Intelligent fusion terminal-based power quality comprehensive monitoring method and system

Abstract

The present application relates to the field of power system electric energy quality monitoring, especially to the electric energy quality comprehensive monitoring method and system based on intelligent fusion terminal, comprising: obtaining and preprocessing the electric energy quality data of the intelligent fusion terminal, dividing the electrical partition, determining the dominant harmonic component, and calculating the electrical distance between terminals in the same partition. Based on the density peak clustering, the similarity distance is obtained by combining the feature difference and the electrical distance weighted fusion, the adaptive cut-off distance is determined by using the low-density area adjustment factor, and the decision graph is drawn to identify the clustering center. The clustering results are screened, the electric energy quality characteristic dominance is analyzed, the mixed cluster is identified and re-clustered, the disturbance source is located by using the attenuation law combined with the significant clustering results and the electrical distance. The present application improves the accuracy of disturbance source positioning by adaptive clustering and mixed cluster analysis, and the adaptive cut-off distance and the adjustment factor of the low-density area are beneficial to accurately identifying the power grid operating condition and multiple disturbance sources.

Description

Technical Field

[0001] This invention relates to the field of power system power quality monitoring. In particular, it relates to a comprehensive power quality monitoring method and system based on an intelligent fusion terminal. Background Technology

[0002] The importance of intelligent integrated terminals in power quality monitoring is becoming increasingly apparent. Power quality is not only related to the stable operation of the power system, but also directly affects the performance and lifespan of various electrical equipment. Through intelligent integrated terminals, key data such as voltage and current in the power grid can be collected in real time, providing a basis for accurate power quality detection. This helps to detect abnormal disturbances in the power grid in a timely manner, ensuring the reliability and quality of power supply, which is crucial for maintaining the operating efficiency of the power system and the overall user experience.

[0003] Utilizing intelligent fusion terminals for power quality monitoring still faces numerous challenges. Multiple disturbance sources exist within the power grid, and the power quality problems they generate are intertwined, making it difficult to accurately distinguish and locate these sources using traditional monitoring methods alone. To address this challenge, clustering methods are commonly employed, attempting to effectively identify disturbance sources by matching power quality characteristics with electrical distance. However, the physical topology of the power grid has clearly defined regional divisions and connectivity characteristics, with significant differences in electrical characteristics between different regions. This leads to the potential for invalid cross-regional clustering during the clustering process, resulting in clustering results that fail to accurately reflect the actual operating conditions of the power grid. Furthermore, the clustering results often contain mixed clusters, where data from different disturbance sources is integrated, further increasing the difficulty of accurately identifying multiple disturbance sources. Summary of the Invention

[0004] To address the challenges of accurately distinguishing and locating power quality issues caused by the intertwining of multiple disturbance sources in the power grid, and the limitations of existing clustering methods in handling cross-regional electrical characteristic differences and mixed clusters, which affect the accurate reflection of the actual operating conditions of the power grid and the effective identification of multiple disturbance sources, this invention provides solutions in the following aspects.

[0005] In the first aspect, the power quality comprehensive monitoring method based on intelligent fusion terminals includes: acquiring and preprocessing the power quality dataset of intelligent fusion terminals, dividing electrical zones, and determining the dominant harmonic components; calculating the electrical distance between intelligent fusion terminals within the same zone by combining the dominant harmonic components in the preprocessed power quality dataset with the electrical paths in the power grid topology; clustering the intelligent fusion terminals within the same zone based on density peaks, calculating the feature differences and electrical distances between each pair of intelligent fusion terminals and performing weighted fusion; according to a preset weight allocation rule, performing weighted fusion of feature differences and electrical distances to obtain a similarity distance; determining an adaptive cutoff distance by combining an adjustment factor for low-density areas; and calculating the local density of each intelligent fusion terminal and the density of each intelligent fusion terminal relative to other intelligent fusion terminals. The relative distance between intelligent fusion terminals with higher density is used to draw a decision map based on local density and relative distance, identify density peak points to determine cluster centers, and obtain preliminary clustering results within the same partition. The clustering results are then screened, the dominance of each power quality feature is analyzed, and it is determined whether they are mixed clusters. Mixed clusters are re-clustered based on the corresponding power quality features to obtain significant clustering results. Combining the significant clustering results and electrical distance, the location of disturbance sources is obtained using the power quality attenuation law. The preset weight allocation rule is as follows: the standard deviation of the power quality feature differences of all terminals within the same partition is divided by the sum of the standard deviations of the power quality feature differences and the standard deviations of the electrical distance to obtain the weight coefficient of similarity distance. The weight coefficient of electrical distance is obtained by subtracting the weight coefficient of similarity distance from 1.

[0006] Preferably, the dominant harmonic component includes:

[0007] Calculate the energy percentage of each harmonic component by dividing the square of the voltage amplitude of each harmonic by the sum of the squares of all harmonic voltage amplitudes. Select the harmonic component with the largest energy percentage as the dominant harmonic number and identify the dominant harmonic component based on the dominant harmonic number.

[0008] Preferably, the calculation of the electrical distance includes:

[0009] Taking any terminal as the target terminal, obtain multiple electrical paths between the target terminal and other terminals, wherein each electrical path includes multiple wire lines and multiple transformers;

[0010] The resistive and inductive reactance components of all conductors in the electrical path are summed separately, and the sums are added together to obtain the total line impedance of the electrical path. The short-circuit impedances of all transformers in the electrical path are summed to obtain the total transformer impedance of the electrical path. The total line impedance and the total transformer impedance are summed, and the complex modulus is taken to obtain the electrical distance between the target terminal and other terminals.

[0011] Preferably, obtaining the resistive and inductive components includes the following steps:

[0012] Taking any conductor as the target conductor, calculate the complex modulus of the resistance per unit length of the target conductor's line and the length of the target conductor's line to obtain the resistance component of the target conductor; convert the imaginary unit in the complex number... The inductive reactance component of the target conductor is obtained by taking the complex modulus between the dominant harmonic component frequency, the corresponding unit length inductance of the conductor, and the length of the line.

[0013] Preferably, the step of obtaining the feature differences includes:

[0014] For each terminal within the same partition, a power quality feature vector is constructed, which includes: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude of dominant harmonic components, voltage sag amplitude, and sag duration.

[0015] Taking any smart fusion terminal as the target terminal, calculate the Euclidean distance between the power quality feature vectors of any two smart fusion terminals in the same partition to obtain the feature differences.

[0016] Preferably, the adjustment factor for the low-density region is calculated using the following method:

[0017] Calculate the average electrical distance between all terminal pairs within the partition. Determine the distance of the nearest neighbor pair used for density calculation based on the target cutoff ratio and the number of sample pairs. Normalize the difference between the average distance and the distance of the nearest neighbor pair and add 1 as an adjustment factor for low-density areas.

[0018] Preferably, the dominance of the power quality characteristics includes:

[0019] The total number of smart fusion terminals contained in the cluster is counted, the number of preset power quality characteristics that exceed the standard value of the corresponding characteristics is selected, and the ratio of the number to the total number is calculated to obtain the characteristic dominance.

[0020] Preferably, the step of obtaining the location of the disturbance source by utilizing the power quality attenuation law includes:

[0021] Based on historical data and clustering results, a harmonic disturbance event database and a sag event database are established to record the location of disturbance sources, grid topology, and electrical energy characteristics of terminals.

[0022] The electrical distance from each terminal to the disturbance source point is determined by the electrical distance calculation method, and the attenuation curves of harmonic and voltage sag disturbances are fitted to obtain their respective attenuation coefficients and attenuation models.

[0023] The smart fusion terminal most affected by the disturbance is selected as the backtracking point. The amplitude of the smart fusion terminal is substituted into the fitted attenuation model to calculate the electrical distance from the disturbance source to the backtracking point. Finally, the location of the disturbance source is determined by combining the power grid topology.

[0024] Preferably, the power quality dataset includes: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude, instantaneous voltage value, voltage sag duration, and voltage sag magnitude;

[0025] Based on the power grid topology, electrical zones are divided. For the power quality dataset after electrical zone division, terminals with abnormal indicators are screened out according to the preset anomaly judgment criteria. For the screened terminals with abnormal indicators, the mean and standard deviation of each power quality dataset are calculated according to the terminal data within the same electrical zone. When the absolute difference between each power quality dataset and the mean is greater than 3 times the standard deviation, the feature value is judged as an outlier and removed. The power quality dataset is then subjected to maximum and minimum standardization to eliminate dimensional differences.

[0026] Secondly, a power quality integrated monitoring system based on an intelligent fusion terminal includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned power quality integrated monitoring method based on an intelligent fusion terminal is implemented.

[0027] The present invention has the following effects:

[0028] 1. This invention, by combining power quality characteristics and electrical distance for adaptive clustering, and further analyzing and re-clustering mixed clusters, can more accurately identify and distinguish multiple disturbance sources, thereby improving the accuracy of disturbance source location. It avoids the location errors caused by cross-regional electrical characteristic differences and mixed clusters in traditional methods, ensuring an accurate reflection of the actual operating conditions of the power grid.

[0029] 2. This invention, through adaptive truncation distance and adjustment factors for low-density regions, can automatically adapt to the data distribution in different density regions, improving the adaptability and reliability of the clustering algorithm. It not only reduces false clustering and improves the accuracy of clustering results, but also enhances the robustness of the clustering algorithm in different scenarios, ensuring accurate reflection of the actual operating conditions of the power grid and effective identification of multiple disturbance sources. Attached Figure Description

[0030] Figure 1 This is a flowchart of steps S1-S4 in the power quality comprehensive monitoring method based on an intelligent fusion terminal according to an embodiment of the present invention.

[0031] Figure 2 This is a structural block diagram of the power quality integrated monitoring system based on an intelligent fusion terminal according to an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0033] Reference Figure 1 The power quality comprehensive monitoring method based on intelligent fusion terminals includes steps S1-S4, as detailed below:

[0034] S1: Obtain the power quality dataset of the intelligent fusion terminal and preprocess it, divide the electrical zones, and determine the dominant harmonic components.

[0035] The power quality dataset includes: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude, instantaneous voltage value, voltage sag duration, and voltage sag magnitude;

[0036] It should be noted that the dominant harmonic component refers to the one or more harmonic components in a power system that have the greatest impact on power quality. Harmonics are periodic components in a power system whose voltage or current waveforms deviate from a sine wave; they can be integer multiples of the fundamental frequency. The dominant harmonic component typically has the largest amplitude and has the most significant impact on the operation of the power grid and the performance of equipment.

[0037] Based on the power grid topology, electrical zones are divided into three levels: substation → feeder → distribution transformer. For example, terminal codes can be represented as substation ID, feeder ID, and distribution transformer ID. Subsequent analysis is performed only on terminals within the same zone to avoid cross-regional disturbances.

[0038] Based on the power quality dataset after electrical partitioning, and by comparing it with preset anomaly judgment criteria, terminals with abnormal indicators are selected. For example, the voltage deviation threshold is set as follows: Frequency deviation setting threshold is The threshold for total harmonic distortion is set as follows: The threshold values ​​for each harmonic voltage amplitude are set as follows: The voltage sag amplitude is set to a threshold value of 100%. The voltage sag duration is set to a threshold value of ; For the screened abnormal terminals, data preprocessing is performed, including outlier removal and standardization. For terminal data within the same electrical zone, the mean and standard deviation of each power quality dataset are calculated. If the absolute difference between any power quality dataset and the mean is greater than three times the standard deviation, the feature value is determined to be an outlier and removed. This is used to eliminate invalid data caused by terminal failures or communication interference. The power quality datasets are then subjected to min-max standardization to eliminate dimensional differences.

[0039] In the data preprocessing stage of the power quality monitoring system, terminals with abnormal indicators are first screened out based on preset anomaly judgment criteria, focusing on terminals requiring key attention while excluding terminals with normal data. Subsequently, invalid data caused by terminal malfunctions or communication interference is further removed from these abnormal terminals to ensure the reliability of data in subsequent analyses. Next, the preprocessed data undergoes standardization to eliminate differences in the dimensions of different indicators, providing reliable data for subsequent operations. Electrical zoning constraints ensure that the analysis focuses on terminals with close electrical connections, conforming to the physical boundaries of disturbance propagation. This improves data reliability and consistency, ensuring the accuracy and effectiveness of subsequent analyses.

[0040] In power quality monitoring, accurately identifying major harmonic components is crucial for diagnosing power grid problems. This invention uses harmonic voltage amplitude data collected by an intelligent fusion terminal to calculate the energy proportion of each harmonic component based on the principle that the square of the harmonic voltage amplitude is proportional to the harmonic power contribution.

[0041] Specifically, the energy proportion of each harmonic component is obtained by dividing the square of the voltage amplitude of each harmonic by the sum of the squares of all harmonic voltage amplitudes. The order of the harmonic component with the largest energy proportion is selected as the dominant harmonic order. By retaining the dominant harmonic component, misjudgments caused by interference from secondary harmonic components can be reduced, thereby improving the overall monitoring accuracy.

[0042] It's important to note that voltage amplitude is a key feature, typically encompassing the amplitudes of the dominant and subharmonics. The dominant harmonic is the one or a few harmonic components with the largest amplitude among all harmonic components. These components have the greatest impact on power quality and are therefore called dominant. However, when performing density peak clustering, special attention is paid to the voltage amplitude of the dominant harmonic because its impact on power quality is most significant.

[0043] The dominant harmonic order refers to the frequency multiple of the harmonic with the largest amplitude in the harmonic spectrum of a power system. For example, if the fundamental frequency is 50Hz or 60Hz, the dominant harmonic order could be the 3rd, 5th, or 7th harmonic, depending on which harmonic has the largest amplitude. Therefore, the specific steps to obtain the dominant harmonic order corresponding to the highest energy proportion in the harmonic voltage amplitude are as follows:

[0044] Specifically, the dominant harmonic order satisfies the following relationship:

[0045] ;

[0046] In the formula, Indicates the dominant harmonic order. Indicates the first The amplitude of the second harmonic voltage, with the squared term reflecting the power contribution of the harmonic, and the harmonic with the highest energy proportion having the greatest impact on propagation. This indicates the maximum harmonic component order that the terminal can acquire. This indicates the lowest harmonic component order that the terminal can collect. It represents the order of the harmonic component with the highest energy proportion among all harmonic voltage amplitudes.

[0047] In power quality monitoring systems, topological information of terminals (such as their associated lines and transformer connections) is obtained from databases like Geographic Information Systems (GIS). This aims to accurately grasp the structure of the power grid and the connections between equipment, providing the monitoring system with a detailed view of the grid layout and enhancing monitoring accuracy. It also clarifies disturbance propagation paths and facilitates faster fault location. Furthermore, topological information-based zonal monitoring strategies can optimize monitoring resource allocation and improve monitoring efficiency. In addition, topological information provides crucial data analysis and intelligent decision-making, helping to implement preventative measures and optimize maintenance plans, thereby significantly improving the reliability and operational efficiency of the power grid.

[0048] S2: Calculate the electrical distance between smart fusion terminals within the same partition by combining the dominant harmonic components in the preprocessed power quality dataset with the electrical paths in the power grid topology.

[0049] Taking any terminal as the target terminal, obtain multiple electrical paths between the target terminal and other terminals, wherein each electrical path includes multiple wire lines and multiple transformers;

[0050] The resistive and inductive reactance components of all conductors in the electrical path are summed separately, and the sums are added together to obtain the total line impedance of the electrical path. The short-circuit impedances of all transformers in the electrical path are summed to obtain the total transformer impedance of the electrical path. The total line impedance and the total transformer impedance are summed, and the complex modulus is taken to obtain the electrical distance between the target terminal and other terminals.

[0051] Specifically, electrical distance satisfies the following relationship:

[0052] ;

[0053] In the formula, Indicates terminal With terminal Electrical distance between them Indicates terminal To the terminal There are a total of Section of conductor line, Represents the resistance component of a wire. This represents the inductive reactance component of the conductor. Indicates terminal With terminal The number of transformers between Indicates the first in the electrical path The short-circuit impedance (Ω) of a transformer, where the short-circuit impedance of the transformer includes the equivalent resistance. and equivalent leakage reactance , , Indicates taking the modulus of a complex number. This reflects the transformer's impediment to current. By taking a complex modulus, the vector sum of resistance and inductive reactance is converted into a scalar value, ensuring that the physical meaning of the electrical distance is clear.

[0054] Taking any conductor as the target conductor, calculate the complex modulus of the resistance per unit length of the target conductor's line and the length of the target conductor's line to obtain the resistance component of the target conductor; convert the imaginary unit in the complex number... The inductive reactance component of the target conductor is obtained by taking the complex modulus between the dominant harmonic component frequency, the corresponding unit length inductance of the conductor, and the length of the line.

[0055] Specifically, the resistive component and the inductive reactance component satisfy the following relationships:

[0056] ;

[0057] ;

[0058] In the formula, Represents the resistance component of a wire. This represents the inductive reactance component of the conductor. This represents the resistance per unit length of a conductor (Ω / km). Indicates the first The length of a conductor line (km). The imaginary unit in a complex number is used to represent the inductive reactance component. Indicates the frequency of the dominant harmonic component. Inductance per unit length of a conductor (Ω / km).

[0059] To further explain, This reflects the energy loss caused by resistance when current flows through a wire. This reflects the resistance of inductance to current in AC circuits. The higher the frequency (such as higher harmonics), the greater the inductive reactance, the stronger the resistance to current, and the faster the harmonics attenuate during propagation.

[0060] Based on physical meaning, it comprehensively considers the resistance and inductive reactance components of the line, as well as the short-circuit impedance of the transformer. By quantifying the equivalent impedance loss of disturbance propagation, it provides a physically meaningful distance metric for cluster analysis. It quantifies the electrical correlation between nodes by considering line impedance and transformer impedance.

[0061] In electrical engineering, the imaginary unit in complex numbers This is used to represent impedance in complex form, where the real part represents resistance and the imaginary part represents inductive reactance. The formula multiplies by the imaginary unit. This is to combine the inductive reactance component with the resistive component to form a complex impedance. This not only comprehensively considers the line's opposition to alternating current, including resistive and inductive losses, but also allows for the calculation of the total impedance using the modulus of the complex number, thus providing a physically meaningful measure for calculating electrical distances.

[0062] Electrical distance can quantify the physical losses during the propagation of disturbances in the power grid. Compared to simple spatial distance measurements, it more closely reflects the actual laws of disturbance attenuation. Furthermore, by calculating only the electrical distance between terminals within the same electrical zone, distance calculation errors caused by cross-zone topological barriers can be effectively avoided.

[0063] S3: Cluster the intelligent fusion terminals within the same partition based on density peaks, calculate the feature differences and electrical distances between each pair of intelligent fusion terminals, and perform weighted fusion of feature differences and electrical distances according to preset weight allocation rules to obtain similarity distances. Combine the adjustment factor of low-density regions to determine the adaptive cutoff distance, calculate the local density of each intelligent fusion terminal and the relative distance between each intelligent fusion terminal and other intelligent fusion terminals with higher densities, draw a decision map based on local density and relative distances, identify density peak points to determine cluster centers, and obtain preliminary clustering results within the same partition.

[0064] For each terminal within the same partition, a power quality feature vector is constructed, which includes: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude of dominant harmonic components, voltage sag amplitude, and sag duration.

[0065] In cluster analysis, voltage amplitude is replaced with the voltage amplitude of the dominant harmonic. Since the voltage amplitude of the dominant harmonic more accurately reflects the main sources of harmonic pollution in the power grid, it allows for more effective identification of terminals affected by the same disturbance source during clustering. This enables more precise location of power quality disturbance sources, improving the accuracy and efficiency of clustering.

[0066] The methods for calculating similarity distance include:

[0067] Taking any smart fusion terminal as the target terminal, calculate the Euclidean distance between the power quality feature vectors of any two smart fusion terminals in the same partition to obtain the feature differences;

[0068] Divide the standard deviation of power quality characteristics of all terminals in the same partition by the sum of the standard deviation of power quality characteristics and the standard deviation of electrical distance to obtain the weighting coefficient of similarity distance. Subtract the weighting coefficient of similarity distance from 1 to obtain the weighting coefficient of electrical distance.

[0069] The similarity distance and electrical distance are weighted and fused based on the weight coefficients of similarity distance and electrical distance to obtain the similarity distance between the target terminal and other terminals.

[0070] A similarity matrix is ​​constructed based on the similarity distances and sorted in descending order. The adaptive cutoff distance is then fused based on the target cutoff ratio and the adjustment factor of low-density regions. In existing density peak clustering methods, the cutoff distance is determined according to the target cutoff ratio. Compared with existing technologies, by introducing the adjustment factor of low-density regions, cluster centers in different density regions can be identified and distinguished more accurately. This improves the adaptability and accuracy of cluster analysis, thereby more effectively identifying disturbance sources in the power grid and optimizing power quality monitoring and maintenance strategies.

[0071] For example, the target cutoff ratio is 2%, which is set with reference to existing technology and will not be described in detail.

[0072] The adjustment factor for low-density regions is calculated using the following methods:

[0073] Calculate the average electrical distance between all terminal pairs within the partition. Determine the distance of the nearest neighbor pair used for density calculation based on the target cutoff ratio and the number of sample pairs. Normalize the difference between the average distance and the distance of the nearest neighbor pair and add 1 as an adjustment factor for low-density areas.

[0074] Specifically, the adjustment factor for low-density regions satisfies the following relationship:

[0075] ;

[0076] In the formula, The adjustment factor represents the low-density region. This represents the average electrical distance between all pairs of intelligent converged terminals within the same partition. Indicates the cutoff ratio according to the target. The selected nearest neighbor distance, Indicates the number of sample pairs. The symbol indicates rounding up.

[0077] Specifically, the adaptive cutoff distance satisfies the following relationship:

[0078] ;

[0079] In the formula, Indicates the adaptive cutoff distance. Indicates the cutoff ratio according to the target. The selected nearest neighbor distance, This represents the adjustment factor for low-density regions.

[0080] The difference between this embodiment and existing technologies lies in the fact that the influence weight of low-density regions during partitioning is analyzed based on electrical distance. By introducing an adjustment factor for low-density regions, the adaptive cutoff distance can adapt to power grid regions of different densities, thereby improving the accuracy of clustering analysis and the robustness of the algorithm. Specifically, the influence weight of electrical distance on low-density region partitioning is considered, optimizing the clustering results. This makes terminals within the same cluster more similar in power quality and electrical connectivity, facilitating more accurate identification and location of disturbance sources in the power grid, and enhancing the efficiency and reliability of power grid operation.

[0081] The remaining sample points are sorted in descending order of local density and then assigned to the clusters containing the nearest points with local densities greater than their own. This step is an existing technique.

[0082] Specifically, the local density satisfies the following relationship:

[0083] ;

[0084] In the formula, Indicates terminal Local density, Indicates terminal With terminal Similarity distance, Indicates the adaptive cutoff distance. Represented by natural numbers An exponential function with base 0.

[0085] Specifically, the relative distance satisfies the following relationship:

[0086] terminal The formula for the relative distance to high-density terminals is:

[0087] ;

[0088] In the formula, Indicates terminal The minimum distance between it and other terminals with a higher density. Indicates terminal With terminal Similarity distance, Indicates terminal Local density, Indicates terminal Local density, This indicates taking the minimum value in the set.

[0089] For local density It is the largest terminal: By constructing " "Decision graph" adaptive selection and The terminals with significantly larger values ​​are used as cluster centers.

[0090] S4: Filter the clustering results, analyze the dominance of each power quality feature, and determine whether it is a mixed cluster. Re-cluster the mixed clusters based on the corresponding power quality features to obtain significant clustering results. Combine the significant clustering results and electrical distance, and use the power quality attenuation law to obtain the location of the disturbance source.

[0091] In power quality analysis, features are divided into two categories: harmonics and voltage sags, and their dominance is calculated separately because these two features represent different physical phenomena with different impacts, consequences, monitoring methods, and mitigation measures.

[0092] Harmonics are primarily associated with nonlinear loads, affecting grid efficiency and equipment lifespan, while voltage sags are typically caused by short circuits or the startup of large equipment, potentially leading to malfunctions in sensitive devices. Distinguishing between these two characteristics helps in more accurately identifying power quality problems in the grid, enabling more effective and targeted mitigation measures to improve grid operational efficiency and reliability.

[0093] Furthermore, this distinction is particularly important in cluster analysis because it can identify and locate different types of disturbance sources, providing more targeted data support for power grid maintenance and optimization.

[0094] The total number of intelligent fusion terminals contained in the cluster is counted. The number of harmonic features and voltage sag features that exceed the standard value of the corresponding features are screened out, and the ratio of the number of features to the total number is calculated to obtain the feature dominance.

[0095] If the characteristic dominance is less than or equal to a preset threshold, it is a single cluster of harmonic disturbances / voltage sag disturbances; otherwise, if it is greater than the preset threshold, it is a mixed cluster of harmonic disturbances / voltage sag disturbances.

[0096] Specifically, the preset threshold is 80%, which can be adjusted according to specific circumstances.

[0097] If the disturbance is a mixed cluster, secondary clustering is required. This involves selecting power quality characteristics corresponding to the dominant disturbance type, eliminating irrelevant features, and applying a specific distance function for secondary clustering, thereby splitting the mixed cluster into multiple individual sub-clusters. This not only improves the accuracy of disturbance source location but also provides more targeted data support for power quality monitoring and maintenance of the power grid, making it particularly suitable for handling complex power grid environments with multiple disturbance types.

[0098] Hybrid clusters are clusters containing the influence of different perturbation sources. In this case, terminals within a single cluster may be affected by multiple different perturbation sources. To improve the accuracy and reliability of clustering results, these hybrid clusters need further analysis and splitting. Through secondary clustering or other methods, hybrid clusters are split into clusters with a single perturbation source, ensuring that each cluster corresponds to a single perturbation type. Accurate clustering results are crucial for subsequent perturbation source localization; the presence of hybrid clusters reduces the accuracy of localization.

[0099] First, a harmonic disturbance event database and a voltage sag event database are established based on historical data and clustering results, recording the location of the disturbance source, the grid topology, and the electrical energy characteristics of the terminals. Then, the electrical distance from each terminal to the disturbance source is determined using an electrical distance calculation method, and attenuation curves are fitted to harmonic and voltage sag disturbances respectively to obtain their respective attenuation coefficients and attenuation models.

[0100] By selecting the terminal most affected by the disturbance as the traceback point and substituting the amplitude of that terminal into the fitted attenuation function, the electrical distance from the disturbance source to the traceback point is calculated. Finally, the location of the disturbance source is determined by combining the power grid topology. This significantly improves the accuracy and efficiency of disturbance source location, reduces the scope of on-site investigation, and achieves precise location of power quality disturbance sources based on intelligent fusion terminal data, thus achieving the core objective of this invention.

[0101] By fitting the attenuation curve and combining historical data with the power grid topology, the approximate location of the disturbance source can be accurately determined. By analyzing the clustering results and electrical distance, the location of the disturbance source can be inferred, thus providing a scientific basis for the maintenance and optimization of the power grid.

[0102] This invention also provides a comprehensive power quality monitoring system based on an intelligent fusion terminal. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the power quality comprehensive monitoring method based on an intelligent fusion terminal according to the first aspect of the present invention. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their configuration and functions are known in the art and will not be described in detail here.

[0103] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A power quality comprehensive monitoring method based on an intelligent fusion terminal, characterized in that, The method comprises the following steps: Obtain the power quality data set of the intelligent fusion terminal and preprocess it, divide the electrical partition, and determine the dominant harmonic component; Calculate the electrical distance between the intelligent fusion terminals in the same partition based on the dominant harmonic component in the preprocessed power quality data set and the electrical path in the topology structure of the power grid; Cluster the intelligent fusion terminals in the same partition based on the density peak value, calculate the feature difference and electrical distance between each two intelligent fusion terminals, weight the feature difference and electrical distance according to the preset weight distribution rule, obtain the similarity distance, determine the adaptive cut-off distance combined with the adjustment factor of the low-density area, calculate the local density of each intelligent fusion terminal and the relative distance between each intelligent fusion terminal and other intelligent fusion terminals with higher density, draw a decision graph based on the local density and the relative distance, identify the density peak point to determine the clustering center, and obtain the preliminary clustering result in the same partition; Screen the clustering result, analyze the dominance of each power quality feature, and determine whether it is a mixed cluster, re-cluster the mixed cluster based on the corresponding power quality feature, and obtain the significant clustering result; Obtain the location of the disturbance source by combining the significant clustering result and the electrical distance and using the power quality attenuation law; Establish a harmonic disturbance event library and a voltage sag event library according to historical data and the clustering result, and record the location of the disturbance source, the topology structure of the power grid, and the power feature values of the terminal; Determine the electrical distance from each terminal to the disturbance source point by using the electrical distance calculation method, and fit the attenuation curve of the harmonic and voltage sag disturbance respectively to obtain the attenuation coefficient and attenuation model of each; Select the intelligent fusion terminal most affected by the disturbance as the backtracking point, and calculate the electrical distance from the disturbance source to the backtracking point by substituting the amplitude of the intelligent fusion terminal into the fitted attenuation model, and finally determine the location of the disturbance source combined with the topology structure of the power grid. The preset weight distribution rule is that the standard deviation of the feature difference of the power quality of all terminals in the same partition is divided by the sum of the standard deviation of the feature difference of the power quality and the standard deviation of the electrical distance, to obtain the weight coefficient of the similarity distance, and the weight coefficient of the electrical distance is obtained by subtracting the weight coefficient of the similarity distance from 1. The calculation of the electrical distance comprises: Taking any terminal as a target terminal, obtaining a plurality of electrical paths between the target terminal and other terminals, wherein each electrical path comprises a plurality of conductor lines and a plurality of transformers; Sum the resistance component and the reactance component of all conductor lines in the electrical path, add the sum, and obtain the total line impedance of the electrical path; sum the short-circuit impedance of all transformers in the electrical path, and obtain the total transformer impedance of the electrical path; sum the total line impedance and the total transformer impedance, take the complex modulus value, and obtain the electrical distance between the target terminal and other terminals.

2. The method according to claim 1, wherein, The dominant harmonic component comprises: Calculate the energy proportion of each harmonic voltage amplitude by squaring the square of all harmonic voltage amplitudes, select the harmonic component with the largest energy proportion as the dominant harmonic component, and identify the dominant harmonic component according to the dominant harmonic component. 3.The method of claim 1, wherein, The method for obtaining the resistance component and the reactance component comprises the following steps: Taking any conductor as a target conductor, a complex modulus value between a unit length resistance of a conductor line of the target conductor and a length of the conductor line of the target conductor is calculated to obtain a resistance component of the target conductor; an imaginary unit in the complex number is replaced by a real number, A complex modulus value between a corresponding unit length inductance and a length of the line of the main harmonic component frequency of the target conductor is obtained to obtain a reactance component of the target conductor.

4. The method according to claim 1, wherein, The step of obtaining the feature difference comprises: constructing an electric energy quality feature vector for each terminal in the same subarea, wherein the electric energy quality feature vector comprises: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude of dominant harmonic component, voltage sag amplitude and sag duration; taking any intelligent fusion terminal as a target terminal, calculating the Euclidean distance between the electric energy quality feature vectors of two intelligent fusion terminals in the same subarea to obtain the feature difference.

5. The method according to claim 1, wherein, The calculation method of the adjustment factor of the low-density area comprises: calculating the average distance of the electrical distance between all terminal pairs in the subarea, determining the distance of the nearest neighbor point pair for density calculation according to the target cutoff ratio and the number of sample pairs, and normalizing the difference between the average distance and the distance of the nearest neighbor point pair and adding 1 as the adjustment factor of the low-density area.

6. The method according to claim 1, wherein the method further comprises: The dominant degree of the electric energy quality feature comprises: counting the total number of intelligent fusion terminals contained in the cluster, screening out the number of preset electric energy quality features exceeding the standard value of the corresponding feature, and calculating the ratio to the total number to obtain the feature dominant degree.

7. The method according to claim 1, wherein the method further comprises: The electric energy quality data set comprises: voltage deviation, frequency deviation, total harmonic distortion, voltage amplitude, instantaneous voltage value, voltage sag duration and voltage sag amplitude; According to the power grid topology structure, the electrical subarea is divided, and the electric energy quality data set after the electrical subarea is divided is screened according to the preset abnormality judgment standard. The index abnormal terminal is screened out, and the mean value and standard deviation of each electric energy quality data set are calculated according to the terminal data in the same electrical subarea. When the absolute difference between each electric energy quality data set and the mean value is greater than 3 times the standard deviation, it is determined that the feature value is an abnormal value, and is removed; the electric energy quality data set is normalized by maximum and minimum to eliminate the dimension difference.

8. The power quality comprehensive monitoring system based on the intelligent fusion terminal, characterized in that, It comprises: a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, the method for comprehensively monitoring electric energy quality based on intelligent fusion terminal according to any one of claims 1-7 is realized.

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