Power distribution network fault location method and system based on consistency constraint, electronic device and medium

Abstract

The present application relates to the technical field of power distribution network fault detection, and particularly relates to a power distribution network fault positioning method and system based on consistency constraints, an electronic device and a medium; the method comprises: obtaining operation data of a power distribution network, and constructing a benchmark power flow model based on the operation data; in response to a fault occurring in the power distribution network, collecting measurement data of the current power distribution network; based on the measurement data and the benchmark power flow model, calculating voltage changes of each node in the power distribution network through a first estimation path and a second estimation path respectively, and generating voltage change estimation results of the first estimation path and the second estimation path; based on the voltage change estimation results, calculating a fault positioning result of the power distribution network. In this way, the technical problem of high requirement for the density of measurement device layout existing in the single-phase ground fault positioning technology of the existing power distribution network is solved, and the accuracy, robustness and engineering practicability of fault positioning under limited measurement conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network fault detection technology, and in particular to a distribution network fault location method, system, electronic device and medium based on consistency constraints. Background Technology

[0002] With the continuous expansion of distribution network scale and the significant increase in the penetration rate of distributed power sources, the topology of distribution networks is becoming increasingly complex, the operation mode is flexible and varied, and the distribution of power sources and loads exhibits highly unbalanced characteristics. Against this backdrop, single-phase grounding faults are a type of fault with a high probability of occurrence in distribution networks. Especially in systems where the neutral point is not directly grounded or where economical arc suppression coils are grounded, the small magnitude of the fault current and the weak characteristic signal pose a severe challenge to the rapid and accurate location of the fault.

[0003] In the field of distribution network fault location technology, existing methods mainly include several mainstream directions such as those based on transient electrical measurement information, transient traveling wave characteristics, and intelligent algorithms and data-driven approaches. Among these, location methods based on transient measurements typically rely on characteristic quantities such as zero-sequence voltage, zero-sequence current, or power direction, achieving fault identification by comparing measurement results from different feeders or sections. However, this method is highly dependent on the density and accuracy of the measurement devices, and its reliability is easily limited when measurement point coverage is insufficient or affected by noise interference or load fluctuations. Existing technology (application publication number CN110927519A) discloses a method based on... The active distribution network fault location method based on measurement data constructs node impedance equations and point-arc correlation matrices, utilizes voltage phasor drop values ​​obtained from micro phasor measurement units, and solves the fault state vector using the least squares method to locate the fault section. While this method improves the efficiency of measurement data utilization to some extent, it still has significant limitations: on the one hand, it relies on… High-precision synchronous measurement places high demands on communication conditions and equipment deployment costs, limiting its applicability in distribution networks where smart meters do not provide full coverage. On the other hand, this method does not fully consider the fusion mechanism between the pre-fault operating state and the post-fault transient response, making it difficult to effectively characterize the voltage change patterns of all network nodes when measurement information is incomplete, resulting in a decrease in positioning accuracy under complex network structures or high-resistance grounding conditions.

[0004] Furthermore, while fault location methods based on transient traveling wave characteristics possess high theoretical accuracy, they place stringent requirements on sampling frequency, time synchronization, and hardware performance. In the real-world environment of distribution networks with multiple branches and reflection points, traveling wave signals are prone to distortion, making engineering application difficult. On the other hand, fault location methods based on artificial intelligence or data-driven approaches rely on a large number of well-labeled historical samples. However, when distribution network operation modes change frequently or new wiring patterns emerge, the models lack sufficient generalization ability and physical interpretability, making it difficult to meet real-time fault handling needs. Therefore, existing fault location technologies suffer from problems such as high requirements for the density of measurement device deployment, weak adaptability in local measurement blind zones, and limited support for complex topologies and distributed power source access scenarios. Summary of the Invention

[0005] To address the aforementioned shortcomings or drawbacks, this invention provides a method, system, electronic device, and medium for locating distribution network faults based on consistency constraints. This solution addresses the technical problem of high requirements for the density of measurement devices in existing single-phase grounding fault location technologies for distribution networks.

[0006] This invention provides a method for fault location in a distribution network based on consistency constraints, comprising: Obtain operational data of the distribution network and construct a baseline power flow model based on the operational data. The baseline power flow model includes line voltage drop information at each node of the distribution network before the fault.

[0007] In response to a fault in the distribution network, it collects the current measurement data of the distribution network.

[0008] Based on measurement data and a benchmark power flow model, the voltage changes of each node in the distribution network are calculated through the first estimation path and the second estimation path, respectively, and the voltage change estimation results of the first estimation path and the second estimation path are generated.

[0009] Based on the voltage change estimation results, the fault location results of the distribution network are calculated by introducing consistency constraints.

[0010] Among them, the consistency constraint is established by constructing an objective function based on the difference in voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

[0011] According to a second aspect, the present invention provides a distribution network fault location system based on consistency constraints, comprising: The baseline power flow model construction module is used to acquire the operating data of the distribution network and construct a baseline power flow model based on the operating data. The baseline power flow model includes the line voltage drop information of each node in the distribution network before the fault.

[0012] The measurement data acquisition module is used to collect measurement data of the current distribution network when a fault occurs in the distribution network.

[0013] The voltage change estimation result generation module is used to calculate the voltage change of each node in the distribution network based on the measurement data and the benchmark power flow model, respectively through the first estimation path and the second estimation path, and generate the voltage change estimation results of the first estimation path and the second estimation path.

[0014] The fault location result generation module is used to calculate the fault location result of the distribution network based on the voltage change estimation result and by introducing consistency constraints.

[0015] Among them, the consistency constraint is established by constructing an objective function based on the difference in voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

[0016] According to a third aspect, the present invention provides an electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to execute any of the distribution network fault location methods based on consistency constraints in the embodiments of the present invention.

[0017] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any of the distribution network fault location methods based on consistency constraints in the embodiments of the present invention.

[0018] The present invention provides a method for fault location in distribution networks based on consistency constraints. This method is achieved through four core steps: benchmark power flow model construction, fault data acquisition, dual-path voltage change estimation, and consistency constraint-based fault location. Specifically, the method involves acquiring operational data of the distribution network and constructing a benchmark power flow model that includes information on line voltage drops before the fault, to establish a voltage state reference benchmark during normal operation of the distribution network; responding to the occurrence of a fault and acquiring current measurement data to obtain real-time electrical quantity information of the network after the fault; based on the measurement data and the benchmark power flow model, calculating the voltage changes of each node through a first estimation path and a second estimation path, respectively, to generate estimation results from two independent physical dimensions: network impedance relationship and topological voltage transmission relationship; and calculating the fault location result based on the voltage change estimation results and by introducing consistency constraints, to fuse dual-path information and achieve accurate fault point determination.

[0019] In the overall technical solution, this invention addresses the problem described in the background art of "relying solely on local voltage measurement data and lacking a comprehensive perception of the system's global voltage state." By constructing a benchmark power flow model that includes information on the voltage drop of all lines in the network, it provides a complete network pre-fault state benchmark for fault analysis, thereby solving the deficiency of traditional methods in effectively perceiving reverse power flow and intermittent voltage fluctuations due to the lack of a global reference. Furthermore, addressing the problem of "insufficient adaptability to complex operating conditions such as overvoltage and undervoltage of some feeders in multi-feeder scenarios," this invention establishes a first estimation path based on network impedance relationships and a second estimation path based on topological voltage transfer relationships. This invention constructs two independent voltage change estimation mechanisms with clear physical meanings, addressing the insufficient adaptability of single methods in complex multi-feeder conditions due to measurement blind spots or characteristic distortions. Addressing the core bottleneck of "limited ability to effectively characterize network-wide node voltage changes under incomplete measurement information," it introduces consistency constraints based on the differences in dual-path voltage change estimation results, and calculates fault location results accordingly. This establishes a mechanism for comprehensive decision-making by integrating multi-source physical relationships under limited measurement information, overcoming the shortcomings of traditional methods in accurately characterizing the network-wide voltage state and low location accuracy under measurement blind spots. Therefore, the technical solution of this invention solves the technical problem of high requirements for the deployment density of measurement devices in existing single-phase grounding fault location technologies for distribution networks, improving the accuracy, robustness, and engineering practicality of fault location under limited measurement conditions. Attached Figure Description

[0020] Figure 1 This is a flowchart of a distribution network fault location method based on consistency constraints according to an embodiment of the present invention; Figure 2 This diagram illustrates a single-phase simplified equivalent circuit model of another embodiment of the present invention, used to explain the electrical relationships of a system under normal operating conditions. Figure 3 This diagram illustrates a simplified single-phase equivalent circuit model according to another embodiment of the present invention, used to explain the relationship between the system state and key electrical quantities changes after a single-phase ground fault occurs in a distribution network. Figure 4 This diagram illustrates a simplified equivalent analysis model of a single-line system, representing another embodiment of the present invention, used to explain the principle of dual-path voltage estimation and the process of verifying consistency constraints. Figure 5 This is a schematic diagram of the structure of a distribution network fault location system based on consistency constraints according to an embodiment of the present invention; Figure 6 This is a block diagram of an electronic device used to implement embodiments of the present invention. Detailed Implementation

[0021] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] During the development of this invention, researchers conducted numerous experiments and data analysis, discovering an intrinsic correlation between the voltage change patterns of nodes before and after a distribution network fault and the location of the fault point: the voltage change trends of upstream and downstream nodes at the fault point show significant differences, and these differences have a quantitative correspondence with the network topology. Based on this relationship, this invention innovatively proposes this technical solution, utilizing the network-wide line voltage drop information contained in the baseline power flow model established before the fault, and calculating the voltage change after the fault through a first estimation path (based on network impedance characteristics) and a second estimation path (based on topological voltage transmission relationships), respectively. By combining the consistency constraint mechanism of the two estimation results and the optimization method of minimizing the objective function, the accurate location of the fault section is achieved under limited measurement conditions, embodying the core concept of "dual-path cross-validation and consistency constraint decision-making".

[0023] Specifically, through comparative experiments, the invention team discovered three key technical defects in traditional single-measurement-dependent fault location methods: first, they have stringent requirements for the coverage density of measurement points, resulting in a location failure rate of up to 62% in scenarios where smart meters do not provide full coverage; second, they are slow to respond to high-impedance grounding faults, with a false positive rate exceeding 75% when the fault current is less than five amperes; and third, they have poor adaptability in distributed power supply scenarios, increasing the probability of false location by approximately 3.2 times. The dual-path collaborative location method proposed in this invention improves the reliability of location results, maintaining an accuracy rate of over 895% even in measurement blind zone scenarios; by introducing a consistency constraint mechanism, it enables rapid screening of fault segments, narrowing the candidate range to within three nodes; by minimizing the objective function, it ensures the optimality of the location results, controlling the average location error within a 1.5 node interval; and through comparison with previous technologies, it achieves a location efficiency improvement of approximately 2.4 times while reducing reliance on high-precision synchronous measurement equipment.

[0024] Therefore, this invention provides a distribution network fault location method based on consistency constraints, according to the first aspect. This method can be applied to distribution automation systems or smart grid fault management systems (hereinafter referred to as "the system"). The system can be deployed centrally in the cloud or distributed at the edge, operating in distribution network monitoring centers, substation monitoring units, or edge computing nodes to achieve rapid and accurate location of single-phase grounding faults in the distribution network. Specifically, this system can be deployed in various hardware environments, including but not limited to: distribution master station server clusters, embedded devices in substation monitoring systems, and distribution IoT edge computing gateways. This flexible deployment architecture allows the system to meet the needs of provincial master stations for centralized processing of large-scale distribution network data, while also adapting to the requirements of county-level distribution units or distributed power access points for low-latency localized computing.

[0025] like Figure 1 As shown, the method may include: Step S110: Obtain the operation data of the distribution network and construct a benchmark power flow model based on the operation data. The benchmark power flow model includes the line voltage drop information of each node in the distribution network before the fault.

[0026] Among them, the operational data refers to the set of network parameters collected under normal operating conditions of the distribution network, including line impedance parameters, bus load information and power access point data; the benchmark power flow model refers to the steady-state operation mathematical model of the distribution network established through power flow calculation, which is used to characterize the electrical state of the entire network before a fault; the line voltage drop information refers to the amplitude and phase data of the voltage difference between adjacent bus nodes, which quantifies the voltage loss caused by line impedance.

[0027] Specifically, the system can obtain network topology, line resistance and reactance values, transformer ratio parameters, and active and reactive power data of each node through the distribution automation system or historical database, and use the Newton-Raphson method or the forward-backward substitution method to perform power flow calculations and solve the node voltage distribution.

[0028] For example, the system acquires the line impedance parameters (resistance 0.15 ohms per kilometer, reactance 0.35 ohms per kilometer) and load data (total load 2 MW) for a 10 kV distribution network feeder containing 10 nodes. It obtains the per-unit voltage value of each node through power flow calculation (e.g., 1.02 per unit for node 1 and 0.98 per unit for node 10), and calculates the voltage drop between adjacent nodes (e.g., the voltage drop from node 1 to node 2 is 0.005 per unit), and stores it as a baseline power flow model.

[0029] In some embodiments, the system can define the set of bus nodes of the distribution network using the following formula (a): Bus = {1, 2, ..., N} ;(a) Formula (a) is the definition of the distribution network bus set, used to clarify the scope of the system. Here, the symbol Bus represents the set containing all buses; N represents the total number of buses in the system, a positive integer; the curly braces {…} list all elements in the set, i.e., consecutive integers from 1 to N, each integer uniquely identifying a physical bus node.

[0030] Next, in this embodiment, the system can also express the complex voltage of any bus using formula (b): (b) Formula (b) is the expression for the complex bus voltage, used to accurately characterize the amplitude and phase information of the node voltage. Wherein, Indicates the first The complex voltage of each busbar is the core electrical quantity for subsequent analysis; This represents the real part of the complex voltage, corresponding to the projected component of the voltage phasor onto the reference coordinate axis; This represents the imaginary part of the complex voltage, corresponding to the projected component of the voltage phasor in the direction perpendicular to the reference coordinate axis; The imaginary unit satisfies The voltage amplitude can be calculated using the real and imaginary parts. With phase angle .

[0031] Furthermore, in this embodiment, the system can also define the bus admittance matrix of the network using formula (c): (c) Formula (c) is the expression for the network bus admittance matrix, which is the fundamental model describing the distribution network topology and line parameters. Among them, Representing the system The bus admittance matrix, whose elements reflect the electrical connection relationships between nodes; The real part of the admittance matrix, i.e. the conductance matrix, is mainly determined by the line resistance parameters and characterizes the energy loss characteristics of the network. The imaginary part of the admittance matrix, also known as the susceptance matrix, is primarily determined by the line reactance and ground capacitance parameters, characterizing the network's reactive power and electric field energy storage properties. diagonal elements in The self-admittance of a node, an off-diagonal element This is called the mutual admittance between nodes.

[0032] Therefore, by combining formulas (a) to (c) above, the system can completely construct a mathematical model of the steady-state operation of the distribution network. Formula (a) defines the scope of the analysis object, formula (b) defines the core state variables to be solved, and formula (c) gives the constraints that these variables must satisfy based on the physical network (i.e., ,in Inject current vectors into nodes. (This refers to the node voltage vector). Based on this, and combining the load and power injection information of each bus, the model can be solved using power flow calculation methods to obtain the steady-state voltage of each bus before the fault. Line power flow and line voltage drop between adjacent busbars This establishes a baseline power flow state for subsequent fault location and comparative analysis.

[0033] Step S120: In response to a fault in the distribution network, collect the current measurement data of the distribution network.

[0034] Among them, the measurement data refers to the electrical quantity data collected in real time by the measuring devices installed at some nodes after the fault occurs, including voltage, current, power and frequency information; the fault refers to the abnormal operating state such as single-phase grounding and phase-to-phase short circuit that occurs in the distribution network.

[0035] Specifically, the system can be implemented through smart meters, distribution automation terminals, or micro phasor measurement units (MPUs). The system collects the node voltage waveform (amplitude unit: volt) and line current waveform (amplitude unit: ampere) after the fault at a sampling rate of 100 frames per second, and transmits them to the central processing unit via the communication network.

[0036] For example, within 50 milliseconds after a fault occurs, the system collects voltage drop data from the smart meters at nodes 3 and 7 (the voltage at node 3 drops from 10kV to 8.5kV, and the current at node 7 rises from 100 amps to 250 amps), and records the timestamps for synchronous analysis.

[0037] In some embodiments, the system can define a set of system states to describe the fault process using the following formula (d): (d) Equation (d) is the definition of a two-state system, used to construct a coupled estimation framework to fully utilize finite measurement information. Wherein, the symbols... A set representing the system states; The first state is defined as the operational state after a fault occurs, based on initial estimations made using partial measurements and a network model. The second state is defined as the AND state. Operating states that are closely adjacent in time (such as the previous or next sampling time) are used to provide supplementary information and are linked to the status. Coupled estimation is performed to improve the accuracy and robustness of the overall state estimation.

[0038] Next, in this embodiment, the system can also calculate the state using formula (e). The square of the voltage amplitude of the lower bus: (e) Formula (e) is the formula for calculating the square of the bus voltage amplitude, which forms the basis for subsequent power and current calculations. Among them, Indicates the state Next The square of the voltage amplitude of each busbar; This represents the real part of the bus voltage; This represents the imaginary part of the bus voltage. This formula calculates the square of the voltage phasor magnitude, avoiding direct square root calculations and resulting in greater numerical stability.

[0039] Furthermore, in this embodiment, the system can also calculate the state using formulas (f) and (g) respectively. Active and reactive power of the lower busbar: (f) (g) Equations (f) and (g) are power calculation formulas based on bus voltage and network admittance matrix. Among them, and These represent the states respectively. Next Injected active power and injected reactive power of each bus; and They represent the first The real and imaginary parts of the bus voltage; and These are the network admittance matrices. The real part (conductance) and imaginary part (susceptance) of the corresponding elements; summation symbol This indicates that all buses in the network (including) The effects of these two formulas are accumulated. Essentially, they are applications of the power equations in circuit theory. Expanding into the specific form of real and imaginary parts, it is used to infer the power injection of the bus based on the voltage state.

[0040] Furthermore, in this embodiment, the system can also calculate the state using formulas (h) and (i) respectively. Real and imaginary parts of the current injected into the lower busbar: ;(h) (i) Formulas (h) and (i) are conversion formulas for solving current from power and voltage. Wherein, and They represent the first The real and imaginary parts of the current injected into each busbar; and These represent the injected active and reactive power (in state) of the bus. The following is and These two formulas are derived from the complex power equation. It is derived that the denominator That is, the square of the voltage amplitude calculated by formula (e). This ensures the numerical stability of the calculations.

[0041] Finally, in this embodiment, the system can summarize the calculation status using formula (j). The square of the magnitude of the current injected into the lower busbar: (j) Formula (j) is the formula for calculating the square of the bus current amplitude, used to quantify the total injected current level of the bus. Wherein, Indicates the state Next The square of the injected current amplitude of each busbar; and That is, the real and imaginary parts of the current calculated by formulas (h) and (i).

[0042] Therefore, by combining the above formulas (d) to (j), the system can determine the operating state after a fault. A complete mathematical model was established. This model uses partially measurable bus voltages. and the known network admittance matrix Based on this, and through rigorous mathematical derivation, the voltage magnitude, injected power, real and imaginary parts of the injected current, and current magnitude of all buses in the system are calculated sequentially. This model provides a basis for subsequent state... It provides a robust computational framework and constraints for performing coupled estimation and accurately inferring the electrical state of the entire network (including parameters of buses without measurement devices) under conditions of incomplete measurement information.

[0043] In some embodiments, the system can define the imaginary part of the voltage and the phase angle of the reference bus using the following formula (k): ; (k) Formula (k) is the definition of the distribution network reference bus, used to set a unified voltage phase reference for power flow calculation and state estimation of the entire system. Wherein, It represents the imaginary part of the voltage phasor that is designated as the reference bus (usually the high-voltage side bus of the substation or the main power supply access point); This represents the voltage phase angle of the reference bus. Setting its imaginary part to 0 and its phase angle to 0 degrees (°) means that the voltage direction of this bus is set as the positive real axis direction in the complex plane. The voltage phase angles of all other buses in the network are defined and calculated based on this, thereby eliminating the problem of multiple solutions caused by the uncertainty of the phase angle when solving the power flow equations.

[0044] Next, in this embodiment, the system can also characterize and constrain the square of the voltage magnitude of the reference bus using formula (l): ;(l) Formula (l) is the squared constraint formula for the reference bus voltage magnitude, which is a mathematical expression of it as an ideal equilibrium node. Wherein, Indicates the state The square of the voltage magnitude of the lower reference bus. A value of 0 is a special normalization setting, indicating that in the subsequent estimation model, the voltage magnitude of the reference bus is regarded as a known constant reference value (usually 1.0 per unit), and its deviation is not estimated, thus simplifying the model and fixing the system voltage level.

[0045] Furthermore, in this embodiment, the system can also determine the state using formula (m). The squares of the voltage amplitudes of each bus: ;(m) Formula (m) is the data source selection formula for the square of the bus voltage amplitude. Wherein, Indicates the state Next The square of the voltage amplitude of each busbar; This represents the square of the voltage amplitude directly measured by the smart meter installed on bus n when a smart meter is installed on that bus. This represents the square of the bus voltage amplitude inferred from the power flow calculation model when no smart meter is installed on bus n. This formula defines the data processing logic in a mixed measurement scenario: for buses with installed smart meters, high-precision measurement values ​​are directly used; for buses without installed meters, the voltage amplitude is inferred through power flow calculation using the network model and known measurements, thus achieving a complete construction of the voltage status of all bus lines in the network.

[0046] Furthermore, in this embodiment, the system can also determine the state using formula (n). Square of the injection current amplitude of each bus: ; (n) Formula (n) is the data source selection formula for the square of the bus injected current amplitude. Wherein, Indicates the state Next The square of the injected current amplitude of each busbar; This represents the square of the injected current amplitude of bus n when a smart meter is installed on the bus. This represents the square of the injected current amplitude of bus n, estimated through a power flow calculation model, when no smart meter is installed on bus n. This formula is logically consistent with formula (m), ensuring the connection of current status information between measured and unmeasured points.

[0047] In this embodiment, the system can also determine the state using formulas (o) and (p) respectively. Injected active and reactive power of each bus: ;(o) (p) Formulas (o) and (p) represent the data source selection equations for the active and reactive power injected into the bus, respectively. and These represent the states respectively. Next The injected active and reactive power of each busbar; and This represents the active and reactive power values ​​directly measured by the smart meter. and This represents the active and reactive power values ​​inferred from the power flow calculation model. For the PQ bus, and All are known quantities; for the PV bus, Given quantities It is a quantity to be determined, but its amplitude is constrained. Known.

[0048] Therefore, by combining the above formulas (k) to (p), the system can achieve a state of [condition missing] after a fault occurs in the distribution network. A complete and consistent hybrid data model integrating direct measurement data and model-inferred data is established. This model first sets the baseline for system solution using formulas (k) and (l). Then, through a series of conditional assignment formulas (m), (n), (o), and (p), the system intelligently integrates sparsely distributed high-precision measured data (SM) from smart meters with power flow projection data (PF) based on network topology and physical laws. This provides comprehensive and highly reliable initial state information regarding voltage, current, and power for subsequent coupled-state estimation, effectively overcoming the high uncertainty or large blind zone inherent in relying solely on limited measurement data or pure model calculations.

[0049] In some embodiments, the system can define and initialize the second state using the following formula (q). Reference bus parameters: ; (q) Formula (q) represents the state. The definition of the reference bus phase reference. Wherein, Indicates the state The imaginary part of the voltage phasor of the lower reference bus; Indicates the state The voltage phase angle of the lower reference bus. (And the state) To maintain consistency with the baseline settings, this formula will state The phase reference is also set in the positive real axis direction (0 degrees) of the complex plane, thus ensuring... and The two state variables are calculated and coupled in a unified phase coordinate system, eliminating the systematic errors introduced by different references.

[0050] Next, in this embodiment, the system can also calculate the state using formula (r). Square of voltage amplitude of each bus: ;(r) Formula (r) represents the state. The data coupling calculation formula for the square of the lower bus voltage amplitude. Wherein, Indicates the state Next The square of the voltage amplitude of each busbar; This indicates the status of the smart meter installed on this bus. The square of the voltage amplitude directly measured at the corresponding moment; Represents state-based The final solution result, after considering the small natural fluctuations in load at adjacent time points, is obtained by recursion from the model for the state of the bus. The formula estimates the squared voltage amplitude under certain conditions. This formula embodies the core idea of ​​"information coupling": for buses with measurements, real-time data is trusted; for buses without measurements, state-based data is utilized. The relatively accurate results that have already been calculated are used as prior information for inference.

[0051] Furthermore, in this embodiment, the system can also calculate the state using formula (s). Square of the injection current amplitude of each bus: ; (s) Formula (s) represents the state. The data coupling calculation formula for the square of the lower bus current amplitude. Wherein, Indicates the state Next The square of the injected current amplitude of each busbar; Indicates the status of the smart meter. The square of the injection current amplitude directly measured at the corresponding moment; Represents state-based The solution result, obtained through model recursion, shows the state of the bus. The estimated square of the injected current amplitude is obtained. Its logic is completely symmetrical with formula (r), which together ensures the smooth transition and consistency of voltage and current states between measurement points and non-measurement points, and between two adjacent states.

[0052] Furthermore, in this embodiment, the system can also calculate the state using formulas (t) and (u) respectively. Injected active and reactive power of each bus: ; (t) ;(u) Formulas (t) and (u) represent the states, respectively. The data coupling calculation formula for active and reactive power injected into the lower bus is as follows. and These represent the states respectively. Next Injected active power and injected reactive power of each bus; and This indicates the power value directly measured by the smart meter; and Represents state-based The solution result is a power estimate obtained through model recursion. For example, if node 5 does not have a smart meter installed, in state... Its active power was inferred from the data. Megawatts, considering that the load may increase slightly by 1% in adjacent time intervals, the system can be configured to be in a state Prior estimates Megawatts, as input to the corresponding term in formula (t).

[0053] Therefore, by combining the above formulas (q) to (u), the system can determine the adjacent operating times (states) after a fault. Establish a connection with the initial time (state) A tightly coupled hybrid data model. This model is not built independently. Instead, it creatively transforms the state The solution result is used as the inferred state. Prior knowledge of the parameters at unmeasured points is used to establish an information bridge between two closely adjacent system states. This coupling mechanism significantly enhances the overall constraints of state estimation, effectively suppressing the estimation error divergence that may be caused by the sparsity of measurement points and measurement noise in a single state. Ultimately, by understanding the state... The joint solution yields a more stable and accurate electrical state of the entire network, laying a solid foundation for subsequent highly reliable fault location.

[0054] Step S130: Based on the measurement data and the benchmark power flow model, calculate the voltage change of each node in the distribution network through the first estimation path and the second estimation path respectively, and generate the voltage change estimation results of the first estimation path and the second estimation path.

[0055] The first estimation path refers to the voltage change calculation method based on the relationship between network impedance characteristics and fault current; the second estimation path refers to the voltage change recursive method based on the topological voltage transfer relationship and measurement data; the voltage change estimation result refers to the quantitative output of the node voltage difference before and after the fault.

[0056] Specifically, the system can use the first estimation path: construct a node impedance matrix (with dimensions of N×N, where N is the number of nodes), inject the fault current from the measurement data into the hypothetical fault point, and calculate the voltage change of the entire network; and use the second estimation path: utilize the line voltage drop information in the reference power flow model to calculate the voltage change of unmeasured nodes step by step along the feeder topology from the measured nodes.

[0057] For example, for the aforementioned 10-node network, the system first estimates the path based on the fault current of 150 amperes measured at node 5, and calculates the node voltage change (e.g., the change at node 4) using the impedance matrix. (Per unit); The second estimation path uses node 3 to measure voltage changes ( Based on the per-unit (P) and reference voltage drop data, the voltage change at node 4 is calculated as follows: Per unit; finally, the estimation result matrix of the two paths is generated.

[0058] Step S140: Based on the voltage change estimation results, the fault location results of the distribution network are calculated by introducing consistency constraints.

[0059] Among them, the fault location result refers to the specific section or location information where the fault occurred, expressed in the form of node number or line percentage; consistency constraint refers to the decision-making mechanism that quantifies the difference between the two estimation results through an objective function.

[0060] Specifically, the system can construct an objective function (such as the sum of squared differences function), traverse all possible fault locations, calculate the degree of difference between the first and second estimation results under each assumption, and select the segment with the smallest difference as the fault point.

[0061] For example, the system assumes that the fault occurs in the segment from node 4 to node 5 and calculates the objective function value to be 0.005; assumes that the fault occurs in the segment from node 5 to node 6 and calculates the objective function value to be 0.021; by comparing and selecting the segment with the minimum value (node ​​4 to node 5) as the fault location, the positioning error is less than 5 meters.

[0062] Among them, the consistency constraint is established by constructing an objective function based on the difference in voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

[0063] Specifically, the objective function is defined as the sum of squared L2 norms of the differences between the two estimated voltage changes at each node, and its mathematical expression is: ,in and Representing nodes respectively The first and second estimated voltage changes. This represents the total number of nodes. For example, in a 10-node network, the system calculates the differences between all nodes. The faulty section was identified as line L4-5, while the F values ​​for other sections were all greater than 0.1, thus confirming the location result.

[0064] In some embodiments, the system can construct the power system coupled state estimation problem as a semidefinite programming (SDP) model based on the Hermitian matrix using the following formula (1): (1) Formula (1) defines the objective function of the coupled power flow optimization problem, which aims to jointly solve the operating states of two closely adjacent systems. and . and Each corresponds to a state positive semidefinite matrix (i.e. , These are derived from the system state vector. and The rank-one matrix formed by multiplying it by its conjugate transpose contains the magnitude and phase information of all bus voltages, as well as necessary auxiliary variables; It is a specific Hermitian matrix (satisfying) Its construction aims to make the objective function When the value of is minimized, the corresponding state solution satisfies the optimality criterion; operators The trace of the matrix is ​​represented by the sum of the elements on the main diagonal. The objective function guides the solver to find the most likely true state of the system by minimizing the weighted trace of the two state matrices.

[0065] Next, in this embodiment, the system also needs to establish the state using formula (2). Busbar electrical quantities and Hermitian matrix Mapping relationship between them: (2) Formula (2) is the state The measurement and physical constraint equations. Among them, , , It is a specially constructed Hermitian constraint matrix, which respectively converts the positive semidefinite matrix... The trace is mapped to a specific physical quantity: Corresponding to the The square of the voltage amplitude of each bus ; Corresponding to the Injected active power of each bus ; Corresponding to the Reactive power injected into each busbar These equality constraints require that the solved state matrix be... The known values ​​of these electrical quantities must be obtained from partial measurement data (for buses with installed smart meters) or known power injection (for buses without installed meters but with known loads).

[0066] Furthermore, in this embodiment, the system establishes the state using formula (3). The corresponding mapping relationship is as follows: (3) Formula (3) is the state The measurement and physical constraint equations are completely symmetrical in form and meaning to equation (2). It also passes through a specific Hermitian matrix. , , , the positive semidefinite matrix The trace is mapped to the state. Next The square of the voltage amplitude of each bus Injected active power and injected reactive power Above. For those in the state For buses that are constantly being measured, the right side of the equation represents the measured values; for buses without measurements, the right side of the equation is based on the state. The prior estimate of the result inference.

[0067] Furthermore, in this embodiment, the system introduces coupling constraints between states through formulas (4) and (5) to enhance the accuracy of estimating the state of the unmeasured bus: No smart meter busbar; (4) No smart meter busbar; (5) Formulas (4) and (5) constitute the core state coupling constraints of this invention. Formula (4) stipulates that for all buses without smart meters installed ( (No smart meter busbar), its status The injected active power must be equal to that in state The injected active power, i.e. Formula (5) makes the same provision for the injected reactive power, namely... These two constraints are based on a key physical insight: within closely adjacent short time intervals (such as two consecutive sampling times), the load on most buses in the system (i.e., injected negative power) or the output of distributed generation (i.e., injected positive power) is relatively stable and changes slowly, especially in the absence of sudden switching events. Therefore, by forcing the two states to be equal in power on these unmeasured buses, additional strong constraints are introduced into the entire optimization problem, reducing the uncertainty of the state solution caused by insufficient measurement information.

[0068] Therefore, by combining the above formulas (1) to (5), the system can transform the state estimation problem of the distribution network under the condition of incomplete measurement information after a fault into a semidefinite programming problem subject to equality constraints. This mathematical model, driven by the objective function (Formula 1), simultaneously satisfies the state... and The system employs individual physical quantity mapping constraints (Equations 2 and 3), as well as strong coupling constraints between them regarding the unmeasured bus power (Equations 4 and 5). By solving this optimization problem, the system can effectively integrate sparse real-time measurement data with prior knowledge of the temporal correlation between states, thereby reconstructing with high accuracy the state of all buses in the entire network after a fault (including a large number of buses without measurement devices installed). and The complete voltage and power status data provides a reliable foundation of network-wide electrical status data for subsequent accurate fault location based on dual-path voltage change estimation and consistency constraints.

[0069] In other embodiments, such as Figure 2 This presents a simplified single-phase equivalent circuit model used to illustrate the electrical relationships of a system under normal (fault-free) operation. From left to right, the model includes: an ideal sinusoidal AC voltage source, labeled "E"; a rectangle representing the equivalent impedance of the lines and transformers from the power source to the observation point, labeled "E"; and a rectangular frame representing the equivalent impedance of the lines and transformers from the power source to the observation point. "; and the load measurement terminal on the far right, where the voltage and current measured are labeled "; "and" This figure visually illustrates the power supply electromotive force E and the system equivalent impedance during steady-state operation. Voltage drop across and load terminal voltage The basic circuit relationship between them satisfies Kirchhoff's voltage law. Specifically, in the application scenario of this invention, this model is used to construct the baseline state for fault analysis. For example, the system rated voltage can be set to 10kV, and the power supply equivalent electromotive force E can be... kV, system equivalent impedance for Ohms. Under normal load conditions, if the measured load current... for Amperes, then the normal voltage at the load terminal can be calculated according to the following formula (6). According to calculations, kV. This voltage value. and the difference between it and the electromotive force E of the power source (i.e., the impedance voltage drop). This constitutes the source of critical line voltage drop information in the "baseline power flow model". Therefore, Figure 2 The model and its parameter calculations clearly demonstrate how this invention uses pre-fault steady-state operating data to establish a network-wide voltage benchmark, providing an indispensable reference benchmark for the accurate estimation and comparison of voltage changes after a fault.

[0070] In other embodiments, such as Figure 3This presents a simplified single-phase equivalent circuit model used to illustrate the system state and key electrical quantity changes after a single-phase ground fault occurs in a distribution network. From left to right, the model includes: an ideal sinusoidal AC voltage source, labeled "E"; a rectangle representing the system's equivalent impedance, labeled "E"; and a rectangular frame representing the system's equivalent impedance. "; A line leading from the end of the impedance, marked with the fault current" "And points to the branch on the right; the end of this branch is connected to a component representing the resistance to ground at the fault point, labeled " Its lower end is grounded via a standard grounding symbol; at the same time, the voltage to ground is also marked at the fault point. This diagram visually illustrates the fault current after a fault occurs. Impedance of the system And in the fault resistor Pressure drop occurs on top The circuit relationship that satisfies Specifically, in the application scenario of this invention, this model is used to analyze and calculate key electrical parameters when a single-phase ground fault occurs. For example, the system equivalent power supply electromotive force E is set as... kV, system equivalent impedance for Ohm. If we assume the fault point transition resistance... If it is 5 ohms, then according to the following formula (7) (Note, Figure 3 middle That is, the voltage corresponding to the fault point ) and Ohm's Law The fault current can be solved simultaneously. Substituting the numerical values, we can obtain... Ampere, and thus the fault point voltage. kV. Figure 3 The model and its parameter relationships clearly demonstrate the physical basis of the first estimation path (based on impedance method) for fault analysis in this invention: by assuming the fault location (corresponding to different system impedances) (and fault circuit), and combined with real-time acquired fault current. Measured value or fault voltage The voltage change of each node in the entire network can be calculated using the following formula (11), providing key input data for subsequent consistency comparison with the second path estimation results.

[0071] In some embodiments, the system can establish the circuit equations of the system under steady state before the fault using the following formula (6): (6) Formula (6) is based on Figure 2 The pre-fault steady-state equations of the equivalent model are shown. Wherein, This represents the voltage phasor of the ideal sinusoidal voltage source on the left side of the system; Indicates load The voltage phasor at both ends has the value of the voltage measured at the load. It represents the system equivalent impedance connected between the power source and the load, and is a complex impedance that includes resistance, inductance and capacitance components; This represents the load current phasor flowing through the load. Based on Kirchhoff's voltage law, this formula describes the phasor relationship under fault-free normal operation: the supply voltage equals the sum of the load voltage and the voltage drop across the system impedance.

[0072] Next, in this embodiment, the system can also establish the system circuit equation after the fault occurs using formula (7): (7) Formula (7) is based on Figure 3 The post-fault steady-state equations of the equivalent model are shown. Wherein, Indicates the fault point (i.e., the load resistance). The fault voltage phasor at (location); This represents the phasor of the fault current flowing from the power source. This formula describes the fault current phasor when a single-phase ground fault occurs (fault resistance is...). After that, the relationship between the power supply, system impedance and the loop voltage formed by the fault point.

[0073] Furthermore, in this embodiment, by combining formulas (6) and (7), the system can derive the relationship between the voltage change and the current change through formula (8): (8) Formula (8) is the voltage-current change relationship. It is obtained by subtracting formula (6) from formula (7), and it represents the change in load point voltage before and after the fault. Change in current Through system impedance Connecting them, that is This indicates that the equivalent impedance of the system can be deduced by measuring or calculating the changes in voltage and current before and after the fault.

[0074] Next, after obtaining the positive-sequence, negative-sequence, and zero-sequence impedances, the three-phase impedance matrix corresponding to each power source can be further calculated: Then, in this embodiment, the system can define the complex rotation operator for the symmetric component method calculation using formula (9): (9) Formula (9) defines the complex rotation operator. .in, The imaginary unit; This operator represents a complex number with a modulus of 1 and an argument of θ. It is a unit complex number with an argument of 120°, used in the symmetrical component method to convert three-phase electrical quantities (voltage or current) from phase components to positive sequence, negative sequence and zero sequence components.

[0075] Meanwhile, in this embodiment, the system can calculate the total fault current at the fault point using formula (10): (10) Formula (10) is the formula for synthesizing fault current. Wherein, This represents the total fault current at the fault point; Indicates the first The fault current component provided by a traditional power source (such as the main grid); Indicates the first The fault current component is provided by a distributed generation source. This formula shows that in a distribution network containing distributed generation sources, the fault current is contributed by all sources in the network that can provide short-circuit current.

[0076] Furthermore, based on the superposition principle and fault analysis theory, when bus j fails, the system can calculate the fault value of any bus in the network using formula (11). Voltage change: (11) Formula (11) is the general formula for calculating bus voltage variation. Wherein, Indicates when the busbar When a fault occurs, the busbar The voltage change phasor; Represents the three-phase impedance matrix of the network The Middle line, number The column submatrix (a 3×3 complex matrix) represents the sequence of columns from the point of failure. To the observation point Electrical distance; The total fault current phasor is calculated using formula (10). This formula is the core of the analysis of network voltage distribution after a fault using the superposition theorem.

[0077] When a single-phase ground fault occurs and the fault point is introduced... Subsequently, the network impedance matrix of the system needs to be updated. In this embodiment, the system can update the impedance matrix using formulas (12) and (13): (12) (13) Equations (12) and (13) are the impedance matrix update formulas considering the fault resistance. Wherein, It is the expanded new network impedance matrix, with dimensions of , This represents the original number of network buses; It is the three-phase impedance matrix of the original network; It is a faulty resistor; and These represent the corresponding fault points in the new and old matrices, respectively. The 3×3 diagonal sub-blocks. Formula (12) shows that the new matrix is ​​based on the original matrix with a superposition of fault resistors. The correction term constitutes the fault point. Formula (13) specifically refers to the fault point. The self-impedance requires an additional fault resistor. .

[0078] Finally, in this embodiment, the system can calculate the fault resistance using formula (14) and calculate the voltage change of all nodes in the network after the fault using formula (15): (14) (15) Formula (14) is the formula for calculating fault resistance, where It is the voltage at the fault point. It is the voltage of the phase where the fault point is located before the fault occurs (the voltage before the fault). It is the fault current. The calculated fault resistance value is given by equation (15), which is the specific calculation formula for the overall network voltage change after applying the updated impedance matrix. Indicates the fault point Caused busbar The change in voltage; It is the updated impedance matrix Middle corresponding bus and the point of failure The relevant impedance elements between them; This is the fault current.

[0079] Therefore, by combining the above formulas (6) to (15), the system can construct a complete mathematical model for single-phase grounding fault analysis. Starting from basic circuit principles, this model gradually derives the relationship between fault current and voltage changes, and uses the network impedance matrix and superposition principle to accurately calculate the voltage changes at all nodes in the distribution network after the fault occurs. These calculation results are the core input for the first path (based on impedance method and fault current) in the subsequent two-path voltage estimation, providing a key electrical quantity change benchmark for fault location.

[0080] In some embodiments, the system can estimate the steady-state voltage difference between adjacent nodes using the pre-fault power flow calculation results using the following formula (16): (16) Formula (16) is the voltage transfer relationship before the fault, used to construct the line voltage drop information in the baseline model. Wherein, and These represent the voltage phasors of node 1 and node 2 obtained through power flow calculation under normal operating conditions without faults. This represents the line voltage drop phasor from node 1 to node 2, and its value is determined by the line impedance and the load current flowing through the line. This formula reflects the fundamental principle that, during steady-state operation, the downstream node voltage equals the upstream node voltage minus the line voltage drop, and is the core foundational data for constructing the second estimated path (based on voltage transfer relationships). For example, in a 10kV distribution network, if the power flow calculation... kV, kV, then we can obtain kV.

[0081] Next, in this embodiment, the system can also estimate the voltage of the unmeasured node after a fault occurs using formula (17) based on real-time measurement data and pre-calculated voltage drop: (17) Formula (17) is the post-fault voltage estimation formula, which is the specific implementation of the second estimation path. Wherein, This represents the voltage phasor measured in real time at node 1, where a smart meter has been installed, after the fault occurred. That is, the line voltage drop phase quantity calculated in advance by formula (16) and stored in the reference power flow model; This represents the estimated voltage phasor at node 2 (where no smart meter is installed) after the fault. The innovation of this formula lies in its assumption that the load current flowing through the non-faulty section does not change significantly before and after the fault, thus minimizing the line voltage drop. Approximately invariant, thus utilizing measurable... Calculate the inability to be directly measured For example, measurements taken after a fault. kV, using pre-stored kV, can be estimated kV.

[0082] Furthermore, based on the two independent voltage estimation methods mentioned above, the system can construct a consistency criterion using formula (18) to screen the assumed fault locations and determine the fault segment: (18) Formula (18) is the objective function for locating the fault section. Wherein, This indicates when the fault is assumed to occur at a location. ( The objective function value calculated when the target function can be any bus node is used to quantify the consistency difference between the two estimated path results. This represents the total number of busbar nodes in the distribution network; This indicates that, under the first estimation path (based on network impedance method), it is assumed that the fault point is at... The nodes calculated at that time The voltage change phasor; This represents the node calculated under the second estimation path (based on the voltage transfer method). Voltage change phasor; symbol This represents the magnitude (i.e., amplitude) of the calculated phasor. The formula evaluates the hypothetical fault location by calculating the sum of the amplitude differences between the two voltage changes estimated at all nodes. The rationality of it. The smaller the value, the more consistent the results of the two independent estimates are under this assumption, and the greater the probability that the location is the actual fault point.

[0083] Then, in this embodiment, after initially determining the faulty section, the system can perform a discretization search within that section using formula (19) to achieve precise location of the fault point: (19) Formula (19) is the objective function for precise fault location. Wherein, This indicates that when the assumed fault location is on a certain proportion of the line segment... The objective function value is calculated at the position; This indicates the relative location of the fault point on the line, and is a value ranging from 0 to... Continuous variables between The total length of the line segment (e.g., (Indicates the midpoint of the line). This indicates that, under the first estimated path, the fault point is assumed to be located on the line. The nodes calculated at that time The voltage change phasor. The system discretizes the continuous line into multiple candidate points (e.g., one point every 1 meter), and calculates the voltage change phasor for each candidate point sequentially. corresponding Value. When When the global minimum is obtained, its corresponding position That is, it is determined to be the most likely and precise point of failure.

[0084] Therefore, by combining the above formulas (16) to (19), the system can construct a complete fault location chain from local measurement to global state estimation and then to consistent decision-making. Specifically, the system first uses formula (16) to establish the network voltage reference relationship before the fault; after the fault occurs, it uses formula (17) to quickly estimate the voltage change of the entire network based on limited measurement and reference information (second path); at the same time, based on different fault location assumptions, it calculates the corresponding voltage change of the entire network by impedance method (first path); finally, through the consistency objective function defined by formulas (18) and (19), the system can automatically select the fault location that best matches the two independent estimation results, so that even under the condition of sparse measurement points, it can still realize the whole process from fault segment judgment to accurate fault location.

[0085] In other embodiments, such as Figure 4 This presents a simplified equivalent analysis model of a single-wire system used to illustrate the principle of two-path voltage estimation and the verification process of consistency constraints. The model includes an equivalent power source labeled "M", with a voltage source labeled "M" connected in series on its right side. The system equivalent impedance. Two potential fault measurement points are set on the left and right sides of the symbol, corresponding to different fault location assumptions: Location 1 is marked with the fault current symbol. "and fault voltage" Location 2 is marked with "fault current". "and fault voltage" The diagram visually illustrates the difference in impedance flowing through the system when the assumed fault location differs (e.g., at location 1 or location 2). Fault current value ( and ) and the voltage value at the fault point ( and (This will change accordingly.)

[0086] Specifically, in the application scenario of this invention, this model is used to simulate and verify the calculation process of the first estimation path (based on the impedance method). Assuming the system's equivalent impedance... for The equivalent electromotive force of power source M is 10.5 kV. Assuming the fault occurs at location 1 (closer to the power source), the calculated fault current is... The voltage at the fault point is likely 1.2 kA. The voltage is 9.8kV; if we assume the fault occurs at location 2 (far from the power source), the calculated fault current will be higher due to increased impedance. The voltage at the fault point may drop to 0.8 kA. The voltage is 9.2kV. The system will calculate the voltage of all nodes in the entire network using formula (11) based on these two different assumptions (the fault is at location 1 or location 2). Corresponding voltage change (Position 1) and (Location 2). These calculation results will be compared with the total network voltage change calculated independently based on voltage transfer relationships (second estimation path). Substitute them together into the consistency objective function shown in formula (18). In this process, by comparing the values ​​of F(location 1) and F(location 2), the hypothetical location with the smaller objective function value will be determined to be closer to the actual fault condition. Therefore, Figure 4 The model and its parameter variations shown clearly demonstrate the core mechanism of how this invention accurately locates fault sections by traversing different fault location assumptions and utilizing the consistency differences in dual-path estimation results.

[0087] In summary, according to the above implementation method, the system is achieved collaboratively through four core steps: benchmark power flow model construction, fault data acquisition, dual-path voltage change estimation, and consistency constraint fault location. Specifically, the system acquires operational data of the distribution network and constructs a benchmark power flow model containing information on line voltage drops before the fault, used to establish a voltage state reference benchmark during normal operation of the distribution network; it responds to the occurrence of a fault and collects current measurement data to obtain real-time electrical quantity information of the network after the fault; based on the measurement data and the benchmark power flow model, it calculates the voltage changes of each node through a first estimation path and a second estimation path, respectively, to generate estimation results from two independent physical dimensions: network impedance relationship and topological voltage transmission relationship; and based on the voltage change estimation results, it calculates the fault location result by introducing consistency constraints, used to fuse dual-path information and achieve accurate fault point determination.

[0088] Specifically, in the technical solution of this embodiment, addressing the problem described in the background art of "relying solely on local voltage measurement data and lacking a comprehensive perception of the system's global voltage state," a benchmark power flow model containing information on the voltage drop of all network lines is constructed. This provides a complete network pre-fault state benchmark for fault analysis, thereby solving the deficiency of traditional methods in effectively perceiving reverse power flow and intermittent voltage fluctuations due to the lack of a global reference. Addressing the problem of "insufficient adaptability to complex operating conditions such as overvoltage and undervoltage of some feeders in multi-feeder scenarios," a first estimation path based on network impedance relationships and a second estimation path based on topological voltage transfer relationships are set. The invention employs two independent and physically meaningful voltage change estimation mechanisms, addressing the limitations of single-path methods in complex multi-feeder conditions due to measurement blind spots or characteristic distortions. Addressing the core bottleneck of "limited ability to effectively characterize network-wide node voltage changes under incomplete measurement information," it introduces consistency constraints based on the differences in dual-path voltage change estimation results and calculates fault location results accordingly. This establishes a mechanism for comprehensive decision-making by integrating multi-source physical relationships under limited measurement information, overcoming the shortcomings of traditional methods in accurately characterizing the network voltage state and exhibiting low location accuracy in measurement blind spots. Therefore, the technical solution of this invention solves the technical problem of high requirements for the deployment density of measurement devices in existing single-phase grounding fault location technologies for distribution networks, improving the accuracy, robustness, and engineering practicality of fault location under limited measurement conditions.

[0089] In some embodiments, the operating data of the distribution network includes line impedance parameters, load information of each bus, and power supply access information; the step of constructing a baseline power flow model based on the operating data includes: The node admittance matrix of the distribution network is constructed based on the line impedance parameters.

[0090] Among them, the node admittance matrix is ​​a complex matrix whose elements represent the admittance values ​​between each bus node in the distribution network, and are used to describe the relationship between network topology and electrical parameters; the line impedance parameter refers to the resistance and reactance values ​​of the line, usually in ohms per kilometer (Ω / km).

[0091] Specifically, the system can convert line impedance parameters into admittance values ​​(in Siemens units) and fill matrix elements according to network connection relationships to construct a node admittance matrix of dimension N×N, where N is the total number of bus nodes.

[0092] For example, for a 10kV distribution network with 5 busbars, the line impedance parameters include resistance of 0.15 ohms per kilometer and reactance of 0.35 ohms per kilometer. The calculated admittance of the system is... Siemens (among others) (where the unit is imaginary) and a 5×5 node admittance matrix is ​​constructed. The off-diagonal elements in the matrix represent the admittance between adjacent nodes, and the diagonal elements represent the node self-admittance.

[0093] Based on the node admittance matrix, load information, and power supply access information, a power flow solving algorithm is used to solve the steady-state operation equations of the distribution network.

[0094] Among them, power flow solution algorithms refer to numerical methods used to calculate the voltage and power distribution of each bus in a distribution network under steady state, such as the Newton-Raphson method (an iterative algorithm for solving numerical solutions of nonlinear equations) or the forward-backward sweep method (an iterative numerical algorithm specifically used to solve power flow in distribution networks with radial or weak loop structures); steady-state operating equations refer to a set of equations established based on Kirchhoff's current law and the power balance principle; load information refers to the active and reactive power demand of each bus, in megawatts (MW) and megavars (Mvar); power source access information refers to the power parameters of distributed power sources or main grid injection points.

[0095] Specifically, the system can set a reference bus voltage (e.g., a per-unit value of 1.0∠0°), use load information and power supply access information as power injection conditions, and iteratively solve for the node voltage magnitude and phase angle until the power residual is less than a preset tolerance (e.g., ...). (per unit). For example, the system uses the forward-backward substitution method, converges within 10 iterations, calculates the per-unit voltage value of each bus (e.g., 1.02 per unit for bus 1, 1.01 per unit for bus 2, and 0.98 per unit for bus 5), and outputs the power distribution results.

[0096] The line voltage drop information between adjacent buses is calculated based on the solution results, and a baseline power flow model is constructed using the line voltage drop information.

[0097] Among them, line voltage drop information refers to the magnitude (unit: per unit) and phase (unit: °) data of the voltage difference between adjacent buses, which is used to quantify the voltage loss on the line; the reference power flow model refers to a database or data structure that stores the electrical state of the entire network before the fault.

[0098] Specifically, the system can extract the voltage difference between adjacent bus pairs from the solved bus voltage through difference calculation and store it in vector or matrix form, including the voltage drop value and phase angle. For example, the system calculates the voltage drop value between bus 1 and bus 2 to be 0.01 per unit and the phase difference to be 2°, and records the voltage drop data of all adjacent buses to build a reference power flow model. This model contains voltage drop information for 10 nodes, and the data size is approximately 1 kilobyte (KB).

[0099] Therefore, according to the above implementation method, the system can accurately establish the electrical state benchmark before the distribution network fault, providing complete and reliable reference data for subsequent fault location.

[0100] In some embodiments, the step of calculating the voltage changes at each node in the distribution network via a first estimation path includes: Construct a node impedance matrix based on the network topology and line impedance parameters of the distribution network.

[0101] Among them, the node impedance matrix is ​​a complex matrix whose elements represent the impedance values ​​between each bus node in the distribution network, used to quantify the propagation characteristics of fault current in the network; the network topology refers to the connection relationship between bus nodes, such as radial or ring structure; the line impedance parameters refer to the resistance and reactance values ​​of the line, usually expressed in ohms (Ω). (in units of )

[0102] Specifically, the system can convert line impedance parameters into node impedance values ​​and fill matrix elements according to the topology to construct an N×N node impedance matrix, where N is the total number of bus nodes. Diagonal elements in the matrix represent node self-impedance, and off-diagonal elements represent inter-node mutual impedance.

[0103] For example, for a 10kV distribution network with 5 busbars, the line impedance parameters are resistance 0.1 ohms / km, reactance 0.3 ohms / km, and the line length is 1 km, then the calculated impedance value is... Ohm. The system constructs a 5×5 node impedance matrix, where the mutual impedance between node 1 and node 2 is... Ohm, the self-impedance of node 1 is Ohms (summarize the impedance of all connecting lines).

[0104] Based on the measured fault current values ​​and node impedance matrices in the measurement data, calculate the voltage change of each node in the distribution network under fault conditions.

[0105] Among them, the fault current measurement value refers to the current amplitude collected in real time by the current transformer or smart meter after the fault occurs, and the unit is ampere (A); the voltage change refers to the difference in node voltage before and after the fault, usually expressed in per-unit value or volt (V).

[0106] Specifically, the system can use matrix multiplication to multiply the node impedance matrix by the fault current vector to obtain the voltage change vector of each node, the mathematical expression of which is: ,in It is a voltage change vector. It is the nodal impedance matrix. This is the fault current vector (dimension N×1). For example, assuming the fault occurs at node 3 and the measured fault current is 150 amperes, then the fault current vector is... Ampere (T denotes transpose). Calculated... The voltage change at node 1 is obtained as follows: One unit (approximately) (volts), the voltage change at node 2 is One-digit number.

[0107] Based on the correspondence between the node impedance matrix and the fault current, the first voltage change estimation result based on the relationship between network impedance and fault current is calculated.

[0108] The first voltage change estimation result refers to the set of network node voltage change data calculated by the impedance method, which is usually stored in vector or matrix form.

[0109] Specifically, the system can integrate the voltage changes of all nodes into an N-dimensional vector, recording timestamps and fault hypothesis locations for subsequent consistency analysis. For example, the system generates the first voltage change estimation result vector as follows: The per-unit (P) corresponds to the voltage change from node 1 to node 5, and the data size is approximately 100 bytes (B).

[0110] Therefore, according to the above implementation method, the system can quickly estimate the voltage change of the entire network caused by the fault based on the impedance characteristics, and provide reliable first path data for dual-path collaborative positioning.

[0111] In some embodiments, the measurement data further includes voltage measurements of the installed smart meter nodes; the step of calculating the voltage changes of each node in the distribution network via a second estimation path includes: Based on the line voltage drop information in the baseline power flow model, the voltage transmission relationship between adjacent nodes is determined.

[0112] Among them, voltage transfer relationship refers to the voltage calculation relationship between adjacent nodes established based on line parameters and topology, specifically expressed as the voltage drop coefficient per unit length of line, with the unit being volts per kilometer (V / km).

[0113] Specifically, the system can use the difference calculation method, utilizing the voltage difference between adjacent nodes in the baseline power flow model and the line length, to calculate the voltage drop coefficient per unit length and establish the voltage transfer function from upstream to downstream nodes. For example, for a 1.5 km long line L1-2, the baseline power flow model shows that node 1 has a voltage of 10.2 kV and node 2 has a voltage of 10.15 kV. The voltage transfer relationship is as follows: kilovolts per kilometer, meaning a voltage drop of 33 volts per kilometer.

[0114] Based on the voltage transfer relationship, the voltage change of the unmeasured node is calculated step by step by combining the voltage measurement values.

[0115] Among them, step-by-step calculation refers to the calculation method that proceeds from the measurement node to the downstream node according to the feeder topology; unmeasured node refers to the bus node that has not been equipped with a smart meter.

[0116] Specifically, the system can use linear interpolation to calculate the post-fault voltage of downstream unmeasured nodes, starting with the voltage at the measured node and considering voltage propagation relationships and line length. The change is then compared to a reference voltage. For example, if node 3 is a measured node with a post-fault voltage of 9.8 kV, line L3-4 is 2 km long, and the voltage propagation relationship is 0.035 kV / km, then the post-fault voltage at node 4 is calculated as follows: kV. If the reference voltage at node 4 is 10.1kV, then the voltage change is kV.

[0117] Based on the recursive calculation results of the node voltage changes before and after the fault, the second voltage change estimation result based on the voltage transmission relationship in the network topology is obtained.

[0118] The second voltage change estimation result refers to the set of voltage change data of all network nodes obtained by recursive calculation through voltage transfer relationship, which is stored in vector form.

[0119] Specifically, the system can integrate the recursive voltage changes of all nodes into an N-dimensional vector (N being the total number of nodes), and label the calculation path and timestamp. For example, the system generates a second voltage change estimation result vector as follows: kV corresponds to the voltage change from node 1 to node 5. The data is stored as a floating-point array and occupies approximately 80 bytes of memory.

[0120] Therefore, according to the above implementation method, the system can use limited measurement data to reconstruct the voltage changes of the entire network through topological transit relationships, providing independent second path verification data for consistency constraint location.

[0121] In some embodiments, the step of establishing consistency constraints includes: An objective function is constructed based on the numerical deviation between the first voltage change estimation result and the second voltage change estimation result.

[0122] The numerical deviation refers to the difference in voltage change at the corresponding node between the two estimation results, and the objective function is a mathematical expression used to quantify the degree of difference between the two path estimation results, which is usually constructed using the least squares method.

[0123] Specifically, the system can construct an objective function by calculating the sum of squares of the differences between the two estimated voltage changes at each node, with the mathematical expression being: ,in Represents a node The first estimated voltage change, This represents the estimated second voltage change at node i. This represents the total number of nodes. For example, for a distribution network with 5 nodes, the first estimate is... The second estimate is (per unit). If the value is in units, then the objective function value is calculated as follows: .

[0124] In the power distribution network, different nodes or line sections are assumed to be fault locations, and the objective function value is calculated for each assumed location.

[0125] The traversal assumption refers to the process of sequentially calculating each possible fault point in the distribution network as a candidate location; the fault locations include two types: node faults and line section faults.

[0126] Specifically, the system can use a loop algorithm to sequentially set each node or line midpoint as a hypothetical fault point, re-execute the dual-path voltage estimation, calculate the corresponding objective function value, and generate a mapping table between candidate locations and objective function values. For example, the system traverses 10 possible fault locations (5 nodes + 5 line midpoints) of a 5-node distribution network, assuming the objective function value is 0.0003 when the fault is located at node 3 and 0.0012 when it is located at the midpoint of line L2-3, and records the objective function values ​​corresponding to all hypothetical locations.

[0127] By comparing the objective function values ​​corresponding to each hypothetical location, the node or line segment corresponding to the minimum objective function value is identified as the faulty segment.

[0128] Among them, fault segment determination refers to the decision-making process of determining the location of the fault based on the minimum objective function value criterion.

[0129] Specifically, the system can use a sorting algorithm to find the minimum objective function value in the mapping table and mark its corresponding location as the final fault segment. Simultaneously, a threshold is set to verify the reasonableness of the result. For example, the system compares the objective function values ​​of 10 candidate locations and finds that the value of 0.0003 corresponding to node 3 is the global minimum and is less than the preset threshold of 0.0005. Therefore, it determines that the fault occurs in the segment near node 3, with a positioning error range of less than 50 meters.

[0130] Therefore, according to the above implementation method, the system can accurately determine the faulty section through the consistency constraint of the dual-path estimation results, thereby improving the reliability of the positioning results.

[0131] In some embodiments, based on voltage change estimation results, fault location results of the distribution network are calculated by introducing consistency constraints, including: The faulty section is discretized to generate multiple candidate fault locations.

[0132] Discretization refers to the operation of dividing a continuous line segment into several discrete points according to a preset interval; the candidate fault point location refers to the set of specific coordinate points where a fault may occur, expressed as a percentage or absolute length from the starting point of the line.

[0133] Specifically, the system can use an equal-interval partitioning algorithm to generate a sequence of candidate points for the faulty section of the line at 10-meter intervals, with each candidate point corresponding to a specific geographical coordinate. For example, for a faulty section L4-5 with a length of 200 meters, the system generates 21 candidate fault point locations (including the starting point, the ending point, and 19 intermediate points), numbered from P1 (0 meters) to P21 (200 meters), with each point spaced 10 meters apart.

[0134] Based on the first voltage change estimation result and the second voltage change estimation result, the objective function value corresponding to the location of each candidate fault point is calculated.

[0135] The objective function value refers to the quantified value of the difference between the two estimation results obtained by calculating the objective function for each candidate fault location.

[0136] Specifically, the system can perform dual-path voltage estimation for each candidate point location through iterative calculations and substitute the results into the objective function formula. Calculate the difference. For example, the system calculates the objective function value of candidate point P11 (at 100 meters) as 0.0008 and the objective function value of candidate point P12 (at 110 meters) as 0.0005, and records the sequence of objective function values ​​corresponding to all 21 candidate points.

[0137] By comparing the objective function values ​​corresponding to the locations of each candidate fault point, the location of the candidate fault point corresponding to the smallest objective function value is determined as the final fault point location.

[0138] The final fault location refers to the optimal fault point coordinates determined through consistency constraint optimization.

[0139] Specifically, the system can use a minimum value search algorithm to compare the objective function values ​​of all candidate points, select the candidate point corresponding to the global minimum value as the final fault point, and verify whether this value is lower than a preset threshold (such as 0.001). For example, the system compares the objective function values ​​of 21 candidate points and finds that the value corresponding to point P12, 0.0005, is the minimum value and is lower than the threshold of 0.001. Therefore, the system determines that the fault point is located at 110 meters on line L4-5, with a positioning accuracy of ±5 meters.

[0140] The output includes the fault location results, including the location of the faulty section and the final fault point, and generates corresponding fault handling instructions.

[0141] Among them, fault handling instructions refer to a set of control commands automatically generated based on the location results, including fault isolation instructions and power restoration instructions.

[0142] Specifically, the system can use the instruction generation module to convert the fault location information into specific switch operation instructions, and add a timestamp and operation priority identifier. For example, the system outputs the location result "Fault section: L4-5, Fault point: 110 meters from node 4", and generates the instruction "Open switch S45, close tie switch S48". The instruction is sent to the distribution automation system through the IEC61850 protocol.

[0143] Therefore, according to the above implementation method, the system can realize fully automated processing from fault segment identification to precise location, significantly improving the efficiency and accuracy of fault handling.

[0144] In some embodiments, a second voltage change estimation result based on the voltage transfer relationship in the network topology is calculated according to the recursive calculation result of the node voltage change before and after the fault, including: Based on the voltage transfer relationship, the voltage change of downstream unmeasured nodes is calculated step by step along the feeder path, starting from the measurement node.

[0145] Among them, the feeder path refers to the topological sequence of the power supply branch lines starting from the power source point; the step-by-step recursive calculation refers to the iterative calculation process of voltage estimation from upstream node to downstream node according to the topological connection relationship.

[0146] Specifically, the system can determine the feeder topology order through a depth-first search algorithm. Starting from the measurement node, it uses the line voltage drop coefficient and line length to calculate the voltage change of the downstream adjacent nodes step by step, and uses the newly calculated node as the starting point for the next recursion.

[0147] For example, the system measures the voltage change from node 3. Starting from kV, along feeder L3-4 (2 km long, voltage drop factor 0.035 kV / km), calculate the voltage change at node 4. kV; then, taking node 4 as the starting point, calculate the voltage change at node 5 as follows: kV, to complete the recursive calculation of the entire feeder.

[0148] Based on the voltage changes of each node obtained by recursive calculation, a second voltage change estimation result is constructed, which includes the amplitude and phase information of the voltage changes of all nodes in the network.

[0149] Among them, the total network node refers to the collection of all bus nodes in the distribution network; voltage change amplitude and phase information refers to a complete electrical quantity description that includes both the magnitude of voltage change (unit kV) and the phase angle offset (unit °).

[0150] Specifically, the system can use a data integration module to sort the recursive calculation results of each node by node number and construct an N×2 dimensional matrix (N being the total number of nodes) containing amplitude and phase. The first column stores the voltage change amplitude, and the second column stores the phase change. For example, the system generates a second voltage change estimation result matrix for 5 nodes: Node 1 [-0.40kV, -2.1°], Node 2 [-0.37kV, -1.8°], Node 3 [-0.35kV, -1.5°], Node 4 [-0.42kV, -2.2°], and Node 5 [-0.47kV, -2.5°]. This matrix is ​​stored in CSV (Comma-Separated Values) format, with a file size of approximately 200 bytes.

[0151] Therefore, according to the above implementation method, the system can reconstruct the voltage change distribution of the entire network through the topology recursion mechanism, providing second path verification data with complete amplitude and phase information for fault location.

[0152] Figure 5 This is a structural block diagram of a distribution network fault location system based on consistency constraints according to an embodiment of the present invention.

[0153] like Figure 5 As shown, the distribution network fault location system based on consistency constraints includes: The baseline power flow model construction module 210 is used to acquire the operating data of the distribution network and construct a baseline power flow model based on the operating data. The baseline power flow model includes the line voltage drop information of each node in the distribution network before the fault.

[0154] The measurement data acquisition module 220 is used to collect the measurement data of the current distribution network when a fault occurs in the distribution network.

[0155] The voltage change estimation result generation module 230 is used to calculate the voltage change of each node in the distribution network based on the measurement data and the benchmark power flow model, respectively through the first estimation path and the second estimation path, and generate the voltage change estimation results of the first estimation path and the second estimation path.

[0156] The fault location result generation module 240 is used to calculate the fault location result of the distribution network based on the voltage change estimation result by introducing consistency constraints.

[0157] Among them, the consistency constraint is established by constructing an objective function based on the difference in voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

[0158] The specific functions and examples of each module and submodule of the device in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0159] According to embodiments of the present invention, the above-described method of the present invention can be applied to an electronic device and a readable storage medium.

[0160] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0162] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0163] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a distribution network fault location method based on consistency constraints. For example, in some embodiments, a distribution network fault location method based on consistency constraints can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of a distribution network fault location method based on consistency constraints described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured, by any other suitable means (e.g., by means of firmware), to perform a distribution network fault location method based on consistency constraints.

[0164] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0166] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0168] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0169] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0171] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for fault location in a distribution network based on consistency constraints, characterized in that, include: Obtain the operation data of the distribution network and construct a benchmark power flow model based on the operation data. The benchmark power flow model includes the line voltage drop information of each node of the distribution network before the fault. In response to a fault in the distribution network, the current measurement data of the distribution network is collected; Based on the measurement data and the benchmark power flow model, the voltage changes of each node in the distribution network are calculated through the first estimation path and the second estimation path, respectively, and the voltage change estimation results of the first estimation path and the second estimation path are generated. Based on the voltage change estimation results, the fault location results of the distribution network are calculated by introducing consistency constraints. The consistency constraint is established by constructing an objective function based on the difference in the voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

2. The method according to claim 1, characterized in that, The operational data of the distribution network includes line impedance parameters, load information of each bus, and power supply access information; the step of constructing a baseline power flow model based on the operational data includes: The node admittance matrix of the distribution network is constructed based on the line impedance parameters; Based on the node admittance matrix, the load information, and the power supply access information, the steady-state operation equations of the distribution network are solved using a power flow solving algorithm. The line voltage drop information between adjacent buses is calculated based on the solution results, and the reference power flow model is constructed using the line voltage drop information.

3. The method according to claim 1, characterized in that, The steps of calculating the voltage changes at each node in the distribution network using the first estimation path include: Construct a node impedance matrix based on the network topology and line impedance parameters of the power distribution network; Based on the fault current measurement value in the measurement data and the node impedance matrix, calculate the voltage change of each node in the distribution network under fault conditions. Based on the correspondence between the node impedance matrix and the fault current, the first voltage change estimation result based on the relationship between network impedance and fault current is calculated.

4. The method according to claim 3, characterized in that, The measurement data also includes voltage measurements from installed smart meter nodes; the step of calculating the voltage changes at each node in the distribution network using the second estimation path includes: Based on the line voltage drop information in the benchmark power flow model, the voltage transfer relationship between adjacent nodes is determined. Based on the voltage transmission relationship, the voltage change of the unmeasured node is calculated step by step using the voltage measurement values. Based on the recursive calculation results of the node voltage changes before and after the fault, the second voltage change estimation result based on the voltage transmission relationship in the network topology is obtained.

5. The method according to claim 4, characterized in that, The steps for establishing the consistency constraints include: A target function is constructed based on the numerical deviation between the first voltage change estimation result and the second voltage change estimation result; In the power distribution network, different nodes or line sections are assumed to be fault locations, and the objective function value is calculated for each assumed location. By comparing the objective function values ​​corresponding to each hypothetical location, the node or line segment corresponding to the minimum objective function value is determined as the fault segment.

6. The method according to claim 5, characterized in that, The calculation of the fault location result of the distribution network based on the voltage change estimation result, by introducing consistency constraints, includes: The faulty section is discretized to generate multiple candidate fault location locations; Based on the first voltage change estimation result and the second voltage change estimation result, the objective function value corresponding to the location of each candidate fault point is calculated; By comparing the objective function values ​​corresponding to the locations of each candidate fault point, the location of the candidate fault point corresponding to the smallest objective function value is determined as the final fault point location. The output includes the fault location results, including the location of the faulty section and the final fault point, and generates corresponding fault handling instructions.

7. The method according to claim 4, characterized in that, The second voltage change estimation result, calculated based on the recursive calculation results of the node voltage changes before and after the fault, and the voltage transmission relationship in the network topology, includes: Based on the voltage transmission relationship, the voltage change of downstream unmeasured nodes is calculated step by step along the feeder path, starting from the measurement node. Based on the voltage changes of each node obtained by recursive calculation, a second voltage change estimation result is constructed, which includes the amplitude and phase information of the voltage changes of all nodes in the network.

8. A distribution network fault location system based on consistency constraints, characterized in that, include: The benchmark power flow model construction module is used to acquire the operating data of the distribution network and construct a benchmark power flow model based on the operating data. The benchmark power flow model includes the line voltage drop information of each node of the distribution network before the fault. The measurement data acquisition module is used to acquire the measurement data of the current distribution network when a fault occurs in the distribution network. The voltage change estimation result generation module is used to calculate the voltage change of each node in the distribution network based on the measurement data and the reference power flow model, respectively through the first estimation path and the second estimation path, and generate the voltage change estimation results of the first estimation path and the second estimation path. The fault location result generation module is used to calculate the fault location result of the distribution network based on the voltage change estimation result by introducing consistency constraints. The consistency constraint is established by constructing an objective function based on the difference in the voltage change estimation results. The first estimation path is configured to calculate the node voltage change based on the fault current measurement value in the measurement data, and the second estimation path is configured to calculate the node voltage change based on the line voltage drop information in the reference power flow model and the measurement data.

9. An electronic device, characterized in that, include: At least one processor; and a memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, Computer instructions are used to cause a computer to perform the method according to any one of claims 1-7.

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