Power grid evaluation method and system based on power load decomposition

Abstract

The invention relates to the technical field of power grid evaluation, in particular to a power grid evaluation method and system based on power load decomposition, and the method comprises the steps: collecting high-frequency harmonic waves at all branches of a power grid through a synchronous phasor sensor, converting the high-frequency harmonic waves into digital signals, and carrying out the preprocessing of the collected digital signals; extracting the fluctuation frequency and the amplitude change speed of the high-frequency waveform, constructing a feature template database as a comparison reference, comparing the extracted parameters with a template, quantifying the matching degree, and distinguishing the power consumption features of the equipment according to the matching degree; injecting a micro-disturbance test signal into the power grid, extracting a phase offset, and finally arranging and storing according to nodes; quantizing the correlation degree of the power consumption characteristics of the equipment and the phase deviation, judging the correlation of the two according to a preset threshold value, and judging the nodes with the correlation degree reaching the preset threshold value to be harmonic sensitive. A synchronous phasor measurement sensor is adopted for data acquisition and data preprocessing, so that the problem of waveform distortion caused by insufficient sampling frequency in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power grid assessment technology, and specifically to a power grid assessment method and system based on power load decomposition. Background Technology

[0002] High-frequency harmonics in the power grid are generated by loads such as frequency converters and electric arc furnaces. Their persistent presence can easily lead to transformer overheating, malfunction of protection devices, and deterioration of power quality, posing a significant threat to the safe and stable operation of the power grid. Existing high-frequency harmonic monitoring and vulnerable node location technologies have the following shortcomings: At the data acquisition level, it is difficult to completely capture the oscillation waveform of high-frequency harmonics, and the preprocessing process does not specifically separate the power frequency component from the high-frequency signal, which is prone to high-frequency characteristic distortion due to power frequency interference, failing to meet the needs of accurate analysis. At the load power consumption characteristic extraction level, existing methods do not combine the operating characteristics of typical equipment for differentiated extraction, but only obtain the overall energy distribution through spectrum analysis, making it difficult to distinguish frequency converters. The power consumption characteristics of equipment and electric arc furnaces result in low accuracy in load type identification, failing to provide effective evidence for harmonic source tracing. At the dynamic response detection level, there is a lack of means to actively stimulate the dynamic characteristics of the power grid, relying solely on passively monitored voltage and current data, making it difficult to quantify the sensitivity of nodes to high-frequency harmonics. Furthermore, the lack of a correlation between load fingerprints and node impedance characteristics makes it impossible to accurately identify key nodes affected by harmonics. At the location and visualization level, the location of vulnerable nodes often relies on a single parameter without combining it with power grid topology information for spatial correlation, resulting in significant location errors. Additionally, the lack of intuitive risk level display methods makes it difficult for maintenance personnel to quickly identify high-risk areas, affecting the efficiency of harmonic mitigation. Summary of the Invention

[0003] This invention addresses the technical problems existing in the prior art by providing a power grid assessment method and system based on power load decomposition.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A power grid assessment method based on power load decomposition, comprising the following steps:

[0005] S101. High-frequency harmonics are collected in each branch of the power grid by synchronous phasor sensors, and waveforms of each frequency band are continuously collected and captured and converted into digital signals. The collected digital signals are preprocessed.

[0006] S102. Extract the fluctuation frequency and amplitude change rate of the high-frequency waveform, construct a feature template database containing frequency conversion equipment and electric arc equipment as a comparison benchmark, compare the extracted parameters with the templates, quantify the matching degree, and distinguish the power consumption characteristics of the equipment based on the matching degree.

[0007] S103. Inject micro-disturbance test signals into the power grid, simultaneously use synchronous phasor measurement sensors to collect response signals from each branch, extract phase offset, and finally organize and store them by node.

[0008] S104. Quantify the correlation between the power consumption characteristics of the equipment and the phase shift, and then determine the correlation between the two based on the preset threshold. Nodes with a correlation reaching the preset threshold are judged as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model according to the coordinates and marked with the color corresponding to the risk level.

[0009] In a preferred embodiment, in step S101, synchronous phasor measurement sensors are deployed at collection points on each branch of the target power grid. Through these collection points, voltage and current signals of each branch of the target power grid are collected to capture high-frequency harmonic oscillation waveforms. The analog voltage and current signals are converted into digital signals by the built-in AD conversion module in the synchronous phasor measurement sensor. The collected digital signals are then denoised using digital filtering technology to suppress interference signals introduced during the collection process. The denoised data is then separated into power frequency components. Utilizing the difference between the power frequency components and high-frequency harmonics in the frequency domain, a high-pass filter is used to block low-order harmonics, retaining only the high-frequency signals.

[0010] In a preferred embodiment, in step S102, waveform feature parameters are extracted from the high-frequency signal after acquisition and denoising. Quantifiable fluctuation patterns, including fluctuation frequency and amplitude change rate, are analyzed from the waveform of the high-frequency signal. Based on historical data and equipment characteristics, a power consumption feature database for typical equipment is pre-constructed. This database includes feature templates for at least two types of typical equipment: frequency converters and arc-type equipment. High-frequency waveform fluctuation pattern benchmarks corresponding to each type of equipment are stored. The extracted waveform feature parameters are compared with the power consumption feature database to calculate the matching degree. The matching degree calculation mainly revolves around the two core feature parameters: fluctuation frequency and amplitude change rate. Let the set of fluctuation frequencies of the waveform to be analyzed be { The frequency range of the device feature template is [ ]. The number of frequencies falling within this interval is The specific formula for calculating the frequency matching degree is as follows: ;

[0011] in, Indicates frequency matching degree, { } represents the set of fluctuation frequency data extracted from the waveform to be analyzed. This represents the i-th fluctuation frequency data point, and n represents the total number of fluctuation frequency data points in the fluctuation frequency set. This indicates the frequency of successful matches. This indicates the lower expected limit of the fluctuation frequency of the voltage waveform during equipment operation. This represents the expected upper limit of the fluctuation frequency of the voltage waveform generated by the equipment during operation. If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the medium-to-high frequency characteristic range of the variable frequency equipment characteristic template, then it is determined to be the corresponding power consumption characteristic of the variable frequency equipment. Let the set of amplitude change rates of the waveform to be analyzed be { The speed threshold of the device feature template is} The number of speeds that meet the threshold condition is The specific formula for calculating the matching degree is as follows: ;

[0012] in, Indicates the matching degree of the rate of change of amplitude. This represents the m-th amplitude change rate data, where m represents the total number of amplitude change rate data in the set of amplitude change rate data. If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the mid-to-high frequency range of the characteristic template of arc-type equipment, then its corresponding arc-type equipment power consumption characteristic is determined.

[0013] In a preferred embodiment, in S103, a set of micro-disturbance test signals are sent to the power grid. The amplitude is a small proportion of the rated voltage of the power grid, which is equal to the expected lower limit of the voltage fluctuation range allowed for normal operation of the power grid. The frequency covers a wide frequency range from low frequency to high frequency. The signal form is a frequency sweep signal, with the frequency linearly increasing from low frequency to high frequency. Energy is injected uniformly throughout the entire frequency band. The test signal is injected into the target power grid through a dynamic reactive power compensation device in the power grid. The signal injection process uses phase-locked loop technology to keep the injected signal in a fixed phase relationship with the fundamental voltage of the power grid.

[0014] Simultaneously with the injection of the test signal, the response signal is acquired through synchronous phasor measurement sensors deployed in each branch. The acquired signals include voltage and current signals of the branch. The phase change of the acquired response signal is extracted, and the offset of the phase difference between voltage and current relative to the fundamental frequency is calculated at different frequencies. At the fundamental frequency, the phase difference between voltage and current in each branch is pre-measured and recorded. The specific calculation formula is as follows: ;

[0015] in, This represents the phase difference between voltage and current. Indicates the measured voltage of the branch. Indicates the measured current of the branch. Indicates voltage signal phase angle, Represents current signal The phase angle is calculated, and the phase difference between voltage and current is recalculated at each frequency point of the test signal. The specific calculation formula is as follows: ;

[0016] in, This represents the phase difference between voltage and current at the test frequency f, reflecting the impedance characteristics of the branch at that high frequency. This represents the voltage response signal at the test frequency f. phase angle, This indicates the current response signal at the test frequency f. The phase angle, the difference between voltage and current, is the phase shift at that frequency. The specific calculation formula is as follows: ;

[0017] in, It represents the phase shift at the test frequency f, that is, the difference between the phase difference at that frequency and the fundamental impedance angle, reflecting the sensitivity of the branch to high-frequency signals.

[0018] The extracted phase offsets of each branch are organized according to node affiliation to form structured data containing node identifier, test signal frequency, and phase offset. For each frequency point in the entire frequency range, there is a corresponding phase offset record. For frequency sweep signals with continuously changing frequencies, the data is discretized and recorded at preset frequency intervals.

[0019] In a preferred embodiment, in S104, the correlation between the power consumption characteristics of the equipment and the phase shift is compared. When the correlation between the power consumption characteristics of a node and the phase shift reaches a preset threshold, it is determined to be highly correlated and the node is sensitive to harmonics. The power grid topology information is called, which includes the physical connection relationship of each node and line and the node coordinate information. For the nodes determined to be sensitive to harmonics, their physical connection paths are traced according to the topology information. If the correlation of the sensitive node is mainly caused by the power consumption characteristics of the equipment on a certain line, then the key connection point of the line is the vulnerable node. Combined with the coordinate information in the topology, its physical location is determined.

[0020] A three-dimensional map is constructed based on power grid topology information. Nodes are represented by three-dimensional icons. The risk level of vulnerable nodes is pre-defined into three levels, corresponding to different risk degrees: high risk, medium risk, and low risk. High-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is highly correlated and the phase shift is significant. Medium-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is at a moderate level and the phase shift is relatively obvious. Low-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is low and the phase shift is slight.

[0021] A mapping relationship between risk level and color is established, with high-risk nodes marked in red, medium-risk nodes marked in yellow, and low-risk nodes marked in green. The identified vulnerable nodes are located in the three-dimensional power grid topology model according to their physical coordinates, and the corresponding color is used to mark them according to their risk level, generating a three-dimensional visualization map containing the complete topology structure and vulnerable node markings.

[0022] The present invention also provides a power grid assessment system based on power load decomposition, comprising:

[0023] Data acquisition module: Collects high-frequency harmonics in each branch of the power grid through synchronous phasor sensors, continuously collects and captures waveforms in each frequency band, converts them into digital signals, and preprocesses the collected digital signals;

[0024] Electricity consumption feature extraction module: Extracts the fluctuation frequency and amplitude change rate of high-frequency waveforms, constructs a feature template database containing frequency converters and electric arc devices as a comparison benchmark, compares the extracted parameters with the templates, quantifies the matching degree, and distinguishes the electricity consumption features of devices based on the matching degree;

[0025] Phase offset calculation module: Injects micro-disturbance test signals into the power grid, simultaneously uses synchronous phasor measurement sensors to collect response signals from each branch, extracts the phase offset, and finally organizes and stores the data by node;

[0026] Vulnerable node location module: Quantifies the correlation between the power consumption characteristics of equipment and phase shift, and then determines the correlation between the two based on a preset threshold. Nodes with a correlation reaching the preset threshold are identified as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model by coordinates and marked with the color corresponding to the risk level.

[0027] The beneficial effects of this invention are as follows: This invention uses a synchronous phasor measurement sensor for data acquisition, covering the main frequency bands of high-frequency harmonics, and preprocesses the data to completely preserve the effective high-frequency signals, solving the waveform distortion problem caused by insufficient sampling frequency in traditional methods. By accurately decomposing the power consumption characteristics of frequency converters and arc-type equipment, it achieves differentiated identification of load types, solving the defect of traditional methods that cannot distinguish equipment characteristics. Based on the micro-disturbance signal injected into the power grid, it synchronously calculates the dynamic impedance phase offset, quantifies the response characteristics of nodes to high-frequency harmonics, and can accurately capture the sensitivity of nodes. Combined with preset thresholds and power grid topology information, it controls the positioning error within a reasonable range. By classifying risk levels through the dual parameters of correlation and phase offset, it avoids misjudgment based on a single parameter, improves the reliability of positioning, and can intuitively display the physical location of vulnerable nodes. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention;

[0029] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0032] like Figure 1 This embodiment provides a power grid assessment method based on power load decomposition, comprising the following steps:

[0033] S101. High-frequency harmonics are collected in each branch of the power grid by synchronous phasor sensors, and waveforms of each frequency band are continuously collected and captured and converted into digital signals. The collected digital signals are preprocessed.

[0034] Furthermore, synchronous phasor measurement sensors are deployed at acquisition points on each branch of the target power grid. These sensors acquire voltage and current signals from each branch to capture high-frequency harmonic oscillation waveforms. The analog voltage and current signals are converted into digital signals using the built-in AD conversion module of the synchronous phasor measurement sensor. The acquired digital signals are then denoised using digital filtering technology to suppress interference signals introduced during acquisition. The noise that may be present in the original digital signal mainly includes: thermal noise from the sensor itself, electromagnetic interference, and random disturbances during data transmission. This noise can mask the true characteristics of high-frequency harmonics. Noise removal preserves the assignment and phase information of the high-frequency harmonic signals while filtering out useless interference components, ensuring that the signal-to-noise ratio of the subsequent analysis data meets the requirements. The denoised data is then subjected to power frequency component separation. Utilizing the difference between the power frequency component and the high-frequency harmonics in the frequency domain, a high-pass filter blocks low-order harmonics, retaining only the high-frequency signal.

[0035] It should be noted that the purpose of separating the power frequency component is to reduce interference with high-frequency harmonic analysis. Power frequency components typically have large amplitudes, and if not separated, they will mask subtle changes in high-frequency harmonics. Furthermore, the frequency stability of power frequency components differs significantly from the dynamic oscillation characteristics of high-frequency harmonics. Separation allows subsequent analysis to focus on the target high-frequency band, improving the targeting and accuracy of harmonic feature extraction. The retained high-frequency signals must meet the following conditions: Data integrity: key features of the original high-frequency waveforms must not be lost; Data consistency: high-frequency signals from different branches and time periods must be comparable through standardization processing.

[0036] S102. Extract the fluctuation frequency and amplitude change rate of the high-frequency waveform, construct a feature template database containing frequency conversion equipment and electric arc equipment as a comparison benchmark, compare the extracted parameters with the templates, quantify the matching degree, and distinguish the power consumption characteristics of the equipment based on the matching degree.

[0037] Waveform feature parameters are extracted from the acquired and denoised high-frequency signals. Quantifiable fluctuation patterns, including fluctuation frequency and amplitude change rate, are analyzed from the waveforms. Based on historical data and equipment characteristics, a power consumption characteristic database for typical equipment is pre-constructed. This database includes feature templates for at least two types of typical equipment: frequency converters and arc-type equipment. High-frequency waveform fluctuation pattern benchmarks for each type of equipment are stored. The extracted waveform feature parameters are compared with the power consumption characteristic database to calculate the matching degree. The matching degree calculation mainly revolves around the two core feature parameters: fluctuation frequency and amplitude change rate. Let the set of fluctuation frequencies of the waveform to be analyzed be { The frequency range of the device feature template is [ ]. The number of frequencies falling within this interval is The specific formula for calculating the frequency matching degree is as follows: ;

[0038] in, Indicates frequency matching degree, { } represents the set of fluctuation frequency data extracted from the waveform to be analyzed. This represents the i-th fluctuation frequency data point, and n represents the total number of fluctuation frequency data points in the fluctuation frequency set. This indicates the frequency of successful matches. This indicates the lower expected limit of the fluctuation frequency of the voltage waveform during equipment operation. This represents the expected upper limit of the fluctuation frequency of the voltage waveform generated by the equipment during operation. If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the medium-to-high frequency characteristic range of the variable frequency equipment characteristic template, then it is determined to be the corresponding power consumption characteristic of the variable frequency equipment. Let the set of amplitude change rates of the waveform to be analyzed be { The speed threshold of the device feature template is} The number of speeds that meet the threshold condition is The specific formula for calculating the matching degree is as follows: ;

[0039] in, Indicates the matching degree of the rate of change of amplitude. This represents the m-th amplitude change rate data, where m represents the total number of amplitude change rate data in the set of amplitude change rate data. If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the mid-to-high frequency range of the characteristic template of arc-type equipment, then its corresponding arc-type equipment power consumption characteristic is determined.

[0040] It should be noted that the fluctuation frequency is determined by the oscillation period of the waveform within a unit of time, and the amplitude change rate is determined by calculating the ratio of the amplitude difference between adjacent peaks to the time interval. One type is frequency converter equipment, whose characteristic template is defined as having a fluctuation frequency concentrated in the mid-to-high frequency characteristic range, a slow amplitude change rate, and obvious waveform periodicity. The other type is arc equipment, whose characteristic template is defined as having a fluctuation frequency dispersed in the mid-to-high frequency range, a fast amplitude change rate, and a waveform without a fixed period. By establishing a benchmark template, a comparison basis is provided for the waveform to be analyzed. The power consumption characteristics of the equipment are determined by its working mechanism and have stability and repeatability. For example, frequency converter equipment achieves speed regulation through high-frequency switching of switching transistors. Its harmonic frequency is related to the switching frequency, and the fluctuation pattern is stable. These characteristics are like the fingerprint of the equipment, which can uniquely identify its motion state.

[0041] S103. Inject micro-disturbance test signals into the power grid, simultaneously use synchronous phasor measurement sensors to collect response signals from each branch, extract phase offset, and finally organize and store them by node.

[0042] Furthermore, a set of micro-disturbance test signals is sent to the power grid, with an amplitude that is a small proportion of the grid's rated voltage. This proportion is equal to the expected lower limit of the voltage fluctuation range allowed for normal grid operation. The frequency covers a wide frequency range from low to high frequency, and the signal form is a frequency sweep signal with the frequency linearly increasing from low frequency to high frequency. Energy is injected uniformly throughout the entire frequency band. The test signal is injected into the target grid through a dynamic reactive power compensation device in the grid. During the signal injection process, phase-locked loop technology is used to ensure that the injected signal maintains a fixed phase relationship with the grid's fundamental voltage.

[0043] Simultaneously with the injection of the test signal, the response signal is acquired through synchronous phasor measurement sensors deployed in each branch. The acquired signals include voltage and current signals of the branch. The phase change of the acquired response signal is extracted, and the offset of the phase difference between voltage and current relative to the fundamental frequency is calculated at different frequencies. At the fundamental frequency, the phase difference between voltage and current in each branch is pre-measured and recorded. The specific calculation formula is as follows: ;

[0044] in, This represents the phase difference between voltage and current. Indicates the measured voltage of the branch. Indicates the measured current of the branch. Indicates voltage signal phase angle, Represents current signal The phase angle is calculated, and the phase difference between voltage and current is recalculated at each frequency point of the test signal. The specific calculation formula is as follows: ;

[0045] in, This represents the phase difference between voltage and current at the test frequency f, reflecting the impedance characteristics of the branch at that high frequency. This represents the voltage response signal at the test frequency f. phase angle, This indicates the current response signal at the test frequency f. The phase angle, the difference between voltage and current, is the phase shift at that frequency. The specific calculation formula is as follows: ;

[0046] in, It represents the phase shift at the test frequency f, that is, the difference between the phase difference at that frequency and the fundamental impedance angle, reflecting the sensitivity of the branch to high-frequency signals.

[0047] The extracted phase offsets of each branch are organized according to node affiliation to form structured data containing node identifier, test signal frequency, and phase offset. For each frequency point in the entire frequency range, there is a corresponding phase offset record. For frequency sweep signals with continuously changing frequencies, the data is discretized and recorded at preset frequency intervals.

[0048] It should be noted that the injection point of the test signal must meet the following conditions: First, it must be located on the main line or key node of the power grid to ensure that the signal can be effectively propagated to each branch. Second, it must be a direct access point of a large impact load to avoid the signal being submerged by harmonics generated by the load itself. The injection device must have high-precision signal generation capability, and the amplitude error of its output signal must be controlled within a very small range, and the frequency accuracy must be maintained at a very high level to ensure the parameter stability of the injected signal.

[0049] S104. Quantify the correlation between the power consumption characteristics of the equipment and the phase shift, and then determine the correlation between the two based on the preset threshold. Nodes with a correlation reaching the preset threshold are judged as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model according to the coordinates and marked with the color corresponding to the risk level.

[0050] Furthermore, by comparing the correlation between the power consumption characteristics of the equipment and the phase shift, when the correlation between the power consumption characteristics of the equipment and the phase shift of a node reaches a preset threshold, it is determined to be highly correlated and the node is sensitive to harmonics. The power grid topology information is called, including the physical connection relationship of each node and line and the node coordinate information. For the nodes determined to be sensitive to harmonics, their physical connection paths are traced according to the topology information. If the correlation of the sensitive node is mainly caused by the power consumption characteristics of the equipment on a certain line, then the key connection point of the line is the vulnerable node. Combined with the coordinate information in the topology, its physical location is determined.

[0051] A three-dimensional map is constructed based on power grid topology information. Nodes are represented by three-dimensional icons. The risk level of vulnerable nodes is pre-defined into three levels, corresponding to different risk degrees: high risk, medium risk, and low risk. High-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is highly correlated and the phase shift is significant. Medium-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is at a moderate level and the phase shift is relatively obvious. Low-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is low and the phase shift is slight.

[0052] A mapping relationship between risk level and color is established, with high-risk nodes marked in red, medium-risk nodes marked in yellow, and low-risk nodes marked in green. The identified vulnerable nodes are located in the three-dimensional power grid topology model according to their physical coordinates, and the corresponding color is used to mark them according to their risk level, generating a three-dimensional visualization map containing the complete topology structure and vulnerable node markings.

[0053] It should be noted that the principle of determining the harmonic sensitivity of a node is that the electrical characteristics of the equipment reflect its harmonic characteristics, and the phase shift reflects the node's response characteristics to harmonics. The high correlation between the two means that the phase shift of a node is mainly caused by the harmonics of the equipment at that node.

[0054] This embodiment also provides a power grid assessment system based on power load decomposition, including:

[0055] Data acquisition module: Collects high-frequency harmonics in each branch of the power grid through synchronous phasor sensors, continuously collects and captures waveforms in each frequency band, converts them into digital signals, and preprocesses the collected digital signals;

[0056] Electricity consumption feature extraction module: Extracts the fluctuation frequency and amplitude change rate of high-frequency waveforms, constructs a feature template database containing frequency converters and electric arc devices as a comparison benchmark, compares the extracted parameters with the templates, quantifies the matching degree, and distinguishes the electricity consumption features of devices based on the matching degree;

[0057] Phase offset calculation module: Injects micro-disturbance test signals into the power grid, simultaneously uses synchronous phasor measurement sensors to collect response signals from each branch, extracts the phase offset, and finally organizes and stores the data by node;

[0058] Vulnerable node location module: Quantifies the correlation between the power consumption characteristics of equipment and phase shift, and then determines the correlation between the two based on a preset threshold. Nodes with a correlation reaching the preset threshold are identified as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model by coordinates and marked with the color corresponding to the risk level.

[0059] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0065] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A power grid assessment method based on power load decomposition, characterized in that, Includes the following steps: S101. High-frequency harmonics are collected in each branch of the power grid by synchronous phasor sensors, and waveforms of each frequency band are continuously collected and captured and converted into digital signals. The collected digital signals are preprocessed. S102. Extract the fluctuation frequency and amplitude change rate of the high-frequency waveform, construct a feature template database containing frequency conversion equipment and electric arc equipment as a comparison benchmark, compare the extracted parameters with the templates, quantify the matching degree, and distinguish the power consumption characteristics of the equipment based on the matching degree. S103. Inject micro-disturbance test signals into the power grid, simultaneously use synchronous phasor measurement sensors to collect response signals from each branch, extract phase offset, and finally organize and store them by node. S104. Quantify the correlation between the power consumption characteristics of the equipment and the phase shift, and then determine the correlation between the two based on the preset threshold. Nodes with a correlation reaching the preset threshold are judged as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model according to the coordinates and marked with the color corresponding to the risk level.

2. The power grid assessment method based on power load decomposition according to claim 1, characterized in that, In S101, synchronous phasor measurement sensors are deployed at acquisition points on each branch of the target power grid. Through these acquisition points, voltage and current signals of each branch of the target power grid are acquired to capture high-frequency harmonic oscillation waveforms. The analog voltage and current signals are converted into digital signals by the built-in AD conversion module in the synchronous phasor measurement sensor. The acquired digital signals are then denoised using digital filtering technology to suppress interference signals introduced during the acquisition process. The denoised data is then separated into power frequency components. Utilizing the difference between the power frequency components and high-frequency harmonics in the frequency domain, a high-pass filter is used to block low-order harmonics, retaining only the high-frequency signals.

3. The power grid assessment method based on power load decomposition according to claim 1, characterized in that, In step S102, waveform feature parameters are extracted from the acquired and denoised high-frequency signal. Quantifiable fluctuation patterns, including fluctuation frequency and amplitude change rate, are analyzed from the waveform. Based on historical data and equipment characteristics, a power consumption characteristic database for typical equipment is pre-constructed. This database includes feature templates for at least two types of typical equipment: frequency converters and arc-type equipment. It stores the high-frequency waveform fluctuation pattern benchmarks for each type of equipment. The extracted waveform feature parameters are compared with the power consumption characteristic database to calculate the matching degree. The matching degree calculation revolves around the two core feature parameters: fluctuation frequency and amplitude change rate. Let the set of fluctuation frequencies of the waveform to be analyzed be { The frequency range of the device feature template is [ ]. The number of frequencies falling within the interval is The specific formula for calculating the frequency matching degree is as follows: ； in, Indicates frequency matching degree, { } represents the set of fluctuation frequency data extracted from the waveform to be analyzed. This represents the i-th fluctuation frequency data point, and n represents the total number of fluctuation frequency data points in the fluctuation frequency set. This indicates the frequency of successful matches. This indicates the lower expected limit of the fluctuation frequency of the voltage waveform during equipment operation. This indicates the expected upper limit of the fluctuation frequency of the voltage waveform generated by the device during operation.

4. The power grid assessment method based on power load decomposition according to claim 3, characterized in that, If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the mid-to-high frequency characteristic range of the characteristic template for frequency converter equipment, then its corresponding power consumption characteristics for frequency converter equipment are determined. Let the set of amplitude change rates of the waveform to be analyzed be { The speed threshold of the device feature template is} The number of speeds that meet the threshold condition is The specific formula for calculating the matching degree is as follows: ； in, Indicates the matching degree of the rate of change of amplitude. This represents the m-th amplitude change rate data, where m represents the total number of amplitude change rate data in the set of amplitude change rate data. If the fluctuation frequency of the waveform characteristic parameter to be analyzed falls within the mid-to-high frequency range of the characteristic template of arc-type equipment, then its corresponding arc-type equipment power consumption characteristic is determined.

5. The power grid assessment method based on power load decomposition according to claim 1, characterized in that, In S103, a set of micro-disturbance test signals are sent to the power grid. The amplitude is a small proportion of the rated voltage of the power grid. This proportion is equal to the expected lower limit of the voltage fluctuation range allowed for normal operation of the power grid. The frequency covers a wide frequency range from low frequency to high frequency. The signal form is a frequency sweep signal. The frequency increases linearly from low frequency to high frequency. Energy is injected uniformly throughout the entire frequency band. The test signal is injected into the target power grid through a dynamic reactive power compensation device in the power grid. The signal injection process uses phase-locked loop technology to keep the injected signal in a fixed phase relationship with the fundamental voltage of the power grid.

6. The power grid assessment method based on power load decomposition according to claim 5, characterized in that, Simultaneously with the injection of the test signal, the response signal is acquired through synchronous phasor measurement sensors deployed in each branch. The acquired signals include voltage and current signals of the branch. The phase change of the acquired response signal is extracted, and the offset of the phase difference between voltage and current relative to the fundamental frequency is calculated at different frequencies. At the fundamental frequency, the phase difference between voltage and current in each branch is pre-measured and recorded. The specific calculation formula is as follows: ； in, This represents the phase difference between voltage and current. Indicates the measured voltage of the branch. Indicates the measured current of the branch. Indicates voltage signal phase angle, Represents current signal The phase angle is calculated, and the phase difference between voltage and current is recalculated at each frequency point of the test signal. The specific calculation formula is as follows: ； in, This represents the phase difference between voltage and current at the test frequency f, reflecting the impedance characteristics of the branch at high frequencies. This represents the voltage response signal at the test frequency f. phase angle, This indicates the current response signal at the test frequency f. The phase angle, the difference between voltage and current, is the phase shift at frequency. The specific calculation formula is as follows: ； in, It represents the phase offset at the test frequency f, that is, the difference between the phase difference at the frequency and the fundamental impedance angle, reflecting the sensitivity of the branch to high-frequency signals.

7. The power grid assessment method based on power load decomposition according to claim 6, characterized in that, The extracted phase offsets of each branch are organized according to node affiliation to form structured data containing node identifier, test signal frequency, and phase offset. For each frequency point in the entire frequency range, there is a corresponding phase offset record. For frequency sweep signals with continuously changing frequencies, the data is discretized and recorded at preset frequency intervals.

8. The power grid assessment method based on power load decomposition according to claim 1, characterized in that, In S104, the correlation between the power consumption characteristics of the equipment and the phase shift is compared. When the correlation between the power consumption characteristics of the equipment and the phase shift of a node reaches a preset threshold, it is determined to be highly correlated and the node is sensitive to harmonics. The power grid topology information is called, which includes the physical connection relationship of each node and line and the node coordinate information. For the nodes determined to be sensitive to harmonics, their physical connection path is traced according to the topology information. If the correlation of the sensitive node is mainly caused by the power consumption characteristics of the equipment on a certain line, then the key connection point of the line is the vulnerable node. Combined with the coordinate information in the topology, its physical location is determined. A three-dimensional map is constructed based on power grid topology information. Nodes are represented by three-dimensional icons. The risk level of vulnerable nodes is pre-defined into three levels, corresponding to different risk degrees: high risk, medium risk, and low risk. High-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is highly correlated and the phase shift is significant. Medium-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is at a moderate level and the phase shift is relatively obvious. Low-risk nodes refer to nodes where the correlation between the power consumption characteristics of the equipment and the phase shift is low and the phase shift is slight. A mapping relationship between risk level and color is established, with high-risk nodes marked in red, medium-risk nodes marked in yellow, and low-risk nodes marked in green. The identified vulnerable nodes are located in the three-dimensional power grid topology model according to their physical coordinates, and the corresponding color is used to mark them according to their risk level, generating a three-dimensional visualization map containing the complete topology structure and vulnerable node markings.

9. A power grid assessment system based on power load decomposition, applied to a power grid assessment method based on power load decomposition as described in any one of claims 1-8, characterized in that, include: Data acquisition module: Collects high-frequency harmonics in each branch of the power grid through synchronous phasor sensors, continuously collects and captures waveforms in each frequency band, converts them into digital signals, and preprocesses the collected digital signals; Electricity consumption feature extraction module: Extracts the fluctuation frequency and amplitude change rate of high-frequency waveforms, constructs a feature template database containing frequency converters and electric arc devices as a comparison benchmark, compares the extracted parameters with the templates, quantifies the matching degree, and distinguishes the electricity consumption features of devices based on the matching degree; Phase offset calculation module: Injects micro-disturbance test signals into the power grid, simultaneously uses synchronous phasor measurement sensors to collect response signals from each branch, extracts the phase offset, and finally organizes and stores the data by node; Vulnerable node location module: Quantifies the correlation between the power consumption characteristics of equipment and phase shift, and then determines the correlation between the two based on a preset threshold. Nodes with a correlation reaching the preset threshold are identified as harmonic sensitive. Then, based on the three-dimensional power grid topology model, the vulnerable nodes are located in the model by coordinates and marked with the color corresponding to the risk level.

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