between energy transfer and periodic fluctuations is analyzed based on the energy transfer delay factor. The energy balance is adjusted based on the degree of matching to obtain an energy balance correction factor. The regulation accuracy error calculation module is used to calculate the local damping dynamic coefficient and the resonant mode coupling coefficient based on the energy transfer delay factor and the energy balance correction factor to obtain the real-time regulation accuracy error; the target electrical data optimization module is used to optimize the instantaneous distortion of the target voltage and the cumulative deviation of the target current based on the real-time regulation accuracy error and the damping decay time constant; the power allocation ratio update module is used to update the power flow allocation ratio of the power system based on the optimized instantaneous distortion of the target voltage and the cumulative deviation of the target current, as well as the local damping dynamic coefficient, to improve the voltage stability of the target node.

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