A method for intelligent quantitative analysis of the impact range of distribution network faults

Abstract

The present invention relates to the field of distribution network fault detection and analysis, and specifically to a distribution network fault impact range assessment method based on automatic generation of distribution network topology, automatic collection of fault information and intelligent quantitative analysis. The steps of the present invention are: 1. Automatic generation of distribution network topology; 2. Automatic collection of fault information; 3. Theoretical calculation of power flow; 4. Dynamic correction of calculation results; 5. Quantitative assessment of fault range. The present invention provides accurate fault propagation path analysis and quantitative assessment through automatic generation of topological structure, multi-source data fusion, theoretical calculation of power flow and intelligent correction technology. The intuitive quantitative results of node scoring and impact range provided provide a scientific basis for operation and maintenance decision-making, contribute to the rational allocation of operation and maintenance resources, and further improve the operation efficiency of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of distribution network fault detection and analysis, and specifically to a distribution network fault impact range assessment method based on automatic generation of distribution network topology, automatic collection of fault information and intelligent quantitative analysis. Background Art

[0002] With the continuous expansion of distribution networks and the increasing diversification of user needs, the complexity of grid operations and the unpredictability of faults have increased significantly. The impact of distribution network faults is no longer limited to the vicinity of the fault point, but can spread over a wider area through electrical coupling relationships, causing voltage fluctuations for other users, increased three-phase imbalance, and even equipment shutdown or tripping. This chain reaction can seriously disrupt users' production and operations, resulting in significant economic losses.

[0003] However, existing technologies have many deficiencies in addressing such issues, making it difficult to meet the actual needs of modern power grid operation and maintenance.

[0004] 1) Traditional methods typically rely on single-point or small amounts of monitoring data. While intelligent monitoring equipment (such as DTUs, FTUs, and protection devices) can provide certain fault information, this information is finitely distributed and has limited coverage, making it difficult to fully reflect the complex electrical coupling relationships within the power grid. Especially when the electrical properties between nodes change dynamically, it is difficult to accurately infer the scope of a fault based on data from only a few monitoring points.

[0005] 2) The quantitative analysis capabilities of existing methods are weak, mainly manifested in the following two aspects:

[0006] ① Lack of accurate fault impact analysis capabilities. Traditional analysis methods mostly use static models, which fail to fully consider the coupling relationship between electrical properties of nodes and make it difficult to characterize the temporal evolution and spatial distribution characteristics of the fault propagation process.

[0007] ② The evaluation indicators are single and lack comprehensiveness. Traditional methods typically use a single indicator (such as voltage amplitude) for impact assessment, failing to consider the combined impact of multiple electrical characteristics such as voltage harmonics and three-phase voltage imbalance on user equipment. Consequently, the assessment results fail to fully reflect the actual impact of the fault.

[0008] 3) Existing methods lack the ability to dynamically correct data and integrate multiple sources when assessing the scope of fault propagation, making it difficult to effectively combine sampled data with theoretical calculation results. On the one hand, theoretical calculation results fail to fully correct for noise and bias in the actual sampled data; on the other hand, the real-time nature of the sampled data prevents effective dynamic adjustment of the power flow calculation model, resulting in a discrepancy between the fault impact assessment results and the actual operating status.

[0009] 4) Traditional assessment methods are often slow to respond. Analyzing and locating the fault scope can take hours or even longer, failing to meet the high real-time and accuracy requirements of modern power grids. This not only delays fault isolation and repair, but can also cause the fault to spread further, compromising grid safety and consumer power reliability.

[0010] These problems restrict the accuracy and efficiency of the assessment of the impact range of distribution network faults. A more comprehensive, intelligent and accurate analysis method is urgently needed to solve the above problems. Summary of the Invention

[0011] In response to the shortcomings of existing distribution network fault analysis methods, the present invention proposes a method for intelligent quantitative analysis of the impact range of distribution network faults, which solves the following problems:

[0012] 1) Realize the automatic generation and update of distribution network topology and support real-time response of fault analysis.

[0013] 2) Through multi-source data fusion technology, combined with calculated values ​​and actual values, multi-dimensional information of fault events can be accurately obtained to solve the problem of data isolation.

[0014] 3) Based on power flow theory calculation and data correction mechanism, the quantitative assessment accuracy of the fault impact range is improved.

[0015] 4) Provide a comprehensive assessment method based on voltage amplitude, voltage harmonic content, and voltage three-phase imbalance to clarify the scope and severity of the impact of fault propagation.

[0016] The method for performing intelligent quantitative analysis of the impact range of a distribution network fault of the present invention comprises the following steps:

[0017] 1. Automatic generation of distribution network topology;

[0018] 2. Automatic collection of fault information;

[0019] 3. Calculation of tidal current theory;

[0020] 4. Dynamic correction of calculation results;

[0021] 5. Quantitative assessment of fault scope;

[0022] Specifically, in step 1, the distribution network topology is automatically generated as follows:

[0023] The main electrical wiring diagram of the distribution network in CAD format is used to automatically generate a topological diagram G(V,E), where V represents a node set, including transformers, loads, and switches, and E represents a line set. By analyzing the nodes and their connection relationships in the main wiring diagram, the system extracts the corresponding key information.

[0024] Specifically, the key information is:

[0025] The electrical properties of the node, including rated voltage and current;

[0026] Spatial attributes of the node, including latitude and longitude coordinates;

[0027] The impedance parameter Z of the line.

[0028] Specifically, the impedance parameter Z of the above-mentioned line is calculated by the following formula:

[0029]

[0030] Where R is the line resistance, L is the inductance, and C is the capacitance; ω is the grid frequency, ω = 2πf.

[0031] The resistance, inductance and capacitance parameters of the line are calculated based on the cable model and line length parsed from the file.

[0032] Ultimately, the generated topology diagram not only includes the electrical coupling relationship between nodes, but also reflects the geographical distribution of nodes and line lengths, providing a basis for subsequent power flow calculations and impact range assessments.

[0033] Specifically, in step 2, the fault information is automatically aggregated into:

[0034] The system automatically collects fault event information from measurement and control terminals such as distribution terminals, feeder terminals, line protection devices, smart switches and multi-function meters through the communication network.

[0035] Specifically, the fault event information includes:

[0036] Fault type: Fault types include phase short circuit and ground short circuit;

[0037] Fault location: the specific coordinates or node number of the fault location;

[0038] Fault characteristic parameters: Fault characteristic parameters include fault current and voltage drop amplitude;

[0039] Power quality information: including voltage harmonic content rate and three-phase imbalance;

[0040] Action information: Action information includes whether reclosing occurs and protection action time.

[0041] After the above data is connected to the system through the real-time transmission module, it will trigger subsequent power flow calculation and analysis.

[0042] Specifically, in step 3, the power flow theory is calculated as:

[0043] After receiving a fault event, the system automatically triggers a power flow calculation to predict the changes in electrical parameters of nodes without sampling devices installed. The power flow calculation includes:

[0044] Construct the node admittance matrix Y: Based on the topological graph G(V,E), construct the node admittance matrix;

[0045] Power flow iteration calculation: The voltage amplitude and phase angle of each node are calculated using the Newton-Raphson method;

[0046] Data interpolation supplement: set nodes every 10% of the distance on the line, and interpolate to calculate the theoretical electrical parameters.

[0047] Covering line sections where no sampling devices are installed;

[0048] Through the calculation results, the electrical parameters such as the theoretical voltage amplitude, voltage harmonic content, and voltage three-phase imbalance of each node are obtained to provide a basis for subsequent analysis.

[0049] Specifically, in step 4, the calculation result is dynamically corrected to:

[0050] In order to improve the evaluation accuracy, a dynamic correction mechanism is introduced to compare the theoretical calculation results with the sampling data, including error analysis and parameter update.

[0051] Furthermore, the error analysis is as follows: comparing the calculated value of the power flow at each sampling node with the actual sampling value, and calculating the error:

[0052] ΔX=X 采样 -X 理论

[0053] P 修正 =f(ΔX,P,d)

[0054] Where X represents the electrical parameters, P represents the topological parameters before correction, and P 修正 represents the modified topological parameters, and d represents the termination condition.

[0055] The parameter update includes: based on the error correction topology parameter P 修正 , recalculate the power flow to ensure the consistency of topology parameters with actual operating status.

[0056] Specifically, in step 5, the fault scope is quantitatively assessed as follows:

[0057] A comprehensive quantitative evaluation is conducted based on three indicators: voltage amplitude, voltage harmonic content, and three-phase imbalance. Each of the three indicators is scored individually, and then a comprehensive score is calculated.

[0058] Single indicator scoring system:

[0059] Specifically, in the voltage amplitude:

[0060] The voltage amplitude score is calculated based on the function model of the actual voltage fluctuation amplitude ΔU. Consider the score value S U Should vary continuously and is defined as follows:

[0061]

[0062] This formula uses a quadratic parabola to decrease between 5% and 10%, reflecting the nonlinear effect of voltage amplitude fluctuations.

[0063] When ΔU ≤ 5%, the score is 100, which means there is no impact on the system;

[0064] When 5%<ΔU≤10%, the score decreases quadratically, and the greater the voltage deviation, the lower the score;

[0065] When ΔU>10%, it is considered a serious impact and scored as 0.

[0066] Specifically, the voltage harmonic content rate is:

[0067] For the total harmonic distortion THD, the scoring formula S is defined THD for:

[0068]

[0069] When the harmonic content is THD ≤ 3%, the full score is 100.

[0070] When the harmonic content is 3%<THD≤5%, the harmonic score decreases linearly. For every 1% increase in the total harmonic content, the score decreases by 20.

[0071] When the harmonic content THD>5%, the score decreases exponentially with the sharp increase of harmonics and eventually approaches 0.

[0072] Specifically, in the three-phase voltage imbalance:

[0073] The three-phase imbalance can be calculated using the symmetrical component method, specifically by measuring the ratio of the voltage positive sequence component V1 to the negative sequence component V2:

[0074]

[0075] Scoring Formula S Unb Defined as:

[0076]

[0077] When ΔUnb ≤ 1%, the score is 100.

[0078] When 1%<ΔUnb≤3%, the score drops rapidly according to a quadratic parabola, reflecting the impact amplification effect of mild imbalance.

[0079] When ΔUnb>3%, the score decreases exponentially, and in severe imbalance conditions, the operation of the power grid is significantly affected.

[0080] Specifically, the comprehensive score calculation

[0081] Comprehensive score S 综合 The fuzzy comprehensive evaluation method and weight dynamic adjustment strategy are adopted, which include: A. establishment of sub-item matrix, B. establishment of weight matrix, C. comprehensive scoring formula and D. score normalization processing.

[0082] A. Establishment of item matrix

[0083] According to the scores of the three indicators, the sub-item evaluation matrix R is constructed:

[0084]

[0085] where r ij It represents the normalized score of the jth indicator of the i-th node, calculated according to the following formula:

[0086]

[0087] m is the number of nodes, j∈{U,THD,Unb}, which represent the three indicators of voltage amplitude, harmonic content rate and three-phase unbalance respectively.

[0088] B. Weight Matrix Establishment

[0089] For each column j, calculate:

[0090]

[0091] Where k = 1 / ln(m).

[0092] Weight distribution: weights are distributed inversely according to entropy values:

[0093]

[0094] C. Comprehensive scoring formula

[0095] The comprehensive score is the product of the fuzzy matrix and the weight:

[0096]

[0097] where r j is the normalized single item score vector, w j is the weight vector.

[0098] D. Rating normalization

[0099] Finally, the composite score is normalized to the interval [0,100] for direct interpretation:

[0100] S 综合最终 =S 综合 ×100

[0101] The comprehensive score is greater than 80 points, and the node is in good operating condition;

[0102] The comprehensive score is between 60 and 80 points, and the node operation is slightly affected;

[0103] If the comprehensive score is lower than 60 points, the node operation is significantly affected and should be paid special attention.

[0104] An electronic device includes one or more processors for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the aforementioned method for intelligent quantitative analysis of the impact range of distribution network faults.

[0105] A storage medium stores a computer program, wherein the computer program is configured to execute the aforementioned method for intelligent quantitative analysis of the impact range of distribution network faults when running.

[0106] This paper proposes a method for intelligent quantitative analysis of the impact range of distribution network faults. By automatically generating topological structures, integrating multi-source data, calculating power flow theory, and using intelligent correction technology, it provides accurate fault propagation path analysis and quantitative assessment. This method includes the following steps and key technologies:

[0107] 1) Automatically generate the distribution network topology by processing the main wiring diagram. Specifically, based on the parsed information of nodes and lines, a topological graph G(V,E) is constructed, consisting of a set of nodes and lines. Nodes V include devices such as transformers, loads, and switches, and lines E include the electrical connections between these devices. By combining electrical properties (such as voltage, current, voltage harmonic content, and three-phase voltage imbalance) with spatial properties (such as node latitude and longitude), dynamic electrical coupling relationships between nodes are further established. This can also be used for spatial location analysis, facilitating spatial mapping of fault propagation ranges.

[0108] 2) When a distribution network fault occurs, real-time fault information is collected from various intelligent devices, such as DTUs, FTUs, protection devices, and intelligent switches. This information includes fault type, protection action time, fault location, electrical parameters, and protection action information. Using multi-source data fusion technology, this data is uniformly formatted and standardized for input into the analysis system.

[0109] 3) Power flow calculation is triggered based on step 2). Unlike traditional analysis that relies directly on sampling points, this method uses power flow calculation to expand the scope of analysis beyond nodes where intelligent monitoring devices are installed, including theoretical calculation nodes without monitoring devices. Power flow calculations are performed at nodes on each side of the transformer, along the high, medium, and low sides, along each section of the line, and at every 10% interval between the beginning and end of the line, yielding theoretical voltage and current values, thereby comprehensively understanding the operating status of the entire network.

[0110] 4) To ensure accuracy, the theoretical values ​​are compared with the actual sampled values ​​during the calculation. Error feedback is used to correct the relevant parameters of the topological network and iteratively update the power flow model. The corrected model re-triggers the power flow calculation, ensuring that the analysis results are more consistent with the actual operating conditions.

[0111] 5) In terms of quantitative evaluation, the present invention first designs three independent scoring systems: the voltage amplitude score is based on the degree of its deviation from the nominal voltage; the harmonic content score is calculated based on the total harmonic distortion rate; and the three-phase imbalance score considers the severity of the three-phase voltage imbalance.

[0112] After completing the above three sets of scoring, a comprehensive scoring is conducted. This is achieved by introducing a sub-item matrix and a nonlinear weighting method to improve the assessment sensitivity and adaptability to extreme situations.

[0113] Beneficial effects:

[0114] To address the shortcomings of traditional distribution network fault impact range analysis methods, this paper proposes an intelligent method based on automatic generation of distribution network topology, automatic collection of fault information, power flow calculation and correction, and quantitative analysis of fault impact range. The method has the following significant improvements and effects:

[0115] 1) The accuracy of fault range analysis is significantly improved

[0116] Traditional methods often rely on single-point or small amounts of monitoring data, making it difficult to fully reflect the fault propagation path and impact range of complex distribution networks. This invention automatically generates a distribution network topology diagram, combining power flow calculations with data correction technology to accurately capture the dynamic electrical coupling relationship between nodes and the impact range of faults.

[0117] The power flow calculation covers all key nodes, not just sampling nodes. By comparing and correcting with monitoring data, it realizes the function of dynamically adjusting topology parameters, further improving the quantification accuracy of the fault impact range.

[0118] 2) Quantitative assessment capability of the impact of fault propagation on power grid users

[0119] A scientific scoring system has been designed for multiple dimensions such as voltage amplitude, voltage harmonic content and voltage three-phase imbalance. Through more scientific and adaptable scoring formulas (such as parabolic decline and exponential decay) and fuzzy comprehensive evaluation methods, it can more accurately reflect the comprehensive impact of different faults on various nodes in the power grid.

[0120] The fault impact scope assessment is based on a comprehensive score and provides clear quantitative results, enabling operation and maintenance personnel to quickly determine the boundaries of the impact scope and reduce the uncertainty of human subjective judgment.

[0121] 3) Improve operation and maintenance efficiency and reduce economic losses

[0122] Through automated fault information collection and power flow calculation, the time for fault analysis is significantly shortened. The analysis process that traditional methods usually take several hours to complete can be completed in 1 minute by the present invention, greatly improving the analysis efficiency.

[0123] The intuitive quantitative results of node scores and impact ranges provided provide a scientific basis for operation and maintenance decisions, help to rationally allocate operation and maintenance resources, and further improve the operation efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] Figure 1 The figure is a schematic diagram of the working process of a method for performing intelligent quantitative analysis on the impact range of a distribution network fault according to the present invention. DETAILED DESCRIPTION

[0125] The specific implementation plan of this program is as follows:

[0126] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and in combination with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0127] To implement a method for intelligent quantitative analysis of the impact range of distribution network faults, this embodiment designs a set of intelligent analysis processes covering automatic topology generation, fault information collection, power flow calculation, dynamic correction, and impact range assessment based on the actual operation data, topology structure, and theoretical power flow calculation results of the distribution network. The specific implementation method is as follows:

[0128] 1) Automatic generation of distribution network topology

[0129] This method uses the main wiring diagram of the distribution network in CAD format to automatically generate a topology diagram G(V,E), where V represents the node set (including transformers, loads, switches, etc.) and E represents the line set. By analyzing the nodes and their connection relationships in the main wiring diagram, the system extracts the following key information:

[0130] ① The electrical properties of the node, including rated voltage, current, etc.;

[0131] ② Spatial attributes of the node, including longitude and latitude coordinates;

[0132] ③The impedance parameter Z of the line is calculated by the following formula:

[0133]

[0134] Where R is the line resistance, L is the inductance, and C is the capacitance; ω is the grid frequency, ω = 2πf.

[0135] The line's resistance, inductance, capacitance and other parameters are calculated based on the cable model and line length parsed from the file.

[0136] Ultimately, the generated topology diagram not only includes the electrical coupling relationship between nodes, but also reflects the geographical distribution of nodes and line lengths, providing a basis for subsequent power flow calculations and impact range assessments.

[0137] 2) Automatic collection of fault information

[0138] The system automatically collects fault event information from measurement and control terminals such as DTU (distribution terminal), FTU (feeder terminal), line protection device, intelligent switch and multi-function meter through the communication network. This information includes:

[0139] ① Fault type: such as phase short circuit, ground short circuit, etc.;

[0140] ②Fault location: the specific coordinates or node number of the fault location;

[0141] ③Fault characteristic parameters: such as fault current and voltage drop amplitude;

[0142] ④ Power quality information: including voltage harmonic content rate and three-phase imbalance;

[0143] ⑤Action information: such as whether reclosing occurs, protection action time, etc.

[0144] After the above data is connected to the system through the real-time transmission module, it will trigger subsequent power flow calculation and analysis.

[0145] 3) Flow theory calculation

[0146] After receiving a fault event, the system automatically triggers a power flow calculation to predict the changes in electrical parameters of nodes that do not have sampling devices installed. The steps of this embodiment are as follows:

[0147] ① Construct the node admittance matrix Y: Based on the topological graph G(V,E), construct the node admittance matrix;

[0148] ② Power flow iteration calculation: The voltage amplitude and phase angle of each node are calculated using the Newton-Raphson method.

[0149] ③Data interpolation supplement: set nodes every 10% of the distance on the line, and interpolate the theoretical electrical

[0150] Parameters, covering line sections where no sampling devices are installed.

[0151] Through the calculation results, the electrical parameters such as the theoretical voltage amplitude, voltage harmonic content, and voltage three-phase imbalance of each node are obtained to provide a basis for subsequent analysis.

[0152] 4) Dynamic correction of calculation results

[0153] In order to improve the evaluation accuracy, this method introduces a dynamic correction mechanism to compare the theoretical calculation results with the sampling data.

[0154] ① Error analysis: Compare the calculated value of the power flow at each sampling node with the actual sampling value and calculate the error:

[0155] ΔX=X 采样 -X 理论

[0156] P 修正 =f(ΔX,P,d)

[0157] Where X represents the electrical parameters, P represents the topological parameters before correction, and P 修正 represents the modified topological parameters, and d represents the termination condition.

[0158] ② Parameter update: Correct the topology parameter P based on the error 修正 , recalculate the power flow to ensure the consistency of topology parameters with actual operating status.

[0159] 5) Quantitative assessment of fault scope

[0160] This method performs comprehensive quantitative evaluation based on three indicators: voltage amplitude, voltage harmonic content and three-phase imbalance.

[0161] Single indicator scoring system:

[0162] ① Voltage amplitude:

[0163] The voltage amplitude score is calculated based on the function model of the actual voltage fluctuation amplitude ΔU. Consider the score value S U Should vary continuously and is defined as follows:

[0164]

[0165] This formula uses a quadratic parabola to decrease between 5% and 10%, reflecting the nonlinear effect of voltage amplitude fluctuations.

[0166] When ΔU ≤ 5%, the score is 100, which means there is no impact on the system;

[0167] When 5%<ΔU≤10%, the score decreases quadratically, and the greater the voltage deviation, the lower the score;

[0168] When ΔU>10%, it is considered a serious impact and scored as 0.

[0169] ② Voltage harmonic content rate:

[0170] For the total harmonic distortion THD, the scoring formula S is defined THD for:

[0171]

[0172] When the harmonic content is THD ≤ 3%, the full score is 100.

[0173] When the harmonic content is 3%<THD≤5%, the harmonic score decreases linearly. For every 1% increase in the total harmonic content, the score decreases by 20.

[0174] When the harmonic content THD>5%, the score decreases exponentially with the sharp increase of harmonics and eventually approaches 0.

[0175] ③ Voltage three-phase imbalance:

[0176] The three-phase imbalance can be calculated using the symmetrical component method, specifically by measuring the ratio of the voltage positive sequence component V1 to the negative sequence component V2:

[0177]

[0178] Scoring Formula S Unb Defined as:

[0179]

[0180] When ΔUnb ≤ 1%, the score is 100.

[0181] When 1%<ΔUnb≤3%, the score drops rapidly according to a quadratic parabola, reflecting the impact amplification effect of mild imbalance.

[0182] When ΔUnb>3%, the score decreases exponentially, and in severe imbalance conditions, the operation of the power grid is significantly affected.

[0183] ④ Comprehensive score calculation

[0184] Comprehensive score S 综合 Adopt fuzzy comprehensive evaluation method and weight dynamic adjustment strategy:

[0185] A. Establishment of item matrix

[0186] According to the scores of the three indicators, the sub-item evaluation matrix R is constructed:

[0187]

[0188] where r ij It represents the normalized score of the jth indicator of the i-th node, calculated according to the following formula:

[0189]

[0190] m is the number of nodes, j∈{U,THD,Unb}, which represent the three indicators of voltage amplitude, harmonic content rate and three-phase unbalance respectively.

[0191] B. Weight Matrix Establishment

[0192] For each column j, calculate:

[0193]

[0194] Where k = 1 / ln(m).

[0195] Weight distribution: weights are distributed inversely according to entropy values:

[0196]

[0197] C. Comprehensive scoring formula

[0198] The comprehensive score is the product of the fuzzy matrix and the weight:

[0199]

[0200] Where rj is the normalized single item score vector, and wj is the weight vector.

[0201] D. Rating normalization

[0202] Finally, the composite score is normalized to the interval [0,100] for direct interpretation:

[0203] S 综合最终 =S 综合 ×100

[0204] The comprehensive score is greater than 80 points, and the node is in good operating condition;

[0205] The comprehensive score is between 60 and 80 points, and the node operation is slightly affected;

[0206] If the comprehensive score is lower than 60 points, the node operation is significantly affected and should be paid special attention.

[0207] It can be seen from the above specific implementation that:

[0208] 1. Systematic modeling and analysis of fault propagation mechanisms. This involves establishing a comprehensive mathematical model of fault propagation within power users, integrating multiple factors such as user device sensitivity, network topology, and fault type into a unified analytical framework. This systematic modeling approach enables more accurate analysis of fault propagation paths and impact areas, providing a theoretical foundation for subsequent prediction and mitigation.

[0209] 2. Develop rapid isolation plans for different types of faults and research innovative multi-party collaborative emergency response mechanisms, integrating the resources and capabilities of power grid companies, power users, and regulatory authorities. By developing a unified information sharing platform and standardized collaborative processes, the efficiency and coordination of fault handling will be significantly improved.

Claims

1. A method for intelligent quantitative analysis of the impact range of distribution network faults, characterized by:

1. Automatic generation of distribution network topology; 2. Automatic collection of fault information; 3. Calculation of power flow theory; 4. Dynamic correction of calculation results; 5. Quantitative assessment of fault scope.

2. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 1 is characterized by: In step 1, the distribution network topology is automatically generated as follows: The topology diagram G(V,E) is automatically generated using the main electrical wiring diagram of the distribution network in CAD format, where V represents a node set, including transformers, loads and switches, and E represents a line set; by parsing the nodes and their connection relationships in the main wiring diagram, the system extracts the corresponding key information; The key information described is: The electrical properties of the node, including rated voltage and current; Spatial properties of nodes, including longitude and latitude coordinates; The impedance parameter Z of the line.

3. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 2 is characterized by: The impedance parameter Z of the above line is calculated by the following formula: Where R is the line resistance, L is the inductance, C is the capacitance; ω is the grid frequency, ω=2πf.

4. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 1 is characterized by: In step 2, the fault information is automatically aggregated as follows: The system automatically collects fault event information from distribution terminals, feeder terminals, line protection devices, intelligent switches, multi-function meters and other measurement and control terminals through the communication network; The fault event information includes: Fault type: Fault types include phase-to-phase short circuit and ground short circuit; Fault location: the specific coordinates or node number of the fault location; Fault characteristic parameters: Fault characteristic parameters include fault current and voltage drop amplitude; Power quality information: including voltage harmonic content and three-phase imbalance; Action information: Action information includes whether reclosing occurs and protection action time; After the above data is connected to the system through the real-time transmission module, it will trigger subsequent power flow calculation and analysis.

5. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 1 is characterized by: In step 3, the power flow theory is calculated as: After receiving a fault event, the system automatically triggers a power flow calculation to predict the changes in electrical parameters of nodes without sampling devices installed; the power flow calculation includes: Construct node admittance matrix Y: Based on the topological graph G(V,E), construct the node admittance matrix; Power flow iteration calculation: The voltage amplitude and phase angle of each node are calculated using the Newton-Raphson method; Data interpolation supplement: set nodes every 10% of the distance on the line, and interpolate and calculate the theoretical electrical parameters. Covering line sections where no sampling devices are installed; Through the calculation results, the electrical parameters such as the theoretical voltage amplitude, voltage harmonic content, and voltage three-phase imbalance of each node are obtained to provide a basis for subsequent analysis.

6. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 1 is characterized by: In step 4, the calculation result is dynamically corrected to: In order to improve the evaluation accuracy, a dynamic correction mechanism is introduced to compare the theoretical calculation results with the sampling data, including error analysis and parameter updating.

7. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 6 is characterized by: The error analysis is as follows: compare the calculated value of the power flow at each sampling node with the actual sampling value and calculate the error: ΔX=X 采样 -X 理论 P 修正 =f(ΔX,P,d) Where X represents the electrical parameters, P represents the topological parameters before correction, and P 修正 represents the modified topological parameters, and d represents the termination condition; The parameter update includes: based on the error correction topology parameter P 修正 , recalculate the power flow to ensure the consistency of topology parameters with actual operating status.

8. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 1 is characterized by: In step 5, the fault scope is quantitatively assessed as follows: A comprehensive quantitative evaluation is conducted based on three indicators: voltage amplitude, voltage harmonic content and three-phase imbalance. Each of the three indicators is scored individually, and then a comprehensive score is calculated.

9. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 8 is characterized by: In the voltage amplitudes described: The voltage amplitude score is calculated based on the function model of the actual voltage fluctuation amplitude ΔU; considering the score value S U Should vary continuously and is defined as follows: This formula uses a quadratic parabola decrease between 5% and 10%, reflecting the nonlinear effect of voltage amplitude fluctuations; When ΔU≤5%, the score is 100, which means there is no impact on the system; When 5%<ΔU≤10%, the score decreases quadratically, and the greater the voltage deviation, the lower the score; When ΔU>10%, it is considered a serious impact and scored as 0.

10. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 8, characterized in that: The voltage harmonic content rate is: For the total harmonic distortion THD, the scoring formula S is defined THD for: When the harmonic content is THD ≤ 3%, the full score is 100; When the harmonic content is 3%<THD≤5%, the harmonic score decreases linearly, and the score decreases by 20 for every 1% increase in the total harmonic content; When the harmonic content THD>5%, the score decreases exponentially with the sharp increase of harmonics and eventually approaches 0.

11. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 8, characterized in that: The three-phase voltage unbalance degree is: The three-phase unbalance can be calculated using the symmetrical component method, specifically measured by the ratio of the voltage positive sequence component V1 to the negative sequence component V2: Scoring formula S Unb Defined as: When ΔUnb ≤ 1%, the score is 100. When 1%<ΔUnb≤3%, the score drops rapidly according to a quadratic parabola, reflecting the impact amplification effect of mild imbalance; When ΔUnb>3%, the score decreases exponentially, and the grid operation is significantly affected in severe imbalance conditions.

12. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 8, characterized in that: The comprehensive score is being calculated. 综合 The fuzzy comprehensive evaluation method and weight dynamic adjustment strategy are adopted, which include: A. establishment of sub-item matrix, B. establishment of weight matrix, C. comprehensive scoring formula and D. scoring normalization processing.

13. The method for intelligent quantitative analysis of the impact range of a distribution network fault according to claim 12, characterized in that: A. Establishment of item matrix According to the scores of the three indicators, construct the sub-item evaluation matrix R: where r ij It represents the normalized score of the jth indicator of the i-th node, which is calculated according to the following formula: m is the number of nodes, j∈{U,THD,Unb}, which represent the three indicators of voltage amplitude, harmonic content rate and three-phase unbalance respectively; B. Weight Matrix Establishment For each column j, calculate: Where K = 1 / ln(m); Weight distribution: weights are distributed inversely according to the entropy value. C. Comprehensive scoring formula The comprehensive score is the product of the fuzzy matrix and the weight: where r j is the normalized single item score vector, w j is the weight vector; D. Rating Normalization Finally, the composite score is normalized to the interval [0,100] for direct interpretation: S 综合最终 =S 综合 ×100 The comprehensive score is greater than 80 points, and the node is running well; The comprehensive score is between 60 and 80 points, and the node operation is slightly affected; If the comprehensive score is lower than 60 points, the node operation is significantly affected and should be paid special attention.

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