Intelligent power grid monitoring system integrating distance measurement technology and feeder terminal unit

Abstract

The invention provides an intelligent power grid monitoring system integrating a distance measurement technology and a feeder terminal unit. The system obtains node information of a corresponding monitoring node through each feeder terminal unit in a feeder terminal unit set, and sends the node information to a server to form a node information set, then the server determines a voltage vector, a current vector and a distance matrix according to the node information set, and then the voltage vector, the current vector and the distance matrix are monitored by using a preset power grid state monitoring model. The power grid state of the power distribution network is determined according to the voltage vector, the current vector and the distance matrix so that potential problems or abnormal conditions in the power grid can be found in time, intelligent decision support is provided for power grid operation and maintenance personnel, and therefore the system can rapidly give out early warning and help the operation and maintenance personnel take measures in time; safe and stable operation of the power grid is ensured.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a smart grid monitoring system integrating ranging technology with a feeder terminal unit. Background Art

[0002] With the continuous development of smart grid technology, higher requirements are placed on the monitoring and management of power grids. Traditional power grid monitoring systems often rely on manual inspections and regular testing, which is not only inefficient but also difficult to detect potential problems in the power grid in a timely manner. Especially in complex distribution network environments, due to the large number of nodes and complex lines, traditional monitoring methods are difficult to achieve comprehensive and real-time monitoring.

[0003] In addition, the existing power grid monitoring system also has deficiencies in data processing and analysis. Traditional monitoring systems can usually only monitor and record basic parameters such as voltage and current of the power grid, and lack in-depth analysis and quantitative evaluation of the power grid status. This makes it difficult for operation and maintenance personnel to accurately judge the operating status of the power grid and to discover and solve problems in the power grid in a timely manner.

[0004] In response to the above problems, how to achieve real-time monitoring and comprehensive analysis of the power grid and improve the operation and maintenance efficiency and safety of the power grid has become an important direction for the current development of smart grid technology. Summary of the invention

[0005] The present application provides an intelligent grid monitoring system that integrates ranging technology with feeder terminal units, which is used to timely discover potential problems or abnormal conditions in the power grid and provide intelligent decision-making support for power grid operation and maintenance personnel, so that the system can quickly issue early warnings and help operation and maintenance personnel take timely measures to ensure the safe and stable operation of the power grid.

[0006] In a first aspect, the present application provides a smart grid monitoring system integrating ranging technology with feeder terminal units, comprising: a server, a distribution network, and a feeder terminal unit set, wherein each feeder terminal unit in the feeder terminal unit set is used to monitor node information of a monitoring node in the distribution network, and each feeder terminal unit in the feeder terminal unit set is respectively connected to the distribution network for communication;

[0007] Each feeder terminal unit in the feeder terminal unit set obtains node information of a corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal, and a monitoring node position;

[0008] The server determines a voltage vector, a current vector and a distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by positions of two corresponding monitoring nodes;

[0009] The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix.

[0010] In the above scheme, through each feeder terminal unit in the feeder terminal unit set, the system can monitor the voltage signal, current signal and position information of each monitoring node in the distribution network in real time. Then, each feeder terminal unit sends the collected node information to the server, and the server receives and integrates this information to form a node information set. Then, the server further determines the voltage vector, current vector and distance matrix based on the node information set. Among them, the voltage vector and current vector reflect the electrical characteristics of the power grid, and the distance matrix provides the spatial position relationship between the monitoring nodes. Then, the server uses the preset power grid state monitoring model, combined with the voltage vector, current vector and distance matrix, to quantitatively evaluate the power grid state of the distribution network. By evaluating the power grid state, the system can promptly discover potential problems or abnormal conditions in the power grid, and provide intelligent decision-making support for power grid operation and maintenance personnel, so that the system can quickly issue an early warning to help the operation and maintenance personnel take measures in time to ensure the safe and stable operation of the power grid.

[0011] Optionally, the server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including:

[0012] The server determines a grid state characteristic value of the distribution network according to the voltage vector, the current vector and the distance matrix;

[0013] If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0014] In the above scheme, the server uses the preset grid state monitoring model to comprehensively analyze the voltage vector, current vector and distance matrix, and extracts the characteristic values ​​that can reflect the grid state. These characteristic values ​​not only include the electrical characteristics of the grid (such as voltage and current distribution), but also incorporate the spatial position relationship between the monitoring nodes, thereby realizing a refined evaluation of the grid state. Then, the system presets a grid state characteristic threshold, which can be based on a large amount of historical data and grid operation experience, and can effectively distinguish between normal and abnormal states. By comparing the actual calculated grid state characteristic value with the preset threshold, the system can more accurately identify the abnormal state of the grid. When the server determines that the grid state characteristic value is greater than the preset grid state characteristic threshold, the system will immediately determine that the distribution network is in an abnormal state and trigger the early warning mechanism. This real-time early warning capability enables operation and maintenance personnel to be aware of the abnormal situation of the grid at the first time, so as to take necessary measures to intervene and avoid the expansion of faults or accidents.

[0015] Optionally, the server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including:

[0016] The server uses Fourier transform to convert the voltage vector and the current vector into corresponding frequency domain voltage vector and frequency domain current vector;

[0017] The server determines a grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix;

[0018] If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0019] In the above scheme, the server converts the voltage vector and current vector in the time domain into the voltage vector and current vector in the frequency domain through Fourier transform. Frequency domain analysis can reveal the components and distribution of signals at different frequencies, so as to have a deeper understanding of the dynamic characteristics of the power grid. This conversion makes the power grid state assessment no longer limited to the time domain characteristics, but can capture more key information hidden in the frequency domain, improving the precision and accuracy of the assessment. In the frequency domain, the server combines the frequency domain voltage vector, the frequency domain current vector and the distance matrix to calculate the characteristic values ​​of the power grid state. These characteristic values ​​not only include the performance of the electrical characteristics of the power grid in the frequency domain, but also incorporate the spatial position relationship between the monitoring nodes, making the identification of abnormal states more sensitive. For example, some weak signal changes that are not easy to detect in the time domain may appear as significant changes in the spectrum components in the frequency domain, so that they can be accurately captured by the system. Then, the system presets the characteristic threshold of the power grid state to distinguish between normal and abnormal states. When the characteristic value of the power grid state calculated by the server exceeds this threshold, the system will immediately determine that the distribution network is in an abnormal state and trigger the early warning mechanism. Since frequency domain analysis can capture more subtle signal changes, the system can achieve early fault warning, buy more time for operation and maintenance personnel to troubleshoot and handle faults, and reduce the impact of faults on power grid operation. In addition, through frequency domain analysis, the system can not only identify abnormal conditions, but also provide more accurate and scientific decision support for operation and maintenance personnel based on the specific values ​​and change trends of frequency domain eigenvalues. For example, by analyzing the changes in frequency domain eigenvalues, operation and maintenance personnel can determine the type and severity of the fault, so as to formulate a more effective operation and maintenance plan. In addition, the system can also automatically allocate operation and maintenance resources, such as maintenance personnel, emergency repair equipment, etc., according to the actual situation of the power grid, to ensure that the fault can be handled in a timely and effective manner. By combining ranging technology with feeder terminal units, the system realizes comprehensive monitoring and real-time early warning of the distribution network. This comprehensive monitoring capability enables the system to timely discover potential risks and problems in the power grid, providing strong support for operation and maintenance personnel. At the same time, the real-time early warning mechanism enables operation and maintenance personnel to be informed of abnormal conditions of the power grid at the first time, so as to quickly take measures to intervene, avoid the expansion of faults or accidents, and improve the safety and stability of the power grid.

[0020] Optionally, before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the method further includes:

[0021] The server determines a weight coefficient according to the frequency domain voltage vector and the frequency domain current vector, the weight coefficient is used to determine the grid state characteristic value, and the weight coefficient is positively correlated with the grid state characteristic value.

[0022] In the above scheme, the server determines the weight coefficients according to the frequency domain voltage vector and the frequency domain current vector. These weight coefficients reflect the importance of different frequency domain voltages or frequency domain currents in the evaluation of the power grid state. By introducing the weight coefficients, the system can give different degrees of importance to the signal components of different frequency domain voltages or frequency domain currents when calculating the characteristic value of the power grid state, thereby improving the accuracy of the characteristic value calculation. Among them, the weight coefficient is positively correlated with the characteristic value of the power grid state, which means that the larger the weight coefficient, the greater the contribution of the corresponding frequency domain signal component in the characteristic value calculation. This design enables the characteristic value to more accurately reflect the actual state of the power grid, especially those frequency domain voltages or frequency domain currents that have a greater impact on the operation of the power grid, thereby enhancing the representativeness of the characteristic value. In addition, when determining the characteristic value of the power grid state using the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the system considers the influence of the weight coefficient. This enables the system to more accurately distinguish between normal signal changes and abnormal signal changes when identifying abnormal states, reduce false alarms and missed alarms, and improve the accuracy of abnormal state identification.

[0023] Optionally, before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the method further includes:

[0024] The server uses the Welch method to determine the auto-power spectral density of the frequency-domain voltage vector according to the frequency-domain voltage vector, determines the auto-power spectral density of the frequency-domain current vector according to the frequency-domain current vector, and determines the cross-power spectral density of the frequency-domain voltage vector and the frequency-domain current vector according to the frequency-domain voltage vector and the frequency-domain current vector;

[0025] The server determines a coherence function of the frequency domain voltage vector and the frequency domain current vector based on the mutual power spectrum density of the frequency domain voltage vector and the frequency domain current vector, the autopower spectrum density of the frequency domain voltage vector and the autopower spectrum density of the frequency domain current vector, the coherence function is used to determine the grid state characteristic value, and the coherence function is positively correlated with the grid state characteristic value.

[0026] In the above scheme, the server uses the Welch method to calculate the auto-power spectrum density and cross-power spectrum density of the frequency domain voltage vector and the frequency domain current vector. The Welch method is an improved periodogram method. Through segmented averaging and windowing processing, it effectively reduces spectrum leakage and noise interference, and improves the accuracy and reliability of signal analysis. This improvement makes the spectrum characteristics of the frequency domain voltage vector and the frequency domain current vector more accurate, providing a solid foundation for the subsequent eigenvalue calculation. Then, the server calculates the coherence function based on the cross-power spectrum density of the frequency domain voltage vector and the frequency domain current vector, the auto-power spectrum density of the frequency domain voltage vector, and the auto-power spectrum density of the frequency domain current vector. The coherence function reflects the correlation between the two signals at different frequencies, that is, the phase synchronization degree and energy transfer relationship between them. By introducing the coherence function, the system can more comprehensively evaluate the power grid state, especially the dynamic relationship between voltage and current in the power grid, so as to capture more potential problems and risks. The coherence function is positively correlated with the eigenvalue of the power grid state, which means that the larger the coherence function value, the more important the corresponding power grid state eigenvalue. This design enables the grid state eigenvalues ​​to more accurately reflect the actual state of the grid, especially those characteristics that are closely related to the dynamic relationship between voltage and current. By comprehensively considering the coherence function and eigenvalues, the system can more accurately judge the operating status of the grid and provide strong support for operation and maintenance decisions. In addition, when determining the grid state eigenvalues ​​using frequency domain voltage vectors, frequency domain current vectors, and distance matrices, the system considers the influence of the coherence function. Since the coherence function can reflect the phase synchronization degree and energy transfer relationship between voltage and current, when abnormal conditions occur in the grid, such as short circuits, overloads, etc., the coherence function value often changes significantly. This makes the system more sensitive and accurate in identifying abnormal conditions, and can detect problems in the grid more promptly and take measures to intervene.

[0027] Optionally, after determining that the power distribution network state is an abnormal state, the method further includes:

[0028] The server determines a power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network.

[0029] In the above scheme, after the grid state is determined to be abnormal, the system can respond quickly and dynamically adjust the power distribution plan according to the distribution of load equipment corresponding to the monitoring node. This instant adjustment capability enables the grid to restore stable operation faster when facing faults or abnormal situations, reduce the time and scope of power outages, and improve the reliability and resilience of the grid. In addition, the system determines the power distribution plan by considering the distribution of load equipment, which can ensure that power resources are more reasonably configured. Under abnormal conditions, the system can give priority to guaranteeing the power supply of key load equipment while reducing the power consumption of non-critical loads, thereby maximizing the utilization of power resources. In addition, by dynamically adjusting the power distribution plan, the system can avoid excessive loss and waste of the grid under abnormal conditions, thereby reducing operation and maintenance costs. At the same time, reasonable power distribution also helps to reduce the loss and failure rate of grid equipment, extend the service life of equipment, and further reduce operation and maintenance costs. In addition, after determining that the grid state is abnormal, the system can automatically determine the power distribution plan according to the distribution of load equipment without manual intervention. This automation and intelligent decision-making capability enables the smart grid system to respond more quickly and accurately when facing a complex and changing grid environment, thereby improving the overall intelligence level of the system.

[0030] Optionally, before the server determines the power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network, the method further includes:

[0031] The server determines a balance factor according to the frequency domain voltage vector, the frequency domain current vector, the distance matrix and the power state of the power distribution network, and the balance factor is used to determine the power distribution scheme.

[0032] In the above scheme, the server determines the balance factor according to the frequency domain voltage vector, the frequency domain current vector, the distance matrix and the power state of the distribution network. As an important reference indicator of the power distribution scheme, the balance factor can comprehensively consider various factors of the power grid, such as voltage stability, current distribution, node distance and power balance. By introducing the balance factor, the system can more scientifically evaluate the power supply requirements of different load devices and the carrying capacity of the power grid when determining the power distribution scheme, so as to formulate a more reasonable and effective power distribution strategy. In addition, the balance factor plays an important guiding role in the power distribution scheme. It can help the system identify potential risks and weak links in the power grid, such as voltage fluctuations, current overloads, power imbalances, etc., and adjust the power distribution scheme accordingly to reduce the impact of these risks on the operation of the power grid. Guided by the balance factor, the system can ensure that the power grid can still maintain high stability and security under abnormal conditions, avoiding large-scale power outages and accidents. In addition, when determining the power distribution scheme, the system will dynamically dispatch power resources according to the balance factor. This means that the system can flexibly adjust the power supply priority and power distribution ratio of each load device according to the real-time status and demand changes of the power grid to maximize the utilization of power resources. Through the role of balancing factors, the system can avoid the waste and over-concentration of power resources and improve the configuration efficiency and economy of the entire power grid. Moreover, the calculation and application process of the balancing factor is completely completed automatically by the server without human intervention. This enables the smart grid monitoring system to make decisions more quickly and accurately when facing a complex and changing power grid environment, and improve the intelligent management level of the system. Through the introduction of balancing factors, the system can realize real-time monitoring, analysis and optimization of the power grid status, providing more accurate and reliable decision support for operation and maintenance personnel.

[0033] In a second aspect, the present application provides a smart grid monitoring method integrating ranging technology and feeder terminal units, which is applied to a smart grid monitoring system integrating ranging technology and feeder terminal units, wherein the smart grid monitoring system integrating ranging technology and feeder terminal units comprises: a server, a distribution network, and a feeder terminal unit set, wherein each feeder terminal unit in the feeder terminal unit set is used to monitor node information of a monitoring node in the distribution network, and each feeder terminal unit in the feeder terminal unit set is respectively connected to the distribution network in communication; the method comprises:

[0034] Each feeder terminal unit in the feeder terminal unit set obtains node information of a corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal, and a monitoring node position;

[0035] The server determines a voltage vector, a current vector and a distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by positions of two corresponding monitoring nodes;

[0036] The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix.

[0037] Optionally, the server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including:

[0038] The server determines a grid state characteristic value of the distribution network according to the voltage vector, the current vector and the distance matrix;

[0039] If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0040] Optionally, the server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including:

[0041] The server uses Fourier transform to convert the voltage vector and the current vector into corresponding frequency domain voltage vector and frequency domain current vector;

[0042] The server determines a grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix;

[0043] If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0044] Optionally, before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the method further includes:

[0045] The server determines a weight coefficient according to the frequency domain voltage vector and the frequency domain current vector, the weight coefficient is used to determine the grid state characteristic value, and the weight coefficient is positively correlated with the grid state characteristic value.

[0046] Optionally, before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the method further includes:

[0047] The server uses the Welch method to determine the auto-power spectral density of the frequency-domain voltage vector according to the frequency-domain voltage vector, determines the auto-power spectral density of the frequency-domain current vector according to the frequency-domain current vector, and determines the cross-power spectral density of the frequency-domain voltage vector and the frequency-domain current vector according to the frequency-domain voltage vector and the frequency-domain current vector;

[0048] The server determines a coherence function of the frequency domain voltage vector and the frequency domain current vector based on the mutual power spectrum density of the frequency domain voltage vector and the frequency domain current vector, the autopower spectrum density of the frequency domain voltage vector and the autopower spectrum density of the frequency domain current vector, the coherence function is used to determine the grid state characteristic value, and the coherence function is positively correlated with the grid state characteristic value.

[0049] Optionally, after determining that the power distribution network state is an abnormal state, the method further includes:

[0050] The server determines a power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network.

[0051] Optionally, before the server determines the power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network, the method further includes:

[0052] The server determines a balance factor according to the frequency domain voltage vector, the frequency domain current vector, the distance matrix and the power state of the power distribution network, and the balance factor is used to determine the power distribution scheme.

[0053] In a third aspect, the present application provides an electronic device, including:

[0054] processor; and,

[0055] A memory, configured to store executable instructions of the processor;

[0056] The processor is configured to perform any possible method described in the second aspect by executing the executable instructions.

[0057] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the second aspect.

[0058] The present application provides an intelligent grid monitoring system that integrates ranging technology and feeder terminal units. The system obtains node information of corresponding monitoring nodes through each feeder terminal unit in a feeder terminal unit set, and sends the node information to a server to form a node information set. Then, the server determines a voltage vector, a current vector and a distance matrix based on the node information set, and then uses a preset grid state monitoring model to determine the grid state of the distribution network based on the voltage vector, the current vector and the distance matrix so as to timely detect potential problems or abnormal conditions in the grid, and provide intelligent decision-making support for grid operation and maintenance personnel, so that the system can quickly issue an early warning, help the operation and maintenance personnel take timely measures, and ensure the safe and stable operation of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 It is a flow chart of a smart grid monitoring method integrating ranging technology with a feeder terminal unit according to an exemplary embodiment of the present application;

[0061] Figure 2 is a flow chart of a smart grid monitoring method integrating ranging technology with a feeder terminal unit according to another exemplary embodiment of the present application;

[0062] Figure 3 is a structural schematic diagram of a smart grid monitoring system integrating ranging technology and a feeder terminal unit according to an exemplary embodiment of the present application;

[0063] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0064] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0065] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0066] In order to solve the above problems, the system in the embodiment provided by the present application mainly consists of three parts: a server, a distribution network and a feeder terminal unit set. Among them, each feeder terminal unit in the feeder terminal unit set is responsible for monitoring the node information of each monitoring node in the distribution network, including voltage signals, current signals and location information of the monitoring nodes. These feeder terminal units are connected to the distribution network in communication to ensure real-time transmission of data.

[0067] Each feeder terminal unit has a data acquisition function, which can monitor the voltage, current and other electrical parameters of the node it is responsible for monitoring in real time, and obtain the location information of the monitoring node through the built-in positioning system. This node information is then sent to the server to form a node information collection. This real-time monitoring method ensures the timeliness and accuracy of the data, and provides a reliable basis for subsequent data analysis and grid status assessment.

[0068] After receiving the node information from each feeder terminal unit, the server integrates and processes the information. First, the server determines the voltage vector and current vector based on the voltage signal and current signal. These two vectors reflect the electrical characteristics of the power grid. Secondly, the server calculates the distance matrix based on the node location information. The matrix provides the spatial position relationship between the monitoring nodes. The generation of these vectors and matrices provides rich data dimensions and quantitative indicators for the subsequent quantitative evaluation of the power grid status.

[0069] In order to accurately evaluate the operating status of the power grid, the server uses the preset power grid status monitoring model, combined with the voltage vector, current vector and distance matrix, to quantitatively evaluate the power grid status of the distribution network. This evaluation process not only takes into account the electrical characteristics of the power grid (such as voltage stability, current balance, etc.), but also incorporates the spatial position relationship between monitoring nodes, thereby achieving a comprehensive analysis of the power grid status. Through quantitative evaluation, the system can promptly detect potential problems or abnormal conditions in the power grid, such as voltage fluctuations, current overloads, short circuits, etc., and provide intelligent decision-making support for operation and maintenance personnel.

[0070] Once the system detects an abnormality in the power grid status, the server will immediately trigger the early warning mechanism and send early warning information to the power grid operation and maintenance personnel. At the same time, the system will also provide intelligent decision-making support for the operation and maintenance personnel based on the early warning situation, such as recommending corresponding adjustment measures, optimizing power distribution plans, etc. These decision-making supports help the operation and maintenance personnel take measures quickly to ensure the safe and stable operation of the power grid.

[0071] The system also has dynamic adjustment capabilities, and can adjust monitoring parameters and warning thresholds in real time according to changes in grid status and operation and maintenance requirements to ensure the continued effectiveness and accuracy of the system. In addition, the system also supports continuous optimization, and continuously improves the accuracy and efficiency of grid status monitoring and warning through continuous learning and accumulation of data.

[0072] To summarize, the smart grid monitoring system that integrates the ranging technology and feeder terminal unit provided in this application realizes comprehensive monitoring and intelligent management of the power grid status through technical means such as real-time monitoring, data integration and processing, quantitative evaluation of power grid status, intelligent early warning and decision support, dynamic adjustment and continuous optimization, thereby improving the operation and maintenance efficiency and safety of the power grid.

[0073] Figure 1 FIG. 1 is a flow chart of a smart grid monitoring method integrating ranging technology with a feeder terminal unit according to an exemplary embodiment of the present application. Figure 1 As shown, the smart grid monitoring method provided by this embodiment that integrates the ranging technology with the feeder terminal unit includes:

[0074] S101. Each feeder terminal unit in a feeder terminal unit set obtains node information of a corresponding monitoring node.

[0075] In this step, each feeder terminal unit in the feeder terminal unit set obtains node information of the corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal and a monitoring node position.

[0076] Specifically, each feeder terminal unit in the feeder terminal unit set collects the voltage signal, current signal and location information of the corresponding monitoring node in real time through built-in sensors and measuring devices. This information reflects the operating status of the power grid at a specific time and place. After collecting the node information, each feeder terminal unit sends the node information to the server through wireless communication or wired communication. The server receives and integrates the information from all feeder terminal units to form a node information set. This set contains the voltage signal, current signal and location information of all monitoring nodes in the distribution network.

[0077] S102: The server determines a voltage vector, a current vector, and a distance matrix according to a node information set.

[0078] In this step, the server determines the voltage vector, the current vector and the distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by the positions of the corresponding two monitoring nodes.

[0079] Specifically, after receiving the node information set, the server further processes the information. First, based on the voltage signal and current signal, the server determines the voltage vector and current vector respectively. These two vectors describe the electrical characteristics of the power grid in mathematical form, such as voltage distribution and current flow. Secondly, the server calculates and determines the distance matrix based on the location information in the node information. The distance matrix is ​​a two-dimensional array in which each element represents the spatial distance between the corresponding two monitoring nodes. This matrix provides the spatial position relationship between the monitoring nodes, which is helpful for the subsequent analysis and evaluation of the power grid status.

[0080] S103: The server uses a preset grid status monitoring model and determines the grid status of the distribution network according to the voltage vector, the current vector and the distance matrix.

[0081] Specifically, the server uses the preset grid state monitoring model, combined with the voltage vector, current vector and distance matrix, to quantitatively evaluate the grid state of the distribution network. The grid state monitoring model is an algorithm model based on machine learning or statistical methods. It can output quantitative indicators of the grid state based on the input electrical characteristics and spatial position relationship. During the evaluation process, the model will consider a variety of factors, such as voltage stability, current balance, and the impact of node distance on grid transmission efficiency. By comprehensively analyzing these factors, the model can accurately determine whether the grid is currently in a normal state, a warning state, or an abnormal state.

[0082] In a possible implementation, the server may use Formula 1 and calculate the voltage vector V = [V1, V2, ..., V i ,…,V n ], current vector I=[I1,I2,…,I i ,…,I n ] and the distance matrix D determine the grid state characteristic value S of the distribution network, and formula 1 is:

[0083]

[0084] Where n is the number of feeder terminal units in the feeder terminal unit set, V i is the amplitude of the i-th voltage signal in the voltage vector V, V j is the amplitude of the jth voltage signal in the voltage vector V, I i is the amplitude of the i-th current signal in the current vector I, I j is the amplitude of the jth current signal in the current vector I, σ V is the voltage standard deviation corresponding to the voltage vector V, σ I is the current standard deviation corresponding to the current vector I, γ is the distance attenuation factor, D ijis the distance between the i-th monitoring node position and the j-th monitoring node position in the distance matrix D, ω ij is the weight coefficient;

[0085] If the server determines that the grid state characteristic value S is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0086] The above formula 1 realizes a comprehensive quantitative evaluation of the power grid state by comprehensively considering the amplitude difference of the voltage signal, the amplitude difference of the current signal and the distance between the monitoring nodes. This method not only takes into account the changes in electrical characteristics, but also incorporates the influence of spatial position relationship, making the evaluation results more accurate and comprehensive. By standardizing the voltage and current signals (i.e., dividing by their respective standard deviations), the influence caused by the difference in the magnitude of electrical parameters between different monitoring nodes is eliminated, so that each factor has the same weight in the evaluation process, improving the accuracy and consistency of the evaluation. In addition, the distance attenuation factor and weight coefficient in formula 1 provide flexibility and adjustability for the evaluation process. The distance attenuation factor can control the degree of influence of the distance factor on the evaluation results, making the evaluation results more consistent with the physical characteristics of the actual power grid. The weight coefficient allows different monitoring nodes or different factors to be given different importance according to actual needs, further improving the pertinence and practicality of the evaluation.

[0087] Then, based on the evaluation results of the power grid status, the server can promptly detect potential problems or abnormal conditions in the power grid and issue early warning signals. The early warning signals can be transmitted to power grid operation and maintenance personnel in a variety of ways, such as mobile phone text messages, emails, or system interface prompts.

[0088] At the same time, the server can also provide intelligent decision support for operation and maintenance personnel based on early warning conditions. For example, when voltage fluctuations or current overloads occur in a certain area of ​​the power grid, the server can recommend corresponding adjustment measures, such as adjusting power distribution, switching backup power supplies, or initiating emergency response. These decision supports help operation and maintenance personnel take quick measures to ensure the safe and stable operation of the power grid.

[0089] In this embodiment, node information of the corresponding monitoring node is obtained through each feeder terminal unit in the feeder terminal unit set, and the node information is sent to the server to form a node information set. Then, the server determines the voltage vector, current vector and distance matrix according to the node information set, and then uses the preset power grid state monitoring model to determine the power distribution network state according to the voltage vector, current vector and distance matrix so as to timely discover potential problems or abnormal conditions in the power grid, and provide intelligent decision-making support for power grid operation and maintenance personnel, so that the system can quickly issue early warnings to help operation and maintenance personnel take timely measures to ensure the safe and stable operation of the power grid.

[0090] Figure 2 FIG. 1 is a flow chart of a smart grid monitoring method integrating ranging technology with a feeder terminal unit according to another exemplary embodiment of the present application. Figure 2 As shown, the smart grid monitoring method provided by this embodiment that integrates the ranging technology with the feeder terminal unit includes:

[0091] S201. Each feeder terminal unit in the feeder terminal unit set obtains node information of a corresponding monitoring node.

[0092] In this step, each feeder terminal unit in the feeder terminal unit set obtains node information of the corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal and a monitoring node position.

[0093] Specifically, each feeder terminal unit in the feeder terminal unit set collects the voltage signal, current signal and location information of the corresponding monitoring node in real time through built-in sensors and measuring devices. This information reflects the operating status of the power grid at a specific time and place. After collecting the node information, each feeder terminal unit sends the node information to the server through wireless communication or wired communication. The server receives and integrates the information from all feeder terminal units to form a node information set. This set contains the voltage signal, current signal and location information of all monitoring nodes in the distribution network.

[0094] S202. The server determines a voltage vector, a current vector, and a distance matrix according to a node information set.

[0095] In this step, the server determines the voltage vector, the current vector and the distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by the positions of the corresponding two monitoring nodes.

[0096] Specifically, after receiving the node information set, the server further processes the information. First, based on the voltage signal and current signal, the server determines the voltage vector and current vector respectively. These two vectors describe the electrical characteristics of the power grid in mathematical form, such as voltage distribution and current flow. Secondly, the server calculates and determines the distance matrix based on the location information in the node information. The distance matrix is ​​a two-dimensional array in which each element represents the spatial distance between the corresponding two monitoring nodes. This matrix provides the spatial position relationship between the monitoring nodes, which is helpful for the subsequent analysis and evaluation of the power grid status.

[0097] S203. The server uses Fourier transform to convert the voltage vector and the current vector into corresponding frequency domain voltage vector and frequency domain current vector.

[0098] In this step, the server uses Fourier transform to transform the voltage vector V = [V1, V2, ..., V i ,…,V n ] and the current vector I=[I1,I2,…,I i ,…,I n ] is converted into the corresponding frequency domain voltage vector V′=[V1′,V2′,…,V i ′,…,V n ′] and the frequency domain current vector I′=[I′1,I′2,…,I i ′,…,I′ n ].

[0099] S204. The server determines a power grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix.

[0100] In this step, the server uses Formula 2 and according to the frequency domain voltage vector V′=[V1′,V2′,…,V i ′,…,V n ′], frequency domain current vector I′=[I′1,I′2,…,I i ′,…,I′ n ] and the distance matrix D to determine the grid state characteristic value S of the distribution network, formula 2 is:

[0101]

[0102] Where n is the number of feeder terminal units in the feeder terminal unit set, V i ′ is the amplitude of the i-th voltage signal in the frequency domain voltage vector V′, V j ′ is the amplitude of the jth voltage signal in the frequency domain voltage vector V′, I i ′ is the amplitude of the i-th current signal in the frequency domain current vector I′, I j ′ is the amplitude of the jth current signal in the frequency domain current vector I′, σ V′ is the voltage standard deviation corresponding to the frequency domain voltage vector V′, σ I′ is the current standard deviation corresponding to the frequency domain current vector I′, γ is the distance attenuation factor, D ij is the distance between the i-th monitoring node position and the j-th monitoring node position in the distance matrix D, ω ij is the weight coefficient, λ is the first adjustable parameter, μ is the second adjustable parameter, Γ VI (f) is the coherence function of the frequency domain voltage vector V′ and the frequency domain current vector I′, min(D) is the minimum value in the distance matrix D, and f is the frequency.

[0103] If the server determines that the grid state characteristic value S is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

[0104] In the above scheme, through Fourier transform, the server converts the voltage vector and current vector from the time domain to the frequency domain to obtain the frequency domain voltage vector and frequency domain current vector. Frequency domain analysis can reveal the components and characteristics of the signal at different frequencies, which is crucial for identifying complex phenomena such as harmonics and oscillations in the power grid. Compared with time domain analysis, frequency domain analysis can more accurately capture subtle changes in the power grid state and improve the sensitivity and accuracy of monitoring. In the frequency domain, the server calculates the amplitude difference of the voltage and current signals and considers their standard deviation for standardization. This processing method helps to eliminate noise and interference in the signal and highlight the true characteristics of the power grid state. At the same time, through the introduction of coherence functions, the server can evaluate the correlation of voltage and current signals at different frequencies, further enriching the information dimension of the power grid state. The server fully considers the spatial position relationship between the monitoring nodes in the evaluation process, and realizes a multi-dimensional comprehensive evaluation of the power grid state through the introduction of distance matrix and weight coefficient. This evaluation method not only considers the changes in electrical characteristics, but also incorporates the influence of spatial position, making the evaluation results more comprehensive and accurate. At the same time, the flexible setting of the weight coefficient allows the importance of different monitoring nodes or different factors to be adjusted according to actual needs, which enhances the robustness and adaptability of the evaluation. In addition, the first adjustable parameter and the second adjustable parameter in Formula 2 provide additional flexibility for the evaluation process. By adjusting these parameters, the server can optimize the accuracy and sensitivity of the evaluation results to meet the monitoring needs under different power grid environments and operating conditions. This adjustability enables the system to better adapt to the complex and changeable power grid environment and improve the effectiveness and reliability of monitoring. Then, the server can quickly identify whether the power grid state of the distribution network is abnormal by comparing the power grid state characteristic value with the preset power grid state characteristic threshold. This abnormal state determination method based on quantitative indicators is efficient and accurate, and can promptly discover potential problems and abnormal conditions in the power grid. Once the power grid state is determined to be abnormal, the server can immediately trigger the early warning mechanism and send early warning information to the power grid operation and maintenance personnel. At the same time, combined with historical data and real-time monitoring information, the server can also provide intelligent decision support for the operation and maintenance personnel to help them quickly locate the root cause of the problem and formulate effective countermeasures. This intelligent early warning and decision support mechanism helps to shorten the fault handling time, reduce fault losses, and improve the safe and stable operation level of the power grid.

[0105] In addition, the server uses Formula 2 and according to the frequency domain voltage vector V′=[V1′,V2′,…,V i ′,…,V n ′], frequency domain current vector I′=[I′1,I′2,…,I i′,…,I′ n ] and the distance matrix D before determining the grid state characteristic value S of the distribution network, it also includes:

[0106] The server uses Formula 3 and according to the frequency domain voltage vector V′=[V1′,V2′,…,V i ′,…,V n ′] and the frequency domain current vector I′=[I′1,I′2,…,I i ′,…,I′ n ]Determine the weight coefficient ω ij , Formula 3 is:

[0107]

[0108] Among them, μ V′ is the voltage mean corresponding to the frequency domain voltage vector V′, μ I′ is the current mean value corresponding to the frequency domain voltage vector V′.

[0109] In the above scheme, Formula 3 dynamically calculates the weight coefficient of each monitoring node pair by considering the deviation of the amplitude of the frequency domain voltage vector and the frequency domain current vector from their corresponding means, as well as the standard deviation of these deviations. This method can capture the characteristic differences of different monitoring nodes in the frequency domain and assign weights accordingly, so that when evaluating the power grid status, more attention is given to node pairs with significant characteristic differences. Through dynamic weight allocation, the system can more accurately reflect the actual status differences of different areas or equipment in the power grid. In the process of power grid status assessment, this highly targeted weight allocation method helps to highlight key problem areas, reduce misjudgments and missed judgments, and improve the accuracy and reliability of the assessment.

[0110] Among them, the operating state of the power grid is dynamically changing, and the electrical characteristics at different times and in different regions may be significantly different. Formula 3 enables the system to adaptively adjust the evaluation strategy to cope with the dynamic changes of the power grid by calculating the weight coefficient in real time. This adaptive adjustment capability enhances the robustness of the system, enabling it to maintain a high evaluation accuracy under different operating conditions. The dynamic weight allocation mechanism enables the system to more flexibly adjust the evaluation parameters when facing different types of power grid structures and operating conditions, thereby improving the generalization ability of the system. This is of great significance for promoting the application of smart grid monitoring systems in power grids of different regions and sizes. By dynamically calculating the weight coefficient, the system can avoid presetting or fixed allocation of the weight coefficient, thereby reducing computational redundancy. In the process of real-time monitoring and evaluation, this optimized calculation method helps to improve the response speed and computing efficiency of the system. In addition, the dynamic weight allocation mechanism enables the system to reasonably allocate computing resources according to actual needs. For node pairs with higher weights, the system can allocate more computing resources for in-depth analysis; while for node pairs with lower weights, the investment in computing resources can be appropriately reduced. This resource allocation method helps to improve the overall performance and resource utilization efficiency of the system.

[0111] Furthermore, formula 2 is used in the server, and according to the frequency domain voltage vector V′=[V1′,V2′,…,V i ′,…,V n ′], frequency domain current vector I′=[I′1,I′2,…,I i ′,…,I′ n ] and the distance matrix D before determining the grid state characteristic value S of the distribution network, it also includes:

[0112] The server uses the Welch method and determines the auto-power spectrum density P of the frequency domain voltage vector V′ according to the frequency domain voltage vector V′ VV (f) Determine the auto-power spectrum density P of the frequency domain current vector I′ according to the frequency domain current vector I′ II (f) Determine the cross power spectral density P of the frequency domain voltage vector V′ and the frequency domain current vector I′ according to the frequency domain voltage vector V′ and the frequency domain current vector I′ VI (f);

[0113] The server uses formula 4 and the cross power spectrum density P of the frequency domain voltage vector V′ and the frequency domain current vector I′ VI (f) The auto-power spectral density P of the frequency domain voltage vector V′ VV (f) and the auto-power spectral density P of the frequency domain current vector I′ II (f) Determine the coherence function of the frequency domain voltage vector V′ and the frequency domain current vector I′, where Formula 4 is:

[0114]

[0115] Among them, Γ VI (f) is the coherence function of the frequency domain voltage vector V′ and the frequency domain current vector I′.

[0116] In the above scheme, the Welch method is used to calculate the auto-power spectrum density and cross-power spectrum density of the frequency domain voltage vector and the frequency domain current vector, so that the system can deeply analyze the frequency domain characteristics of the power grid signal. These frequency domain characteristics contain information about various electrical phenomena in the power grid, such as harmonics, interharmonics, voltage fluctuations, etc., which provide a more comprehensive and accurate data basis for the subsequent power grid status assessment. In addition, the coherence function describes the coherence of the frequency domain voltage vector and the frequency domain current vector at a specific frequency. By calculating the coherence function, the system can identify the correlation between different signal sources in the power grid, distinguish between noise and useful signals, and thus improve the accuracy of the power grid status assessment. In particular, in the presence of multiple signal sources or in a complex electrical environment, coherence function analysis can effectively reduce interference and improve the reliability of the assessment results. In actual power grids, noise is inevitable. Through the Welch method and coherence function analysis, the system can effectively identify and suppress noise signals. The Welch method reduces spectrum leakage and noise interference through data segmentation and windowing; while the coherence function analysis further distinguishes noise and useful signals by evaluating the coherence between signals, thereby enhancing the system's anti-interference ability. Through frequency domain characteristic analysis and coherence function analysis, the system can extract purer and more reliable power grid signals. These high-quality signals provide strong support for subsequent state evaluation and fault detection, enabling the system to maintain stable performance in a complex and changing power grid environment. The power grid state monitoring method based on the Welch method and coherence function analysis can significantly improve the response speed of the system. When the power grid state changes, the system can quickly capture these changes and provide accurate decision support for operation and maintenance personnel through real-time analysis and evaluation. This is of great significance to improving the safety and stability of the power grid.

[0117] S205. The server determines a power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the distribution network.

[0118] Specifically, the server uses Formula 5 and determines the power distribution plan P according to the distribution of load devices corresponding to the monitoring nodes in the distribution network. opt , where Formula 5 is:

[0119]

[0120] Among them, P is the load device set consisting of the load devices corresponding to the monitoring nodes in the distribution network, P i is the power allocation of the i-th load device in the load device set, Pj is the power distribution of the jth load device in the load device set, P i,ref is the reference power distribution of the ith load device in the load device set, ε i is the power distribution fluctuation range of the i-th load device in the load device set, δ is the balance factor, A ij is the connection relationship matrix between the i-th load device and the j-th load device in the load device set. If there is a connection between the i-th load device and the j-th load device, the corresponding element is 1; if there is no connection between the i-th load device and the j-th load device, the corresponding element is 0.

[0121] In the above scheme, when the grid status of the distribution network is judged to be abnormal, the system can immediately start the power distribution optimization process. Through Formula 5, the system can quickly calculate the optimal power distribution plan to deal with the current abnormal state. This real-time response mechanism helps to reduce the duration and impact range of power grid failures and improve the overall stability of the system. The reference power distribution amount and power distribution fluctuation range in Formula 5 represent the reference power distribution amount and power distribution fluctuation range of each load device, respectively, which reflects the system's consideration of the individual differences of load devices. Through personalized power distribution, the system can better meet the actual needs of each load device and avoid equipment failure or performance degradation due to uneven power distribution. In addition, the reference power distribution amount and power distribution fluctuation range in Formula 5 represent the reference power distribution amount and power distribution fluctuation range of each load device, respectively, which reflects the system's consideration of the individual differences of load devices. Through personalized power distribution, the system can better meet the actual needs of each load device and avoid equipment failure or performance degradation due to uneven power distribution. In addition, represents the balance of power distribution between load devices. By adjusting the balance factor, the system can balance the power grid load as much as possible while ensuring that the power demand of each load device is met, reduce overload and underload phenomena in the power grid, and thus improve the operation efficiency of the power grid. By optimizing power distribution, the system can reduce unnecessary energy waste. Under abnormal conditions, some load devices may not work properly due to insufficient or excessive power supply, resulting in energy waste. Formula 5 can ensure that each load device obtains appropriate power supply by globally optimizing power distribution, thereby reducing energy waste and improving energy utilization efficiency. In addition, the power distribution plan in Formula 5 is dynamically calculated based on the current power grid state and the distribution of load devices. This means that the system can adjust the power distribution plan in real time according to different power grid environments and load changes to adapt to complex power grid environments. This dynamic adjustment capability enhances the adaptability and flexibility of the system, enabling it to better cope with various emergencies and challenges. By optimizing power distribution, the system can reduce the risk and probability of power grid failures. Under abnormal conditions, the system can quickly adjust the power distribution plan to reduce the pressure on the faulty load device and prevent the fault from spreading to other load devices. This mechanism helps to improve the reliability and stability of the system and ensure the safe operation of the power grid.

[0122] Furthermore, the server uses Formula 5 and determines the power distribution scheme P according to the distribution of load devices corresponding to the monitoring nodes in the distribution network. opt Previously, it also included:

[0123] The server uses Formula 6 to determine the balance factor based on the frequency domain voltage vector V′, the frequency domain current vector I′, the distance matrix D, and the power state of the distribution network. Formula 6 is:

[0124]

[0125] Among them, τ is the preset adjustment coefficient, ΔP is the power difference between the current state output power of the distribution network and the preset stable state output power, P total is the total output power of the distribution network.

[0126] In the above scheme, Formula 6 dynamically calculates the balancing factor by introducing the frequency domain voltage vector, frequency domain current vector, distance matrix and power state of the distribution network. This enables the system to respond to changes in the grid state in real time, especially when the grid fluctuates or is abnormal, and to quickly adjust the balancing factor to adapt to the current grid environment. The balancing factor in Formula 5 plays a role in regulating the balance of power distribution between load devices. By dynamically calculating the balancing factor, the system can flexibly adjust the power distribution plan according to the load demand and power changes under different grid states, ensure that each load device obtains appropriate power supply, and improve the overall stability and operating efficiency of the system. δ in Formula 6 int The part (i.e., the initial value of the balancing factor) takes into account the variance of the frequency domain voltage and current vectors and the distance between nodes, which helps the system identify potential overloaded and underloaded areas in the power grid. By dynamically adjusting the balancing factor, the system can give priority to these areas during the power distribution process, reduce the occurrence of overload and underload phenomena, and thus improve the operation efficiency and reliability of the power grid. By optimizing power distribution, the system can reduce unnecessary energy waste. Dynamic calculation of the balancing factor enables the system to more accurately predict and respond to changes in the power grid load, thereby avoiding over-allocation or under-allocation of power and improving energy efficiency. In addition, the preset adjustment coefficient and power difference in Formula 6 enable the system to cope with a variety of power grid states, including normal state, fluctuating state, and abnormal state. By dynamically adjusting the balancing factor, the system can maintain stable performance under different power grid states and improve the robustness and reliability of the system. When an emergency occurs in the power grid, such as equipment failure or load mutation, the system can quickly adjust the balancing factor to cope with these changes. This helps the system reduce the scope and time of the impact of the failure and improve the system's ability to cope with emergencies.

[0127] Figure 3FIG. 1 is a schematic diagram of a smart grid monitoring system that integrates ranging technology and feeder terminal units according to an exemplary embodiment of the present application. Figure 3 As shown, the smart grid monitoring system 300 provided in this embodiment that integrates the ranging technology and the feeder terminal unit includes:

[0128] A server 310, a distribution network 320, and a feeder terminal unit set 330, wherein each feeder terminal unit in the feeder terminal unit set 330 is used to monitor node information of a monitoring node in the distribution network 320, and each feeder terminal unit in the feeder terminal unit set 330 is respectively connected to the distribution network 320 for communication;

[0129] Each feeder terminal unit in the feeder terminal unit set 330 obtains node information of a corresponding monitoring node, and sends the node information to the server 310 to form a node information set, wherein the node information includes a voltage signal, a current signal, and a monitoring node position;

[0130] The server 310 determines a voltage vector, a current vector and a distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by positions of two corresponding monitoring nodes;

[0131] The server 310 uses a preset grid state monitoring model and determines the grid state of the distribution network 320 according to the voltage vector, the current vector and the distance matrix.

[0132] Optionally, the server 310 uses a preset grid state monitoring model and determines the grid state of the distribution network 320 according to the voltage vector, the current vector and the distance matrix, including:

[0133] The server 310 determines a grid state characteristic value of the distribution network 320 according to the voltage vector, the current vector and the distance matrix;

[0134] If the server 310 determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network 320 is an abnormal state.

[0135] Optionally, the server 310 uses a preset grid state monitoring model and determines the grid state of the distribution network 320 according to the voltage vector, the current vector and the distance matrix, including:

[0136] The server 310 converts the voltage vector and the current vector into corresponding frequency domain voltage vector and frequency domain current vector by Fourier transform;

[0137] The server 310 determines a grid state characteristic value of the distribution network 320 according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix;

[0138] If the server 310 determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network 320 is an abnormal state.

[0139] Optionally, before the server 310 determines the grid state characteristic value of the distribution network 320 according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the further comprising:

[0140] The server 310 determines a weight coefficient according to the frequency domain voltage vector and the frequency domain current vector, wherein the weight coefficient is used to determine the grid state characteristic value, and the weight coefficient is positively correlated with the grid state characteristic value.

[0141] Optionally, before the server 310 determines the grid state characteristic value of the distribution network 320 according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, the further comprising:

[0142] The server 310 uses the Welch method to determine the auto-power spectral density of the frequency-domain voltage vector according to the frequency-domain voltage vector, determines the auto-power spectral density of the frequency-domain current vector according to the frequency-domain current vector, and determines the cross-power spectral density of the frequency-domain voltage vector and the frequency-domain current vector according to the frequency-domain voltage vector and the frequency-domain current vector;

[0143] The server 310 determines the coherence function of the frequency domain voltage vector and the frequency domain current vector based on the mutual power spectrum density of the frequency domain voltage vector and the frequency domain current vector, the autopower spectrum density of the frequency domain voltage vector and the autopower spectrum density of the frequency domain current vector, the coherence function is used to determine the grid state characteristic value, and the coherence function is positively correlated with the grid state characteristic value.

[0144] Optionally, after determining that the power grid state of the power distribution network 320 is an abnormal state, the method further includes:

[0145] The server 310 determines a power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network 320 .

[0146] Optionally, before the server 310 determines the power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network 320, the method further includes:

[0147] The server 310 determines a balance factor according to the frequency domain voltage vector, the frequency domain current vector, the distance matrix, and the power state of the power distribution network 320 , and the balance factor is used to determine the power distribution scheme.

[0148] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0149] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0150] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0151] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0152] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0153] The bus 403 is used to connect the memory 402 and the processor 401 .

[0154] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0155] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0156] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0157] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A smart grid monitoring system integrating ranging technology and feeder terminal unit, characterized in that: include: A server, a distribution network and a feeder terminal unit set, each feeder terminal unit in the feeder terminal unit set is used to monitor node information of a monitoring node in the distribution network, and each feeder terminal unit in the feeder terminal unit set is respectively connected to the distribution network for communication; Each feeder terminal unit in the feeder terminal unit set obtains node information of a corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal, and a monitoring node position; The server determines a voltage vector, a current vector and a distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by positions of two corresponding monitoring nodes; The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix.

2. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to claim 1 is characterized in that: The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including: The server determines a grid state characteristic value of the distribution network according to the voltage vector, the current vector and the distance matrix; If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

3. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to claim 1 is characterized in that: The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix, including: The server converts the voltage vector and the current vector into corresponding frequency domain voltage vector and frequency domain current vector by Fourier transform; The server determines a grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix; If the server determines that the grid state characteristic value is greater than a preset grid state characteristic threshold, it determines that the grid state of the distribution network is an abnormal state.

4. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to claim 3 is characterized in that: Before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, it also includes: The server determines a weight coefficient according to the frequency domain voltage vector and the frequency domain current vector, the weight coefficient is used to determine the grid state characteristic value, and the weight coefficient is positively correlated with the grid state characteristic value.

5. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to claim 3 is characterized in that: Before the server determines the grid state characteristic value of the distribution network according to the frequency domain voltage vector, the frequency domain current vector and the distance matrix, it also includes: The server uses the Welch method to determine the auto-power spectral density of the frequency-domain voltage vector according to the frequency-domain voltage vector, determines the auto-power spectral density of the frequency-domain current vector according to the frequency-domain current vector, and determines the cross-power spectral density of the frequency-domain voltage vector and the frequency-domain current vector according to the frequency-domain voltage vector and the frequency-domain current vector; The server determines a coherence function of the frequency domain voltage vector and the frequency domain current vector based on the mutual power spectrum density of the frequency domain voltage vector and the frequency domain current vector, the autopower spectrum density of the frequency domain voltage vector and the autopower spectrum density of the frequency domain current vector, the coherence function is used to determine the grid state characteristic value, and the coherence function is positively correlated with the grid state characteristic value.

6. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to any one of claims 3 to 5, characterized in that: After determining that the power distribution network state is an abnormal state, the method further includes: The server determines a power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network.

7. The smart grid monitoring system integrating ranging technology and feeder terminal unit according to claim 6 is characterized in that: Before the server determines the power distribution plan according to the distribution of load devices corresponding to the monitoring nodes in the power distribution network, the method further includes: The server determines a balance factor according to the frequency domain voltage vector, the frequency domain current vector, the distance matrix and the power state of the power distribution network, and the balance factor is used to determine the power distribution scheme.

8. A smart grid monitoring method integrating ranging technology with feeder terminal units, characterized in that: A smart grid monitoring system for integrating ranging technology and feeder terminal units, the smart grid monitoring system for integrating ranging technology and feeder terminal units comprising: a server, a distribution network and a feeder terminal unit set, each feeder terminal unit in the feeder terminal unit set being used to monitor node information of a monitoring node in the distribution network, each feeder terminal unit in the feeder terminal unit set being respectively connected to the distribution network in communication; the method comprising: Each feeder terminal unit in the feeder terminal unit set obtains node information of a corresponding monitoring node, and sends the node information to the server to form a node information set, wherein the node information includes a voltage signal, a current signal, and a monitoring node position; The server determines a voltage vector, a current vector and a distance matrix according to the node information set, wherein each distance element in the distance matrix is ​​a distance determined by positions of two corresponding monitoring nodes; The server uses a preset grid state monitoring model and determines the grid state of the distribution network according to the voltage vector, the current vector and the distance matrix.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to perform the method of claim 8 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to claim 8 when executed by a processor.

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