Power grid inertia evaluation disturbance identification method and system based on adaptive wavelet transform

Abstract

The invention relates to the technical field of power grid inertia evaluation, in particular to a power grid inertia evaluation disturbance identification method and system based on self-adaptive wavelet transform, and the method comprises the steps: obtaining active power data in a power system, carrying out the preprocessing of the data, carrying out the self-adaptive wavelet transform of the preprocessed data, and carrying out the disturbance identification of the power grid inertia evaluation. Extracting a wavelet detail coefficient and an approximation coefficient; step disturbance is identified by using a detail coefficient through a multi-scale digital-analog maximum value method; for slope disturbance, carrying out sliding window linear goodness-of-fit threshold value identification on the data after the step disturbance identification part is carried out; for noise-like disturbance, performing disturbance identification through mean value stability and variance stability dual-stability detection and wavelet entropy verification; and outputting types, time windows and key parameters of various disturbances. The method can quickly decompose the measurement data to different frequency bands, provides accurate and reliable disturbance data for system inertia evaluation, and is helpful for maintaining the system frequency stability.

Description

Technical Field

[0001] This invention relates to the field of power grid inertia assessment technology, and in particular to a method and system for power grid inertia assessment disturbance identification based on adaptive wavelet transform. Background Technology

[0002] As renewable energy sources such as wind and solar power account for an increasingly larger share of the power system, the system inertia level continues to decline. This puts enormous pressure on the frequency stability of the power grid. At the same time, power disturbances within the grid exhibit characteristics of multiple types of complex disturbances. Against this backdrop, accurate grid inertia assessment is crucial. It is the core technical support for early warning of potential risks, taking targeted control measures, and ensuring the safe and stable operation of the system.

[0003] Traditional disturbance identification methods mostly target single disturbances and lack the ability to identify multiple types of power disturbances. Therefore, there is an urgent need for a precise disturbance identification method for power grid inertia assessment that can track and analyze system disturbance power in real time, provide timely and accurate disturbance identification results, and prepare data for power grid inertia assessment.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for power grid inertia assessment and disturbance identification based on adaptive wavelet transform, thereby effectively solving the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a power grid inertia assessment and disturbance identification method based on adaptive wavelet transform, comprising the following steps:

[0007] The active power data of the power system is acquired and preprocessed. Adaptive wavelet transform is then performed on the preprocessed data to extract wavelet detail coefficients and approximation coefficients.

[0008] For step disturbances, the multi-scale numerical modulus maxima method is used to identify them using the aforementioned detail coefficients.

[0009] For slope disturbances, the method of using a sliding window linear fitting goodness threshold to identify the data after the step disturbance identification part based on the approximation coefficient is used.

[0010] For noise-like disturbances, a dual stability detection method using mean stability and variance stability is employed, and disturbance identification is performed through wavelet entropy verification.

[0011] Output the type, time window, and key parameters of various disturbances.

[0012] Furthermore, the data preprocessing includes:

[0013] Normalize the active power data:

[0014] ;

[0015] In the formula: To standardize the power value, This is the measured value of the disturbance power. Reference power;

[0016] High-frequency noise is suppressed using a Butterworth low-pass filter.

[0017] Furthermore, the step of performing adaptive wavelet transform on the preprocessed data to extract wavelet detail coefficients and approximation coefficients includes:

[0018] Different wavelet basis functions are used depending on the type of disturbance to be identified. For high-frequency components, the db4 wavelet is used to extract detail coefficients, and for low-frequency components, the sym8 wavelet is used to extract approximate coefficients.

[0019] Furthermore, the identification of step disturbances using the detailed coefficients via a multi-scale numerical model maxima method includes:

[0020] Extract the wavelet detail coefficients d1 of the highest frequency band and detect the modulus maxima of the d1 component. As a candidate step jump point The corresponding time is recorded as ; Take the average of the modulo maxima among the detail coefficients from d1 to d3 As a reference value, each candidate step point is checked to see if it meets the step disturbance judgment threshold. Candidate step points with amplitudes greater than the judgment threshold are recorded as step disturbance occurrence points. The judgment process uses the following formula:

[0021] ;

[0022] In the formula, Threshold for identifying step disturbances;

[0023] The d1 component point that satisfies the step disturbance criterion is considered the moment when the step disturbance occurs, and the corresponding time is... At this point, the timing of the output step disturbance and the change in step disturbance power are... Its formula is:

[0024] ;

[0025] In the formula: This represents the change in power during a step disturbance. The active power value at the moment of disturbance. This represents the active power value at the moment before the disturbance occurred.

[0026] Furthermore, the method for identifying slope disturbances by applying a sliding window linear fitting goodness-of-fit threshold to the data after the step disturbance identification portion based on the approximation coefficients includes:

[0027] Set the wavelet approximation coefficients for traversing the power perturbation using a sliding window, perform linear fitting on the data within the window, and calculate the goodness of fit. Set the goodness-of-fit threshold for linear fit. When the linear fit goodness within the window is detected to be greater than or equal to the threshold, the center of the window is taken as the slope perturbation breakpoint. After traversing the perturbation approximation coefficients, the preliminary slope segmentation is obtained. The slope perturbation linear threshold requirement is expressed by the following formula:

[0028] ;

[0029] The initial segmentation results of the slope disturbance are processed by removing excessively short segments, calculating the average slope of each segment, merging adjacent segments with similar slopes, and outputting the start and end times of the slope disturbance and the average slope of the disturbance segment to obtain the slope disturbance identification and segmentation results.

[0030] Furthermore, for noise-like disturbances, the method employs dual stability detection (mean stability and variance stability) and disturbance identification via wavelet entropy verification, including:

[0031] The remaining approximation coefficients are iterated using the sliding window method. The average magnitude and variance within the window are calculated. Thresholds for magnitude stability and variance stability are set. Perturbation segments that meet the dual stability threshold requirements are denoted as candidate segments for noise-like perturbations. The dual stability check is expressed by the following formula:

[0032] ;

[0033] ;

[0034] In the formula: This represents the maximum or minimum power change within the sliding window. The mean stability threshold, The power variance within the sliding window. This is the variance stability threshold;

[0035] The wavelet entropy of the candidate noise-like segments is calculated for further screening. If the wavelet entropy is greater than or equal to the threshold requirement, it is considered a noise-like disturbance. The start and end times of the noise-like disturbance are output and segmented to obtain the noise-like disturbance segmentation results. The wavelet entropy threshold requirement is expressed by the following formula:

[0036] ;

[0037] In the formula, The wavelet entropy value, The length of the sliding window. The threshold value is the wavelet entropy threshold.

[0038] The present invention also includes a power grid inertia assessment and disturbance identification system based on adaptive wavelet transform, using the method described above, wherein the system comprises:

[0039] The adaptive wavelet transform unit is used to acquire active power data in the power system, preprocess the data, perform adaptive wavelet transform on the preprocessed data, and extract wavelet detail coefficients and approximation coefficients.

[0040] A step disturbance identification unit is used to identify step disturbances using the detailed coefficients in a multi-scale digital modulus maxima method.

[0041] The slope disturbance identification unit is used to identify slope disturbances by applying a sliding window linear fitting goodness threshold to the data after the step disturbance identification part based on the approximation coefficients.

[0042] The noise disturbance identification unit is used to identify noise disturbances by employing dual stability detection of mean stability and variance stability and by wavelet entropy verification.

[0043] The output unit is used to output the type, time window, and key parameters of various disturbances.

[0044] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0045] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0046] The beneficial effects of this invention are as follows: Using wavelet transform to analyze the characteristics of power grid disturbances, and through multi-scale, multi-frequency band decomposition, the definite type of power disturbance and its start and end times are obtained, which helps improve the accuracy of inertia assessment. Compared with other methods, wavelet transform can quickly decompose measurement data into different frequency bands, obtaining more accurate disturbance power, providing accurate and reliable disturbance data for system inertia assessment, and helping to maintain system frequency stability. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of the method in Example 1;

[0049] Figure 2 This is a schematic diagram of the system structure in Example 1;

[0050] Figure 3 This is a flowchart of the method in Example 2;

[0051] Figure 4 This is a diagram showing the disturbance identification results in Example 2;

[0052] Figure 5 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0054] Example 1:

[0055] like Figure 1 As shown: A method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform, comprising the following steps:

[0056] The active power data of the power system is acquired and preprocessed. Adaptive wavelet transform is then performed on the preprocessed data to extract wavelet detail coefficients and approximation coefficients.

[0057] For step disturbances, the multi-scale numerical model maxima method using detail coefficients is used for identification.

[0058] For slope disturbances, the method of using a sliding window linear fitting goodness threshold based on the approximation coefficient to identify the data after the step disturbance identification part is used.

[0059] For noise-like disturbances, a dual stability detection method using mean stability and variance stability is employed, and disturbance identification is performed through wavelet entropy verification.

[0060] Output the type, time window, and key parameters of various disturbances.

[0061] Analyzing the power disturbance characteristics of the power grid using wavelet transform, and obtaining definite power disturbance types and their start and end times through multi-scale and multi-frequency band decomposition, helps improve the accuracy of inertia assessment. Compared with other methods, wavelet transform can quickly decompose measurement data into different frequency bands, obtaining more accurate disturbance power, providing accurate and reliable disturbance data for system inertia assessment, and helping to maintain system frequency stability.

[0062] In this embodiment, data preprocessing includes:

[0063] Normalize the active power data:

[0064] ;

[0065] In the formula: To standardize the power value, This is the measured value of the disturbance power. Reference power;

[0066] High-frequency noise is suppressed using a Butterworth low-pass filter.

[0067] The process involves performing an adaptive wavelet transform on the preprocessed data to extract wavelet detail coefficients and approximation coefficients, including:

[0068] Different wavelet basis functions are used depending on the type of disturbance to be identified. For high-frequency components, the db4 wavelet is used to extract detail coefficients, and for low-frequency components, the sym8 wavelet is used to extract approximate coefficients.

[0069] As a preferred embodiment of the above embodiments, for step disturbances, identification is performed using a multi-scale numerical modulus maxima method with detail coefficients, including:

[0070] Extract the wavelet detail coefficients d1 of the highest frequency band and detect the modulus maxima of the d1 component. As a candidate step jump point The corresponding time is recorded as ; Take the average of the modulo maxima among the detail coefficients from d1 to d3 As a reference value, each candidate step point is checked to see if it meets the step disturbance judgment threshold. Candidate step points with amplitudes greater than the judgment threshold are recorded as step disturbance occurrence points. The judgment process uses the following formula:

[0071] ;

[0072] In the formula, Threshold for identifying step disturbances;

[0073] The d1 component point that satisfies the step disturbance criterion is considered the moment when the step disturbance occurs, and the corresponding time is... At this point, the timing of the output step disturbance and the change in step disturbance power are... Its formula is:

[0074] ;

[0075] In the formula: This represents the change in power during a step disturbance. The active power value at the moment of disturbance. This represents the active power value at the moment before the disturbance occurred.

[0076] For slope disturbances, identification is performed on the data after the step disturbance identification phase using a sliding window linear fitting goodness-of-fit threshold method, including:

[0077] Set the wavelet approximation coefficients for traversing the power perturbation using a sliding window, perform linear fitting on the data within the window, and calculate the goodness of fit. Set the goodness-of-fit threshold for linear fit. When the linear fit goodness within the window is detected to be greater than or equal to the threshold, the center of the window is taken as the slope perturbation breakpoint. After traversing the perturbation approximation coefficients, the preliminary slope segmentation is obtained. The slope perturbation linear threshold requirement is expressed by the following formula:

[0078] ;

[0079] The initial segmentation results of the slope disturbance are processed by removing excessively short segments, calculating the average slope of each segment, merging adjacent segments with similar slopes, and outputting the start and end times of the slope disturbance and the average slope of the disturbance segment to obtain the slope disturbance identification and segmentation results.

[0080] In this embodiment, for noise-like perturbations, a dual stability detection method (mean stability and variance stability) is used, and perturbation identification is performed through wavelet entropy verification, including:

[0081] The remaining approximation coefficients are iterated using the sliding window method. The average magnitude and variance within the window are calculated. Thresholds for magnitude stability and variance stability are set. Perturbation segments that meet the dual stability threshold requirements are denoted as candidate segments for noise-like perturbations. The dual stability check is expressed by the following formula:

[0082] ;

[0083] ;

[0084] In the formula: This represents the maximum or minimum power change within the sliding window. The mean stability threshold, The power variance within the sliding window. This is the variance stability threshold;

[0085] The wavelet entropy of the candidate noise-like segments is calculated for further screening. If the wavelet entropy is greater than or equal to the threshold requirement, it is considered a noise-like disturbance. The start and end times of the noise-like disturbance are output and segmented to obtain the noise-like disturbance segmentation results. The wavelet entropy threshold requirement is expressed by the following formula:

[0086] ;

[0087] In the formula, The wavelet entropy value, The length of the sliding window. The threshold value is the wavelet entropy threshold.

[0088] like Figure 2 As shown, this embodiment also includes a power grid inertia assessment disturbance identification system based on adaptive wavelet transform, using the method described above. The system includes:

[0089] The adaptive wavelet transform unit is used to acquire active power data in the power system, preprocess the data, perform adaptive wavelet transform on the preprocessed data, and extract wavelet detail coefficients and approximation coefficients.

[0090] A step disturbance identification unit is used to identify step disturbances using a multi-scale numerical model maxima method with detail coefficients.

[0091] The slope perturbation identification unit is used to identify slope perturbations by applying a sliding window linear fitting goodness threshold to the data after the step perturbation identification part.

[0092] The noise disturbance identification unit is used to identify noise disturbances by employing dual stability detection of mean stability and variance stability and by wavelet entropy verification.

[0093] The output unit is used to output the type, time window, and key parameters of various disturbances.

[0094] Example 2:

[0095] like Figure 3 As shown, a method for accurate identification of power grid inertia assessment disturbances based on adaptive wavelet transform is studied, and its steps are as follows:

[0096] A. Collect active power data from the power system. Preprocess the collected data, including filtering, noise reduction, and per-unit normalization, to ensure data integrity and consistency. For the preprocessed data, a method for accurate identification of power grid inertia assessment disturbances based on adaptive wavelet transform is proposed. This method utilizes wavelet transform to process the preprocessed data. Wavelet detail coefficients and approximation coefficients are extracted for subsequent disturbance identification. For step disturbances, a multi-scale wavelet detail coefficient modulus maxima method is proposed for identification. For slope disturbance identification in the power system, a sliding window linear fitting goodness-of-fit threshold method is used for identification after extracting the step disturbance portion. Noise-like disturbance identification employs dual stability detection (mean stability and variance stability) and wavelet entropy verification for disturbance identification.

[0097] B. For the wavelet detail coefficients extracted in step A, first detect the modulus maxima of the detail coefficients in the highest frequency band d1 of the wavelet. This is denoted as a candidate step point, and then the average of the modulo maxima among the detail coefficients from d1 to d3 is taken. As a reference, each candidate step point is checked to see if it meets the step disturbance judgment threshold. Candidate step points with amplitudes greater than the judgment threshold are recorded as step disturbance occurrence points, and the occurrence time and power change of the step disturbance are output.

[0098] C. Based on the wavelet approximation coefficients extracted in step A, reconstruct the approximation coefficient a5 for accurate identification of slope disturbances. Set a sliding window to traverse the wavelet approximation coefficients of power disturbances, perform linear fitting on the data within the window, and calculate the goodness of fit. Set the goodness-of-fit threshold for linear fit. When the linear fit goodness within the window is detected to be greater than or equal to the threshold, the center of the window is taken as the slope disturbance breakpoint. After traversing the disturbance approximation coefficients, the initial slope segmentation is obtained. Then, excessively short segments are removed, the average slope of each segment is calculated, and adjacent segments with similar slopes are merged to obtain the slope disturbance identification segmentation result.

[0099] D. Based on the wavelet approximation coefficients extracted in step A, reconstruct the approximation coefficient a5, remove the slope disturbance part from step C, and then perform accurate slope disturbance identification. Use the sliding window method to iterate through the remaining approximation coefficients, calculate the average amplitude and variance within the window, set amplitude stability and variance stability thresholds, and record disturbance segments that meet the dual stability threshold requirements as noise-like disturbance candidate segments. Calculate the wavelet entropy of the noise-like candidate segments for further filtering to obtain the noise-like disturbance segmentation results. The disturbance identification results are as follows: Figure 4 As shown.

[0100] The specific data preprocessing steps outlined in step A are as follows:

[0101] A1. In data preprocessing, active power data is normalized to improve its applicability to power disturbance identification across multiple voltage levels. The active power normalization formula is as follows:

[0102] (1)

[0103] in: To standardize the power value, This is the measured value of the disturbance power. This is the reference power.

[0104] A2. A Butterworth low-pass filter is used to suppress high-frequency noise, reducing the actual noise in the power system while preserving noise-like disturbance characteristics as much as possible. Power disturbance data is acquired from the power system by the PMU. The data is recorded in time series format, with the time unit being seconds.

[0105] A3. Adaptive wavelet transform is performed on the preprocessed data. Different wavelet basis functions are used according to the type of disturbance to be identified. For high-frequency components, the db4 wavelet is used to extract detail coefficients, and for low-frequency components, the sym8 wavelet is used to extract approximate coefficients, so as to meet the identification accuracy of components in different frequency bands.

[0106] The specific process of researching the precise identification method for step disturbances described in step B is as follows:

[0107] B1. Extract the wavelet detail coefficients d1 of the highest frequency band and detect the modulus maxima of the d1 component. As a candidate step jump point The corresponding time is recorded as Then, the average of the modulo maxima among the detail coefficients from d1 to d3 is taken. As a reference value, each candidate step point is checked to see if it meets the step disturbance judgment threshold. Candidate step points with amplitudes greater than the judgment threshold are recorded as step disturbance occurrence points. The judgment process can be expressed by the following formula:

[0108] (2)

[0109] in, Thresholds are used to identify step disturbances.

[0110] B2. The d1 component point that satisfies the step disturbance criterion is considered the moment when the step disturbance occurs, and the corresponding time is... At this point, the timing of the output step disturbance and the change in step disturbance power are... Its formula is:

[0111] (3)

[0112] in: This represents the change in power during a step disturbance. The active power value at the moment of disturbance. This represents the active power value at the moment before the disturbance occurred.

[0113] The specific process for slope disturbance identification proposed in step C is as follows:

[0114] C1. Reconstruct the wavelet approximation coefficients a5 to accurately identify slope disturbances. Set a sliding window to traverse the wavelet approximation coefficients of the power disturbance, perform linear fitting on the data within the window, and calculate the goodness of fit. Set the goodness-of-fit threshold for linear fit. When the linear fit goodness within the window is detected to be greater than or equal to the threshold, the center of the window is taken as the slope perturbation breakpoint. After traversing the perturbation approximation coefficients, the preliminary slope segmentation is obtained. The slope perturbation linear threshold requirement can be expressed by the following formula:

[0115] (4)

[0116] C2. Remove excessively short segments from the preliminary segmentation results of the slope disturbance, calculate the average slope of each segment, merge adjacent segments with similar slopes, and output the start and end times of the slope disturbance and the average slope of the disturbance segment to obtain the slope disturbance identification and segmentation results.

[0117] The specific process for noise perturbation identification proposed in step D is as follows:

[0118] D1. After reconstructing the approximation coefficients a5 and removing the slope disturbance portion from step C, perform accurate slope disturbance identification. Use the sliding window method to iterate through the remaining approximation coefficients, calculate the average amplitude and variance within the window, and set amplitude stability and variance stability thresholds. Disturbance segments that meet the dual stability threshold requirements are recorded as candidate noise-like disturbance segments. The dual stability check can be expressed by the following formula:

[0119] (5)

[0120] (6)

[0121] In the formula: This represents the maximum or minimum power change within the sliding window. The mean stability threshold, The power variance within the sliding window. This is the variance stability threshold.

[0122] D2. Further filter the candidate noise-like segments by calculating their wavelet entropy. Segments with wavelet entropy greater than or equal to a threshold are considered noise-like disturbances. The start and end times of these disturbances are then segmented to obtain the noise-like disturbance segmentation results. The wavelet entropy threshold requirement can be expressed by the following formula:

[0123] (7)

[0124] In the formula, The wavelet entropy value, The length of the sliding window. The threshold value is the wavelet entropy threshold.

[0125] Compared with the prior art, this embodiment has the following beneficial effects:

[0126] (1) The wavelet transform method is used to analyze the power disturbance characteristics of the power grid. Through multi-scale and multi-frequency band decomposition, the definite power disturbance type and its start and end time are obtained, which helps to improve the accuracy of inertia assessment.

[0127] (2) Compared with other methods, wavelet transform can quickly decompose measurement data into different frequency bands, obtain more accurate disturbance power, provide accurate and reliable disturbance data for system inertia assessment, and help maintain system frequency stability.

[0128] Please see Figure 5 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0129] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0130] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0131] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0132] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0133] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0134] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0136] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0138] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform, characterized in that, Includes the following steps: The active power data of the power system is acquired and preprocessed. Adaptive wavelet transform is then performed on the preprocessed data to extract wavelet detail coefficients and approximation coefficients. For step disturbances, the multi-scale numerical modulus maxima method is used to identify them using the aforementioned detail coefficients. For slope disturbances, the method of using a sliding window linear fitting goodness threshold to identify the data after the step disturbance identification part based on the approximation coefficient is used. For noise-like disturbances, a dual stability detection method using mean stability and variance stability is employed, and disturbance identification is performed through wavelet entropy verification. Output the type, time window, and key parameters of various disturbances.

2. The method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform according to claim 1, characterized in that, The data preprocessing includes: Normalize the active power data: ； In the formula: To standardize the power value, This is the measured value of the disturbance power. Reference power; High-frequency noise is suppressed using a Butterworth low-pass filter.

3. The method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform according to claim 1, characterized in that, The step of performing adaptive wavelet transform on the preprocessed data to extract wavelet detail coefficients and approximation coefficients includes: Different wavelet basis functions are used depending on the type of disturbance to be identified. For high-frequency components, the db4 wavelet is used to extract detail coefficients, and for low-frequency components, the sym8 wavelet is used to extract approximate coefficients.

4. The method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform according to claim 1, characterized in that, The method for identifying step disturbances using the detailed coefficients via a multi-scale numerical model maxima method includes: Extract the wavelet detail coefficients d1 of the highest frequency band and detect the modulus maxima of the d1 component. As a candidate step jump point The corresponding time is recorded as ; Take the average of the modulo maxima among the detail coefficients from d1 to d3 As a reference value, each candidate step point is checked to see if it meets the step disturbance judgment threshold. Candidate step points with amplitudes greater than the judgment threshold are recorded as step disturbance occurrence points. The judgment process uses the following formula: ； In the formula, Threshold for identifying step disturbances; The d1 component point that satisfies the step disturbance criterion is considered the moment when the step disturbance occurs, and the corresponding time is... At this point, the timing of the step disturbance and the change in step disturbance power are as follows: Its formula is: ； In the formula: This represents the change in power during a step disturbance. The active power value at the moment of disturbance. This represents the active power value at the moment before the disturbance occurred.

5. The method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform according to claim 1, characterized in that, The method for identifying slope disturbances, based on the approximation coefficients and applying a sliding window linear fitting goodness-of-fit threshold to the data after the step disturbance identification portion, includes: Set the wavelet approximation coefficients for traversing the power perturbation using a sliding window, perform linear fitting on the data within the window, and calculate the goodness of fit. Set the goodness-of-fit threshold for linear fit. When the linear fit goodness within the window is detected to be greater than or equal to the threshold, the center of the window is taken as the slope perturbation breakpoint. After traversing the perturbation approximation coefficients, the preliminary slope segmentation is obtained. The slope perturbation linear threshold requirement is expressed by the following formula: ； The initial segmentation results of the slope disturbance are processed by removing excessively short segments, calculating the average slope of each segment, merging adjacent segments with similar slopes, and outputting the start and end times of the slope disturbance and the average slope of the disturbance segment to obtain the slope disturbance identification and segmentation results.

6. The method for power grid inertia assessment and disturbance identification based on adaptive wavelet transform according to claim 1, characterized in that, For noise-like disturbances, the method employs dual stability detection (mean stability and variance stability) and disturbance identification via wavelet entropy verification, including: The remaining approximation coefficients are iterated using the sliding window method. The average magnitude and variance within the window are calculated. Thresholds for magnitude stability and variance stability are set. Perturbation segments that meet the dual stability threshold requirements are denoted as candidate segments for noise-like perturbations. The dual stability check is expressed by the following formula: ； ； In the formula: This represents the maximum or minimum power change within the sliding window. The mean stability threshold, The power variance within the sliding window. This is the variance stability threshold; The wavelet entropy of the candidate noise-like segments is calculated for further screening. If the wavelet entropy is greater than or equal to the threshold requirement, it is considered a noise-like disturbance. The start and end times of the noise-like disturbance are output and segmented to obtain the noise-like disturbance segmentation results. The wavelet entropy threshold requirement is expressed by the following formula: ； In the formula, The wavelet entropy value, The length of the sliding window. The threshold value is the wavelet entropy threshold.

7. A power grid inertia assessment and disturbance identification system based on adaptive wavelet transform, characterized in that, Using the method of any one of claims 1 to 6, the system comprises: The adaptive wavelet transform unit is used to acquire active power data in the power system, preprocess the data, perform adaptive wavelet transform on the preprocessed data, and extract wavelet detail coefficients and approximation coefficients. A step disturbance identification unit is used to identify step disturbances using the detailed coefficients in a multi-scale digital modulus maxima method. The slope disturbance identification unit is used to identify slope disturbances by applying a sliding window linear fitting goodness threshold to the data after the step disturbance identification part based on the approximation coefficients. The noise disturbance identification unit is used to identify noise disturbances by employing dual stability detection of mean stability and variance stability and by wavelet entropy verification. The output unit is used to output the type, time window, and key parameters of various disturbances.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

Images