An online inertia and damping assessment method, device and system for a power system

Abstract

This invention discloses an online evaluation method, apparatus, and system for the inertia and damping of a power system, belonging to the field of power system control technology. The online evaluation method includes: slicing a set of observation points for a preset time period into slices at preset time intervals; inputting each slice's observation set into a neural network embedded with physical information to output the evaluation results of the slice's inertia and damping; and then fusing the evaluation results of each slice to obtain the evaluation results of the target inertia and damping. This invention divides the measurement data into multiple slices, then uses corresponding neural networks to evaluate each slice, and finally summarizes the results to obtain the evaluation results. This method solves the problem of random errors caused by quasi-steady-state disturbances and damage measurements, improving the accuracy and robustness of the evaluation.

Description

Technical Field

[0001] This invention belongs to the field of power system control technology, and more specifically, relates to a method, apparatus and system for online evaluation of the inertia and damping of a power system. Background Technology

[0002] The rapid development and large-scale grid connection of new energy sources have led to a continuous decrease in the proportion of traditional synchronous machines in the power grid, resulting in low inertia and weak damping characteristics. As crucial factors in maintaining power grid stability, the decline in inertia and damping levels inevitably threatens the safe operation of the system. To promptly detect and mitigate the risk of low-frequency oscillations in the system and to formulate corresponding stability control measures in advance, power grid operators need to conduct real-time and precise monitoring of the system's inertia and damping levels.

[0003] Previous studies on inertia and damping assessment based on quasi-steady-state data include the power spectral density method and methods based on variable covariance. However, after accurate modeling of the quasi-steady state, the power spectral density method can no longer observe the oscillation mode of the center of inertia, thus failing to estimate the system inertia. Covariance-based assessment methods require the assumption that the Jacobian matrix is ​​known or that there is a linear relationship between active power and voltage phase angle, which hinders their application in practical power grids. Meanwhile, the small fluctuation amplitude in the quasi-steady state makes the effective inertia response easily submerged in measurement noise, and measurement data transmitted over a wide area inevitably contains poor or missing data. These factors that impair measurements all contribute to a decrease in assessment accuracy. Therefore, there is an urgent need for a simpler and more practical method that can effectively handle random disturbances and impaired measurements in the quasi-steady state. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method, device and system for online evaluation of inertia and damping of power systems. Its purpose is to solve the technical problem of poor accuracy of inertia and damping evaluation caused by random errors due to quasi-steady-state disturbances and damaged measurements.

[0005] To achieve the above objectives, according to one aspect of the present invention, an online evaluation method for the inertia and damping of a power system is provided, comprising: S1: When the inertial steady-state measurement of the power system is damaged, the operating state parameters of the power system collected by the phasor measurement unit at each sampling time within a preset time period and the timestamps corresponding to the sampling times are used as observation points, thereby obtaining the set of observation points corresponding to the preset time period. S2: Slice the set of observation points corresponding to the preset time period at a preset time interval to obtain multiple sliced ​​observation sets; S3: Input each of the slice observation sets into the corresponding preset neural network embedded with physical information, so that the preset neural network outputs the slice inertia evaluation result and slice damping evaluation result of each of the slice observation sets; the physical information includes the rotor motion equations corresponding to the inertia to be measured and the damping to be measured. S4: The slice inertia evaluation results and slice damping evaluation results corresponding to each slice observation set are fused to obtain the target inertia evaluation results and target damping evaluation results.

[0006] Further, S2 includes: dividing the set of observation points corresponding to the preset time period into multiple slices by a preset time interval; and randomly sampling within the time domain of each slice to obtain at least one slice observation set corresponding to the slice.

[0007] Further, S4 includes: S41: Delete the inertia outlier and the damping outlier from the slice inertia evaluation results and the slice damping evaluation results corresponding to all the slice observation sets, respectively; S42: The remaining slice inertia evaluation results are weighted and fused to obtain the target inertia evaluation result; the remaining slice damping evaluation results are weighted and fused to obtain the target damping evaluation result.

[0008] Further, S41 includes: Using formula Identify the inertia anomalies and damping anomalies from the slice inertia assessment results and slice damping assessment results corresponding to all the slice observation sets, and delete them respectively; in, Let m be the training loss of the i-th preset neural network. The preset neural network with the smallest training loss is indexed as m, and its training loss is denoted as m. , This indicates the set threshold for filtering abnormal losses.

[0009] Further, S41 includes: Using formula Identify the inertia anomalies and damping anomalies from the slice inertia assessment results and slice damping assessment results corresponding to all the slice observation sets, and delete them respectively; in, and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the mean values ​​of the inertia assessment results and the damping assessment results, respectively. , The standard deviations of the inertia assessment results and the damping assessment results are represented by their respective values. , These represent the set threshold values ​​for inertia anomalies and damping anomalies, respectively.

[0010] Further, S42 includes: using the formula The remaining slice inertia assessment results are weighted and fused to obtain the target inertia assessment result. Using formulas The remaining damping evaluation results of each slice are weighted and fused to obtain the target damping evaluation result. ; in, Let i be the training loss of the i-th preset neural network. and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the number of remaining slice inertia assessment results and slice damping assessment results, respectively.

[0011] Furthermore, the operating status parameters include: frequency. f and power imbalance P The observation point is represented as { f , P , t}, t Indicates the sampling time.

[0012] According to another aspect of the present invention, an online inertia and damping evaluation apparatus for a power system is provided, for performing the online inertia and damping evaluation method for the power system, comprising: The sampling module is used to take the operating status parameters of the power system collected by the phasor measurement unit at each sampling time within a preset time period and the timestamp corresponding to the sampling time as observation points when the inertial steady-state measurement of the power system is damaged, and then obtain the set of observation points corresponding to the preset time period. The slicing module is used to slice the set of observation points corresponding to the preset time period at preset time intervals to obtain multiple sliced ​​observation sets. An evaluation module is used to input each of the slice observation sets into a preset neural network with embedded physical information, so that the preset neural network outputs the slice inertia evaluation results and slice damping evaluation results for each of the slice observation sets; the physical information includes the rotor motion equations corresponding to the inertia to be measured and the damping to be measured. The fusion module is used to fuse the slice inertia evaluation results and the slice damping evaluation results corresponding to each slice observation set to obtain the target inertia evaluation results and the target damping evaluation results.

[0013] According to another aspect of the present invention, an online evaluation system for the inertia and damping of a power system is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the online evaluation method for the inertia and damping of the power system.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the online evaluation method for the inertia and damping of the power system.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: (1) This invention provides an online evaluation method for the inertia and damping of a power system. By dividing the measurement data into multiple slices, evaluating each slice separately using a separate neural network, and finally summing the evaluation results of all slices to obtain statistical values, this method solves the problem of random errors caused by quasi-steady-state disturbances and damaged measurements, and greatly improves the accuracy and robustness of the evaluation.

[0016] (2) Considering the limited training data and the potential risk of measurement data corruption, the accuracy of evaluation results based on short time slices cannot be guaranteed. Therefore, it is necessary to summarize the evaluation results of multiple time slices for statistical analysis to improve the evaluation accuracy. This process consists of two steps: removing outliers and weighted averaging. The inertia and damping evaluations of each time slice correspond to a training process aimed at minimizing the loss function. A smaller training loss means a more favorable evaluation result. Therefore, the training loss of each evaluation is used as the confidence index for subsequent statistical processing. (3) The outlier removal step of this scheme filters outliers from both the training loss and the evaluation value itself. Outliers can be removed from multiple aspects, thereby improving the accuracy of the entire online evaluation algorithm. Attached Figure Description

[0017] Figure 1 A flowchart of the online evaluation method for the inertia and damping of a power system provided in Embodiment 1 of the present invention; Figure 2 A schematic diagram of the online evaluation method for the inertia and damping of a power system provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the error distribution of 50 short-time slices of 10 generators provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram illustrating the relative error of inertial constant evaluation in various regions under different wind power penetration rates, provided in Embodiment 1 of the present invention. Figure 5 This is a schematic diagram showing the distribution of inertia constant evaluation errors for all slices in a single region before and after removing outliers, as provided in Embodiment 1 of the present invention. Figure 6 This is a schematic diagram illustrating the error assessment of the inertial constant and damping coefficient of three regions when measuring damage using sp-PINN provided in Embodiment 1 of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Example 1 like Figure 1 and Figure 2 As shown, this embodiment provides an online evaluation method for the inertia and damping of a power system, including: S1: When the inertial steady-state measurement of the power system is damaged, the operating state parameters of the power system collected by the phasor measurement unit at each sampling time within a preset time period and the timestamps corresponding to the sampling times are used as observation points to obtain the observation point set corresponding to the preset time period; S2: The observation point set corresponding to the preset time period is sliced ​​at a preset time interval to obtain multiple corresponding slice observation sets; S3: Each slice observation set is input into a corresponding preset neural network embedded with physical information so that the preset neural network outputs the slice inertia evaluation result and slice damping evaluation result of each slice observation set; the physical information includes the rotor motion equations corresponding to the inertia and damping to be measured; S4: The slice inertia evaluation result and slice damping evaluation result corresponding to each slice observation set are fused to obtain the target inertia evaluation result and target damping evaluation result.

[0020] Regarding S1, operating status parameters are acquired using a phasor measurement unit (PMU). As an optional implementation, the operating status parameters may include: frequency. f and power imbalance P。 Using PMUs deployed in the power system, the frequency and power imbalance of individual generators (or aggregated generators representing the entire region) and their corresponding timestamps are collected to form observation points. f , P , t The sampling observation points obtained within a time window under quasi-steady state are collected and used as input data for the evaluation method. In the given embodiment, the PMU's acquisition frequency is set to 100Hz, and the acquisition period is a 100s time window of data under quasi-steady state.

[0021] Regarding S2, time windows are segmented. The collected long-term window observation dataset is divided into smaller time intervals, an operation called slicing. The observation points in each slice form an observation set. As an optional implementation, S2 includes: dividing the observation point set corresponding to a preset time period into multiple slices at preset time intervals; and performing random sampling within the time domain of each slice to obtain at least one slice observation set corresponding to the slice. To enhance sample diversity, random sampling is performed within the time domain of each slice to generate a pairing set. The observation set and the pairing set together constitute the training set of PINN.

[0022] In the given embodiment, the length of each short time slice is set to 2 seconds, meaning that 100 seconds of data is divided into 50 short slices. The number of pairing sets is set to twice that of the observation set, so for each short slice, its training set contains a total of 200 observation points and 400 pairing points.

[0023] Regarding S3, parallel evaluation is employed. Independent physical-informed neural networks (PINNs) are used for evaluation of each slice. The rotor motion equations (or region-equivalent aggregated rotor motion equations) containing unknown inertia and damping are embedded as known physical information within the neural networks. Thanks to their strong generalization properties, these PINNs can use identical hyperparameters. Furthermore, since the evaluation of different time slices is performed independently, they can be scheduled to run in parallel to reduce time consumption.

[0024] In the given embodiments, the hyperparameter settings of PINN are as follows:

[0025] Regarding S4, statistical analysis: Once all evaluations are complete, the results based on different time slices are merged together. Finally, statistical processing, including outlier removal and weighted averaging, is performed to produce the final evaluation results.

[0026] It's important to note that the goal of this step is to segment the long-term frequency dynamics into short-term frequency dynamics so that PINN can better fit them. Long-term frequency dynamics are crucial for inertia and damping assessments because the accuracy of assessments based on a single short slice cannot be guaranteed, and assessments may even fail. For example, PINN might converge to a local optimum during training, or the sampled data might be corrupted by measurement noise or anomalies. Therefore, long-term dynamics containing more observational data are needed to ensure the accuracy and robustness of the assessment. When determining the length of each short slice, two main factors are considered. First, the time slice needs sufficient data to ensure normal PINN training, so it cannot be too short. Second, the slice cannot be too long, exceeding PINN's fitting capability. Therefore, a trial-and-error approach can be used to select the optimal slice length.

[0027] Furthermore, when long-term dynamics are divided into multiple short-time slices, it means that multiple evaluations need to be performed, and the accumulated time consumption increases proportionally with the number of slices. To avoid this problem, a parallel evaluation strategy is needed. Each slice uses a PINN with the same structure to estimate inertia and damping separately. In this way, the training time for long-term dynamics is actually equivalent to the evaluation time of a single slice. In addition, compared to using PINN to evaluate the entire long-term dynamics, each PINN deals with less data, which further reduces time consumption and improves evaluation efficiency.

[0028] Specifically, due to limited training data and the potential risk of measurement data corruption, the accuracy of evaluation results based on short time slices cannot be guaranteed. Therefore, it is necessary to aggregate the evaluation results from multiple time slices for statistical analysis to improve evaluation accuracy. This process consists of two steps: outlier removal and weighted averaging. The inertia and damping evaluations for each time slice correspond to a training process aimed at minimizing the loss function. A smaller training loss implies a more favorable evaluation result. Therefore, the training loss of each evaluation is used as a confidence metric for subsequent statistical processing. As an optional implementation, S4 includes: S41: deleting inertia outliers and damping outliers from the slice inertia evaluation results and slice damping evaluation results corresponding to all slice observation sets, respectively; S42: performing weighted fusion on the remaining slice inertia evaluation results to obtain the target inertia evaluation result; performing weighted fusion on the remaining slice damping evaluation results to obtain the target damping evaluation result.

[0029] As an optional implementation, S41 includes: using the formula From the slice inertia assessment results and slice damping assessment results corresponding to all slice observation sets, identify inertia outliers and damping outliers, and delete them respectively; among them, Let m be the training loss of the i-th preset neural network. The preset neural network with the smallest training loss is indexed as m, and its training loss is denoted as m. , This indicates the set threshold for filtering abnormal losses.

[0030] As an optional implementation, S41 includes: using the formula From the slice inertia assessment results and slice damping assessment results corresponding to all slice observation sets, identify inertia outliers and damping outliers, and delete them respectively; among them, and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the mean values ​​of the inertia assessment results and the damping assessment results, respectively. , The standard deviations of the inertia assessment results and the damping assessment results are represented by their respective values. , These represent the set thresholds for inertia anomalies and damping anomalies, respectively, and can typically be set to 2 or 3.

[0031] It should be noted that the outlier removal step filters out outliers from both the training loss and the evaluation value itself. In other words, it can include both methods mentioned above, meaning two outlier removal operations are performed simultaneously.

[0032] As an optional implementation, the weights of the weighted average are set to the reciprocal of the training loss; that is, the smaller the training loss, the larger its corresponding weight. S42 includes: using the formula The inertia assessment results of the remaining individual slices are weighted and fused to obtain the target inertia assessment result. Using formulas The remaining damping evaluation results of each slice are weighted and fused to obtain the target damping evaluation result. ;in, Let i be the training loss of the i-th preset neural network. and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the number of remaining slice inertia assessment results and slice damping assessment results, respectively.

[0033] like Figure 3As shown in the violin plot, the error distribution of 50 short-time slices for 10 generators is illustrated. The results show that the evaluation error of the 50 slices exhibits randomness; while the evaluation error is small in some slices, it exceeds 6% in others. This demonstrates that the results of evaluation using only a single short-time slice are unreliable. However, it is noteworthy that the evaluation error for each generator nearly follows a normal distribution with a mean of approximately 0. This indicates that the statistical values ​​from multiple slices can eliminate random errors caused by short time intervals or PINN training.

[0034] like Figure 4 The figure shows a schematic diagram illustrating the relative error of inertia constant assessment in three different regions under different wind power penetration rates. The wind power penetration rates in the three regions are 9.37%, 32.18%, and 49.87%, respectively. The assessment results are compared with the commonly used ARMAX (Autoregressive moving average with exogenous inputs) method in the field. As shown in the figure, the inertia assessment error of the sp-PINN method is less than 1% for different assessment objects under different renewable energy penetration levels, and is significantly better than the compared ARMAX method. This result verifies the assessment accuracy of the proposed method and its adaptability to different renewable energy penetration levels.

[0035] like Figure 5 The figure shows the distribution of inertia constant evaluation errors for all slices in a single region before and after outlier removal under measurement constraints. The measurement impairment scenario was defined as 5% of the measurement data exhibiting anomalies, including 4% missing data and 1% data jumping to a maximum / minimum value. As can be seen from the figure, before outlier removal, the evaluation error for some slices exceeded 50%, with the maximum exceeding 100%, indicating that the evaluation of these slices had actually failed due to noise / data anomalies. Fortunately, such evaluation failures were isolated incidents; the evaluation error for the vast majority of slices remained below 15%, and was almost uniformly distributed around 0%. However, the introduction of noise / data anomalies did significantly weaken the algorithm's evaluation accuracy, thus demonstrating the superiority of the proposed long-term multi-slice parallel evaluation. After removing outliers from the evaluation results of all slices, the distribution range of the evaluation error was reduced.

[0036] like Figure 6 The diagram illustrates the error assessment of the inertial constant and damping coefficient in three different regions using sp-PINN when measurements are impaired. Two scenarios were set up for measurement impairment: one with only 10% noise injection, and the other with an additional 5% data anomaly superimposed on top of the noise. The results are presented in... Figure 5The final result was obtained by weighted averaging after outlier removal. As shown in the figure, even with only measurement noise, the maximum evaluation error of the proposed algorithm does not exceed 3%. Even with the addition of data anomalies, the overall evaluation error remains below 6%. This demonstrates the accuracy of the proposed evaluation method and its robustness to damaged measurements.

[0037] Example 2 This embodiment provides an online evaluation device for the inertia and damping of a power system, used to perform the above-described online evaluation method for the inertia and damping of a power system, including: The sampling module is used to take the operating state parameters of the power system and the corresponding timestamps collected by the phasor measurement unit at each sampling time within a preset time period as observation points when the inertial steady-state measurement of the power system is damaged, and then obtain the set of observation points corresponding to the preset time period. The slicing module is used to slice the set of observation points corresponding to a preset time period at a preset time interval to obtain multiple sliced ​​observation sets. The evaluation module is used to input each slice observation set into the corresponding preset neural network embedded with physical information, so that the preset neural network outputs the slice inertia evaluation results and slice damping evaluation results for each slice observation set; the physical information includes the rotor motion equations corresponding to the inertia and damping to be measured. The fusion module is used to fuse the slice inertia assessment results and slice damping assessment results corresponding to each slice observation set to obtain the target inertia assessment result and target damping assessment result.

[0038] Example 3 The present invention also relates to an online evaluation system for the inertia and damping of a power system, comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described online evaluation method for the inertia and damping of a power system.

[0039] The online evaluation system for the inertia and damping of this power system can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The processor 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, or discrete hardware components. The memory can be used to store computer programs and / or modules. The processor implements the various functions of the online evaluation system for the inertia and damping of the power system by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0040] Example 4 The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described online evaluation method for the inertia and damping of a power system.

[0041] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0042] Example 5 This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0043] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" in this invention are intended to illustrate the invention and are not intended to limit the invention.

[0044] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for online evaluation of the inertia and damping of a power system, characterized in that, include: S1: When the quasi-steady-state measurement of the power system is damaged, the operating state parameters of the power system collected by the phasor measurement unit at each sampling time within the preset time period and the timestamps corresponding to the sampling time are used as observation points, thereby obtaining the set of observation points corresponding to the preset time period. S2: Slice the set of observation points corresponding to the preset time period at a preset time interval to obtain multiple sliced ​​observation sets; S3: Input each of the slice observation sets into the corresponding preset neural network embedded with physical information, so that the preset neural network outputs the slice inertia evaluation result and slice damping evaluation result of each of the slice observation sets; The physical information includes the rotor motion equations corresponding to the inertia and damping to be measured. S4: The slice inertia evaluation results and slice damping evaluation results corresponding to each slice observation set are fused to obtain the target inertia evaluation results and target damping evaluation results respectively; S4 includes: S41: deleting inertia outliers and damping outliers from the slice inertia evaluation results and slice damping evaluation results corresponding to all slice observation sets, respectively; S42: The remaining slice inertia evaluation results are weighted and fused to obtain the target inertia evaluation result; The remaining damping evaluation results of each slice are weighted and fused to obtain the target damping evaluation result; S42 includes: using the formula The remaining slice inertia assessment results are weighted and fused to obtain the target inertia assessment result. Using formulas The remaining damping evaluation results of each slice are weighted and fused to obtain the target damping evaluation result. ;in, Let i be the training loss of the i-th preset neural network. and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the number of remaining slice inertia assessment results and slice damping assessment results, respectively.

2. The online evaluation method for the inertia and damping of a power system as described in claim 1, characterized in that, S2 includes: dividing the set of observation points corresponding to the preset time period into multiple slices by a preset time interval; and randomly sampling within the time domain of each slice to obtain at least one slice observation set corresponding to the slice.

3. The online evaluation method for the inertia and damping of a power system as described in claim 1, characterized in that, S41 includes: using the formula From all the slice inertia assessment results and slice damping assessment results corresponding to the slice observation sets, identify the inertia outliers and the damping outliers, and delete them respectively; wherein, Let m be the training loss of the i-th preset neural network. The preset neural network with the smallest training loss is indexed as m, and its training loss is denoted as m. , This indicates the set threshold for filtering abnormal losses.

4. The online evaluation method for the inertia and damping of a power system as described in claim 1, characterized in that, S41 includes: using the formula From all the slice inertia assessment results and slice damping assessment results corresponding to the slice observation sets, identify the inertia outliers and the damping outliers, and delete them respectively; wherein, and These represent the inertia evaluation result and the damping evaluation result of the i-th slice, respectively. , These represent the mean values ​​of the inertia assessment results and the damping assessment results, respectively. , The standard deviations of the inertia assessment results and the damping assessment results are represented by their respective values. , These represent the set threshold values ​​for inertia anomalies and damping anomalies, respectively.

5. The online evaluation method for the inertia and damping of a power system as described in any one of claims 1-4, characterized in that, The operating status parameters include: frequency f and power imbalance P The observation point is represented as { f , P , t }, t Indicates the sampling time.

6. An online evaluation device for the inertia and damping of a power system, characterized in that, The method for performing online evaluation of the inertia and damping of a power system according to any one of claims 1-5 includes: The sampling module is used to take the operating state parameters of the power system collected by the phasor measurement unit at each sampling time within a preset time period and the timestamp corresponding to the sampling time as observation points when the quasi-steady-state measurement of the power system is damaged, and then obtain the set of observation points corresponding to the preset time period. The slicing module is used to slice the set of observation points corresponding to the preset time period at preset time intervals to obtain multiple sliced ​​observation sets. An evaluation module is used to input each of the slice observation sets into a preset neural network with embedded physical information, so that the preset neural network outputs the slice inertia evaluation results and slice damping evaluation results for each of the slice observation sets; the physical information includes the rotor motion equations corresponding to the inertia to be measured and the damping to be measured. The fusion module is used to fuse the slice inertia evaluation results and the slice damping evaluation results corresponding to each slice observation set to obtain the target inertia evaluation results and the target damping evaluation results.

7. An online evaluation system for the inertia and damping of a power system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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