Voltage sag detection method and system for 0.4 kilovolt low-voltage distribution network

Abstract

The invention discloses a voltage sag detection method for a 0.4 kilovolt low-voltage power distribution network, and relates to the technical field of electric energy quality detection, and the method comprises the steps: collecting electric power data, calculating an instantaneous synchronization degree by using a cosine function, calculating an initial disturbance index by using a direct difference method, iteratively updating the disturbance index by using a weighted propagation iteration method, and constructing an updated disturbance vector. Generating a virtual sequence by using bitwise XOR operation, calculating a first-order difference of the virtual sequence, calculating a mean value, defining the mean value as a disturbance sensitivity index, regarding the disturbance sensitivity index as a scalar field, calculating vorticity of nodes, and defining the vorticity as global vorticity intensity; instantaneous phase difference of nodes is calculated by Hilbert transform, disturbance indexes are updated in combination with a cosine function and a weighted propagation iteration method, detection sensitivity and accuracy are improved, a mode of generating a virtual sequence through bitwise exclusive-or operation is introduced, global vorticity intensity is calculated in combination with connection weight between the nodes, and the detection accuracy is improved. And the precision and reliability of disturbance analysis are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality detection, and particularly to a method and system for detecting voltage sags in a 0.4 kV low-voltage distribution network. Background Art

[0002] The low-voltage distribution network plays a crucial role in the modern power system, especially in the last link of the hospital power supply. With the rapid development of smart grid technology, the automation and intelligence of the low-voltage distribution network have become the key means to improve the reliability and efficiency of power supply. Voltage sag, as a common power quality problem, is usually caused by load fluctuations, equipment failures or other power network abnormalities, which poses a serious threat to the safe operation of the hospital power system and the stability of user equipment.

[0003] Existing voltage sag detection technologies still have several deficiencies in practical applications. Traditional amplitude-based detection methods are insufficient in responding to the dynamic characteristics of voltage sags (such as instantaneous phase changes and disturbance propagation), and it is difficult to accurately capture the propagation law of sag events in the power grid. Especially in complex topological structures, existing phase analysis methods usually only focus on the signal characteristics of a single node, ignoring the coupling relationship between nodes and the global network dynamics, resulting in an insufficiently comprehensive evaluation of the overall impact of voltage sags. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for detecting voltage sags in a 0.4 kV low-voltage distribution network, which solves the problems that traditional amplitude-based detection methods are insufficient in responding to the dynamic characteristics of voltage sags (such as instantaneous phase changes and disturbance propagation), and it is difficult to accurately capture the propagation law of sag events in the power grid. Especially in complex topological structures, existing phase analysis methods usually only focus on the signal characteristics of a single node, ignoring the coupling relationship between nodes and the global network dynamics, resulting in an insufficiently comprehensive evaluation of the overall impact of voltage sags.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for detecting voltage sags in a 0.4 kV low-voltage distribution network, which includes, Collecting power data, defining smart sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, positioning it as the phase difference of the node, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector; Generate a virtual sequence using bitwise exclusive OR operation, calculate the first-order difference of the virtual sequence, and calculate the mean value, which is defined as the perturbation sensitivity index. Consider the perturbation sensitivity index as a scalar field, calculate the vorticity of the nodes, calculate the sum of the squares of all vorticities, and take the square root, which is defined as the global vorticity intensity; Detect voltage sags and calculate the location of the sag source, and use the K-means clustering algorithm to cluster the voltage sags; Construct a visualization interface to display the clustering results, and collect and analyze the generated power data.

[0007] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, wherein: for collecting power data, define intelligent sensors as nodes, calculate the difference between the instantaneous phase and the reference phase, and locate it as the phase difference of the nodes. Calculate the instantaneous synchronization degree using the cosine function, calculate the initial perturbation index using the direct difference method, and use the weighted propagation iteration method to iteratively update the perturbation index to construct an updated perturbation vector, including: Deploy voltage and current sensors at the key nodes of the 0.4 kV low-voltage distribution network to collect power data, including voltage, current, and resistance data; Define intelligent sensors as nodes, sort the voltage data in chronological order, and construct a time series matrix; Based on the voltage sequence in the time series matrix, calculate the instantaneous phase of the nodes using Hilbert transform, define the reference phase using the reference phase formula, and calculate the difference between the instantaneous phase and the reference phase, which is located as the phase difference of the nodes; Based on the voltage and current data, calculate the instantaneous active power and calculate the mean value, and calculate the power flow based on the resistance data; Obtain the topological structure information of the 0.4 kV low-voltage distribution network from the hospital power distribution system. If the nth node is connected to the jth node, calculate the connection weight between the nodes, otherwise it is 0. Calculate the sum of the connection weights of the nodes, calculate the instantaneous synchronization degree using the cosine function, and calculate the mean value, and calculate the initial perturbation index using the direct difference method; Use the weighted propagation iteration method to iteratively update the perturbation index, and set the convergence threshold using statistical analysis method When , stop the update, arrange in the order of node numbers, and construct an updated perturbation vector, where is the perturbation index of the nth node updated at time t, is the perturbation index of the nth node at time t-1.

[0008] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, the steps include: generating a virtual sequence using bitwise exclusive OR operation, calculating the first-order difference of the virtual sequence, and calculating the mean value defined as the perturbation sensitivity index. Regarding the perturbation sensitivity index as a scalar field, calculating the vorticity of the nodes, calculating the sum of the squares of all vorticities, and taking the square root, which is defined as the global vorticity intensity, including: Normalize the updated perturbation vector and voltage respectively, use linear mapping to map the normalized perturbation vector and voltage to the integer range [0, 255] respectively, and generate a virtual sequence using bitwise exclusive OR operation; Calculate the first-order difference of the virtual sequence to obtain the change rate of the sequence, and calculate the mean value defined as the perturbation sensitivity index; Define a neighbor set based on the connection weights between nodes , regarding the perturbation sensitivity index as a scalar field, calculate the vorticity of the nodes, where is the connection weight between the nth node and the jth node; Calculate the sum of the squares of all vorticities and take the square root, which is defined as the global vorticity intensity.

[0009] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, the steps of detecting the voltage sag and calculating the location of the sag source include: Set a detection threshold using the empirical rule. Determine that a voltage sag has occurred if the global vorticity intensity is greater than the detection threshold, and record the occurrence time; otherwise, it is normal. Extract the occurrence time during the voltage sag, calculate the time difference between nodes, which is defined as the time delay, and calculate the initial estimated distance between the voltage sag source and the nodes. Select the three nodes with the smallest time delay, use the comparison triangulation method to construct a triangulation, and use the least squares method to solve the location of the sag source.

[0010] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, the steps of clustering the voltage sag using the K-means clustering algorithm include: Extract the voltage sequence and instantaneous phase of the nodes corresponding to the location of the sag source, perform a fast Fourier transform on the voltage sequence of the nodes corresponding to the location of the sag source, extract the main frequency, and define the difference between the instantaneous phase of the nodes corresponding to the location of the sag source and the reference phase as the sag phase; Normalize the main frequency and the sag phase respectively, construct a feature vector using the feature concatenation method, set the classification threshold using the ROC curve method, set the number of clusters K using the elbow method, randomly select K feature vectors as the initial cluster centers, calculate the Euclidean distance from the feature vectors to the K distance centers using the Euclidean distance formula, assign the feature vectors to the nearest cluster center, and recalculate the K cluster centers after each assignment. Stop the iteration when the calculated cluster centers are less than the classification threshold to obtain the K classified clustering results.

[0011] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, wherein: constructing a visualization interface to display the clustering results includes: Use the visualization tool Matplotlib to construct a visualization interface, layout a chart area in the middle of the page to display the clustering results, and layout chart areas around the page to display the detection results and the sag phase; Allow users who have passed real-name verification to view.

[0012] As a preferred solution of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in the present invention, wherein: collecting and analyzing the generated power data includes: Store the collected power data and the generated clustering results in the central database, and set security access measures. The central database backs up the stored data to the cloud, and regularly performs integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored in the central database.

[0013] In a second aspect, the present invention provides a voltage sag detection system for a 0.4 kV low-voltage distribution network, including A collection and update module, used to collect power data, define intelligent sensors as nodes, calculate the difference between the instantaneous phase and the reference phase, locate the phase difference as the node, calculate the instantaneous synchronization degree using the cosine function, calculate the initial disturbance index using the direct difference method, iteratively update the disturbance index using the weighted propagation iteration method, and construct an updated disturbance vector; A vorticity calculation module, used to generate a virtual sequence using the exclusive OR operation bit by bit, calculate the first-order difference of the virtual sequence, and calculate the mean value defined as the disturbance sensitivity index. Regard the disturbance sensitivity index as a scalar field, calculate the vorticity of the node, calculate the sum of the squares of all vorticities, and take the square root, defined as the global vorticity intensity; A detection and clustering module, used to detect voltage sags and calculate the location of the sag source, and cluster the voltage sags using the K-means clustering algorithm; A visualization and storage module, used to construct a visualization interface to display the clustering results, and collect and analyze the generated power data.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the voltage sag detection method for a 0.4 kV low-voltage distribution network as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the voltage sag detection method for a 0.4 kV low-voltage distribution network as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: By collecting power data, defining intelligent sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, positioning it as the phase difference of the nodes, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector; generating a virtual sequence using bitwise exclusive OR operation, calculating the first-order difference of the virtual sequence, and calculating the mean value defined as the disturbance sensitivity index, regarding the disturbance sensitivity index as a scalar field, calculating the vorticity of the nodes, calculating the sum of the squares of all vorticities, and taking the square root, defined as the global vorticity intensity; detecting voltage sags, calculating the location of the sag source, calculating the instantaneous phase difference of the nodes using Hilbert transform, and updating the disturbance index by combining the cosine function and the weighted propagation iteration method, improving the sensitivity and accuracy of detection, introducing a way of generating a virtual sequence using bitwise exclusive OR operation, combining the connection weights between nodes, and calculating the global vorticity intensity, enhancing the accuracy and reliability of disturbance analysis. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of the voltage sag detection method for a 0.4 kV low-voltage distribution network in Embodiment 1.

[0019] Figure 2 It is a schematic diagram of the voltage sag detection system for a 0.4 kV low-voltage distribution network in Embodiment 1.

[0020] Figure 3 It is a flowchart of regarding the disturbance sensitivity index as a scalar field in Embodiment 1.

[0021] Figure 4 It is a flowchart of defining the global vorticity intensity in Embodiment 1. Specific Embodiments

[0022] To make the above objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0025] Embodiment 1, referring to Figures 1 to 4 , which is the first embodiment of the present invention. This embodiment provides a method for detecting voltage sags in a 0.4 kV low-voltage distribution network, including the following steps: S1. Collect power data, define intelligent sensors as nodes, calculate the difference between the instantaneous phase and the reference phase, locate it as the phase difference of the nodes, calculate the instantaneous synchronization degree using the cosine function, calculate the initial disturbance index using the direct difference method, iteratively update the disturbance index using the weighted propagation iteration method, and construct an updated disturbance vector; Specifically, collecting power data, defining intelligent sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, locating it as the phase difference of the nodes, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector includes: Deploy voltage and current sensors at key nodes (power input points, branch line connection points, and electrical equipment connection points) in the 0.4 kV low-voltage distribution network to collect power data, including voltage, current, and resistance data; The intelligent sensors include voltage, current, and resistance sensors; Use the time synchronization protocol to align the power data in time, use the IQR method to identify and delete abnormal data, use the mean filling method to fill in missing data, and standardize the power data; Define intelligent sensors as nodes, sort the voltage data in chronological order, and construct a time series matrix, where each row of the matrix represents a voltage sequence; Based on the voltage sequence in the time series matrix, use the Hilbert transform to calculate the instantaneous phase of the node, define the reference phase using the reference phase formula, calculate the difference between the instantaneous phase and the reference phase, and locate it as the phase difference of the node; Based on the voltage and current data, calculate the instantaneous active power and calculate the mean value. The formula is: , where is the instantaneous active power of the nth node at time t, is the voltage data of the nth node at time t, is the current data of the nth node at time t, is the phase difference of the nth node at time t; Calculate the power flow based on the resistance data. The formula is: , where is the power flow between the nth node and the jth node, and are the mean values of the instantaneous active powers of the nth node and the jth node respectively, is the resistance between the nth node and the jth node; Obtain the topological structure information of the 0.4 kV low-voltage distribution network from the hospital power distribution system. If the nth node is connected to the jth node, calculate the connection weight between the nodes, otherwise it is 0 (if there is no connection, the connection weight between the nodes is 0). The formula is: , where is the connection weight between the nth node and the jth node, is the power flow between the nth node and the jth node, is the Euclidean distance between the nth node and the jth node (calculated using the Euclidean distance formula), is the attenuation coefficient (set using exponential decay fitting) reflecting the influence of phase difference on the connection strength, and are the instantaneous phases of the nth node and the jth node respectively; Calculate the sum of the connection weights of the nodes. The formula is: , where is the sum of the connection weights of the nth node, and N is the number of nodes; Use the cosine function to calculate the instantaneous synchronization degree and calculate the mean value. The formula is: , where is the instantaneous synchronization degree of the nth node, is the phase difference of the jth node at time t; The initial perturbation index is calculated using the direct difference method, and the formula is: , where is the initial perturbation index of the nth node, is the mean value of the instantaneous synchronization degree of the nth node; The perturbation index is iteratively updated using the weighted propagation iteration method, and the convergence threshold is set using the statistical analysis method , when , stop the update, arrange in the order of node numbers, and construct the updated perturbation vector. The formula is: , where is the updated perturbation index of the nth node at time t, and are the perturbation indices of the nth node and the jth node at time t - 1 respectively, is the damping factor (e.g., 0.85).

[0026] By deploying intelligent sensors at key nodes, the voltage, current, and resistance data of the hospital can be monitored in real time. By calculating the difference between the instantaneous phase and the reference phase, the present invention can accurately determine the phase difference of the nodes, and then analyze the dynamic changes of the power grid. By updating the perturbation index through the mutual relationship between the nodes, the perception ability of the hospital power system to perturbations is effectively improved. The calculation of the global vorticity intensity not only reflects the perturbation of a single node, but also evaluates the overall stability of the power grid from a global perspective. Synchronously process various power data in the power grid, and then accurately evaluate the state of each node. By real-time monitoring of power data, the voltage sag problem can be quickly located, and measures can be taken in time to reduce the impact of voltage sag on equipment.

[0027] S2. Generate a virtual sequence using bitwise exclusive OR operation, calculate the first-order difference of the virtual sequence, and calculate the mean value defined as the perturbation sensitivity index. Regard the perturbation sensitivity index as a scalar field, calculate the vorticity of the nodes, calculate the sum of the squares of all vorticities, and take the square root, which is defined as the global vorticity intensity; Specifically, generate a virtual sequence using bitwise exclusive OR operation, calculate the first-order difference of the virtual sequence, and calculate the mean value defined as the perturbation sensitivity index. Regard the perturbation sensitivity index as a scalar field, calculate the vorticity of the nodes, calculate the sum of the squares of all vorticities, and take the square root, which is defined as the global vorticity intensity, including: Normalize the updated perturbation vector and voltage respectively, use linear mapping to map the normalized perturbation vector and voltage to the integer range [0, 255] respectively, and generate a virtual sequence using bitwise XOR operation; Calculate the first-order difference of the virtual sequence to obtain the change rate of the sequence, and calculate the mean value defined as the perturbation sensitivity index; Define a neighbor set based on the connection weights between nodes , indicating the nodes directly connected to the nth node. Regard the perturbation sensitivity index as a scalar field and calculate the vorticity of the node. The formula is: , where is the vorticity of the nth node, is the neighbor set of the nth node, and are the perturbation sensitivity indices of the jth and nth nodes respectively, is the power flow direction, taking values of +1 (flowing to j), −1 (flowing to n), or 0 (no direction); Calculate the sum of the squares of all vorticities and take the square root, which is defined as the global vorticity intensity.

[0028] The step of generating a virtual sequence through bitwise XOR operation can effectively convert the original power data into a format suitable for analysis. The perturbation sensitivity index obtained by calculating the mean value can effectively measure the sensitivity of the hospital power system to different perturbation sources. The calculation of vorticity helps to reveal the complexity of the interaction between nodes in the hospital power system, making the propagation path and intensity of the perturbation more clearly presented. The global vorticity intensity can provide an overall stability assessment for the hospital power system. Especially when a voltage sag event occurs, the global vorticity intensity can reflect the overall perturbation intensity of the system. The global vorticity intensity and vorticity analysis provide an in-depth perspective on power flow and perturbation propagation, which helps the hospital power system to perform precise scheduling when facing complex grid loads, thereby effectively optimizing the grid resource allocation and improving the overall efficiency and stability of the system.

[0029] S3. Detect voltage sags and calculate the location of the sag source, and use the K-means clustering algorithm to cluster the voltage sags; Specifically, detecting voltage sags and calculating the location of the sag source includes: Set a detection threshold using the empirical rule. Determine that a voltage sag has occurred for the global vorticity intensity greater than the detection threshold, and record the occurrence time, otherwise it is normal; Extract the occurrence time during the voltage sag, calculate the time difference between nodes, which is defined as the time delay, and calculate the initial estimated distance between the voltage sag source and the node. The formula is: , Wherein is the initial estimated distance between the voltage sag source and the nth node, c is the propagation speed, is the time delay of the nth node; Select the three nodes with the smallest time delay, use the comparison triangulation method to construct triangulation, and use the least squares method to solve the position of the sag source.

[0030] The threshold judgment based on the global vorticity intensity improves the response speed and accuracy of voltage sag detection, avoids the limitations of manual intervention. The calculation of time delay provides key data for voltage sag source location, enabling the detection process not only to be limited to the occurrence time but also to further trace back to the source location. The triangulation method combined with time delay information can achieve relatively accurate positioning through geometric relationships. The least squares method obtains the optimal solution by minimizing the sum of squared errors, thereby realizing the accurate positioning of the voltage sag source. Whether in a simple distribution network or a complex multi-level power grid, the system can accurately locate the voltage sag source and complete the solution of the source position through simple calculation steps.

[0031] Furthermore, use the K-means clustering algorithm to cluster voltage sags, including: Extract the voltage sequence and instantaneous phase of the node corresponding to the sag source position, perform a fast Fourier transform on the voltage sequence of the node corresponding to the sag source position, extract the main frequency, and define the difference between the instantaneous phase of the node corresponding to the sag source position and the reference phase as the sag phase; Normalize the main frequency and sag phase respectively, use the feature splicing method to construct a feature vector, use the ROC curve method to set the classification threshold, use the elbow method to set the number of clusters K, randomly select K feature vectors as the initial cluster centers, use the Euclidean distance formula to calculate the Euclidean distance from the feature vector to the K distance centers, assign the feature vector to the nearest cluster center, and recalculate the K cluster centers after each assignment. Stop the iteration when the calculated cluster center is less than the classification threshold to obtain the K classification results after clustering.

[0032] Transfer the voltage data from the time domain to the frequency domain to reveal the frequency characteristics of the signal, so as to better capture the essence of voltage sag events. The phase differences of different sag sources may reflect their spatial and temporal positional relationships, thus providing an important basis for the location and classification of voltage sag sources. The feature splicing method can effectively enhance the applicability of the K-means clustering algorithm, enabling it to obtain more accurate clustering results in a complex hospital power system environment. The ROC curve can help select the optimal classification threshold, thereby improving the accurate recognition rate of voltage sag events. The elbow method helps select the most suitable number of clusters by calculating the error change trend under different K values, so as to achieve the best effect in the clustering process. The K-means algorithm calculates the Euclidean distance between the feature vector and the cluster center and continuously updates the cluster center, finally converging to an optimal classification result. It can not only accurately cluster and classify voltage sag events, but also help the hospital power system identify potential power quality problems in advance through real-time monitoring and data analysis.

[0033] S4. Construct a visualization interface to display the clustering results, and collect and analyze the generated power data; Specifically, constructing a visualization interface to display the clustering results includes: Use the visualization tool Matplotlib to construct a visualization interface. Layout the chart area in the middle of the page to display the clustering results, and layout the chart areas around the page to display the detection results and sag phases; Allow users who have passed real-name verification to view.

[0034] As a powerful chart-drawing tool, Matplotlib can generate various high-quality charts based on the clustering analysis results in the hospital power system. Through this layout, users can clearly see the clustering categories of each node and the distribution among different categories. In the operation and monitoring process of the hospital power system, the accuracy and security of data are crucial. Real-name verification not only prevents unauthorized personnel from accessing sensitive data, but also provides a reliable guarantee for data traceability and accountability.

[0035] Furthermore, collecting and analyzing the generated power data includes: Store the collected power data and the generated clustering results in the central database, and set security access measures. The central database backs up the stored data to the cloud and regularly conducts integrity checks on the stored data and backup data. After the check is completed, an integrity check record is generated and synchronously stored in the central database.

[0036] To ensure the security of power data and clustering results, the present invention prevents unauthorized personnel from accessing data by setting up security access measures. By storing data in the cloud, not only can the long-term availability of the data be ensured, but also data recovery can be conveniently carried out. Through integrity detection, data damage or tampering can be promptly discovered, and necessary measures can be taken for repair or recovery.

[0037] This embodiment also provides a voltage sag detection system for a 0.4 kV low-voltage distribution network, including: A collection and update module, which is used to collect power data, define intelligent sensors as nodes, calculate the difference between the instantaneous phase and the reference phase, locate the phase difference as the node, calculate the instantaneous synchronization degree using the cosine function, calculate the initial disturbance index using the direct difference method, use the weighted propagation iteration method to iteratively update the disturbance index, and construct an updated disturbance vector; A vorticity calculation module, which is used to generate a virtual sequence using bitwise exclusive OR operation, calculate the first-order difference of the virtual sequence, and calculate the mean value defined as the disturbance sensitivity index. Regarding the disturbance sensitivity index as a scalar field, calculate the vorticity of the node, calculate the sum of the squares of all vorticities, and take the square root, which is defined as the global vorticity intensity; A detection and clustering module, which is used to detect voltage sags, calculate the location of the sag source, and cluster voltage sags using the K-means clustering algorithm; A visualization and storage module, which is used to construct a visualization interface to display the clustering results and collect and analyze the generated power data.

[0038] This embodiment also provides a computer device applicable to the case of the voltage sag detection method for a 0.4 kV low-voltage distribution network, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the voltage sag detection method for a 0.4 kV low-voltage distribution network as proposed in the above embodiment.

[0039] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0040] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the voltage sag detection method for a 0.4 kV low-voltage distribution network as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0041] In summary, the present invention collects power data, defines intelligent sensors as nodes, calculates the difference between the instantaneous phase and the reference phase, locates the phase difference of the nodes, calculates the instantaneous synchronization degree using the cosine function, calculates the initial disturbance index using the direct difference method, iteratively updates the disturbance index using the weighted propagation iteration method, and constructs an updated disturbance vector; generates a virtual sequence using bitwise exclusive OR operation, calculates the first-order difference of the virtual sequence, and calculates the mean value defined as the disturbance sensitivity index, regards the disturbance sensitivity index as a scalar field, calculates the vorticity of the nodes, calculates the sum of the squares of all vorticities, and takes the square root, defined as the global vorticity intensity; detects voltage sags, calculates the location of the sag source, calculates the instantaneous phase difference of the nodes using Hilbert transform, combines the cosine function and the weighted propagation iteration method to update the disturbance index, improves the sensitivity and accuracy of detection, introduces a way to generate a virtual sequence using bitwise exclusive OR operation, combines the connection weights between nodes, calculates the global vorticity intensity, and enhances the accuracy and reliability of disturbance analysis.

[0042] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A voltage sag detection method for a 0.4 kV low-voltage distribution network, characterized in that: including Collecting power data, defining intelligent sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, positioning it as the phase difference of the nodes, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector; Generating a virtual sequence using bitwise exclusive OR operation, calculating the first-order difference of the virtual sequence, and calculating the mean value defined as the disturbance sensitivity index. Regarding the disturbance sensitivity index as a scalar field, calculating the vorticity of the nodes, calculating the sum of the squares of all vorticities, and taking the square root, which is defined as the global vorticity intensity; Detecting voltage sags and calculating the location of the sag source, and clustering the voltage sags using the K-means clustering algorithm; Constructing a visualization interface to display the clustering results and collecting and analyzing the generated power data.

2. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 1, wherein: The steps of collecting power data, defining intelligent sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, positioning it as the phase difference of the nodes, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector include: Deploying voltage and current sensors at key nodes of the 0.4 kV low-voltage distribution network to collect power data, including voltage, current, and resistance data; Defining intelligent sensors as nodes, sorting the voltage data in chronological order, and constructing a time series matrix; Based on the voltage sequence in the time series matrix, calculating the instantaneous phase of the nodes using the Hilbert transform, defining the reference phase using the reference phase formula, calculating the difference between the instantaneous phase and the reference phase, and positioning it as the phase difference of the nodes; Based on the voltage and current data, calculating the instantaneous active power and calculating the mean value, and calculating the power flow based on the resistance data; Obtaining the topological structure information of the 0.4 kV low-voltage distribution network from the hospital power distribution system. If the nth node is connected to the jth node, calculating the connection weight between the nodes, otherwise it is 0. Calculating the sum of the connection weights of the nodes, calculating the instantaneous synchronization degree using the cosine function, and calculating the mean value, and calculating the initial disturbance index using the direct difference method; Iteratively update the perturbation index using the weighted propagation iteration method, and set the convergence threshold using the statistical analysis method , when , stop the update, arrange in the order of node numbers, and construct the updated perturbation vector, where is the perturbation index of the nth node updated at time t, is the perturbation index of the nth node at time t-1.

3. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 2, characterized in that: The steps of generating a virtual sequence using bitwise exclusive OR operation, calculating the first-order difference of the virtual sequence, and calculating the mean value defined as the disturbance sensitivity index. Regarding the disturbance sensitivity index as a scalar field, calculating the vorticity of the nodes, calculating the sum of the squares of all vorticities, and taking the square root, which is defined as the global vorticity intensity include: Normalizing the updated disturbance vector and voltage respectively, using linear mapping to map the normalized disturbance vector and voltage to the integer range [0, 255] respectively, and generating a virtual sequence using bitwise exclusive OR operation; Calculating the first-order difference of the virtual sequence to obtain the change rate of the sequence, and calculating the mean value defined as the disturbance sensitivity index; Define the neighbor set based on the connection weights between nodes , regard the perturbation sensitivity index as a scalar field, and calculate the vorticity of the node, where is the connection weight between the nth node and the jth node; Calculating the sum of the squares of all vorticities and taking the square root, which is defined as the global vorticity intensity.

4. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 3, characterized in that: The steps of detecting voltage sags and calculating the location of the sag source include: Setting a detection threshold using the empirical rule, determining that a voltage sag has occurred when the global vorticity intensity is greater than the detection threshold, and recording the occurrence time, otherwise it is normal; Extract the occurrence time during the voltage sag, calculate the time difference between nodes, which is defined as the time delay, and calculate the initial estimated distance from the voltage sag source to the nodes. Select the three nodes with the smallest time delay, use the comparison triangulation method to construct triangulation, and use the least squares method to solve the position of the sag source.

5. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 4, wherein: The use of the K-means clustering algorithm to cluster voltage sags includes: Extract the voltage sequence and instantaneous phase of the nodes corresponding to the position of the sag source, perform a fast Fourier transform on the voltage sequence of the nodes corresponding to the position of the sag source, extract the main frequency, and define the difference between the instantaneous phase of the nodes corresponding to the position of the sag source and the reference phase as the sag phase. Normalize the main frequency and sag phase respectively, use the feature splicing method to construct a feature vector, use the ROC curve method to set the classification threshold, use the elbow method to set the number of clusters K, randomly select K feature vectors as the initial cluster centers, use the Euclidean distance formula to calculate the Euclidean distance from the feature vector to the K distance centers, assign the feature vector to the nearest cluster center, and recalculate the K cluster centers after each assignment. Stop the iteration when the calculated cluster centers are less than the classification threshold to obtain the K classification results after clustering.

6. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 5, wherein: The construction of the visualization interface to display the clustering results includes: Use the visualization tool Matplotlib to construct a visualization interface, layout a chart area in the middle of the page to display the clustering results, and layout chart areas around the page to display the detection results and sag phase. Allow users who have passed real-name verification to view.

7. The voltage sag detection method for a 0.4 kV low-voltage distribution network according to claim 6, characterized in that: The collection and analysis of the generated power data includes: Store the collected power data and the generated clustering results in the central database, and set security access measures. The central database backs up the stored data to the cloud, and regularly performs integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored in the central database.

8. A voltage sag detection system for a 0.4 kV low-voltage distribution network, based on the voltage sag detection method for a 0.4 kV low-voltage distribution network according to any one of claims 1 to 7, characterized in that: Including, A collection and update module for collecting power data, defining intelligent sensors as nodes, calculating the difference between the instantaneous phase and the reference phase, positioning it as the phase difference of the nodes, calculating the instantaneous synchronization degree using the cosine function, calculating the initial disturbance index using the direct difference method, iteratively updating the disturbance index using the weighted propagation iteration method, and constructing an updated disturbance vector. A vorticity calculation module for generating a virtual sequence using the exclusive OR operation for each bit, calculating the first-order difference of the virtual sequence, and calculating the mean value defined as the disturbance sensitivity index. Regarding the disturbance sensitivity index as a scalar field, calculate the vorticity of the nodes, calculate the sum of the squares of all vorticities, and take the square root, which is defined as the global vorticity intensity. A detection and clustering module for detecting voltage sags and calculating the position of the sag source, and clustering voltage sags using the K-means clustering algorithm. A visualization and storage module for constructing a visualization interface to display the clustering results and collecting and analyzing the generated power data.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the voltage sag detection method for the 0.4 kV low-voltage distribution network described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the voltage sag detection method for a 0.4 kV low-voltage distribution network according to any one of claims 1 to 7.

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