A power grid evaluation method and system based on power load decomposition

Abstract

The present application relates to the technical field of power grid evaluation, in particular to a power grid evaluation method and system based on power load decomposition, comprising: collecting high-frequency harmonics on each branch of the power grid through a synchronous phasor sensor and converting the high-frequency harmonics into digital signals, and preprocessing the collected digital signals; extracting fluctuation frequency and amplitude change speed of the high-frequency waveform, constructing a feature template database as a comparison benchmark, comparing the extracted parameters with the template, quantifying the matching degree, and distinguishing the equipment power consumption characteristics according to the matching degree; injecting a micro-disturbance test signal into the power grid, extracting the phase shift amount, and finally storing according to the nodes; quantifying the correlation degree of the equipment power consumption characteristics and the phase shift, and determining the correlation of the two according to the preset threshold, and the nodes with the correlation degree reaching the preset threshold are determined as harmonic sensitive. The synchronous phasor measurement sensor is used for data collection, and the data is preprocessed, so that the waveform distortion problem caused by the insufficient traditional sampling frequency is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid evaluation, in particular to a power grid evaluation method and system based on power load decomposition. BACKGROUND

[0002] The high-frequency harmonic of the power grid is generated by the operation of variable frequency equipment, electric arc furnace and other loads, and its persistence can easily lead to transformer overheating, protection device misoperation and power quality decline, which poses a significant threat to the safe and stable operation of the power grid. The existing high-frequency harmonic monitoring and vulnerable node positioning technology has the following shortcomings: at the data acquisition level, it is difficult to completely capture the oscillation waveform of high-frequency harmonic, and the pre-processing process does not specifically separate the power frequency component and high-frequency signal, which can easily distort the high-frequency characteristics due to power frequency interference, and cannot meet the demand for accurate analysis. At the load power consumption feature extraction level, the existing method does not differentiate extraction combined with the working characteristics of typical equipment, but only obtains the overall energy distribution through frequency spectrum analysis, which makes it difficult to distinguish the power consumption characteristics of variable frequency equipment and electric arc furnace, resulting in low load type recognition accuracy and providing no effective basis for harmonic tracing. At the dynamic response detection level, there is a lack of means to actively stimulate the dynamic characteristics of the power grid, and only the voltage and current data are relied on for passive monitoring, which makes it difficult to quantify the sensitivity of the node to high-frequency harmonic, and the correlation between the load fingerprint and the node impedance characteristics is not established, which cannot accurately determine the key node affected by the harmonic. At the positioning and visualization level, the vulnerable node positioning relies on a single parameter, and the spatial correlation is not combined with the power grid topology information, resulting in large positioning error. At the same time, there is a lack of intuitive risk level display means, which makes it difficult for operation and maintenance personnel to quickly identify high-risk areas, affecting the efficiency of harmonic control. SUMMARY

[0003] The present application provides a power grid evaluation method and system based on power load decomposition to solve the technical problems in the prior art.

[0004] The technical solution of the present application to solve the above technical problems is as follows: a power grid evaluation method based on power load decomposition, comprising the following steps:

[0005] S101, collecting high-frequency harmonic on each branch of the power grid through a synchronous phasor sensor, continuously collecting and capturing waveforms of each frequency band, and converting them into digital signals, and pre-processing the collected digital signals;

[0006] S102, extracting the fluctuation frequency and amplitude change speed of the high-frequency waveform, and constructing a feature template database containing variable frequency and electric arc devices as a comparison benchmark, comparing the extracted parameters with the template, quantifying the matching degree, and distinguishing the power consumption characteristics of the equipment according to the matching degree;

[0007] S103, injecting a perturbation test signal to the power grid, synchronously collecting response signals of each branch using a synchronous phasor measurement sensor, extracting the phase shift, and finally storing 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] ;

[0012] 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. The expected upper limit value of the fluctuation frequency of the voltage waveform generated by the display device when it is running, if the fluctuation frequency of the waveform characteristic parameter to be analyzed falls in the medium-high frequency characteristic interval in the variable frequency type device characteristic template, it is determined that it corresponds to the variable frequency type device power consumption characteristic, the amplitude variation speed set of the waveform to be analyzed is , the speed threshold of the device characteristic template is , the number of speed that meets the threshold condition is , and the specific calculation formula of the matching degree is as follows:

[0013] ;

[0014] Among them, indicates the amplitude variation speed matching degree, indicates the mth amplitude variation speed data, m indicates the total number of amplitude variation speed data in the amplitude variation speed set, and if the fluctuation frequency of the waveform characteristic parameter to be analyzed falls in the medium-high frequency interval of the arc type device characteristic template, it is determined that it corresponds to the arc type device power consumption characteristic.

[0015] In a preferred embodiment, in S103, a group 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, and the proportion is equal to the expected lower limit value of the voltage fluctuation range allowed by the normal operation of the power grid. The frequency covers a wide frequency range from low frequency to high frequency, the signal form is a sweep signal, the frequency is linearly increased from low frequency to high frequency, and energy is uniformly injected in the entire frequency band. The test signal is injected into the target power grid through the dynamic reactive power compensation device in the power grid, and the signal injection process is realized by the phase-locked loop technology to make the injected signal and the fundamental wave voltage of the power grid maintain a fixed phase relationship.

[0016] At the same time of injecting the test signal, the synchronous phasor measurement sensor deployed in each branch is used to collect the response signal, the collection object includes the voltage signal and the current signal of the branch, the phase change of the collected response signal is extracted, and the phase difference between the voltage and the current under different frequencies is calculated. The offset amount relative to the fundamental wave state, at the fundamental frequency, the voltage and current phase difference of each branch is measured and recorded in advance, and the specific calculation formula is as follows:

[0017] ;

[0018] Among them, indicates the phase difference between the voltage and the current, indicates the measured voltage of the branch, indicates the measured current of the branch, indicates the phase angle of the voltage signal , indicates the phase angle of the 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:

[0019] ;

[0020] 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:

[0021] ;

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] A mapping relationship between risk levels and colors is established, high-risk nodes correspond to red annotations, medium-risk nodes correspond to yellow annotations, and low-risk nodes correspond to green annotations, the determined vulnerable nodes are positioned in the three-dimensional power grid topology model according to their physical coordinates, the corresponding colors are called according to the risk levels, and a three-dimensional visualization atlas containing complete topology structure and vulnerable node annotations is generated.

[0027] The application further provides a power grid evaluation system based on power load decomposition, comprising:

[0028] The data acquisition module: through the synchronous phasor sensor, high-frequency harmonics are collected at each branch of the power grid, each frequency band waveform is continuously collected and captured, and the waveform is converted into a digital signal, and the collected digital signal is preprocessed;

[0029] The power consumption feature extraction module: the fluctuation frequency and amplitude change speed of the high-frequency waveform are extracted, a feature template database containing variable frequency and arc type devices is constructed as a comparison benchmark, the extracted parameters are compared with the template, the matching degree is quantified, and the power consumption features of the devices are distinguished according to the matching degree;

[0030] The phase shift calculation module: a perturbation test signal is injected into the power grid, the synchronous phasor measurement sensor is used to collect the response signal of each branch synchronously, and the phase shift is extracted, and finally the node is arranged and stored;

[0031] The vulnerable node positioning module: the correlation degree of the device power consumption features and the phase shift is quantified, and the correlation of the two is determined according to the preset threshold, the nodes with a correlation degree reaching the preset threshold are determined as harmonic sensitive, and the vulnerable nodes are positioned in the model according to the coordinates and the color corresponding to the risk level is marked according to the three-dimensional power grid topology model.

[0032] The beneficial effects of the application are: the synchronous phasor measurement sensor is used for data acquisition, covering the main distribution frequency band of high-frequency harmonics, and the data is preprocessed, the high-frequency effective signal is completely retained, the waveform distortion problem caused by insufficient traditional sampling frequency is solved, the power consumption features of variable frequency devices and arc type devices are accurately decomposed, the load type differentiation identification is realized, the defect that the traditional method cannot distinguish the device characteristics is solved, the micro-perturbation signal injected into the power grid is used to calculate the dynamic impedance phase shift synchronously, the response characteristics of the nodes to the high-frequency harmonics are quantified, the sensitivity of the nodes can be accurately captured, the preset threshold and the power grid topology information are combined, the positioning error is controlled within a reasonable range, the risk level is divided by the correlation degree and the phase shift, the misjudgment of a single parameter is avoided, the reliability of positioning is improved, and the physical position of the vulnerable node can be intuitively displayed. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The method flowchart of the application;

[0034] Figure 2 System diagram of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work are within the scope of protection of the present application.

[0036] In the description of the present application, the term "for example" is used to indicate "as an example, an illustration, or a description". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail in order to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope in accordance with the principles and characteristics disclosed in the present application.

[0037] As Figure 1 The present embodiment provides a power load decomposition-based power grid evaluation method, comprising the following steps:

[0038] S101, collecting high-frequency harmonics on each branch of the power grid through a synchronous phasor sensor, continuously collecting and capturing waveforms in each frequency band, and converting them into digital signals, and pre-processing the collected digital signals;

[0039] Further, the synchronous phasor measurement sensor is arranged at each branch of the target power grid to collect the voltage and current signals of each branch of the target power grid to capture the high-frequency harmonic oscillation waveform. The analog voltage and current signals are converted into digital signals by the AD conversion module built in the synchronous phasor measurement sensor. The collected digital signals are denoised by digital filtering technology to suppress the interference signals introduced in the collection process. The noise that may be contained in the original digital signal mainly includes the thermal noise of the sensor itself, electromagnetic environmental interference, and random disturbance in the data transmission process. These noises will mask the true characteristics of the high-frequency harmonics. Through noise removal processing, the amplitude and phase information of the high-frequency harmonic signal can be retained, and the useless interference components can be filtered out to ensure that the signal-to-noise ratio of the subsequent analysis data meets the requirements. The power frequency component is separated from the denoised data. The high-frequency harmonics are separated from the low-frequency harmonics by a high-pass filter.

[0040] It should be noted that the purpose of the power frequency component separation is to analyze the interference of the high-frequency harmonics. The power frequency component usually has a large amplitude, and if it is not separated, it will mask the subtle changes of the high-frequency harmonics. At the same time, the frequency stability of the power frequency component is significantly different from the dynamic oscillation characteristics of the high-frequency harmonics. After separation, the subsequent analysis can focus on the target high-frequency band, improving the relevance and accuracy of the harmonic feature extraction. The retained high-frequency signal needs to meet the following conditions: data integrity: no loss of key features of the original collected high-frequency band waveform; data consistency: different branches and different time periods of high-frequency signals are comparable through standardization processing.

[0041] S102, extract the fluctuation frequency and amplitude change speed of the high-frequency waveform, construct a feature template database of the variable frequency type and arc type device as a comparison benchmark, compare the extracted parameters with the template, quantify the matching degree, and distinguish the device power consumption characteristics according to the matching degree;

[0042] The high-frequency signal collected and denoised is subjected to waveform feature parameter extraction, and the quantifiable fluctuation rule index is analyzed from the waveform of the high-frequency signal, including the fluctuation frequency and the amplitude change speed. Based on the historical data and the device characteristics, a power consumption characteristic database of typical devices is constructed in advance. The power consumption characteristic database at least includes the feature templates of two types of typical devices, one being a variable frequency type device and the other being an arc type device. The corresponding high-frequency waveform fluctuation rule benchmarks of various devices are stored. The extracted waveform feature parameters are compared with the power consumption characteristic database to calculate the matching degree. The calculation of the matching degree mainly focuses on the two core feature parameters of the fluctuation frequency and the amplitude change speed. Let the fluctuation frequency set of the waveform to be analyzed be , the frequency interval of the device feature template be , and the number of frequencies falling within the interval be The specific calculation formula of the frequency matching degree is as follows:

[0043] ;

[0044] wherein, represents the frequency matching degree, represents a set of fluctuation frequency data extracted from the waveform to be analyzed, represents the i-th fluctuation frequency data, and n represents the total number of fluctuation frequency data in the fluctuation frequency set, represents the number of matched frequencies, represents the expected lower limit value of the fluctuation frequency generated by the voltage waveform of the device when it is running, represents the expected upper limit value of the fluctuation frequency generated by the voltage waveform of the device when it is running, if the fluctuation frequency of the characteristic parameter of the waveform to be analyzed falls in the medium-high frequency characteristic interval in the variable frequency device characteristic template, it is determined that it corresponds to the variable frequency device power consumption characteristic, and the amplitude variation speed set of the waveform to be analyzed is , the speed threshold of the device characteristic template is , and the number of speeds meeting the threshold condition is The specific calculation formula of the matching degree is as follows:

[0045] ;

[0046] wherein, represents the amplitude variation speed matching degree, represents the m-th amplitude variation speed data, and m represents the total number of amplitude variation speed data in the amplitude variation speed set, if the fluctuation frequency of the characteristic parameter of the waveform to be analyzed falls in the medium-high frequency interval of the arc device characteristic template, it is determined that it corresponds to the arc device power consumption characteristic.

[0047] It should be noted that the fluctuation frequency is determined by counting the oscillation period of the waveform per unit time, and the amplitude variation speed is determined by calculating the ratio of the amplitude difference between adjacent peak values and the time interval. One is a variable frequency device, and its characteristic template is defined as the fluctuation frequency being concentrated in the medium-high frequency characteristic interval, the amplitude variation speed being slow, and the waveform being periodic. Another is an arc device, and its characteristic template is positioned as the fluctuation frequency being scattered in the medium-high frequency interval, the amplitude variation speed being fast, and the waveform having no fixed period. By establishing a reference template, a comparison basis is provided for the waveform to be analyzed. The power consumption characteristics of the device are determined by its working mechanism and have stability and repeatability. For example, a variable frequency device realizes speed regulation through high-frequency switching of a switching tube, and its harmonic frequency is related to the switching frequency. The fluctuation law is stable. These characteristics are like fingerprints of the device, which can uniquely identify the motion state.

[0048] 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.

[0049] 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.

[0050] 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:

[0051] ;

[0052] 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:

[0053] ;

[0054] 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:

[0055] ;

[0056] in, The phase shift at the test frequency f, i.e. the difference between the phase difference at the frequency and the fundamental impedance angle, reflects the sensitivity of the branch to high-frequency signals;

[0057] The extracted phase shifts of the branches are sorted according to the nodes to form structured data containing the node identifier, test signal frequency and phase shift. For each frequency point in the entire frequency range, there is a phase shift record. For a frequency-swept signal with continuous frequency variation, the discrete recording is performed at a preset frequency interval.

[0058] It should be noted that the injection point of the test signal needs to meet the following conditions: first, it is located on the main line or key node of the power grid to ensure that the signal can be effectively transmitted to each branch; second, it is the direct access point of a large-scale impact load to avoid the signal being submerged by the harmonics generated by the load itself. The injection device needs to have high-precision signal generation capability, with the amplitude error of the output signal controlled within a very small range and the frequency accuracy maintained at a very high level to ensure the stability of the injected signal parameters.

[0059] S104, the correlation degree of the device power consumption characteristics and the phase shift is quantified, and the correlation of the two is determined according to a preset threshold. The node whose correlation degree reaches the preset threshold is determined to be harmonic sensitive. According to the three-dimensional power grid topology model, the vulnerable node is located in the model according to the coordinates and labeled with the color corresponding to the risk level.

[0060] Further, the correlation degree of the device power consumption characteristics and the phase shift is compared. When the correlation degree of the device power consumption characteristics and the phase shift of the node reaches the 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 link relationship of each node and line and the node coordinate information. For the node determined to be sensitive to harmonics, the physical connection path is traced according to the topology information. If the correlation degree of the sensitive node is mainly caused by the device power consumption characteristics on a certain line, the key connection point of the line is the vulnerable node. Combined with the coordinate information in the topology, the physical position is determined;

[0061] A three-dimensional map is constructed based on the power grid topology information. The node is represented by a three-dimensional icon. The risk level of the preset vulnerable node is divided into three levels, corresponding to different risk levels: high risk, medium risk and low risk. The high-risk node refers to the node whose correlation degree of device power consumption characteristics and phase shift reaches a high level and whose phase shift is significant. The medium-risk node refers to the node whose correlation degree of device power consumption characteristics and phase shift is at a medium level and whose phase shift is obvious. The low-risk node refers to the node whose correlation degree of device power consumption characteristics and phase shift is low and whose phase shift is slight.

[0062] A mapping relationship between the risk levels and colors is established, a high-risk node corresponds to a red mark, a medium-risk node corresponds to a yellow mark, and a low-risk node corresponds to a green mark, the determined vulnerable nodes are positioned to the three-dimensional power grid topology model according to their physical coordinates, a corresponding color is called according to the risk level, and a three-dimensional visualization atlas containing complete topology structure and vulnerable node marks is generated.

[0063] It should be noted that the principle of node harmonic sensitivity determination is that the device power consumption characteristics reflect the harmonic characteristics, and the phase shift reflects the response characteristics of the node to the harmonic, and the high correlation means that the phase shift of the node is mainly caused by the harmonic of the device of the node.

[0064] The embodiment also provides a power grid evaluation system based on power load decomposition, including:

[0065] The data acquisition module acquires high-frequency harmonics through the synchronous phasor sensor in each branch of the power grid, continuously acquires and captures waveforms in each frequency band, and converts them into digital signals, and pre-processes the acquired digital signals;

[0066] The power consumption characteristic extraction module extracts the fluctuation frequency and amplitude change speed of the high-frequency waveform, constructs a feature template database containing variable frequency and arc devices as a comparison benchmark, compares the extracted parameters with the template, quantifies the matching degree, and distinguishes the device power consumption characteristics according to the matching degree;

[0067] The phase shift calculation module injects a perturbation test signal into the power grid, synchronously acquires response signals of each branch using the synchronous phasor measurement sensor, extracts the phase shift, and finally stores them according to nodes;

[0068] The vulnerable node positioning module quantifies the correlation degree of the device power consumption characteristics and the phase shift, and then determines the correlation of the two according to a preset threshold, and the node whose correlation degree reaches the preset threshold is determined as a harmonic sensitive node, and then the vulnerable node is positioned to the model according to the coordinates and marked with the color corresponding to the risk level according to the three-dimensional power grid topology model.

[0069] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0070] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented 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.

[0071] The present application is described in reference to the flowchart and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart 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, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0072] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for carrying out the function specified by the flowchart block or blocks.

[0074] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Therefore, it is intended that the scope of the application be limited only by the appended claims and equivalents thereof.

[0075] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

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. While injecting the test signal into S103, the response signal is acquired by synchronous phasor measurement sensors deployed in each branch. The acquired signals include the voltage and current signals of the branches. The phase change of the acquired response signal is extracted, and the phase difference between the voltage and current at different frequencies is calculated relative to the fundamental frequency. At the fundamental frequency, the phase difference between the voltage and current of 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 phase offset at the test frequency f, is calculated using the following formula: in, This represents the phase offset at the test frequency f.

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 limit of the expected 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 1, 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.

7. 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.

8. 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 1-7, 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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