Neutral point ungrounded resonance monitoring method and system for smart power grid

Abstract

The invention relates to the technical field of data processing, in particular to an ungrounded neutral point resonance monitoring method and system for a smart power grid, and solves the technical problem of insufficient monitoring accuracy in the prior art. The method comprises the following steps: acquiring multi-source electric quantity time sequence data of a neutral ungrounded system of a smart power grid; determining resonance excitation combined potential energy according to the multi-source electric quantity time sequence data; the resonance excitation combined potential energy is used for representing the ferromagnetic resonance excitation capability of the system after transient impact; determining a resonance risk dynamic evolution coefficient according to the resonance excitation combined potential energy and the nonlinear saturation index of the iron core; determining a system-level resonance dynamic risk coefficient according to the resonance excitation combined potential energy and the resonance risk dynamic evolution coefficient; the system-level resonance dynamic risk coefficient is used for representing the current resonance instability risk level of the system.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and system for monitoring neutral point ungrounded resonance in a smart grid. Background Technology

[0002] With the widespread integration of distributed power sources into smart grids, the increased system capacitive current significantly raises the risk of ferroresonance in ungrounded neutral systems. Overvoltages generated by ferroresonance can severely damage the insulation performance of power equipment, causing equipment failures and directly impacting the reliability of the power grid, posing a serious threat to the safe and stable operation of smart grids.

[0003] Currently, discrete spectrum analysis based on steady-state threshold criteria and fixed time windows is commonly used to diagnose electrical quantity characteristics in a fragmented, one-dimensional manner to determine the resonance state and achieve resonance monitoring of ungrounded neutral systems in smart grids. However, this monitoring method is difficult to adapt to the dynamic and complex operating conditions of smart grids, resulting in significantly insufficient monitoring accuracy. Therefore, how to achieve real-time and accurate monitoring of ferroresonance in ungrounded neutral systems of smart grids has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the technical problem of insufficient accuracy in existing monitoring technologies, this application aims to provide a method for monitoring neutral point ungrounded resonance in smart grids. The specific technical solution adopted is as follows: Acquire multi-source electrical quantity time-series data of the neutral point ungrounded system of the smart grid.

[0005] Based on the time-series data of multi-source electrical quantities, the joint potential energy of resonant excitation is determined. This joint potential energy characterizes the system's ability to induce ferromagnetic resonances after being subjected to transient shocks.

[0006] The dynamic evolution coefficient of resonance risk is determined based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core. This coefficient characterizes the degree of risk in the evolution of resonance from the energy accumulation stage to the energy release stage.

[0007] The system-level resonant dynamic risk coefficient is determined based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk. The system-level resonant dynamic risk coefficient characterizes the current resonant instability risk level of the system.

[0008] In one possible implementation, the multi-source electrical quantity time-series data includes: zero-sequence circuit electrical quantity time-series data, voltage transformer electrical quantity time-series data, and secondary-side voltage time-series data. Based on the multi-source electrical quantity time-series data, the resonant excitation joint potential energy is determined, including: determining a comprehensive transient impact index based on the zero-sequence circuit electrical quantity time-series data; the comprehensive transient impact index is used to characterize the intensity and duration of the voltage transient impact that triggers resonance; determining a core nonlinearity saturation index based on the voltage transformer electrical quantity time-series data and the secondary-side voltage time-series data; the core nonlinearity saturation index is used to characterize the depth to which the voltage transformer core deviates from the linear operating state; and determining the resonant excitation joint potential energy based on the comprehensive transient impact index and the core nonlinearity saturation index.

[0009] In one possible implementation, a comprehensive transient impact index is determined based on the zero-sequence loop electrical quantity time-sequence data from the multi-source electrical quantity time-sequence data. This includes: extracting the zero-sequence voltage change trend curve from the zero-sequence loop electrical quantity time-sequence data and identifying the extreme points in the zero-sequence voltage change trend curve; determining the degree of abrupt change in voltage change within the neighborhood of each extreme point; determining the average dynamic mutability of voltage abrupt changes based on the degree of abrupt changes at each extreme point; determining the average stability of voltage amplitude based on the voltage amplitude difference between each pair of adjacent extreme points; determining the chaos index of transient impact based on the average dynamic mutability and average stability; and determining the comprehensive transient impact index based on the chaos index and the statistical characteristics of the duration of voltage abrupt changes.

[0010] In one possible implementation, the core nonlinear saturation index is determined based on the voltage transformer electrical quantity time-series data and secondary voltage time-series data in the multi-source electrical quantity time-series data. This includes: determining the current envelope evolution characteristics based on the excitation current data in the voltage transformer electrical quantity time-series data; determining the core saturation point and the saturation interval between each saturation point based on the current envelope evolution characteristics; determining the equivalent impedance change characteristics of the excitation and the waveform distortion characteristics of the excitation current within the core saturation interval; and determining the core nonlinear saturation index based on the current characteristics, rated current value, equivalent impedance change characteristics, and waveform distortion characteristics at the saturation point.

[0011] In one possible implementation, the dynamic evolution coefficient of resonance risk is determined based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core. This includes: obtaining a first exponential sequence of the joint potential energy of resonance excitation and a second exponential sequence of the nonlinear saturation index of the iron core within a continuous time window; determining the system state evolution trajectory based on the first and second exponential sequences; determining the path stability index of resonance energy accumulation based on the evolution characteristics of the state points relative to the steady-state reference point in the system state evolution trajectory; obtaining the potential energy sequence and path stability index of the joint potential energy of resonance excitation within a continuous time window; analyzing the evolutionary correlation characteristics between the potential energy sequence and the path stability index, and determining the vicious cycle intensity index of resonance development based on the evolutionary correlation characteristics and the relative intensity of the joint potential energy of resonance excitation in the current time window; and determining the dynamic evolution coefficient of resonance risk based on the vicious cycle intensity index and the path stability index.

[0012] In one possible implementation, the path stability index for resonant energy accumulation is determined based on the evolution characteristics of state points relative to steady-state reference points in the system state evolution trajectory. This includes: calculating the distance from each state point in the system state evolution trajectory to the steady-state reference point and generating a distance sequence; calculating the change in the distance sequence; and determining the path stability index based on the central tendency and dispersion of the change.

[0013] In one possible implementation, the evolutionary correlation feature is the statistical correlation between the potential energy sequence and the path stability index within a sliding time window; the relative strength is a parameter determined by comparing the joint potential energy of resonant excitation based on the current time window with the extreme value of the joint potential energy of resonant excitation within a preset time range.

[0014] In one possible implementation, the system-level resonant dynamic risk coefficient is determined based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk, including: determining the nonlinear gain term based on the nonlinear saturation index of the iron core; and comprehensively calculating the joint potential energy of resonant excitation, the dynamic evolution coefficient of resonant risk, and the nonlinear gain term to obtain the system-level resonant dynamic risk coefficient.

[0015] In one possible implementation, after determining the system-level resonant dynamic risk coefficient based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk, the method further includes: evaluating the resonant state level based on the system-level resonant dynamic risk coefficient; and outputting a corresponding early warning signal based on the preset threshold range in which the resonant state level is located.

[0016] This application also provides a neutral point ungrounded resonance monitoring system for a smart grid, the system comprising: The acquisition unit is used to acquire multi-source electrical quantity time-series data of the neutral point ungrounded system of the smart grid.

[0017] The potential energy determination unit is used to determine the joint potential energy of resonant excitation based on the time-series data of multi-source electrical quantities. The joint potential energy of resonant excitation is used to characterize the system's ability to induce ferromagnetic resonance after being subjected to transient shocks.

[0018] The evolution coefficient calculation unit is used to determine the dynamic evolution coefficient of resonance risk based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core. The dynamic evolution coefficient of resonance risk is used to characterize the degree of risk in the evolution of resonance from the energy accumulation stage to the energy release stage.

[0019] The system risk determination unit is used to determine the system-level resonant dynamic risk coefficient based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk. The system-level resonant dynamic risk coefficient is used to characterize the current resonant instability risk level of the system.

[0020] This application offers the following advantages: By acquiring multi-source electrical quantity time-series data, it comprehensively captures key electrical characteristics related to resonance, avoiding the limitations of traditional single-dimensional data acquisition; by determining the joint potential energy of resonance excitation, it accurately characterizes the system's fundamental ability to induce ferromagnetic resonance; based on this potential energy and the core nonlinear saturation index, the dynamic evolution coefficient of resonance risk effectively reflects the dynamic risk trend of resonance development; finally, by combining these two key parameters to obtain the system-level resonance dynamic risk coefficient, it achieves an accurate assessment of the system's resonance instability risk level. This solves the problems of insufficient real-time performance and accuracy in traditional monitoring methods, adapts to the dynamic and complex operating conditions of smart grids, and enables accurate monitoring of ferromagnetic resonance in ungrounded neutral point systems of smart grids, providing reliable monitoring support for the safe and stable operation of the power grid. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for monitoring neutral point ungrounded resonance in a smart grid, provided as an embodiment of this application; Figure 2 This is a schematic diagram of the system architecture of a smart grid neutral point ungrounded resonance monitoring system provided in one embodiment of this application. Detailed Implementation

[0023] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a neutral point ungrounded resonance monitoring method for a smart grid proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0025] Unless otherwise specified, the normalization function Norm() mentioned in this application uses maximum and minimum value normalization. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculation result exceeds the [0,1] interval, a truncation function is used to limit it to the [0,1] range (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the evaluation index.

[0026] The following description, in conjunction with the accompanying drawings, details the specific scheme of a neutral point ungrounded resonance monitoring method for a smart grid provided in this application.

[0027] Please see Figure 1 It illustrates a flowchart of a method for monitoring the neutral point ungrounded resonance of a smart grid according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the multi-source electrical quantity time sequence data of the neutral point ungrounded system of the smart grid.

[0028] Among them, multi-source electrical quantity time series data refers to the sequence data that can reflect the changes of various electrical parameters over time in the resonance state of a neutral point ungrounded system. It covers the electrical characteristics of the entire cycle of resonance from "excitation to development to extinction" and serves as the basic data support for subsequent resonance analysis and risk assessment.

[0029] For example, high-precision electrical quantity acquisition terminals are deployed on the bus side, the secondary side of the potential transformer (PT), the outgoing terminals of each distribution branch, and the grid connection points of distributed power sources in a smart grid ungrounded neutral point system. All acquisition terminals achieve nanosecond-level time synchronization based on a unified time synchronization mechanism to ensure data time consistency. A synchronous acquisition system with a sampling frequency of 20kHz is used to perform continuous full-area sampling of the target monitoring area to ensure the high frequency and integrity of the data.

[0030] In some embodiments, the collected multi-source electrical quantity time-series data includes, but is not limited to, zero-sequence circuit electrical quantity time-series data and voltage transformer electrical quantity time-series data. After the data is collected, the raw data needs to be preprocessed, including using wavelet threshold denoising to filter out high-frequency noise introduced by electromagnetic interference, automatically screening and removing data frames with quality defects based on quantitative indicators such as data signal-to-noise ratio, sampling integrity, and amplitude fluctuation rationality, and finally encapsulating the preprocessed valid data into standard data frames in a structured format of "timestamp-acquisition node number-parameter type-value", and transmitting them to the background monitoring server via industrial Ethernet to provide high-quality data for subsequent analysis.

[0031] Step 102: Determine the resonant excitation joint potential energy based on the multi-source electrical quantity timing data.

[0032] Among them, the resonant excitation joint potential energy is used to characterize the system's ability to induce ferromagnetic resonance after being subjected to a transient shock. In other words, the resonant excitation joint potential energy is used to characterize the system's ability to induce ferromagnetic resonance after being subjected to a transient shock. Taking into account both the intensity of the transient shock and the nonlinear characteristics of the system itself (mainly reflected in the saturation degree of the PT core), these two factors jointly determine whether the system has the basic conditions for inducing ferromagnetic resonance.

[0033] In actual power grid operation, transient impacts (such as instantaneous ground faults and sudden changes in distributed power output) can disrupt the original capacitive-inductive balance of the system, while PT core saturation can further exacerbate this imbalance. The synergistic effect of these two factors is key to the excitation of ferroresonance. Therefore, by extracting characteristic parameters related to these two factors from multi-source electrical quantity time-series data and then fusing them to obtain the joint potential energy for resonant excitation, it is possible to accurately reflect the system's potential for ferroresonance excitation.

[0034] Step 103: Determine the dynamic evolution coefficient of resonance risk based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core.

[0035] Among them, the dynamic evolution coefficient of resonance risk is used to characterize the degree of risk in the evolution of resonance from the energy accumulation stage to the energy release stage. The development of resonance is a dynamic process, and it will exhibit different characteristics from energy accumulation to energy release. The joint potential energy excited by resonance alone cannot fully reflect the risk changes in this process, while the core nonlinear saturation index can continuously characterize the degree of nonlinear enhancement of the system. The combination of the two can effectively capture the dynamic risk in the resonance development process.

[0036] Specifically, determining the dynamic evolution coefficient of resonance risk requires analyzing the path stability and vicious cycle effects during the resonance development process: if the system state evolution path is unstable and there is a vicious cycle of "deteriorating path stability - enhanced excitation potential energy", the risk of resonance evolving into the energy release stage will increase significantly, and vice versa. By integrating these dynamic characteristics, the dynamic evolution coefficient of resonance risk can accurately quantify the risk trend of resonance development.

[0037] Step 104: Determine the system-level resonant dynamic risk coefficient based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk.

[0038] Among them, the system-level resonant dynamic risk coefficient is used to characterize the current resonant instability risk level of the system, and is a comprehensive quantitative evaluation index of the system's resonant state.

[0039] The joint potential energy of resonance excitation provides the basic risk of resonance initiation, while the dynamic evolution coefficient of resonance risk provides the dynamic risk increment of resonance development. The combination of the two can comprehensively cover the risk characteristics of the entire process of resonance from initiation to instability. In actual calculations, the two parameters can be comprehensively calculated in conjunction with the continuous influence of the system's nonlinear characteristics. The final system-level resonance dynamic risk coefficient can intuitively reflect the current instability probability and overvoltage hazard of the system, providing a direct basis for subsequent graded early warning.

[0040] Based on the above technical solution, this application comprehensively captures key electrical characteristics related to resonance by acquiring multi-source electrical quantity time-series data, avoiding the limitations of traditional single-dimensional data acquisition. By determining the joint potential energy of resonance excitation, it accurately characterizes the system's fundamental ability to induce ferromagnetic resonance. Based on this potential energy and the core nonlinear saturation index, the dynamic evolution coefficient of resonance risk effectively reflects the dynamic risk trend of resonance development. Finally, by combining these two key parameters to obtain the system-level resonance dynamic risk coefficient, it achieves an accurate assessment of the system's resonance instability risk level. This solves the problems of insufficient real-time performance and accuracy of traditional monitoring methods, adapts to the dynamic and complex operating conditions of smart grids, and enables accurate monitoring of ferromagnetic resonance in ungrounded neutral point systems of smart grids, providing reliable monitoring support for the safe and stable operation of the power grid.

[0041] In one possible implementation, the multi-source electrical quantity timing data includes: zero-sequence circuit electrical quantity timing data, voltage transformer electrical quantity timing data, and secondary-side voltage timing data. Correspondingly, the above step: determining the specific implementation process of the resonant excitation joint potential energy based on the multi-source electrical quantity timing data includes: Step 201: Determine the comprehensive transient impact index based on the zero-sequence circuit electrical quantity time sequence data in the multi-source electrical quantity time sequence data.

[0042] Among them, the timing data of the zero-sequence circuit electrical quantities include the instantaneous values ​​of the zero-sequence voltage and the zero-sequence current of the bus. This type of data can directly reflect whether there is an unbalanced state in the system. The timing data of the voltage transformer electrical quantities include the instantaneous value of the PT excitation current, which can reflect the working state (linear or saturated) of the PT core. The timing data of the secondary voltage includes the instantaneous value of the PT secondary voltage, which can help verify the saturation state of the PT core and abnormal changes in the system voltage.

[0043] The comprehensive transient impulse index is used to characterize the intensity and duration of voltage transient impulses that trigger resonance. Transient impulses are the direct cause of resonance excitation. The greater the intensity and the longer the duration of the transient impulse, the higher the probability of breaking the capacitive-inductive balance of the system, and the greater the possibility of triggering resonance.

[0044] This application analyzes the time-series electrical quantities of the zero-sequence circuit, extracts characteristic parameters related to the intensity and duration of transient impacts, and then fuses them to obtain a comprehensive transient impact index. This index can accurately quantify the impact of transient impacts on resonant excitation and provides key input for the calculation of the joint potential energy of resonant excitation.

[0045] Optionally, the process of determining the comprehensive transient impact index specifically includes: extracting the zero-sequence voltage change trend curve from the time-sequence data of the zero-sequence circuit electrical quantities, and identifying the extreme points in the zero-sequence voltage change trend curve; determining the degree of abrupt change in voltage change within the neighborhood of each extreme point; determining the average dynamic abruptness of voltage abrupt change based on the degree of abrupt change of each extreme point; determining the average stability of voltage amplitude based on the voltage amplitude difference between each pair of adjacent extreme points; determining the chaos index of transient impact based on the average dynamic abruptness and average stability; and determining the comprehensive transient impact index based on the chaos index and the statistical characteristics of the duration of voltage abrupt change.

[0046] Specifically, the zero-sequence voltage variation trend curve is extracted from the time-series data of zero-sequence circuit electrical quantities, and the extreme points in the zero-sequence voltage variation trend curve are identified. The zero-sequence voltage variation trend curve can intuitively reflect the variation law of zero-sequence voltage over time, and its extreme points correspond to the peaks and troughs of the zero-sequence voltage, which are key nodes for analyzing voltage change characteristics. For example, the instantaneous value time-series data of zero-sequence voltage is first fitted with a fifth-order polynomial to generate a smooth voltage variation fitting curve. This curve effectively eliminates minor fluctuations in the data, highlighting the overall trend of zero-sequence voltage changes; then, a zero-point detection algorithm is used to identify... For all extreme points, denote the zero-sequence voltage value corresponding to the extreme point as... ( (Number of extreme points).

[0047] The degree of abrupt change in voltage directly reflects the intensity of the transient impact; the greater the degree of abrupt change, the stronger the transient impact. After determining the extreme points, a 0.5s neighborhood range is defined on both sides of each extreme point (this range can cover the complete duration of the transient impact), and the zero-sequence voltage change trend curve is analyzed. Find the first derivative This derivative value directly reflects the degree of voltage change over time; The absolute value of the slope difference in the neighborhood of the extreme point As the degree of abrupt change in voltage, The larger the value, the stronger the sudden shock of the zero-sequence voltage at the extreme point, and the greater the intensity of transient energy injection.

[0048] The average dynamic abrupt change in voltage is determined based on the degree of abrupt change at each extreme point. The average dynamic abrupt change reflects the overall abrupt change characteristics of the zero-sequence voltage in the precursory stage of resonance, rather than the isolated characteristics of a single extreme point. Specifically, the average dynamic abrupt change is calculated for all extreme points. The arithmetic mean, denoted as , The larger the value, the more severe the average sudden change in zero-sequence voltage during the pre-resonance stage, and the greater the overall intensity of the transient shock.

[0049] The average stability of the voltage amplitude is determined by analyzing the voltage amplitude differences between adjacent extreme points. The stability of the voltage amplitude reflects the disorder of the transient impact; higher disorder makes resonance more likely. Specifically, the stability of the voltage amplitude is calculated between any two adjacent extreme points. and absolute value of voltage difference Take the average of all adjacent extreme point pairs. , The smaller the value, the more stable the zero-sequence voltage oscillation amplitude, and the resonance is still in a controllable nascent stage; The larger the voltage, the more violent the disordered fluctuations in voltage amplitude, and the easier it is to quickly develop into a chaotic overvoltage.

[0050] The degree of chaos in a transient shock is jointly determined by the intensity of the voltage jump and the disorder of the amplitude fluctuation; the combined effect of these two factors influences the excitation probability of resonance. Therefore, the average dynamic abrupt change... With average stability Multiplying them yields an index of the degree of chaos in the transient impact. ,Right now: in, It is the arithmetic mean of the absolute values ​​of the differences in the slope of voltage change within the neighborhood of all extreme points, characterizing the average dynamic abruptness of the zero-sequence voltage in the pre-resonance stage; The mean of the absolute values ​​of the voltage differences between all adjacent extreme points represents the average stability of the zero-sequence voltage amplitude; both are dimensionless parameters.

[0051] Based on the statistical characteristics of the chaos level index and the duration of voltage surges, a comprehensive transient impact index is determined. The longer the duration of a transient impact, the more likely it is to induce the PT core to enter the saturation region. Therefore, it is necessary to introduce a weighted average of the duration of voltage surges to correct the chaos level index.

[0052] For example, the comprehensive transient impact index The calculation formula is: in, This is an indicator of the degree of chaos in a transient shock. This represents the percentage of voltage surge duration (calculated as the ratio of the total duration of the extreme point neighborhood to the duration of the analysis time window, which is typically taken as 1 minute in history). It is a natural constant. Used to quantify the gain effect of duration on resonant excitation; () is the maximum and minimum value normalization function, used to map the calculation results to the [0,1] interval to eliminate dimensional differences. Optionally, the process of determining the total duration of the neighborhood of the extreme point is as follows: based on the previously defined neighborhood range of 0.5 seconds on both sides of each extreme point (i.e., each extreme point corresponds to a neighborhood interval with a duration of 1 second), the neighborhood intervals corresponding to all extreme points are merged on the time axis; if the neighborhood intervals of different extreme points overlap in time, the overlapping part is only calculated once. The total time span obtained after merging is the total duration of the neighborhood of the extreme point.

[0053] Step 202: Determine the core nonlinear saturation index based on the voltage transformer electrical quantity time sequence data and the secondary voltage time sequence data in the multi-source electrical quantity time sequence data.

[0054] The core nonlinearity saturation index is used to characterize the depth to which the voltage transformer core deviates from its linear operating state. As the core nonlinear component of the system, the saturation of the PT core is a key factor exacerbating the capacitive-inductive imbalance and promoting the continuous development of resonance. The greater the depth of the core deviation from its linear operating state, the stronger the nonlinear characteristics of the system and the more significant the gain effect on resonance. By using the timing data of the voltage transformer's electrical quantities and the timing data of the secondary voltage, the process of the PT core changing from linear to saturated can be captured, saturation-related characteristic parameters can be extracted, and the core nonlinearity saturation index can be obtained. This accurately quantifies the nonlinear characteristics of the system and provides another key input for calculating the joint potential energy of resonance excitation.

[0055] Optionally, the process of determining the core nonlinear saturation index specifically includes: determining the current envelope evolution characteristics based on the excitation current data in the voltage transformer electrical quantity time series data; determining the core saturation point and the saturation interval between each saturation point based on the current envelope evolution characteristics; determining the equivalent impedance change characteristics of the excitation and the waveform distortion characteristics of the excitation current within the core saturation interval; and determining the core nonlinear saturation index based on the current characteristics, rated current value, equivalent impedance change characteristics, and waveform distortion characteristics at the saturation point.

[0056] Specifically, it involves acquiring the excitation current data of the bus voltage transformer (PT) synchronized with the bus zero-sequence voltage. and secondary voltage As the core nonlinear component of ferroresonant resonance, the saturation of the core of the voltage transformer (PT) will lead to a nonlinear increase in the excitation current, which will further aggravate the capacitive-inductive imbalance of the system and form the gain condition for continuous resonant oscillation.

[0057] Using Hilbert transform, from excitation current data The current envelope evolution characteristics of the excitation current are extracted. This is used to characterize the saturation evolution trend of the excitation current; optionally, the current envelope evolution characteristics are also included. To obtain excitation current data Extract the envelope signal of the excitation current. Perform a linear fit and calculate the slope of the fitted curve. The larger this value, the faster the core saturation speed, directly reflecting the rate at which the PT switches from the linear operating region to the nonlinear operating region. A faster rate makes it easier to trigger resonance. By fitting the curve and obtaining the points where the curvature changes most and second most between adjacent data points, these points are considered the PT saturation inflection points. , ; will be located at the PT saturation inflection point , The interval between these intervals is considered the saturation interval.

[0058] Obtain the equivalent excitation impedance within the saturation range (Calculated using voltage-current relationship), thereby obtaining the equivalent impedance change characteristics in the saturation range. As an example, the characteristics of equivalent impedance variation Satisfy the following formula: in, The impedance value at the initial saturation moment. To saturate impedance value at time, The duration of saturation. The larger the value, the more drastic the decrease in inductance caused by PT saturation, and the more severe the system's capacitive-inductance imbalance. For parameter tuning coefficients, the value should be a very small positive number (e.g., 0.01) to avoid a denominator of 0.

[0059] Obtained using Fast Fourier Transform (FFT) The fitting curve is used to determine all harmonic components within the PT saturation period. Then, the mean difference in current amplitude among all harmonic components is calculated, and finally, the average of this mean difference is calculated. , will This is denoted as waveform distortion characteristic. The larger the value, the more severe the distortion of the current PT excitation current waveform and the stronger the nonlinearity of the iron core.

[0060] Constructing the nonlinear saturation index of PT core It satisfies the following formula: in, This is the rated excitation current value of the PT. This is the average value of the saturation inflection point current. It comprehensively reflects the shift of the saturation point, the distortion area of ​​the hysteresis loop in the saturation region, and the harmonic distortion of the current waveform, and is an indicator that fully characterizes the depth of deviation of the PT core from the linear state. The larger the value, the deeper PT has entered the nonlinear region, providing a strong gain condition for resonance.

[0061] Step 203: Determine the combined potential energy of resonance excitation based on the comprehensive transient impact index and the core nonlinear saturation index.

[0062] Optionally, since transient impact provides the energy basis for resonant excitation and PT core saturation provides the gain condition for resonant excitation, the synergistic effect of the two determines the system's ability to excite ferromagnetic resonance. Therefore, by adopting a reasonable fusion method to combine the comprehensive transient impact index with the core nonlinear saturation index, a resonant excitation joint potential energy that can fully characterize this capability can be obtained.

[0063] As an example, resonant excitation of joint potential energy Satisfy the following formula: in, The larger the value, the more sufficient the current system excitation conditions are, and the extremely high risk of resonance initiation.

[0064] Based on the above technical solution, this application clarifies the specific types of multi-source electrical quantity time-series data, ensuring the relevance and comprehensiveness of data acquisition and avoiding redundant acquisition of invalid data. By separately determining the comprehensive transient impact index and the core nonlinear saturation index, the two key influencing factors of resonance excitation (transient impact and PT core saturation) are accurately quantified. The joint potential energy of resonance excitation determined based on these two indices can more realistically and accurately reflect the system's ability to excite ferromagnetic resonance, providing high-quality intermediate parameters for subsequent risk assessment and further improving the accuracy of the entire monitoring method.

[0065] In one possible implementation, the above steps—determining the dynamic evolution coefficient of the resonance risk based on the joint potential energy of the resonance excitation and the nonlinear saturation index of the iron core—specifically include: Step 301: Obtain the first exponential sequence of the comprehensive transient impact index and the second exponential sequence of the core nonlinear saturation index within a continuous time window.

[0066] Optionally, this application employs a rolling time window analysis method to obtain the first index sequence of the comprehensive transient impact index and the second index sequence of the core nonlinear saturation index. Specifically, based on the current moment, five consecutive analysis time windows of 200ms each are extracted forward. The comprehensive transient impact index and the core nonlinear saturation index are calculated independently within each time window, thereby obtaining five consecutive comprehensive transient impact indices as the first index sequence. And five consecutive PT core nonlinear saturation indices, as the second index sequence. .

[0067] Step 302: Determine the system state evolution trajectory based on the first exponential sequence and the second exponential sequence.

[0068] Specifically, with The horizontal axis represents the impact driving force. The vertical axis represents the system's nonlinearity, and the values ​​of each analysis time window are plotted. Data points are mapped in chronological order onto a two-dimensional coordinate system and connected by directed line segments to generate a time-series trajectory of system state evolution.

[0069] Step 303: Determine the path stability index of resonant energy accumulation based on the evolution characteristics of the state points relative to the steady-state reference point in the system state evolution trajectory.

[0070] Optionally, this step can be implemented as follows: calculate the distance from each state point in the system state evolution trajectory to the steady-state reference point, and generate a distance sequence; calculate the change in the distance sequence; and determine the path stability index based on the central tendency and dispersion of the change.

[0071] Specifically, the stability of the dynamic process of resonance from excitation to development is primarily reflected in the evolution of the excitation source itself. If the evolution path of the excitation conditions (impact and saturation) tends to diverge, the resonant system may rapidly become unstable; if the path tends to converge, the resonance may self-destruct. Calculating each state point ( , Euclidean distance to the origin The origin represents the ideal linear steady state, i.e., no shock and no saturation.

[0072] Construct distance sequence Calculate the first difference of the distance sequence (i.e., the change in distance between adjacent windows): This reflects the rate at which the state point moves away from or towards the steady-state origin window by window. Optionally, the central tendency of the changes is represented by the arithmetic mean, and the dispersion is represented by the standard deviation. When arithmetic mean When the value is large, it indicates that the system state is continuously moving away from the steady-state origin, the resonant energy tends to accumulate, and the system instability increases; when Standard deviation A large value indicates that the process is far from stable and the system may be on the verge of dynamic imbalance, making it extremely sensitive to external disturbances.

[0073] Calculate the stability index of the current resonant energy accumulation path. It satisfies the following formula: in, It is the arithmetic mean of the changes; The standard deviation of the variation; the arithmetic mean of the path stability and the variation. The absolute values ​​are negatively correlated and affected by the standard deviation of the change. The amplification adjustment. When the system state deviates rapidly and drastically from the steady state, the denominator increases. A value approaching 0 indicates an extremely unstable path, suggesting the current evolution trend of the excitation source: the resonant system is already at high risk of dynamic instability; conversely, if the state hovers or approaches the origin, A value close to 1 indicates that the path is stable.

[0074] Step 304: Obtain the potential energy sequence and path stability index within a continuous time window.

[0075] For example, in this step, the resonant excitation joint potential energy sequence is obtained for five consecutive time windows (the same as the five time windows in step 301). and path stability sequence .

[0076] Step 305: Analyze the evolutionary correlation characteristics between the potential energy sequence and the path stability index, and determine the vicious cycle intensity index of resonance development based on the evolutionary correlation characteristics and the relative intensity of the joint potential energy of resonance excitation in the current time window.

[0077] Among them, the evolutionary correlation feature is the statistical correlation between the potential energy sequence and the path stability index within the sliding time window; the relative strength is a parameter determined by comparing the joint potential energy of resonant excitation based on the current time window with the extreme value of the joint potential energy of resonant excitation within a preset time range.

[0078] Specifically, after a transient shock, the path stability of a healthy system should tend to recover, and the induced potential energy should tend to decay, indicating that the system has the ability to self-calm down. Conversely, if the system falls into a vicious cycle, it will manifest as a continuous deterioration of path stability while the induced potential energy continues to increase, with the two forming a negative synergy that accelerates the evolution of the system towards a deeply nonlinear instability state.

[0079] Optionally, the statistical correlation mentioned above is characterized using the Pearson correlation coefficient. The sliding window Pearson correlation coefficient between the potential energy sequence and the path stability index is calculated and analyzed over time: the Pearson correlation coefficient of the two sequences is calculated based on the past N sampling points (e.g., N=10) across all time window combinations, and the correlation coefficient sequence is obtained sequentially. Pearson correlation coefficient The range of values ​​is The absolute value of the correlation coefficient reflects the strength of the linear correlation, while its sign reflects the direction of the correlation. If the correlation coefficient sequence is consistently significantly negative, it indicates that the system has fallen into a vicious cycle of "high risk-instability," and the resonance will continue to develop; if the correlation coefficient fluctuates around zero, it indicates that the system has not yet formed a fixed evolutionary pattern.

[0080] The relative intensity can be specifically expressed as the normalized relative intensity of the current excited potential energy, using the formula... Characterization; reflects the degree of deviation of the system from its most recent lowest excitation state.

[0081] Correspondingly, the vicious cycle intensity index of the current time window Satisfy the following formula: in, These are parameter tuning coefficients; their values ​​should be extremely small positive numbers (e.g., 0.01) to avoid denominators of 0. The correlation coefficient is calculated using the latest sliding window ending at the current time window. To generate joint potential energy for resonance in the current time window, For the five consecutive time windows analyzed The maximum and minimum values; The range of values ​​is The larger the value, the more it indicates that the system is not only in a significant negative feedback loop of instability and potential energy growth, but also that the current excitation potential energy has climbed to a relatively high level recently. The driving force of the vicious cycle is strong, and the risk of continued resonance is extremely high. This indicates taking the maximum value between 1 and -r.

[0082] Step 306: Determine the dynamic evolution coefficient of resonance risk based on the vicious cycle intensity index and path stability index.

[0083] Specifically, path instability is the foundation of risk, while vicious cycles amplify it: when path stability is poor, the system state is already in a vulnerable phase, rapidly deviating from steady state. If a significant vicious cycle is superimposed, it means that each deviation not only cannot be recovered but also generates greater excitation potential energy through the system's nonlinear feedback, leading to even more severe deviations and forming a positive feedback-driven acceleration of instability. Conversely, if the path is relatively stable, even with some negative correlation, the system possesses strong anti-interference capabilities, and the overall risk is controllable.

[0084] As an example, the dynamic evolution coefficient of resonance risk Satisfy the following formula: in, Characterizes the level of underlying path instability. The lower the value, the larger the value, indicating a high base risk of the system state; It characterizes the amplification effect of vicious cycles on fundamental instability; the larger the value, the stronger the amplification effect. The larger the value, the greater the risk that the system is not only on a highly unstable dynamic path, but also that this instability is being continuously amplified and strengthened by the nonlinear feedback mechanism inside the system. The greater the risk that the resonance will evolve from the energy accumulation stage (accumulation state) to the energy release stage (release state) that produces destructive overvoltage.

[0085] Based on the above technical solutions, this application captures the dynamic process of resonance development by acquiring sequence data within a continuous time window, avoiding the limitations of single-point-of-time data; by constructing a system state evolution trajectory, it intuitively reflects the changing trend of the system state; based on the path stability index determined by the evolution trajectory, it quantifies the degree of system deviation from steady state; by analyzing the correlation characteristics between the potential energy sequence and the path stability index, it determines the vicious cycle intensity index, capturing the negative feedback cycle characteristics of the system; finally, based on these two indicators, the dynamic evolution coefficient of resonance risk can accurately characterize the evolution risk of resonance from energy accumulation to energy release, providing key dynamic risk parameters for the calculation of system-level resonance dynamic risk coefficients, and further improving the monitoring method's ability to accurately control the resonance development process.

[0086] In one possible implementation, the specific process of determining the system-level resonant dynamic risk coefficient based on the resonant excitation joint potential energy and the resonant risk dynamic evolution coefficient includes: Step 401: Determine the nonlinear gain term based on the core nonlinear saturation index.

[0087] Core nonlinear saturation index It reflects the depth of the PT core's deviation from the linear operating state. The larger the value, the stronger the nonlinear characteristics of the system and the more significant the continuous strengthening effect on resonant oscillation. Therefore, it is regarded as the core parameter of the nonlinear gain term.

[0088] For example, the nonlinear gain term is set as ,in This represents the core nonlinear saturation index for the current time window. When... When the core is in a fully linear state, the gain term is 1, with no additional gain on the risk factor; when When the iron core enters a nonlinear state, the gain term is greater than 1, and The larger the value, the larger the gain term, and the stronger the gain effect on the risk coefficient, which can effectively reflect the continuous driving effect of PT core nonlinear saturation on resonance instability.

[0089] Step 402: Perform a comprehensive calculation on the joint potential energy of resonant excitation, the dynamic evolution coefficient of resonant risk, and the nonlinear gain term to obtain the system-level resonant dynamic risk coefficient.

[0090] Optionally, the system-level resonant dynamic risk coefficient can be obtained by comprehensively calculating the joint potential energy of resonant excitation, the dynamic evolution coefficient of resonant risk, and the nonlinear gain term. These three parameters reflect the resonant risk of the system from different dimensions: joint potential energy of resonant excitation... It provides the fundamental risk of resonance initiation; the dynamic evolution coefficient of resonance risk Provides dynamic risk increments for resonant development; nonlinear gain term The continuous gain effect of the system's nonlinear characteristics on resonance is provided. The synergistic effect of these three factors determines the current resonance instability risk level of the system; therefore, multiplication is used for comprehensive integration.

[0091] For example, the dynamic risk coefficient of system-level resonance. The calculation formula is: Based on the above technical solution, this embodiment fully considers the continuous impact of PT core nonlinear saturation on resonance instability by determining the nonlinear gain term, thus overcoming the limitations of relying solely on the basic potential energy and dynamic evolution coefficient. Through comprehensive multiplication of the three key parameters, the basic risk of resonance initiation, the dynamic risk of development, and the continuous nonlinear gain risk are fully integrated. The resulting system-level resonance dynamic risk coefficient can more comprehensively and accurately characterize the current resonance instability risk level of the system, providing an intuitive and reliable quantitative indicator for subsequent graded early warning, and further improving the practicality and accuracy of the monitoring method.

[0092] Optionally, after determining the system-level resonant dynamic risk coefficient based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk, the method further includes: Step 403: Evaluate the resonance state level based on the system-level resonance dynamic risk coefficient.

[0093] Optionally, the system-level resonant dynamic risk coefficient can be adjusted. Maximum and minimum values ​​are normalized to a range of [0,1]. Based on the magnitude of this coefficient and the actual safety requirements of the power grid operation, threshold intervals corresponding to four resonance state levels are preset, as follows: Attention Level: At this point, the risk of system resonance instability is low, and the resonance is in its nascent stage or has already subsided on its own.

[0094] Warning level: At this point, the risk of system resonance instability is moderate, and the resonance is in the energy accumulation stage, with a tendency to develop further.

[0095] Emergency Level: At this point, the system is at high risk of resonance instability, as the resonance is about to enter the energy release stage, which may generate a slight overvoltage.

[0096] Crisis Level: At this point, the risk of system resonance instability is extremely high. The resonance has entered the energy release stage, which will generate severe overvoltage and seriously threaten equipment safety.

[0097] The specific values ​​within the threshold range can be derived based on historical resonance event statistics and simulation verification. In practical applications, adjustments can be made according to different power grid topologies, equipment parameters, and operating conditions to ensure adaptability to actual operating scenarios.

[0098] Step 404: Output the corresponding warning signal according to the preset threshold range where the resonance state level is located.

[0099] For different resonance state levels, corresponding early warning signals are output, and targeted handling suggestions are provided for reference, as follows: Attention Level: A blue alert signal is issued. The recommended action is to strengthen routine monitoring. No additional action is required. Continue to track changes in system status.

[0100] Warning level: Yellow warning signal is issued. The recommended action is to activate enhanced monitoring mode, increase data collection frequency, and prepare for the switching of arc suppression devices.

[0101] Emergency Level: An orange emergency warning signal is issued. The recommended action is to immediately switch on the arc suppression device, adjust the operation mode of the distributed generation (DG) to reduce output, and alleviate the system's capacitive-inductive imbalance.

[0102] Crisis Level: A red crisis warning signal is issued. The recommended response is to quickly disconnect some non-critical loads, isolate the area affected by resonance, and prevent overvoltage from causing large-scale equipment damage.

[0103] Early warning signals can be output through audible and visual alarms, SMS notifications, and platform push notifications from the background monitoring system, ensuring that power grid operation and management personnel can receive them in a timely manner and take appropriate measures.

[0104] The beneficial effects of this embodiment are as follows: This embodiment divides the resonance state levels clearly based on the system-level resonance dynamic risk coefficient, making the risk assessment results more intuitive and easy to understand; it outputs corresponding early warning signals and handling suggestions for different levels, realizing closed-loop management from risk assessment to actual handling, transforming the abstract risk coefficient into specific power grid operation guidance, effectively improving the power grid's rapid response capability to resonance risks, and further ensuring the safe and stable operation of the smart grid neutral point ungrounded system.

[0105] Please see Figure 2This diagram illustrates a system architecture of a smart grid neutral point ungrounded resonance monitoring system according to an embodiment of the present invention. The system includes: an acquisition unit 201, a potential energy determination unit 202, an evolution coefficient calculation unit 203, and a system risk determination unit 204. The units communicate bidirectionally via a communication link to ensure real-time interaction of collected data and analysis results. The communication link can employ wired or wireless transmission methods to meet the communication needs of different monitoring scenarios.

[0106] Acquisition unit 201 is used to acquire multi-source electrical quantity time sequence data of the neutral point ungrounded system of the smart grid.

[0107] The potential energy determination unit 202 is used to determine the joint potential energy of resonant excitation based on the time-series data of multi-source electrical quantities. The joint potential energy of resonant excitation is used to characterize the ability of the system to excite ferromagnetic resonance after being subjected to transient impact.

[0108] The evolution coefficient calculation unit 203 is used to determine the dynamic evolution coefficient of resonance risk based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core. The dynamic evolution coefficient of resonance risk is used to characterize the degree of risk in the evolution of resonance from the energy accumulation stage to the energy release stage.

[0109] The system risk determination unit 204 is used to determine the system-level resonant dynamic risk coefficient based on the joint potential energy of resonant excitation and the dynamic evolution coefficient of resonant risk. The system-level resonant dynamic risk coefficient is used to characterize the current resonant instability risk level of the system.

[0110] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring neutral point ungrounded resonance in a smart grid, characterized in that, The method includes: Acquire multi-source electrical quantity time-series data of the neutral point ungrounded system of the smart grid; Based on the multi-source electrical quantity time-series data, the resonant excitation joint potential energy is determined; wherein, the resonant excitation joint potential energy is used to characterize the system's ability to excite ferromagnetic resonance after being subjected to transient shocks; Based on the resonant excitation joint potential energy and the core nonlinear saturation index, the dynamic evolution coefficient of resonant risk is determined; the dynamic evolution coefficient of resonant risk is used to characterize the degree of risk of the resonance evolving from the energy accumulation stage to the energy release stage. Based on the joint potential energy of the resonance excitation and the dynamic evolution coefficient of the resonance risk, the system-level resonance dynamic risk coefficient is determined; the system-level resonance dynamic risk coefficient is used to characterize the current resonance instability risk level of the system.

2. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 1, characterized in that, The multi-source electrical quantity timing data includes: zero-sequence circuit electrical quantity timing data, voltage transformer electrical quantity timing data, and secondary side voltage timing data; Based on the multi-source electrical quantity timing data, the resonant excitation joint potential energy is determined, including: Based on the zero-sequence circuit electrical quantity time series data in the multi-source electrical quantity time series data, a comprehensive transient impact index is determined; the comprehensive transient impact index is used to characterize the intensity and duration of the voltage transient impact that triggers resonance; Based on the voltage transformer electrical quantity time series data and secondary voltage time series data in the multi-source electrical quantity time series data, the core nonlinearity saturation index is determined; the core nonlinearity saturation index is used to characterize the depth of the voltage transformer core deviating from the linear operating state. The resonant excitation combined potential energy is determined based on the comprehensive transient impact index and the core nonlinear saturation index.

3. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 2, characterized in that, Based on the zero-sequence circuit electrical quantity time series data in the multi-source electrical quantity time series data, the comprehensive transient impact index is determined, including: Extract the zero-sequence voltage variation trend curve from the zero-sequence circuit electrical quantity time-series data, and identify the extreme points in the zero-sequence voltage variation trend curve; Determine the degree of abrupt change in voltage change within the neighborhood of each extreme point; The average dynamic variability of voltage mutations is determined based on the degree of mutation at each extreme point. The average stability of the voltage amplitude is determined based on the voltage amplitude difference between each pair of adjacent extreme points. Based on the average dynamic mutability and the average stability, the chaos index of the transient shock is determined; The comprehensive transient impact index is determined based on the statistical characteristics of the chaos level index and the duration of voltage change.

4. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 2, characterized in that, Based on the voltage transformer electrical quantity time series data and secondary voltage time series data in the multi-source electrical quantity time series data, the core nonlinear saturation index is determined, including: The current envelope evolution characteristics are determined based on the excitation current data in the electrical quantity time series data of the voltage transformer. The saturation point of the iron core and the saturation interval between each saturation point are determined based on the current envelope evolution characteristics. The equivalent impedance variation characteristics of excitation and the waveform distortion characteristics of excitation current within the saturation range of the iron core are determined. The nonlinear saturation index of the iron core is determined based on the current characteristics at the saturation point, the rated current value, the equivalent impedance change characteristics, and the waveform distortion characteristics.

5. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 2, characterized in that, Based on the resonant excitation joint potential energy and the core nonlinear saturation index, the dynamic evolution coefficient of resonant risk is determined, including: Obtain the first exponential sequence of the comprehensive transient impact index and the second exponential sequence of the core nonlinear saturation index within a continuous time window; The system state evolution trajectory is determined based on the first exponential sequence and the second exponential sequence; Based on the evolution characteristics of the state points relative to the steady-state reference point in the system state evolution trajectory, the path stability index of resonant energy accumulation is determined. Obtain the potential energy sequence of the resonant excitation joint potential energy and the path stability index within a continuous time window; The evolutionary correlation characteristics between the potential energy sequence and the path stability index are analyzed, and the vicious cycle intensity index of resonance development is determined based on the evolutionary correlation characteristics and the relative intensity of the joint potential energy of resonance excitation in the current time window. The dynamic evolution coefficient of the resonance risk is determined based on the vicious cycle intensity index and the path stability index.

6. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 5, characterized in that, Based on the evolution characteristics of the state points relative to the steady-state reference point in the system state evolution trajectory, the path stability index for resonant energy accumulation is determined, including: Calculate the distance from each state point in the system state evolution trajectory to the steady-state reference point, and generate a distance sequence; Calculate the change in the distance sequence; The path stability index is determined based on the central tendency and dispersion of the changes.

7. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 5, characterized in that, The evolutionary correlation feature is the statistical correlation between the potential energy sequence and the path stability index within a sliding time window; The relative strength is a parameter determined by comparing the resonant excitation joint potential energy based on the current time window with the extreme value of the resonant excitation joint potential energy within a preset time range.

8. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 1, characterized in that, Based on the resonant excitation joint potential energy and the resonant risk dynamic evolution coefficient, the system-level resonant dynamic risk coefficient is determined, including: The nonlinear gain term is determined based on the nonlinear saturation index of the iron core. The system-level resonant dynamic risk coefficient is obtained by comprehensively calculating the resonant excitation joint potential energy, the resonant risk dynamic evolution coefficient, and the nonlinear gain term.

9. The method for monitoring neutral point ungrounded resonance in a smart grid according to claim 1, characterized in that, After determining the system-level resonant dynamic risk coefficient based on the resonant excitation joint potential energy and the resonant risk dynamic evolution coefficient, the method further includes: The resonance state level is assessed based on the system-level resonance dynamic risk coefficient. Based on the preset threshold range in which the resonance state level is located, a corresponding warning signal is output.

10. A neutral point ungrounded resonance monitoring system for a smart grid, characterized in that, The system includes: The acquisition unit is used to acquire multi-source electrical quantity time-series data of the neutral point ungrounded system of the smart grid; The potential energy determination unit is used to determine the resonant excitation joint potential energy based on the multi-source electrical quantity time-series data; wherein, the resonant excitation joint potential energy is used to characterize the ability of the system to excite ferromagnetic resonance after being subjected to transient impact; The evolution coefficient calculation unit is used to determine the dynamic evolution coefficient of resonance risk based on the joint potential energy of resonance excitation and the nonlinear saturation index of the iron core; the dynamic evolution coefficient of resonance risk is used to characterize the degree of risk of resonance evolving from the energy accumulation stage to the energy release stage; The system risk determination unit is used to determine the system-level resonant dynamic risk coefficient based on the resonant excitation joint potential energy and the resonant risk dynamic evolution coefficient; the system-level resonant dynamic risk coefficient is used to characterize the current resonant instability risk level of the system.

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