Short-circuit fault judgment method based on the change of positive sequence voltage amplitude at load end

Abstract

The present invention discloses a short-circuit fault judgment method based on the change in the amplitude of the positive-sequence voltage at the load end, comprising the following steps: S1, collecting three-phase voltage signals and performing preprocessing; S2, converting the filtered voltage signal into an instantaneous voltage signal and calculating the positive-sequence voltage component; S3, using a dynamic topology mode to track and calculate the amplitude change of the positive-sequence voltage component in real time and identify potential anomalies; S4, constructing an adaptive topology disturbance sensitive network, analyzing the change trend and its impact on the network topology, and judging short-circuit faults; S5, using a multi-dimensional quantum state trajectory analysis method to determine the severity of the fault and trigger an alarm mechanism; S6, when a load imbalance or short-circuit fault is detected, performing hierarchical dynamic topology reconstruction; S7, optimizing the load distribution strategy through a phase space adaptive fuzzy rule system; S8, deploying an intelligent control module of a nanomechanical resonator to accurately adjust the load. The present invention improves the stability and response speed of the power system in a complex environment.

Description

Technical Field

[0001] The invention relates to the technical field of power system protection, and in particular to a short-circuit fault judgment method based on a load-end positive-sequence voltage amplitude variation. Background Art

[0002] With the continuous development of power systems, the operating environment of power grids has become increasingly complex, and the safety and stability of power systems have become the focus of attention of power departments in various countries. In particular, with the increasing load and increasingly complex power system structure, the incidence of short-circuit faults has increased. How to detect and handle short-circuit faults in a timely and accurate manner has become a key issue to ensure the safe operation of the power grid.

[0003] In the existing technology, traditional short-circuit fault detection methods usually rely on changes in electrical quantities such as voltage and current, and determine whether a fault has occurred by setting a certain threshold. These methods include overcurrent protection, overvoltage protection, and distance protection. Although these technologies played an important role in early power systems, with the increasing complexity of modern power systems, these traditional methods have gradually exposed some shortcomings:

[0004] 1. Insufficient response to complex faults: Traditional short-circuit fault detection methods are usually based on a single electrical quantity, such as voltage or current. When complex or multiple faults occur in the power system, these single detection methods often fail to provide accurate fault location and judgment. In addition, as the load of the power system increases, the operating conditions of the power grid become complex and changeable, and traditional methods are unable to respond quickly and accurately judge the faults caused by these changes.

[0005] 2. Lack of dynamic adaptability: Traditional fault detection systems often use fixed thresholds or rules to judge faults, and lack the ability to dynamically adapt to real-time changes in the operating status of the power grid. This static detection method is prone to false alarms or missed alarms when faced with load fluctuations, changes in the power grid topology, etc., and cannot adapt to the complex operating environment of modern power grids.

[0006] 3. Unable to process large amounts of multidimensional data: With the popularization of sensors in power systems, the amount of monitoring data in power grids has increased dramatically. Traditional fault detection methods are unable to cope with these massive amounts of data. Traditional methods lack effective data processing and analysis methods and are unable to extract key information from complex multidimensional data to determine the occurrence of faults. In addition, faced with the real-time requirements of big data, traditional methods are also difficult to respond in the first place.

[0007] 4. Lack of ability to analyze topological changes: The topological structure of the power system is complex and changes dynamically. Traditional fault detection methods focus on local electrical quantity analysis and lack the ability to comprehensively analyze changes in the topological structure of the entire system. This makes it difficult for traditional methods to fully grasp the system status when structural changes occur in the power grid, resulting in delays or errors in fault judgment.

[0008] 5. Failure to fully utilize emerging technologies: With the development of emerging technologies such as quantum computing, artificial intelligence, and fuzzy logic, traditional power system fault detection methods have failed to fully utilize these advanced technologies due to technical limitations, resulting in deficiencies in handling complex faults and improving detection accuracy. For example, quantum computing can greatly improve the speed and accuracy of fault detection, but it has hardly been used in traditional methods.

[0009] Therefore, how to provide a short-circuit fault judgment method based on the change in the positive-sequence voltage amplitude at the load end is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0010] One purpose of the present invention is to propose a short-circuit fault judgment method based on the change in the positive-sequence voltage amplitude at the load end. The present invention makes full use of technologies such as dynamic topology pattern recognition, quantum state trajectory analysis, and adaptive fuzzy rule system, and describes in detail an algorithm for realizing real-time fault detection and load optimization in a complex power system. It has the advantages of fast response speed, high detection accuracy, good load distribution optimization effect, and strong system stability.

[0011] According to an embodiment of the present invention, a short-circuit fault judgment method based on the load terminal positive sequence voltage amplitude change includes the following steps:

[0012] S1, collect three-phase voltage signals, convert them into digital signals, and reduce noise and nonlinear distortion through bandpass filtering and normalization processing;

[0013] S2, the filtered three-phase voltage signal is passed through Transformation to The instantaneous voltage signal on the axis is used to calculate the positive sequence voltage component ;

[0014] S3. Use the dynamic topology pattern recognition voltage change detection method to track and calculate the amplitude change of the positive sequence voltage component in real time , and dynamically compare it with the set historical benchmark value to identify potential abnormal voltage changes;

[0015] S4, build an adaptive topology disturbance sensitive network, adjust the weight and connection strength of network nodes based on real-time topology information, and analyze The changing trend and its impact on network topology, identify disturbance signals in the system, and determine whether a short circuit fault occurs;

[0016] S5. Use multi-dimensional quantum state trajectory analysis method to perform multi-dimensional analysis on disturbance signals, determine the severity of the fault through quantum superposition and quantum state transfer mechanism, and output the fault level to trigger the corresponding alarm mechanism;

[0017] S6. When load imbalance or short circuit fault is detected, hierarchical dynamic topology reconstruction is performed to dynamically adjust the topology of the power system and optimize the distribution of power load by redistributing the load path;

[0018] S7, through the phase space adaptive fuzzy rule system, the fuzzy rules and membership functions are automatically adjusted based on real-time monitoring data, so as to optimize the load distribution strategy and cope with the dynamic changes in the operation status of the power grid;

[0019] S8. Deploy intelligent control modules based on nanomechanical resonators to collect and analyze load data in real time, and use nanoscale vibration detection and response mechanisms to accurately adjust the load, so that the power system can be intelligently and adaptively controlled.

[0020] Optionally, S3 includes the following steps:

[0021] S31, get through After transformation Shaft instantaneous voltage signal, calculate positive sequence voltage component :

[0022] ;

[0023] in, and Respectively indicate at time Next Axis and The instantaneous voltage signal on the axis, is the sensitivity factor, used to respond under specific conditions, is a phase adjustment parameter used to adjust the phase difference between signals;

[0024] S32, the positive sequence voltage component obtained in real time Perform continuous sampling to form a discrete time series {V}_{1}\left [ {n} \right ] ,in Indicates the number of the current sampling point;

[0025] S33, using an adaptive multi-layer topology detection architecture, the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] of each sampling point Perform the calculation:

[0026] ;

[0027] where {V}_{1}\left [ {n-1} \right ] represents the positive sequence voltage component of the previous sampling point, To prevent the denominator from being zero, the adjustment factor is is a nonlinear adjustment factor used to respond to large changes;

[0028] S34. In the adaptive multi-layer topology detection architecture, the calculated positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] Compared with the historical benchmark value B\left [ {n} \right ] Perform dynamic comparison, identify potential anomalies in voltage changes through multi-level analysis, generate comprehensive anomaly detection indicators based on the analysis results at each level, and provide multi-dimensional evaluation of voltage fluctuations;

[0029] S35, extracting key feature points in the comprehensive anomaly detection index, calculating the difference between these feature points and the historical benchmark value, the difference is obtained by subtracting the corresponding historical benchmark value from the value of the feature point, reflecting the degree of deviation between the current voltage fluctuation and the historical benchmark;

[0030] S36, inputting the difference calculated above into the adjustment module of the adaptive multi-layer topology detection architecture, the adjustment module adaptively adjusts the weights of each layer, and optimizes the subsequent positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] The detection accuracy can be improved to respond to abnormal voltage changes.

[0031] Optionally, S4 includes the following steps:

[0032] S41. Based on the construction of an adaptive topology disturbance sensitive network, real-time topology information is obtained from the power system, including node connection status, load distribution data, time dimension changes and geographic space distribution information, and a multi-dimensional topology change tracking matrix is ​​constructed based on the obtained multi-dimensional topology information;

[0033] S42. Based on the multi-dimensional topology change tracking matrix, the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] is used to calculate the positive sequence voltage amplitude change. As input signal, calculate the comprehensive perturbation path of topological changes in each dimension :

[0034] ;

[0035] in, For the The connection matrix in dimensions, is the dimension weight coefficient, is the total number of dimensions examined, is a smoothing factor used to adjust the impact between different dimensions. is the adjustment factor used to control the dynamic changes of the disturbance path. is the historical perturbation path matrix, is the comprehensive disturbance path at the previous moment;

[0036] S43. Calculated comprehensive disturbance path Perform analysis and compare with the historical benchmark path. If the change in the real-time path exceeds the preset threshold, it is determined that a short circuit fault may occur.

[0037] S44. Automatically adjust node weights and connection strengths in the network after identifying potential failures:

[0038] ;

[0039] in, Indicates at time The node weight at is the adjustment factor, represents the change in weight, is a regularization parameter used to avoid the denominator being zero. is the number of nodes in the network;

[0040] S45. Through the above adjustment process, the structure of the adaptive topology disturbance sensitive network is updated, and the load changes in the power system are continuously monitored in the multi-dimensional topological space, and the topological changes in each dimension are tracked to identify and judge the disturbance signals in the system.

[0041] Optionally, S5 includes the following steps:

[0042] S51, converting the disturbance signal in the power system into a multi-dimensional quantum state, wherein each characteristic quantity of the disturbance signal is mapped to each dimension of the quantum state to form a quantum state representation for describing the voltage disturbance characteristics and the dynamic behavior of the system;

[0043] S52. After generating a multidimensional quantum state, a multi-layer trajectory diagram of the quantum state is constructed. Each layer of the trajectory diagram corresponds to a different analysis dimension. Each point in the quantum state trajectory diagram corresponds to a quantum state at a moment. The overall evolution of the trajectory diagram is combined with the comprehensive perturbation path. The information is used to reflect the dynamic changes of fault signals in multiple dimensions;

[0044] S53. Analyze the multi-layer quantum state trajectory diagram, track the evolution of each layer of quantum state over time, extract key trajectory information, and integrate the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] and the integrated perturbation path :

[0045] ;

[0046] in, represents the quantum state coefficients of different dimensions, is the regulating factor, is the dimension weight coefficient, is the connection matrix, Used to detect and analyze fault signals;

[0047] S54. Based on the integration results, combined with the trajectory information in the multi-layer quantum state trajectory diagram, the quantum state transfer mechanism is used to analyze the transfer path and evolution trend of the fault signal between different dimensions, and the severity of the fault is determined by quantifying the transfer path and severity between different fault feature layers;

[0048] S55: Output the corresponding fault level according to the severity of the fault , according to the level of fault, the corresponding alarm mechanism is triggered to respond to and handle the fault.

[0049] Optionally, the S6 comprises the following steps:

[0050] S61. During the operation of the power system, the load imbalance in the system is detected by real-time monitoring of the load conditions. And short circuit fault signal:

[0051] ;

[0052] in, For the The load of the nodes, is the reference voltage value, is the regulating factor, is the historical perturbation path matrix of the node, \Delta {V}_{1}\left [ {j} \right ] is the change in positive sequence voltage amplitude, is the comprehensive disturbance path, is the balance coefficient, when When the preset threshold is exceeded or a short-circuit fault signal is detected, hierarchical dynamic topology reconstruction is triggered;

[0053] S62, based on the detected load imbalance or short circuit fault signal, start the hierarchical dynamic topology reconstruction mechanism, the system divides the power network into multiple layers, each layer represents a specific topology structure or area, and selects the layer that needs to be adjusted for topology reconstruction based on the load distribution and topological connection of each layer;

[0054] S63. In hierarchical dynamic topology reconstruction, quantum computing-assisted algorithms are used to optimize load paths, and the parallel processing capabilities of quantum computing are used to quickly determine the optimal path. :

[0055] ;

[0056] in, is the quantum state coefficient, is the regulating factor, is the comprehensive disturbance path, It shows that the whole minimization process is completed under the framework of quantum computing;

[0057] S64. Execute the quantum computing-assisted optimization topology reconstruction scheme, dynamically adjust the power transmission paths at each level, and monitor the system response in real time during the topology adjustment process. Key indicators include voltage stability and load balancing , and evaluate the topology reconstruction effect based on the monitoring data;

[0058] S65. After completing the topology reconstruction, evaluate the system's operating status. The evaluation indicators include load balancing. , System Stability and the power transmission efficiency after reconstruction ,By analyzing these indicators, the effectiveness of quantum computing-assisted topological reconstruction is determined, and subsequent operations are adjusted based on the evaluation results.

[0059] Optionally, the S7 includes the following steps:

[0060] S71. Construct a real-time phase space adaptive fuzzy rule system, adjust the fuzzy rules through the quantum fuzzy rule optimization algorithm, and define the fuzzy input variables, including the positive sequence voltage amplitude change \Delta {V}_{1}\left [ {n} \right ] , load current I\left [ {n} \right ] and system temperature T\left [ {n} \right ] ;

[0061] S72. In the process of quantum fuzzy rule optimization, the fuzzy rules are converted into quantum states, the combination of different fuzzy rules is represented by the quantum superposition principle, and the optimization problem of fuzzy rules is modeled as a quantum optimization problem. The rules are optimized through the superposition and interference effects in quantum computing, and the quantum state is set. is a fuzzy rule state, and its optimization goal is:

[0062] ;

[0063] in, The quantum state obtained by optimization is is to find the quantum state with the minimum sum of the cost function and the system error , represents the optimization cost function of the fuzzy rule, is the regulating factor, The optimal path based on quantum computing;

[0064] S73, use quantum optimization algorithm to optimize fuzzy rules, and use quantum optimization algorithm to optimize quantum states Evolving to obtain the optimal fuzzy rule combination, and using it to dynamically adjust the fuzzy rule base to adapt to the load distribution strategy when the system state changes;

[0065] S74, based on the optimized fuzzy rules, the adjusted rules are applied to the real-time monitoring data through the fuzzy reasoning mechanism, the optimized load distribution strategy is calculated, and the integrated disturbance path is used And the results of quantum state trajectory analysis are used to make comprehensive judgments to cope with the dynamic changes in the operation status of the power grid;

[0066] S75, provide real-time feedback on the load distribution results after applying the optimization rules, update the fuzzy rule parameters in the quantum optimization algorithm, and combine the historical perturbation path matrix and load imbalance , continuously optimize the system's load distribution strategy to adapt to the dynamic changes of the power grid.

[0067] The beneficial effects of the present invention are:

[0068] (1) The present invention significantly improves the early warning capability of potential faults in the power system by combining dynamic topology pattern recognition with real-time voltage amplitude change detection. It can accurately identify and judge short-circuit faults under complex load conditions, overcoming the problem of insufficient response to multiple faults of traditional methods.

[0069] (2) By introducing an adaptive topology disturbance sensitive network, the present invention can dynamically adjust the weights and connection strengths of power grid nodes, achieve real-time response and adjustment to power grid topology changes, enhance the system's adaptability to complex operating conditions, and solve the problem that traditional methods cannot handle dynamic changes in power grid topology.

[0070] (3) The present invention adopts a multi-dimensional quantum state trajectory analysis method, which can analyze the disturbance signal of the power system from multiple dimensions, accurately assess the severity of the fault, and quickly determine the fault level through the quantum state transfer mechanism, significantly improving the speed and accuracy of fault handling.

[0071] (4) The present invention optimizes the load distribution strategy of the power grid through a phase space adaptive fuzzy rule system, and can automatically adjust the fuzzy rules and membership functions based on real-time monitoring data, effectively responding to the dynamic changes in the operating status of the power grid and ensuring the stable operation of the power grid.

[0072] (5) The present invention utilizes the intelligent control module of the nanomechanical resonator to achieve precise adjustment of the power system load. Combined with the above-mentioned technical means, it improves the stability and response speed of the power system in complex environments, and provides an efficient, safe and intelligent power system fault detection and load optimization solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0074] Figure 1 This is a flow chart of a short-circuit fault judgment method based on the load-side positive-sequence voltage amplitude variation proposed by the present invention;

[0075] Figure 2 This is a schematic diagram of the structure of the dynamic topology pattern recognition voltage change detection proposed by the present invention;

[0076] Figure 3 This is a schematic diagram of the multi-dimensional quantum state trajectory analysis method proposed in the present invention. DETAILED DESCRIPTION

[0077] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0078] refer to Figure 1-3 , a short-circuit fault judgment method based on the load terminal positive sequence voltage amplitude change includes the following steps:

[0079] S1, collect three-phase voltage signals, convert them into digital signals, and reduce noise and nonlinear distortion through bandpass filtering and normalization processing;

[0080] S2, the filtered three-phase voltage signal is passed through Transformation to The instantaneous voltage signal on the axis is used to calculate the positive sequence voltage component ;

[0081] S3. Use the dynamic topology pattern recognition voltage change detection method to track and calculate the amplitude change of the positive sequence voltage component in real time , and dynamically compare it with the set historical benchmark value to identify potential abnormal voltage changes;

[0082] S4, build an adaptive topology disturbance sensitive network, adjust the weight and connection strength of network nodes based on real-time topology information, and analyze The changing trend and its impact on network topology, identify disturbance signals in the system, and determine whether a short circuit fault occurs;

[0083] S5. Use multi-dimensional quantum state trajectory analysis method to perform multi-dimensional analysis on disturbance signals, determine the severity of the fault through quantum superposition and quantum state transfer mechanism, and output the fault level to trigger the corresponding alarm mechanism;

[0084] S6. When load imbalance or short circuit fault is detected, hierarchical dynamic topology reconstruction is performed to dynamically adjust the topology of the power system and optimize the distribution of power load by redistributing the load path;

[0085] S7, through the phase space adaptive fuzzy rule system, the fuzzy rules and membership functions are automatically adjusted based on real-time monitoring data, so as to optimize the load distribution strategy and cope with the dynamic changes in the operation status of the power grid;

[0086] S8. Deploy intelligent control modules based on nanomechanical resonators to collect and analyze load data in real time, and use nanoscale vibration detection and response mechanisms to accurately adjust the load, so that the power system can be intelligently and adaptively controlled.

[0087] In this implementation, S3 includes the following steps:

[0088] S31, get through After transformation Shaft instantaneous voltage signal, calculate positive sequence voltage component :

[0089] ;

[0090] in, and Respectively indicate at time Next Axis and The instantaneous voltage signal on the axis, is the sensitivity factor, used to respond under specific conditions, is a phase adjustment parameter used to adjust the phase difference between signals;

[0091] S32, the positive sequence voltage component obtained in real time Perform continuous sampling to form a discrete time series {V}_{1}\left [ {n} \right ] ,in Indicates the number of the current sampling point;

[0092] S33, using an adaptive multi-layer topology detection architecture, the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] of each sampling point Perform the calculation:

[0093] ;

[0094] where {V}_{1}\left [ {n-1} \right ] represents the positive sequence voltage component of the previous sampling point, To prevent the denominator from being zero, the adjustment factor is is a nonlinear adjustment factor used to respond to large changes;

[0095] S34. In the adaptive multi-layer topology detection architecture, the calculated positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] Compared with the historical benchmark value B\left [ {n} \right ] Perform dynamic comparison, identify potential anomalies in voltage changes through multi-level analysis, generate comprehensive anomaly detection indicators based on the analysis results at each level, and provide multi-dimensional evaluation of voltage fluctuations;

[0096] S35, extracting key feature points in the comprehensive anomaly detection index, calculating the difference between these feature points and the historical benchmark value, the difference is obtained by subtracting the corresponding historical benchmark value from the value of the feature point, reflecting the degree of deviation between the current voltage fluctuation and the historical benchmark;

[0097] S36, inputting the difference calculated above into the adjustment module of the adaptive multi-layer topology detection architecture, the adjustment module adaptively adjusts the weights of each layer, and optimizes the subsequent positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] The detection accuracy can be improved to respond to abnormal voltage changes.

[0098] In this implementation manner, S4 includes the following steps:

[0099] S41. Based on the construction of an adaptive topology disturbance sensitive network, real-time topology information is obtained from the power system, including node connection status, load distribution data, time dimension changes and geographic space distribution information, and a multi-dimensional topology change tracking matrix is ​​constructed based on the obtained multi-dimensional topology information;

[0100] S42. Based on the multi-dimensional topology change tracking matrix, the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] is used to calculate the positive sequence voltage amplitude change. As input signal, calculate the comprehensive perturbation path of topological changes in each dimension :

[0101] ;

[0102] in, For the The connection matrix in dimensions, is the dimension weight coefficient, is the total number of dimensions examined, is a smoothing factor used to adjust the impact between different dimensions. is the adjustment factor used to control the dynamic changes of the disturbance path. is the historical perturbation path matrix, is the comprehensive disturbance path at the previous moment;

[0103] S43. Calculated comprehensive disturbance path Perform analysis and compare with the historical benchmark path. If the change in the real-time path exceeds the preset threshold, it is determined that a short circuit fault may occur.

[0104] S44. Automatically adjust node weights and connection strengths in the network after identifying potential failures:

[0105] ;

[0106] in, Indicates at time The node weight at is the adjustment factor, represents the change in weight, is a regularization parameter used to avoid the denominator being zero. is the number of nodes in the network;

[0107] S45. Through the above adjustment process, the structure of the adaptive topology disturbance sensitive network is updated, and the load changes in the power system are continuously monitored in the multi-dimensional topological space, and the topological changes in each dimension are tracked to identify and judge the disturbance signals in the system.

[0108] In this implementation manner, S5 includes the following steps:

[0109] S51, converting the disturbance signal in the power system into a multi-dimensional quantum state, wherein each characteristic quantity of the disturbance signal is mapped to each dimension of the quantum state to form a quantum state representation for describing the voltage disturbance characteristics and the dynamic behavior of the system;

[0110] S52. After generating a multidimensional quantum state, a multi-layer trajectory diagram of the quantum state is constructed. Each layer of the trajectory diagram corresponds to a different analysis dimension. Each point in the quantum state trajectory diagram corresponds to a quantum state at a moment. The overall evolution of the trajectory diagram is combined with the comprehensive perturbation path. The information is used to reflect the dynamic changes of fault signals in multiple dimensions;

[0111] S53. Analyze the multi-layer quantum state trajectory diagram, track the evolution of each layer of quantum state over time, extract key trajectory information, and integrate the positive sequence voltage amplitude change {\Delta V}_{1}\left [ {n} \right ] and the integrated perturbation path :

[0112] ;

[0113] in, represents the quantum state coefficients of different dimensions, is the regulating factor, is the dimension weight coefficient, is the connection matrix, Used to detect and analyze fault signals;

[0114] S54. Based on the integration results, combined with the trajectory information in the multi-layer quantum state trajectory diagram, the quantum state transfer mechanism is used to analyze the transfer path and evolution trend of the fault signal between different dimensions, and the severity of the fault is determined by quantifying the transfer path and severity between different fault feature layers;

[0115] S55: Output the corresponding fault level according to the severity of the fault , according to the level of fault, the corresponding alarm mechanism is triggered to respond to and handle the fault.

[0116] In this implementation manner, S6 includes the following steps:

[0117] S61. During the operation of the power system, the load imbalance in the system is detected by real-time monitoring of the load conditions. And short circuit fault signal:

[0118] ;

[0119] in, For the The load of the nodes, is the reference voltage value, is the regulating factor, is the historical perturbation path matrix of the node, \Delta {V}_{1}\left [ {j} \right ] is the change in positive sequence voltage amplitude, is the comprehensive disturbance path, is the balance coefficient, when When the preset threshold is exceeded or a short-circuit fault signal is detected, hierarchical dynamic topology reconstruction is triggered;

[0120] S62, based on the detected load imbalance or short circuit fault signal, start the hierarchical dynamic topology reconstruction mechanism, the system divides the power network into multiple layers, each layer represents a specific topology structure or area, and selects the layer that needs to be adjusted for topology reconstruction based on the load distribution and topological connection of each layer;

[0121] S63. In hierarchical dynamic topology reconstruction, quantum computing-assisted algorithms are used to optimize load paths, and the parallel processing capabilities of quantum computing are used to quickly determine the optimal path. :

[0122] ;

[0123] in, is the quantum state coefficient, is the regulating factor, is the comprehensive disturbance path, It shows that the whole minimization process is completed under the framework of quantum computing;

[0124] S64. Execute the quantum computing-assisted optimization topology reconstruction scheme, dynamically adjust the power transmission paths at each level, and monitor the system response in real time during the topology adjustment process. Key indicators include voltage stability and load balancing , and evaluate the topology reconstruction effect based on the monitoring data;

[0125] S65. After completing the topology reconstruction, evaluate the system's operating status. The evaluation indicators include load balancing. , System Stability and the power transmission efficiency after reconstruction ,By analyzing these indicators, the effectiveness of quantum computing-assisted topological reconstruction is determined, and subsequent operations are adjusted based on the evaluation results.

[0126] In this implementation manner, the S7 includes the following steps:

[0127] S71. Construct a real-time phase space adaptive fuzzy rule system, adjust the fuzzy rules through the quantum fuzzy rule optimization algorithm, and define the fuzzy input variables, including the positive sequence voltage amplitude change \Delta {V}_{1}\left [ {n} \right ] , load current I\left [ {n} \right ] and system temperature T\left [ {n} \right ] ;

[0128] S72. In the process of quantum fuzzy rule optimization, the fuzzy rules are converted into quantum states, the combination of different fuzzy rules is represented by the quantum superposition principle, and the optimization problem of fuzzy rules is modeled as a quantum optimization problem. The rules are optimized through the superposition and interference effects in quantum computing, and the quantum state is set. is a fuzzy rule state, and its optimization goal is:

[0129] ;

[0130] in, The quantum state obtained by optimization is is to find the quantum state with the minimum sum of the cost function and the system error , represents the optimization cost function of the fuzzy rule, is the regulating factor, The optimal path based on quantum computing;

[0131] S73. Use quantum optimization algorithms to optimize fuzzy rules and use quantum optimization algorithms to optimize quantum states. Evolving to obtain the optimal fuzzy rule combination, and using it to dynamically adjust the fuzzy rule base to adapt to the load distribution strategy when the system state changes;

[0132] S74, based on the optimized fuzzy rules, the adjusted rules are applied to the real-time monitoring data through the fuzzy reasoning mechanism, the optimized load distribution strategy is calculated, and the integrated disturbance path is used And the results of quantum state trajectory analysis are used to make comprehensive judgments to cope with the dynamic changes in the operation status of the power grid;

[0133] S75, provide real-time feedback on the load distribution results after applying the optimization rules, update the fuzzy rule parameters in the quantum optimization algorithm, and combine the historical perturbation path matrix and load imbalance , continuously optimize the system's load distribution strategy to adapt to the dynamic changes of the power grid.

[0134] Embodiment 1:

[0135] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the smart grid system of a large city, where the power grid covers multiple areas and includes a large number of substations, distribution lines and user loads. Especially during the peak period of electricity consumption in summer, due to the sharp increase in power load, short-circuit faults frequently occur in the power grid. Traditional fault detection methods are difficult to identify and handle these faults in a timely and accurate manner, resulting in large-scale power outages and user complaints. In this embodiment, we selected the short-circuit fault judgment method based on the load-side positive sequence voltage amplitude change of the present invention for application.

[0136] First, the sensor network in the power grid system collects three-phase voltage signals in real time in each area, converts these signals into digital signals, and performs bandpass filtering and normalization to eliminate noise and nonlinear distortion. The transformation converts the filtered voltage signal into The instantaneous voltage signal on the axis is used to calculate the positive sequence voltage component.

[0137] During this process, the system monitors the changes in the positive sequence voltage component in real time, and uses the dynamic topology pattern recognition method to dynamically compare the voltage amplitude changes with the historical benchmark value to quickly identify potential voltage anomalies. For example, in a certain area, when the positive sequence voltage component fluctuates abnormally, the system automatically starts the adaptive topology disturbance sensitive network to analyze the impact of voltage fluctuations on the grid topology structure and determine whether a short circuit fault has occurred. The network dynamically adjusts the weights and connection strengths of network nodes based on the topology information obtained in real time.

[0138] When a short circuit fault is detected in the power grid, the system uses a multi-dimensional quantum state trajectory analysis method to perform a multi-dimensional analysis of the voltage abnormality signal, including voltage changes, load current, system temperature and other factors, and calculates the severity of the fault through a quantum state transfer mechanism. According to the level of the fault, the system will automatically trigger the corresponding alarm mechanism to notify maintenance personnel for timely processing.

[0139] After the fault is initially controlled, the system will perform hierarchical dynamic topology reconstruction according to the real-time load situation, optimize the distribution of power load by redistributing the load path, reduce the load in the fault area, and prevent the fault from spreading further. In addition, the system uses a phase space adaptive fuzzy rule system to dynamically adjust fuzzy rules according to real-time monitoring data, optimize load distribution strategies, and ensure that the power grid can quickly resume normal operation.

[0140] To further verify the beneficial effects of the present invention, we conducted a detailed analysis of the operating data of the city's smart grid from July 1 to July 31, 2023.

[0141] Table 1: Performance comparison between the traditional method and the method of the present invention in power system fault handling and load optimization

[0142]

[0143] The results show that after applying the method of the present invention, the average detection time of short-circuit faults was shortened from 5.7 seconds of the traditional method to 2.1 seconds, a reduction of 63.2%. The fault handling time was shortened from 12.4 minutes to 4.8 minutes, a reduction of 61.3%. The power outage time was shortened from 16.8 minutes to 6.5 minutes, a reduction of 61.3%. During the peak period of electricity consumption in July, the user complaint rate dropped from 4.2% last year to 1.7%, a reduction of 59.5%.

[0144] In addition, through the application of the method of the present invention, the overall load fluctuation rate of the power grid has been reduced by 27%. Under high load conditions, the method of the present invention effectively prevents large-scale power outages caused by load imbalance, and significantly improves the stability and operating efficiency of the power grid. In particular, in terms of load optimization, the system can intelligently distribute power loads, so that the power demand of each region can be effectively met, ensuring the safe operation of the power grid.

[0145] The data analysis results in this embodiment show that the present invention not only significantly improves the efficiency of short-circuit fault detection and processing, but also effectively reduces power outage time and improves the overall stability of the power grid through load optimization and dynamic topology reconstruction, providing a scientific and reliable solution for the power system of modern cities.

[0146] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A short-circuit fault judgment method based on the change in the positive sequence voltage amplitude at the load end, characterized in that: The steps include: S1, collect three-phase voltage signals, convert them into digital signals, and reduce noise and nonlinear distortion through bandpass filtering and normalization processing; S2, convert the filtered three-phase voltage signal into The instantaneous voltage signal on the axis is used to calculate the positive sequence voltage component ; S3. Use the dynamic topology pattern recognition voltage change detection method to track and calculate the amplitude change of the positive sequence voltage component in real time , n represents the number of the current sampling point, and dynamically compares it with the set historical benchmark value to identify potential abnormal voltage changes; S4, build an adaptive topology disturbance sensitive network, adjust the weight and connection strength of network nodes based on real-time topology information, and analyze The changing trend and its impact on network topology, identify disturbance signals in the system, and determine whether a short circuit fault occurs; S5. Use multi-dimensional quantum state trajectory analysis method to perform multi-dimensional analysis on disturbance signals, determine the severity of the fault through quantum superposition and quantum state transfer mechanism, and output the fault level to trigger the corresponding alarm mechanism; S6. When load imbalance or short circuit fault is detected, hierarchical dynamic topology reconstruction is performed to dynamically adjust the topology of the power system and optimize the distribution of power load by redistributing the load path; S7, through the phase space adaptive fuzzy rule system, the fuzzy rules and membership functions are automatically adjusted based on real-time monitoring data, so as to optimize the load distribution strategy and cope with the dynamic changes in the operation status of the power grid; S8. Deploy intelligent control modules based on nanomechanical resonators to collect and analyze load data in real time, use nano-level vibration detection and response mechanisms to accurately adjust loads, and enable intelligent adaptive control of power systems; The S4 specifically includes: S41. Based on the construction of an adaptive topology disturbance sensitive network, real-time topology information is obtained from the power system, including node connection status, load distribution data, time dimension changes and geographic space distribution information, and a multi-dimensional topology change tracking matrix is ​​constructed based on the obtained multi-dimensional topology information; S42, based on the multi-dimensional topology change tracking matrix, using the positive sequence voltage amplitude change As input signal, calculate the comprehensive perturbation path of topological changes in each dimension : ； in, is the connection matrix in the kth dimension, is the dimension weight coefficient, d is the total number of dimensions examined, is a smoothing factor used to adjust the impact between different dimensions. is the adjustment factor used to control the dynamic changes of the disturbance path. is the historical perturbation path matrix, is the comprehensive disturbance path at the previous moment; S43. Calculated comprehensive disturbance path Perform analysis and compare with the historical benchmark path. If the change in the real-time path exceeds the preset threshold, it is determined that a short circuit fault may occur. S44. Automatically adjust node weights and connection strengths in the network after identifying potential failures: ; Among them, W(t+1) represents the node weight at time t+1, is the adjustment factor, represents the change in weight, is the regularization parameter used to avoid the denominator being zero, and n is the number of nodes in the network; S45. Through the above adjustment process, the structure of the adaptive topology disturbance sensitive network is updated, and the load changes in the power system are continuously monitored in the multi-dimensional topological space, and the topological changes in each dimension are tracked to identify and judge the disturbance signal in the system; The S5 specifically includes: S51, converting the disturbance signal in the power system into a multi-dimensional quantum state, wherein each characteristic quantity of the disturbance signal is mapped to each dimension of the quantum state to form a quantum state representation for describing the voltage disturbance characteristics and the dynamic behavior of the system; S52. After generating a multidimensional quantum state, a multi-layer trajectory diagram of the quantum state is constructed. Each layer of the trajectory diagram corresponds to a different analysis dimension. Each point in the quantum state trajectory diagram corresponds to a quantum state at a moment. The overall evolution of the trajectory diagram is combined with the comprehensive perturbation path. The information is used to reflect the dynamic changes of fault signals in multiple dimensions; S53. Analyze the multi-layer quantum state trajectory diagram, track the evolution of each layer of quantum state over time, extract key trajectory information, and integrate the positive sequence voltage amplitude change and the integrated perturbation path : ; in, represents the quantum state coefficients of different dimensions, is the regulating factor, is the dimension weight coefficient, is the connection matrix, Used to detect and analyze fault signals; S54. Based on the integration results, combined with the trajectory information in the multi-layer quantum state trajectory diagram, the quantum state transfer mechanism is used to analyze the transfer path and evolution trend of the fault signal between different dimensions, and the severity of the fault is determined by quantifying the transfer path and severity between different fault feature layers; S55: Output the corresponding fault level according to the severity of the fault , according to the level of fault, the corresponding alarm mechanism is triggered to respond to and handle the fault.

2. The short-circuit fault judgment method based on the load-side positive-sequence voltage amplitude variation according to claim 1 is characterized in that: The S3 specifically includes: S31, obtain the Clarke transform Shaft instantaneous voltage signal, calculate positive sequence voltage component : ; in, and They represent the Axis and The instantaneous voltage signal on the shaft, is the sensitivity factor, used to respond under specific conditions, is a phase adjustment parameter used to adjust the phase difference between signals; S32, the positive sequence voltage component obtained in real time Perform continuous sampling to form a discrete time series , where n represents the number of the current sampling point; S33, using an adaptive multi-layer topology detection architecture, the positive sequence voltage amplitude change at each sampling point Perform the calculation:

3. Among them, represents the positive sequence voltage component of the previous sampling point, To prevent the denominator from being zero, the adjustment factor is is a nonlinear adjustment factor used to respond to large changes; S34, in the adaptive multi-layer topology detection architecture, the calculated positive sequence voltage amplitude change Compared with historical benchmark Perform dynamic comparison, identify potential anomalies in voltage changes through multi-level analysis, generate comprehensive anomaly detection indicators based on the analysis results at each level, and provide multi-dimensional evaluation of voltage fluctuations; S35, extracting key feature points in the comprehensive anomaly detection index, calculating the difference between these feature points and the historical benchmark value, the difference is obtained by subtracting the corresponding historical benchmark value from the value of the feature point, reflecting the degree of deviation between the current voltage fluctuation and the historical benchmark; S36, input the difference calculated above into the adjustment module of the adaptive multi-layer topology detection architecture, and the adjustment module adaptively adjusts the weights of each layer to optimize the subsequent positive sequence voltage amplitude change. The detection accuracy can be improved to respond to abnormal voltage changes.

4. The short-circuit fault judgment method based on the load-side positive-sequence voltage amplitude variation according to claim 1 is characterized in that: The S6 specifically includes: S61. During the operation of the power system, the load imbalance in the system is detected by real-time monitoring of the load conditions. And short circuit fault signal: ; in, is the load of the jth node, is the reference voltage value, is the regulating factor, is the historical perturbation path matrix of the node, is the change in positive sequence voltage amplitude, is the comprehensive disturbance path, is the balance coefficient, when When the preset threshold is exceeded or a short-circuit fault signal is detected, hierarchical dynamic topology reconstruction is triggered; S62, based on the detected load imbalance or short circuit fault signal, start the hierarchical dynamic topology reconstruction mechanism, the system divides the power network into multiple layers, each layer represents a specific topology structure or area, and selects the layer that needs to be adjusted for topology reconstruction based on the load distribution and topological connection of each layer; S63. In hierarchical dynamic topology reconstruction, quantum computing-assisted algorithms are used to optimize load paths, and the parallel processing capabilities of quantum computing are used to quickly determine the optimal path. : ; in, is the quantum state coefficient, is the regulating factor, To synthesize the perturbation path, quantum shows that the entire minimization process is completed under the quantum computing framework; S64. Execute the quantum computing-assisted optimization topology reconstruction scheme, dynamically adjust the power transmission paths at each level, and monitor the system response in real time during the topology adjustment process. Key indicators include voltage stability and load balancing , and evaluate the topology reconstruction effect based on the monitoring data; S65. After completing the topology reconstruction, evaluate the system's operating status. The evaluation indicators include load balancing. , System Stability and the power transmission efficiency after reconstruction ,By analyzing these indicators, the effectiveness of quantum computing-assisted topological reconstruction is determined, and subsequent operations are adjusted based on the evaluation results.

5. The short-circuit fault judgment method based on the load-side positive-sequence voltage amplitude variation according to claim 3 is characterized in that: The S7 specifically includes: S71. Build a real-time phase space adaptive fuzzy rule system, adjust the fuzzy rules through the quantum fuzzy rule optimization algorithm, and define the fuzzy input variables, including the positive sequence voltage amplitude change. , load current and system temperature ; S72. In the process of quantum fuzzy rule optimization, the fuzzy rules are converted into quantum states, the combination of different fuzzy rules is represented by the quantum superposition principle, and the optimization problem of fuzzy rules is modeled as a quantum optimization problem. The rules are optimized through the superposition and interference effects in quantum computing, and the quantum state is set. is a fuzzy rule state, and its optimization goal is: ; in, The quantum state obtained by optimization is is to find the quantum state with the minimum sum of the cost function and the system error , represents the optimization cost function of the fuzzy rule, is the regulating factor, The optimal path based on quantum computing; S73, use quantum optimization algorithm to optimize fuzzy rules, and use quantum optimization algorithm to optimize quantum states Evolving to obtain the optimal fuzzy rule combination, and using it to dynamically adjust the fuzzy rule base to adapt to the load distribution strategy when the system state changes; S74, based on the optimized fuzzy rules, the adjusted rules are applied to the real-time monitoring data through the fuzzy reasoning mechanism, the optimized load distribution strategy is calculated, and the integrated disturbance path is used And the results of quantum state trajectory analysis are used to make comprehensive judgments to cope with the dynamic changes in the operation status of the power grid; S75, provide real-time feedback on the load distribution results after applying the optimization rules, update the fuzzy rule parameters in the quantum optimization algorithm, and combine the historical perturbation path matrix and load imbalance , continuously optimize the system's load distribution strategy to adapt to the dynamic changes of the power grid.

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